system

Abstract

The system according to this embodiment aims to optimally match employees' career information with the needs of the organization. [Solution] The system according to the embodiment comprises a career collection unit, a career analysis unit, an organization collection unit, an organization analysis unit, a matching unit, and a provision unit. The career collection unit collects career information of employees. The career analysis unit analyzes the career information collected by the career collection unit to identify the strengths and aptitudes of employees. The organization collection unit collects information on organizations and projects that are short of personnel. The organization analysis unit analyzes the information collected by the organization collection unit to identify the needs of organizations and projects. The matching unit performs optimal matching between employees and organizations or projects based on the information obtained by the career analysis unit and the organization analysis unit. The provision unit provides the matching results obtained by the matching unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to efficiently match the career information of employees with the needs of the organization, and there is room for improvement.

[0005] The system according to the embodiment aims to optimally match the career information of employees with the needs of the organization.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a career collection unit, a career analysis unit, an organization collection unit, an organization analysis unit, a matching unit, and a provision unit. The career collection unit collects career information of employees. The career analysis unit analyzes the career information collected by the career collection unit to identify the strengths and aptitudes of employees. The organization collection unit collects information on organizations and projects that are experiencing personnel shortages. The organization analysis unit analyzes the information collected by the organization collection unit to identify the needs of organizations and projects. The matching unit performs optimal matching between employees and organizations or projects based on the information obtained by the career analysis unit and the organization analysis unit. The provision unit provides the matching results obtained by the matching unit. [Effects of the Invention]

[0007] The system according to this embodiment can optimally match employee career information with the needs of the organization. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact. The microphone 38B receives user input by voice by detecting the voice of the user. 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, a specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

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

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

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

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

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

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

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

[0028] (Example of form 1) The career matching system according to an embodiment of the present invention is a system that uses AI to propose the optimal match between employees seeking their next career and organizations or projects facing talent shortages. The career matching system collects employee career information, and the AI ​​analyzes the collected career information to identify the employee's strengths and aptitudes. Furthermore, the AI ​​collects information on organizations and projects facing talent shortages to understand current talent needs, required skill sets, and project details. Next, the AI ​​matches the employee's career information with the information on organizations and projects to propose the optimal match. Finally, the AI ​​provides the matching results to the employee and the organization. This system supports employees nearing mandatory retirement age to make the most of the careers they have cultivated so far in a new environment, thereby revitalizing both the organization and its people. For example, the career matching system collects information such as the employee's past work experience, skills, qualifications, and achievements. Next, the career matching system uses AI to analyze the collected career information to identify the employee's strengths and aptitudes. Furthermore, the career matching system uses AI to collect information on organizations and projects facing talent shortages to understand current talent needs, required skill sets, and project details. Next, the career matching system uses AI to match employees' career information with organizational and project information, proposing the optimal match. Finally, the career matching system uses AI to provide the matching results to the employee and the organization. This allows the career matching system to efficiently match employees' career information with organizational needs, enabling optimal career proposals.

[0029] The career matching system according to this embodiment comprises a career collection unit, a career analysis unit, an organization collection unit, an organization analysis unit, a matching unit, and a provision unit. The career collection unit collects employee career information. For example, the career collection unit collects information such as an employee's past work experience, skills, qualifications, and achievements. For example, the career collection unit can collect information such as an employee's past job titles, responsibilities, and project experience. The career collection unit can also collect information such as an employee's technical skills, soft skills, and language skills. Furthermore, the career collection unit can collect professional qualifications and industry certifications held by an employee. The career analysis unit analyzes the collected career information to identify an employee's strengths and aptitudes. For example, the career analysis unit analyzes career information using data mining techniques. The career analysis unit can also analyze career information using statistical analysis techniques. Furthermore, the career analysis unit can also analyze career information using machine learning algorithms. The organization collection unit collects information on organizations and projects that are experiencing personnel shortages. For example, the organization collection unit collects information such as the personnel needs of organizations and projects, the required skill sets, and project details. The Organizational Data Collection Department can collect information such as the skill sets and available positions that an organization needs. It can also collect detailed information such as project objectives, duration, and required resources. Furthermore, it can collect information about the organization's culture and values. The Organizational Analysis Department analyzes the collected information to identify the needs of the organization and projects. The Organizational Analysis Department analyzes organizational information using techniques such as data mining, statistical analysis, and machine learning algorithms. The Matching Department uses the information obtained by the Career Analysis and Organizational Analysis Departments to perform optimal matching between employees and organizations or projects. The Matching Department uses techniques such as skill matching to align the needs of employees and organizations, and cultural fit techniques to align the values ​​of employees and organizations.Furthermore, the matching unit can use AI to evaluate the compatibility between employees and organizations and perform optimal matching. The provision unit provides the matching results obtained by the matching unit to employees and organizations. The provision unit provides the matching results, for example, through a web application or a mobile application. The provision unit can also provide the matching results via email or paper. In addition, the provision unit can update the matching results in real time and provide them to employees and organizations quickly. As a result, the career matching system according to this embodiment can efficiently match employees' career information with the needs of organizations and make optimal career proposals.

[0030] The Career Collection Department collects employee career information. For example, it collects information such as employees' past work experience, skills, qualifications, and achievements. Specifically, it meticulously records details of projects employees have worked on in the past, their roles in those projects, and the results they achieved. Regarding technical skills, it collects information such as experience using programming languages ​​and tools, and expertise in specific technical areas. It also evaluates and records soft skills such as leadership, communication skills, and problem-solving abilities. Furthermore, it meticulously collects information on professional and industry certifications held by employees, including the type of certification, acquisition date, and renewal status. This information can be collected not only through employee self-reporting, but also by integrating data from internal evaluation systems and external certification bodies. The Career Collection Department centrally manages this information and updates it as needed, ensuring that career information is always up-to-date.

[0031] The Career Analysis Department analyzes collected career information to identify employees' strengths and aptitudes. For example, it uses data mining techniques to analyze career information. Specifically, it analyzes what types of work employees are suited for based on their past work experience and skill sets. It also uses statistical analysis techniques to analyze employee performance and evaluation data to identify performance trends and strengths. Furthermore, it can use machine learning algorithms to predict employees' career paths and propose future career directions. For example, based on past data, it can analyze the career paths that employees with specific skill sets tend to follow and propose the optimal career plan. The Career Analysis Department can also utilize the results of psychological tests and aptitude tests to evaluate employees' suitability. This allows the Career Analysis Department to comprehensively evaluate employees' strengths and aptitudes and provide optimal career recommendations.

[0032] The Organizational Information Collection Department collects information on organizations and projects facing talent shortages. For example, it gathers information such as the personnel needs of an organization or project, the required skill sets, and project details. Specifically, it collects detailed information on the current talent shortages an organization is facing, the skill sets needed for future projects, and the availability of positions. It can also collect detailed information on project objectives, duration, required resources, and budget. Furthermore, it collects information on the organization's culture and values, using it as data to assess cultural fit with employees. This information can be collected not only directly from the organization's HR department and project managers, but also from internal databases and external job posting sites. The Organizational Information Collection Department centrally manages this information and updates it as needed, ensuring that it always maintains up-to-date organizational information.

[0033] The Organizational Analysis Department analyzes collected information to identify the needs of organizations and projects. For example, it uses data mining techniques to analyze organizational information. Specifically, it analyzes what kind of personnel are needed based on the skill sets and position availability required by the organization. It can also use statistical analysis techniques to analyze past project data and personnel placement data to identify commonalities in successful projects and the causes of unsuccessful projects. Furthermore, it can use machine learning algorithms to predict the organization's future personnel needs and propose appropriate personnel placements. For example, based on past data, it can analyze which projects personnel with specific skill sets tend to succeed in and propose optimal personnel placements. The Organizational Analysis Department can also analyze information on the organization's culture and values ​​and evaluate cultural fit with employees. In this way, the Organizational Analysis Department can evaluate the needs of organizations and projects from multiple perspectives and make optimal personnel placements.

[0034] The Matching Department performs optimal matching between employees and organizations or projects based on information obtained by the Career Analysis Department and the Organizational Analysis Department. For example, the Matching Department uses skill matching technology to align the needs of employees and organizations. Specifically, it compares the skill sets of employees with those required by the organization to achieve the optimal match. It can also use cultural fit technology to align the values ​​of employees and organizations. For example, it evaluates whether an employee's values ​​and work style align with the organization's culture and values. Furthermore, it can use AI to evaluate the compatibility between employees and organizations and perform optimal matching. The AI ​​learns from past matching data and success stories to build an optimal matching algorithm. This allows the Matching Department to perform optimal matching between employees and organizations or projects with high accuracy. In addition, the Matching Department can continuously evaluate the matching results and improve the algorithm based on feedback. As a result, the Matching Department can always provide highly accurate matching based on the latest information and make optimal career proposals for both employees and organizations.

[0035] The Service Provider department provides the matching results obtained by the Matching Department to employees and organizations. The Service Provider department provides matching results, for example, through web or mobile applications. Specifically, it sets up a dedicated portal site accessible to employees and organizations, where the matching results are displayed. The Service Provider department can also provide matching results via email or paper. For example, important matching results are notified via email with a detailed report attached. Furthermore, the Service Provider department can update matching results in real time and provide them quickly to employees and organizations. For example, if matching results are updated, notifications are sent immediately to provide the latest information. This allows the Service Provider department to ensure that employees and organizations are always aware of the latest matching information and can respond quickly. The Service Provider department can also collect feedback on the matching results and use it to improve the system. This allows the Service Provider department to provide high-quality services to employees and organizations and maximize the effectiveness of the career matching system.

[0036] The Career Data Collection Department can collect information on employees' past work experience, skills, qualifications, and achievements. For example, it can collect information on employees' past job titles, responsibilities, and project experience. It can also collect information on employees' technical skills, soft skills, and language skills. Furthermore, it can collect information on professional qualifications and industry certifications held by employees. This allows for more accurate matching by collecting detailed career information on employees. Some or all of the above processing in the Career Data Collection Department may be performed using AI, for example, or not. For example, the Career Data Collection Department can input information on employees' past work experience and skills into an AI, which can then automatically collect the information.

[0037] The Career Analysis Department can analyze collected career information to identify employees' strengths and aptitudes. For example, the Career Analysis Department can analyze career information using data mining techniques. For instance, it can identify employees' strengths and aptitudes from the collected career information. The Career Analysis Department can also analyze career information using statistical analysis techniques. For example, it can use statistical analysis to evaluate employees' skill sets and performance. Furthermore, the Career Analysis Department can analyze career information using machine learning algorithms. For example, it can use machine learning to predict employees' aptitudes. This allows for optimal career proposals by identifying employees' strengths and aptitudes. Some or all of the above processes in the Career Analysis Department may be performed using AI, or not. For example, the Career Analysis Department can input collected career information into an AI, which can automatically identify strengths and aptitudes.

[0038] The Organizational Information Collection Department can collect information such as the personnel needs of organizations and projects, the required skill sets, and project details. For example, the Organizational Information Collection Department collects information on the skill sets and available positions that organizations require. For example, the Organizational Information Collection Department collects information on the necessary skill sets and available positions to understand the personnel needs of organizations. The Organizational Information Collection Department can also collect detailed information on projects, such as their objectives, duration, and required resources. For example, the Organizational Information Collection Department collects detailed project information to understand the project's objectives, duration, and required resources. Furthermore, the Organizational Information Collection Department can also collect information on organizations' culture and values. For example, the Organizational Information Collection Department collects information on organizations' culture and values ​​to understand the characteristics of organizations. This allows for more accurate matching by collecting detailed information on organizations and projects. Some or all of the above processing in the Organizational Information Collection Department may be performed using AI, or not. For example, the Organizational Information Collection Department can input information on organizations and projects into an AI, which can then automatically collect the information.

[0039] The Organizational Analysis Department can analyze collected information and identify the needs of organizations and projects. For example, the Organizational Analysis Department can analyze organizational information using data mining techniques. For instance, it can identify the needs of organizations and projects from the collected organizational information. The Organizational Analysis Department can also analyze organizational information using statistical analysis techniques. For example, it can use statistical analysis to evaluate the organization's personnel needs and skill sets. Furthermore, the Organizational Analysis Department can analyze organizational information using machine learning algorithms. For example, it can use machine learning to predict organizational needs. This allows for optimal career proposals by identifying the needs of organizations and projects. Some or all of the above processes in the Organizational Analysis Department may be performed using AI, or not. For example, the Organizational Analysis Department can input collected organizational information into an AI, which can then automatically identify the needs.

[0040] The matching department can perform optimal matching between employees and organizations or projects based on information obtained by the career analysis department and the organizational analysis department. For example, the matching department can use skill matching technology to match the needs of employees and organizations. For example, the matching department can match the skill sets of employees with the needs of organizations. The matching department can also use culture fit technology to match the values ​​of employees and organizations. For example, the matching department can match the culture and values ​​of employees and organizations. Furthermore, the matching department can use AI to evaluate the compatibility between employees and organizations and perform optimal matching. For example, the matching department can use AI to evaluate the compatibility between employees and organizations and perform optimal matching. This enables optimal matching between employees and organizations or projects. Some or all of the above processes in the matching department may be performed using AI, or not. For example, the matching department can input information obtained by the career analysis department and the organizational analysis department into the AI, which can then automatically perform optimal matching.

[0041] The service provider can provide matching results to employees and organizations. For example, the service provider can provide matching results through web applications or mobile applications. The service provider can also provide matching results via email or paper. For example, the service provider can send matching results to employees and organizations via email. Furthermore, the service provider can update matching results in real time and provide them quickly to employees and organizations. This allows for faster decision-making by employees and organizations. Some or all of the above processes in the service provider may be performed using AI, or not. For example, the service provider can input matching results into an AI, which can then automatically provide the results.

[0042] The Career Information Collection Department can analyze employees' past career information and select the optimal collection method. For example, the Career Information Collection Department can identify areas of expertise from past career information and prioritize the collection of information related to those areas. The Career Information Collection Department can also collect information on new skills and qualifications that employees may be interested in, based on past career information. Furthermore, the Career Information Collection Department can analyze past career information and collect information to complement any skills or experience that employees lack. This allows the Career Information Collection Department to select the optimal information collection method by analyzing past career information. Some or all of the above processes in the Career Information Collection Department may be performed using AI, for example, or not. For example, the Career Information Collection Department can input past career information into AI, which can automatically select the optimal collection method.

[0043] The career information collection unit can filter the collected career information based on the employee's current projects and areas of interest. For example, the career information collection unit can prioritize collecting skills and experience related to the current project. The career information collection unit can also filter and collect relevant career information based on the employee's areas of interest. Furthermore, the career information collection unit can collect necessary information in a timely manner according to the progress of the current project. This allows for the collection of highly relevant information by filtering information based on the current project and areas of interest. Some or all of the above processing in the career information collection unit may be performed using AI, or not. For example, the career information collection unit can input information on the employee's current projects and areas of interest into an AI, which can then automatically perform the filtering.

[0044] The career information gathering unit can prioritize the collection of highly relevant information by considering the employee's geographical location when gathering career information. For example, the career information gathering unit can prioritize the collection of job postings in the area where the employee is currently located. The career information gathering unit can also prioritize the collection of career information related to the employee's desired work location. Furthermore, the career information gathering unit can also prioritize the collection of career options within the employee's commuting range. For example, the career information gathering unit can prioritize the collection of career options within the employee's commuting range. This allows for the priority collection of highly relevant information by considering geographical location. Some or all of the above processing in the career information gathering unit may be performed using AI, or not. For example, the career information gathering unit can input the employee's geographical location information into AI, which can then automatically prioritize the collection of highly relevant information.

[0045] The Career Information Collection Department can collect relevant information by analyzing employees' social media activity when collecting career information. For example, the Career Information Collection Department can collect career information related to areas that employees show interest in on social media. The Career Information Collection Department can also collect relevant career information based on industry trends and news that employees follow. Furthermore, the Career Information Collection Department can also collect relevant career information by analyzing the activities of online communities and groups that employees participate in. This allows for the collection of highly relevant information by analyzing social media activity. Some or all of the above processes in the Career Information Collection Department may be performed using AI, for example, or not. For example, the Career Information Collection Department can input employee social media activity data into AI, which can then automatically collect relevant information.

[0046] The Career Analysis Department can adjust the level of detail in its analysis of career information based on the employee's importance. For example, it can analyze information on important skills and experiences in detail to clarify an employee's strengths. The Career Analysis Department can also analyze less important information concisely to maintain overall balance. Furthermore, the Career Analysis Department can prioritize and analyze information related to an employee's career goals in detail. By adjusting the level of detail in the analysis based on the employee's importance, it can provide more appropriate analysis results. Some or all of the above processes in the Career Analysis Department may be performed using AI, for example, or not. For example, the Career Analysis Department can input employee importance data into AI, which can automatically adjust the level of detail in the analysis.

[0047] The Career Analysis Department can apply different analysis algorithms to employees depending on their category when analyzing career information. For example, the Career Analysis Department can apply an analysis algorithm specifically designed to evaluate technical skills to employees in technical positions. Similarly, the Career Analysis Department can apply an analysis algorithm specifically designed to evaluate leadership and management skills to employees in management positions. Furthermore, the Career Analysis Department can apply an analysis algorithm specifically designed to evaluate creativity and design skills to employees in creative positions. By applying analysis algorithms according to employee categories, more appropriate analysis results can be provided. Some or all of the above processes in the Career Analysis Department may be performed using AI, or not. For example, the Career Analysis Department can input employee category data into an AI, which can then automatically apply different analysis algorithms.

[0048] The Career Analysis Department can prioritize the analysis of career information based on when the information was submitted. For example, the Career Analysis Department can prioritize the analysis of recently submitted information to reflect the latest career status. The Career Analysis Department can also prioritize the analysis of information related to important projects. Furthermore, the Career Analysis Department can re-analyze older information as needed to bring it up to date. This allows the department to reflect the latest career status by prioritizing the analysis based on when the information was submitted. Some or all of the above processes in the Career Analysis Department may be performed using AI, for example, or not. For example, the Career Analysis Department can input information submission date data into an AI, which can then automatically determine the analysis priority.

[0049] The Career Analysis Department can adjust the order of analysis based on the relevance of the information when analyzing career information. For example, the Career Analysis Department can prioritize the analysis of information related to important skills and experiences. For example, the Career Analysis Department can prioritize the analysis of information related to employees' career goals. For example, the Career Analysis Department can prioritize the analysis of information related to employees' career goals. Furthermore, the Career Analysis Department can prioritize the analysis of information directly related to project success. For example, the Career Analysis Department can prioritize the analysis of information directly related to project success. By adjusting the order of analysis based on the relevance of the information, more appropriate analysis results can be provided. Some or all of the above processes in the Career Analysis Department may be performed using AI, for example, or not using AI. For example, the Career Analysis Department can input information relevance data into AI, and the AI ​​can automatically adjust the order of analysis.

[0050] The Organizational Data Collection Department can analyze the organization's past project information and select the optimal data collection method. For example, the Organizational Data Collection Department can identify the skill sets the organization needs from past project information and prioritize the collection of information related to those skills. The Organizational Data Collection Department can also collect information on new technologies and trends that the organization might be interested in, based on past project information. Furthermore, the Organizational Data Collection Department can analyze past project information and collect information to supplement any skills or resources the organization lacks. This allows the Organizational Data Collection Department to select the optimal data collection method by analyzing past project information. Some or all of the above processes in the Organizational Data Collection Department may be performed using AI, or not. For example, the Organizational Data Collection Department can input past project information into an AI, which can automatically select the optimal data collection method.

[0051] The organizational information collection unit can filter organizational information based on current projects and areas of interest. For example, it can prioritize collecting skills and experience related to current projects. The organizational information collection unit can also filter and collect relevant information based on the organization's areas of interest. Furthermore, the organizational information collection unit can collect necessary information in a timely manner according to the progress of current projects. This allows for the collection of highly relevant information by filtering information based on current projects and areas of interest. Some or all of the above processing in the organizational information collection unit may be performed using AI, or not. For example, the organizational information collection unit can input information on current projects and areas of interest into an AI, which can then automatically perform the filtering.

[0052] The Organization Information Collection Unit can prioritize the collection of highly relevant information by considering geographical location information when collecting organizational information. For example, the Organization Information Collection Unit can prioritize the collection of job postings in the area where the organization is currently located. The Organization Information Collection Unit can also prioritize the collection of information related to the work location desired by the organization. Furthermore, the Organization Information Collection Unit can prioritize the collection of career options within the commutable distance of the organization. This allows for the priority collection of highly relevant information by considering geographical location information. Some or all of the above processing in the Organization Information Collection Unit may be performed using AI, for example, or without AI. For example, the Organization Information Collection Unit can input the organization's geographical location information into AI, which can then automatically prioritize the collection of highly relevant information.

[0053] The Organization Data Collection Department can collect relevant information by analyzing social media activity when collecting organizational information. For example, the Organization Data Collection Department can collect information related to areas in which the organization shows interest on social media. The Organization Data Collection Department can also collect relevant information based on industry trends and news that the organization follows. Furthermore, the Organization Data Collection Department can also collect relevant information by analyzing the activities of online communities and groups in which the organization participates. This allows for the collection of highly relevant information by analyzing social media activity. Some or all of the above processes in the Organization Data Collection Department may be performed using AI, or not. For example, the Organization Data Collection Department can input the organization's social media activity data into AI, which can then automatically collect relevant information.

[0054] The organizational analysis department can adjust the level of detail of its analysis based on the importance of the projects when analyzing organizational information. For example, the organizational analysis department can analyze information related to important projects in detail to clarify the organization's needs. The organizational analysis department can also analyze less important information concisely to maintain overall balance. Furthermore, the organizational analysis department can prioritize and analyze information related to the organization's goals in detail. For example, the organizational analysis department prioritizes and analyzes information related to the organization's goals in detail. By adjusting the level of detail of the analysis based on the importance of the projects, more appropriate analysis results can be provided. Some or all of the above processes in the organizational analysis department may be performed using AI, for example, or not. For example, the organizational analysis department can input project importance data into the AI, and the AI ​​can automatically adjust the level of detail of the analysis.

[0055] The Organizational Analysis Department can apply different analysis algorithms depending on the project category when analyzing organizational information. For example, the Organizational Analysis Department can apply an analysis algorithm specialized in evaluating technical skills to technical projects. For example, the Organizational Analysis Department can apply an analysis algorithm specialized in evaluating leadership and management skills to management projects. For example, the Organizational Analysis Department can apply an analysis algorithm specialized in evaluating leadership and management skills to management projects. Furthermore, the Organizational Analysis Department can apply an analysis algorithm specialized in evaluating creativity and design skills to creative projects. For example, the Organizational Analysis Department can apply an analysis algorithm specialized in evaluating creativity and design skills to creative projects. By applying an analysis algorithm according to the project category, more appropriate analysis results can be provided. Some or all of the above processing in the Organizational Analysis Department may be performed using AI, for example, or not using AI. For example, the Organizational Analysis Department can input project category data into AI, and the AI ​​can automatically apply different analysis algorithms.

[0056] The organizational analysis department can prioritize the analysis of organizational information based on when the information was submitted. For example, the organizational analysis department can prioritize the analysis of recently submitted information to reflect the latest project status. The organizational analysis department can also prioritize the analysis of information related to important projects. Furthermore, the organizational analysis department can re-analyze older information as needed to bring it up to date. This allows the department to reflect the latest project status by prioritizing the analysis based on when the information was submitted. Some or all of the above processes in the organizational analysis department may be performed using AI, for example, or not. For example, the organizational analysis department can input information submission date data into an AI, which can then automatically determine the analysis priority.

[0057] The organizational analysis unit can adjust the order of analysis based on the relevance of the information when analyzing organizational information. For example, the organizational analysis unit can prioritize the analysis of information related to important skills and experience. For example, the organizational analysis unit can prioritize the analysis of information related to organizational goals. For example, the organizational analysis unit can prioritize the analysis of information related to organizational goals. Furthermore, the organizational analysis unit can prioritize the analysis of information that is directly related to the success of a project. For example, the organizational analysis unit can prioritize the analysis of information that is directly related to the success of a project. By adjusting the order of analysis based on the relevance of the information, more appropriate analysis results can be provided. Some or all of the above processes in the organizational analysis unit may be performed using AI, for example, or not using AI. For example, the organizational analysis unit can input information relevance data into AI, and the AI ​​can automatically adjust the order of analysis.

[0058] The matching unit can improve the accuracy of matching by considering the interrelationship between employees and organizations during the matching process. For example, the matching unit can perform matching that considers the interrelationship based on data from past successful matches. The matching unit can also analyze the mutual feedback between employees and organizations to perform highly accurate matching. Furthermore, the matching unit can consider the communication history between employees and organizations to perform compatible matching. This allows for more accurate matching by considering the interrelationship between employees and organizations. Some or all of the above processes in the matching unit may be performed using AI, or not. For example, the matching unit can input employee-organizational relationship data into AI, which can automatically improve the accuracy of matching.

[0059] The matching unit can perform matching by considering the attribute information of employees and organizations. For example, the matching unit can match an employee's skill set with the organization's needs. The matching unit can also match an employee's career goals with the organization's project goals. Furthermore, the matching unit can match an employee's preferred work location with the organization's work location. By considering the attribute information of employees and organizations, more appropriate matching becomes possible. Some or all of the above processes in the matching unit may be performed using AI, or not. For example, the matching unit can input employee and organization attribute information into AI, and the AI ​​can perform matching automatically.

[0060] The matching unit can perform matching while considering geographical distribution. For example, the matching unit can match an employee's preferred work location with the organization's work location. The matching unit can also prioritize matching within an employee's commuting range. Furthermore, the matching unit can prioritize matching geographically close organizations with employees. This allows for more appropriate matching by considering geographical distribution. Some or all of the above processing in the matching unit may be performed using AI, or not. For example, the matching unit can input geographical distribution data of employees and organizations into AI, which can then automatically perform the matching.

[0061] The matching unit can improve the accuracy of matching by referring to relevant literature during the matching process. For example, the matching unit can refer to relevant research papers and apply the latest matching algorithms. The matching unit can also refer to industry best practices to perform highly accurate matching. Furthermore, the matching unit can refer to past success stories and perform matching under similar conditions. This allows for more accurate matching by referring to relevant literature. Some or all of the above processes in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input relevant literature data into AI, which can automatically improve the accuracy of matching.

[0062] The information delivery department can select the optimal delivery method by referring to past feedback from employees and the organization at the time of delivery. For example, the information delivery department can identify the information delivery methods preferred by employees from past feedback and provide information based on those preferences. The information delivery department can also select the optimal information delivery method based on feedback from the organization. For example, the information delivery department can select the optimal information delivery method based on feedback from the organization. Furthermore, the information delivery department can analyze past feedback and select an information delivery method that suits the needs of employees and the organization. For example, the information delivery department can analyze past feedback and select an information delivery method that suits the needs of employees and the organization. This makes it possible to provide more appropriate information by referring to past feedback. Some or all of the above processes in the information delivery department may be performed using AI, for example, or not using AI. For example, the information delivery department can input past feedback data into AI, and the AI ​​can automatically select the optimal delivery method.

[0063] The information delivery unit can select the optimal delivery method by considering device information at the time of delivery. For example, if an employee is using a smartphone, the information delivery unit can provide an information delivery method that is adapted to the screen size. For example, if an organization is using tablets, the information delivery unit can provide an information delivery method that is adapted to the screen size. For example, if an organization is using tablets, the information delivery unit can provide an information delivery method that is adapted to the larger screen. For example, if an employee is using a smartwatch, the information delivery unit can provide a concise and highly visible information delivery method. For example, if an employee is using a smartwatch, the information delivery unit can provide a concise and highly visible information delivery method. This makes it possible to provide more appropriate information by considering device information. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or not using AI. For example, the information delivery unit can input device information into AI, and the AI ​​can automatically select the optimal delivery method.

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

[0065] The career matching system can also include a learning history collection unit that collects employees' learning histories. This unit collects and analyzes information about employees' past learning histories and acquired qualifications to understand their skill sets. For example, it collects information on training sessions and seminars employees have attended. It can also analyze information on acquired qualifications and certifications to evaluate their skill sets. Furthermore, the learning history collection unit can make appropriate career suggestions based on employees' learning histories. For instance, if an employee acquires a new skill, the unit can suggest projects that utilize that skill. This enables career suggestions that consider employees' learning histories, allowing for both skill development and career growth.

[0066] The career matching system can also include a communication analysis department that analyzes employees' communication styles. This department collects and analyzes information on employees' communication history and style to understand their communication characteristics. For example, it can collect employees' email and chat histories. It can also analyze employees' meeting and presentation histories to evaluate their communication styles. Furthermore, based on employees' communication styles, the communication analysis department can make appropriate career suggestions. For instance, if an employee has a leadership style, the department can suggest projects where they can demonstrate leadership. This enables career suggestions that consider employees' communication styles, supporting their career growth by leveraging their strengths.

[0067] The career matching system can also include a career goal collection unit that gathers employees' career goals. This unit collects and analyzes information about employees' career goals to understand their future aspirations. For example, it collects both short-term and long-term career goals set by employees. It can also analyze information about desired career paths and positions and evaluate those goals. Furthermore, based on employees' career goals, the unit can provide appropriate career suggestions. For instance, if an employee wants to demonstrate leadership, the unit can suggest projects where they can do so. This enables career suggestions that consider employees' career goals, supporting their goal achievement and career growth.

[0068] The career matching system may also include an anonymization unit that anonymizes employees' career information. The anonymization unit anonymizes employees' career information and protects their privacy. For example, the anonymization unit removes personal information such as employees' names and contact information. The anonymization unit can also treat employees' career information as statistical data, ensuring that individuals cannot be identified. Furthermore, the anonymization unit can use the anonymized career information to perform matching according to the needs of the organization or project. For example, the anonymization unit can anonymize employees' skill sets and experience to perform matching according to the organization's needs. This makes it possible to make appropriate career suggestions while protecting employee privacy. Some or all of the above processing in the anonymization unit may be performed using AI, for example, or not using AI. For example, the anonymization unit can input employee career information into AI, and the AI ​​can automatically perform the anonymization.

[0069] The career matching system may also include an update unit that periodically updates employees' career information. The update unit periodically collects employees' career information and updates it to the latest information. For example, if an employee acquires a new skill, the update unit collects that information and updates the career information. The update unit can also collect experience when an employee participates in a new project and update the career information. Furthermore, the update unit can periodically review employees' career information and update it to reflect the latest situation. For example, the update unit periodically reviews employees' career information and updates it to reflect the latest situation. This ensures that employees' career information is always up-to-date, enabling more appropriate career suggestions. Some or all of the above processes in the update unit may be performed using AI, for example, or not. For example, the update unit can input employee career information into an AI, which can then automatically perform the updates.

[0070] The following briefly describes the processing flow for example form 1.

[0071] Step 1: The Career Information Department collects employee career information. For example, it collects information such as employees' past work experience, skills, qualifications, achievements, positions, responsibilities, project experience, technical skills, soft skills, language skills, professional qualifications, and industry certifications. Step 2: The Career Analysis Department analyzes the collected career information to identify employees' strengths and aptitudes. For example, it uses data mining techniques, statistical analysis techniques, and machine learning algorithms to analyze career information. Step 3: The Organizational Information Gathering Department collects information on organizations and projects that are experiencing personnel shortages. For example, they gather information on the personnel needs of the organization or project, the required skill sets, project details, available positions, project objectives and duration, necessary resources, and the organization's culture and values. Step 4: The organizational analysis department analyzes the collected information to identify the needs of the organization and the project. For example, they analyze organizational information using data mining techniques, statistical analysis techniques, and machine learning algorithms. Step 5: The Matching Department uses information obtained from the Career Analysis Department and the Organizational Analysis Department to perform optimal matching between employees and organizations or projects. For example, it uses skill matching technology, cultural fit technology, and AI to evaluate the compatibility between employees and organizations and perform optimal matching. Step 6: The provisioning department provides the matching results obtained by the matching department to the employees and organizations. For example, the matching results are provided via web applications, mobile applications, email, or paper media, and are updated in real time.

[0072] (Example of form 2) The career matching system according to an embodiment of the present invention is a system that uses AI to propose the optimal match between employees seeking their next career and organizations or projects facing talent shortages. The career matching system collects employee career information, and the AI ​​analyzes the collected career information to identify the employee's strengths and aptitudes. Furthermore, the AI ​​collects information on organizations and projects facing talent shortages to understand current talent needs, required skill sets, and project details. Next, the AI ​​matches the employee's career information with the information on organizations and projects to propose the optimal match. Finally, the AI ​​provides the matching results to the employee and the organization. This system supports employees nearing mandatory retirement age to make the most of the careers they have cultivated so far in a new environment, thereby revitalizing both the organization and its people. For example, the career matching system collects information such as the employee's past work experience, skills, qualifications, and achievements. Next, the career matching system uses AI to analyze the collected career information to identify the employee's strengths and aptitudes. Furthermore, the career matching system uses AI to collect information on organizations and projects facing talent shortages to understand current talent needs, required skill sets, and project details. Next, the career matching system uses AI to match employees' career information with organizational and project information, proposing the optimal match. Finally, the career matching system uses AI to provide the matching results to the employee and the organization. This allows the career matching system to efficiently match employees' career information with organizational needs, enabling optimal career proposals.

[0073] The career matching system according to this embodiment comprises a career collection unit, a career analysis unit, an organization collection unit, an organization analysis unit, a matching unit, and a provision unit. The career collection unit collects employee career information. For example, the career collection unit collects information such as an employee's past work experience, skills, qualifications, and achievements. For example, the career collection unit can collect information such as an employee's past job titles, responsibilities, and project experience. The career collection unit can also collect information such as an employee's technical skills, soft skills, and language skills. Furthermore, the career collection unit can collect professional qualifications and industry certifications held by an employee. The career analysis unit analyzes the collected career information to identify an employee's strengths and aptitudes. For example, the career analysis unit analyzes career information using data mining techniques. The career analysis unit can also analyze career information using statistical analysis techniques. Furthermore, the career analysis unit can also analyze career information using machine learning algorithms. The organization collection unit collects information on organizations and projects that are experiencing personnel shortages. For example, the organization collection unit collects information such as the personnel needs of organizations and projects, the required skill sets, and project details. The Organizational Data Collection Department can collect information such as the skill sets and available positions that an organization needs. It can also collect detailed information such as project objectives, duration, and required resources. Furthermore, it can collect information about the organization's culture and values. The Organizational Analysis Department analyzes the collected information to identify the needs of the organization and projects. The Organizational Analysis Department analyzes organizational information using techniques such as data mining, statistical analysis, and machine learning algorithms. The Matching Department uses the information obtained by the Career Analysis and Organizational Analysis Departments to perform optimal matching between employees and organizations or projects. The Matching Department uses techniques such as skill matching to align the needs of employees and organizations, and cultural fit techniques to align the values ​​of employees and organizations.Furthermore, the matching unit can use AI to evaluate the compatibility between employees and organizations and perform optimal matching. The provision unit provides the matching results obtained by the matching unit to employees and organizations. The provision unit provides the matching results, for example, through a web application or a mobile application. The provision unit can also provide the matching results via email or paper. In addition, the provision unit can update the matching results in real time and provide them to employees and organizations quickly. As a result, the career matching system according to this embodiment can efficiently match employees' career information with the needs of organizations and make optimal career proposals.

[0074] The Career Collection Department collects employee career information. For example, it collects information such as employees' past work experience, skills, qualifications, and achievements. Specifically, it meticulously records details of projects employees have worked on in the past, their roles in those projects, and the results they achieved. Regarding technical skills, it collects information such as experience using programming languages ​​and tools, and expertise in specific technical areas. It also evaluates and records soft skills such as leadership, communication skills, and problem-solving abilities. Furthermore, it meticulously collects information on professional and industry certifications held by employees, including the type of certification, acquisition date, and renewal status. This information can be collected not only through employee self-reporting, but also by integrating data from internal evaluation systems and external certification bodies. The Career Collection Department centrally manages this information and updates it as needed, ensuring that career information is always up-to-date.

[0075] The Career Analysis Department analyzes collected career information to identify employees' strengths and aptitudes. For example, it uses data mining techniques to analyze career information. Specifically, it analyzes what types of work employees are suited for based on their past work experience and skill sets. It also uses statistical analysis techniques to analyze employee performance and evaluation data to identify performance trends and strengths. Furthermore, it can use machine learning algorithms to predict employees' career paths and propose future career directions. For example, based on past data, it can analyze the career paths that employees with specific skill sets tend to follow and propose the optimal career plan. The Career Analysis Department can also utilize the results of psychological tests and aptitude tests to evaluate employees' suitability. This allows the Career Analysis Department to comprehensively evaluate employees' strengths and aptitudes and provide optimal career recommendations.

[0076] The Organizational Information Collection Department collects information on organizations and projects facing talent shortages. For example, it gathers information such as the personnel needs of an organization or project, the required skill sets, and project details. Specifically, it collects detailed information on the current talent shortages an organization is facing, the skill sets needed for future projects, and the availability of positions. It can also collect detailed information on project objectives, duration, required resources, and budget. Furthermore, it collects information on the organization's culture and values, using it as data to assess cultural fit with employees. This information can be collected not only directly from the organization's HR department and project managers, but also from internal databases and external job posting sites. The Organizational Information Collection Department centrally manages this information and updates it as needed, ensuring that it always maintains up-to-date organizational information.

[0077] The Organizational Analysis Department analyzes collected information to identify the needs of organizations and projects. For example, it uses data mining techniques to analyze organizational information. Specifically, it analyzes what kind of personnel are needed based on the skill sets and position availability required by the organization. It can also use statistical analysis techniques to analyze past project data and personnel placement data to identify commonalities in successful projects and the causes of unsuccessful projects. Furthermore, it can use machine learning algorithms to predict the organization's future personnel needs and propose appropriate personnel placements. For example, based on past data, it can analyze which projects personnel with specific skill sets tend to succeed in and propose optimal personnel placements. The Organizational Analysis Department can also analyze information on the organization's culture and values ​​and evaluate cultural fit with employees. In this way, the Organizational Analysis Department can evaluate the needs of organizations and projects from multiple perspectives and make optimal personnel placements.

[0078] The Matching Department performs optimal matching between employees and organizations or projects based on information obtained by the Career Analysis Department and the Organizational Analysis Department. For example, the Matching Department uses skill matching technology to align the needs of employees and organizations. Specifically, it compares the skill sets of employees with those required by the organization to achieve the optimal match. It can also use cultural fit technology to align the values ​​of employees and organizations. For example, it evaluates whether an employee's values ​​and work style align with the organization's culture and values. Furthermore, it can use AI to evaluate the compatibility between employees and organizations and perform optimal matching. The AI ​​learns from past matching data and success stories to build an optimal matching algorithm. This allows the Matching Department to perform optimal matching between employees and organizations or projects with high accuracy. In addition, the Matching Department can continuously evaluate the matching results and improve the algorithm based on feedback. As a result, the Matching Department can always provide highly accurate matching based on the latest information and make optimal career proposals for both employees and organizations.

[0079] The Service Provider department provides the matching results obtained by the Matching Department to employees and organizations. The Service Provider department provides matching results, for example, through web or mobile applications. Specifically, it sets up a dedicated portal site accessible to employees and organizations, where the matching results are displayed. The Service Provider department can also provide matching results via email or paper. For example, important matching results are notified via email with a detailed report attached. Furthermore, the Service Provider department can update matching results in real time and provide them quickly to employees and organizations. For example, if matching results are updated, notifications are sent immediately to provide the latest information. This allows the Service Provider department to ensure that employees and organizations are always aware of the latest matching information and can respond quickly. The Service Provider department can also collect feedback on the matching results and use it to improve the system. This allows the Service Provider department to provide high-quality services to employees and organizations and maximize the effectiveness of the career matching system.

[0080] The Career Data Collection Department can collect information on employees' past work experience, skills, qualifications, and achievements. For example, it can collect information on employees' past job titles, responsibilities, and project experience. It can also collect information on employees' technical skills, soft skills, and language skills. Furthermore, it can collect information on professional qualifications and industry certifications held by employees. This allows for more accurate matching by collecting detailed career information on employees. Some or all of the above processing in the Career Data Collection Department may be performed using AI, for example, or not. For example, the Career Data Collection Department can input information on employees' past work experience and skills into an AI, which can then automatically collect the information.

[0081] The Career Analysis Department can analyze collected career information to identify employees' strengths and aptitudes. For example, the Career Analysis Department can analyze career information using data mining techniques. For instance, it can identify employees' strengths and aptitudes from the collected career information. The Career Analysis Department can also analyze career information using statistical analysis techniques. For example, it can use statistical analysis to evaluate employees' skill sets and performance. Furthermore, the Career Analysis Department can analyze career information using machine learning algorithms. For example, it can use machine learning to predict employees' aptitudes. This allows for optimal career proposals by identifying employees' strengths and aptitudes. Some or all of the above processes in the Career Analysis Department may be performed using AI, or not. For example, the Career Analysis Department can input collected career information into an AI, which can automatically identify strengths and aptitudes.

[0082] The Organizational Information Collection Department can collect information such as the personnel needs of organizations and projects, the required skill sets, and project details. For example, the Organizational Information Collection Department collects information on the skill sets and available positions that organizations require. For example, the Organizational Information Collection Department collects information on the necessary skill sets and available positions to understand the personnel needs of organizations. The Organizational Information Collection Department can also collect detailed information on projects, such as their objectives, duration, and required resources. For example, the Organizational Information Collection Department collects detailed project information to understand the project's objectives, duration, and required resources. Furthermore, the Organizational Information Collection Department can also collect information on organizations' culture and values. For example, the Organizational Information Collection Department collects information on organizations' culture and values ​​to understand the characteristics of organizations. This allows for more accurate matching by collecting detailed information on organizations and projects. Some or all of the above processing in the Organizational Information Collection Department may be performed using AI, or not. For example, the Organizational Information Collection Department can input information on organizations and projects into an AI, which can then automatically collect the information.

[0083] The Organizational Analysis Department can analyze collected information and identify the needs of organizations and projects. For example, the Organizational Analysis Department can analyze organizational information using data mining techniques. For instance, it can identify the needs of organizations and projects from the collected organizational information. The Organizational Analysis Department can also analyze organizational information using statistical analysis techniques. For example, it can use statistical analysis to evaluate the organization's personnel needs and skill sets. Furthermore, the Organizational Analysis Department can analyze organizational information using machine learning algorithms. For example, it can use machine learning to predict organizational needs. This allows for optimal career proposals by identifying the needs of organizations and projects. Some or all of the above processes in the Organizational Analysis Department may be performed using AI, or not. For example, the Organizational Analysis Department can input collected organizational information into an AI, which can then automatically identify the needs.

[0084] The matching department can perform optimal matching between employees and organizations or projects based on information obtained by the career analysis department and the organizational analysis department. For example, the matching department can use skill matching technology to match the needs of employees and organizations. For example, the matching department can match the skill sets of employees with the needs of organizations. The matching department can also use culture fit technology to match the values ​​of employees and organizations. For example, the matching department can match the culture and values ​​of employees and organizations. Furthermore, the matching department can use AI to evaluate the compatibility between employees and organizations and perform optimal matching. For example, the matching department can use AI to evaluate the compatibility between employees and organizations and perform optimal matching. This enables optimal matching between employees and organizations or projects. Some or all of the above processes in the matching department may be performed using AI, or not. For example, the matching department can input information obtained by the career analysis department and the organizational analysis department into the AI, which can then automatically perform optimal matching.

[0085] The service provider can provide matching results to employees and organizations. For example, the service provider can provide matching results through web applications or mobile applications. The service provider can also provide matching results via email or paper. For example, the service provider can send matching results to employees and organizations via email. Furthermore, the service provider can update matching results in real time and provide them quickly to employees and organizations. This allows for faster decision-making by employees and organizations. Some or all of the above processes in the service provider may be performed using AI, or not. For example, the service provider can input matching results into an AI, which can then automatically provide the results.

[0086] The career information gathering department can estimate employees' emotions and adjust the timing of information collection based on those emotions. For example, if an employee is feeling stressed, the department can adjust the collection timing so that information is provided in a relaxed state. The career information gathering department can also proactively collect career information and obtain detailed information if an employee is highly motivated. Furthermore, if an employee is busy, the department can adjust the timing so that information can be collected in a short time between tasks. This allows for more appropriate information collection by adjusting the collection timing according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processes described above in the career data collection department may be performed using AI, for example, or without AI. For example, the career data collection department can input employee emotional data into AI, which can then automatically adjust the timing of data collection.

[0087] The Career Information Collection Department can analyze employees' past career information and select the optimal collection method. For example, the Career Information Collection Department can identify areas of expertise from past career information and prioritize the collection of information related to those areas. The Career Information Collection Department can also collect information on new skills and qualifications that employees may be interested in, based on past career information. Furthermore, the Career Information Collection Department can analyze past career information and collect information to complement any skills or experience that employees lack. This allows the Career Information Collection Department to select the optimal information collection method by analyzing past career information. Some or all of the above processes in the Career Information Collection Department may be performed using AI, for example, or not. For example, the Career Information Collection Department can input past career information into AI, which can automatically select the optimal collection method.

[0088] The career information collection unit can filter the collected career information based on the employee's current projects and areas of interest. For example, the career information collection unit can prioritize collecting skills and experience related to the current project. The career information collection unit can also filter and collect relevant career information based on the employee's areas of interest. Furthermore, the career information collection unit can collect necessary information in a timely manner according to the progress of the current project. This allows for the collection of highly relevant information by filtering information based on the current project and areas of interest. Some or all of the above processing in the career information collection unit may be performed using AI, or not. For example, the career information collection unit can input information on the employee's current projects and areas of interest into an AI, which can then automatically perform the filtering.

[0089] The career information gathering department can estimate employees' emotions and prioritize the career information to collect based on those estimated emotions. For example, if an employee is feeling stressed, the career information gathering department will prioritize collecting information that helps them relax. Similarly, if an employee is highly motivated, the career information gathering department can prioritize collecting information related to challenging projects. Furthermore, if an employee is feeling tired, the career information gathering department can prioritize collecting information related to less demanding career options. This allows for more appropriate information gathering by prioritizing information according to employees' emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processes described above in the career data collection department may be performed using AI, for example, or without AI. For example, the career data collection department can input employee sentiment data into AI, which can then automatically determine the priority of the information.

[0090] The career information gathering unit can prioritize the collection of highly relevant information by considering the employee's geographical location when gathering career information. For example, the career information gathering unit can prioritize the collection of job postings in the area where the employee is currently located. The career information gathering unit can also prioritize the collection of career information related to the employee's desired work location. Furthermore, the career information gathering unit can also prioritize the collection of career options within the employee's commuting range. For example, the career information gathering unit can prioritize the collection of career options within the employee's commuting range. This allows for the priority collection of highly relevant information by considering geographical location. Some or all of the above processing in the career information gathering unit may be performed using AI, or not. For example, the career information gathering unit can input the employee's geographical location information into AI, which can then automatically prioritize the collection of highly relevant information.

[0091] The Career Information Collection Department can collect relevant information by analyzing employees' social media activity when collecting career information. For example, the Career Information Collection Department can collect career information related to areas that employees show interest in on social media. The Career Information Collection Department can also collect relevant career information based on industry trends and news that employees follow. Furthermore, the Career Information Collection Department can also collect relevant career information by analyzing the activities of online communities and groups that employees participate in. This allows for the collection of highly relevant information by analyzing social media activity. Some or all of the above processes in the Career Information Collection Department may be performed using AI, for example, or not. For example, the Career Information Collection Department can input employee social media activity data into AI, which can then automatically collect relevant information.

[0092] The Career Analysis Department can estimate employees' emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if an employee is stressed, the Career Analysis Department can provide simple and visually easy-to-understand analysis results. For example, if an employee is stressed, the Career Analysis Department can provide simple and visually easy-to-understand analysis results. For example, if an employee is relaxed, the Career Analysis Department can provide detailed analysis results to promote a deeper understanding. For example, if an employee is relaxed, the Career Analysis Department can provide detailed analysis results to promote a deeper understanding. For example, if an employee is in a hurry, the Career Analysis Department can provide concise analysis results that get straight to the point. For example, if an employee is in a hurry, the Career Analysis Department can provide concise analysis results that get straight to the point. In this way, by adjusting the presentation of the analysis according to the employee's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the processes described above in the Career Analysis Department may be performed using AI, for example, or without AI. For example, the Career Analysis Department can input employee emotional data into AI, which can then automatically adjust how the analysis is presented.

[0093] The Career Analysis Department can adjust the level of detail in its analysis of career information based on the employee's importance. For example, it can analyze information on important skills and experiences in detail to clarify an employee's strengths. The Career Analysis Department can also analyze less important information concisely to maintain overall balance. Furthermore, the Career Analysis Department can prioritize and analyze information related to an employee's career goals in detail. By adjusting the level of detail in the analysis based on the employee's importance, it can provide more appropriate analysis results. Some or all of the above processes in the Career Analysis Department may be performed using AI, for example, or not. For example, the Career Analysis Department can input employee importance data into AI, which can automatically adjust the level of detail in the analysis.

[0094] The Career Analysis Department can apply different analysis algorithms to employees depending on their category when analyzing career information. For example, the Career Analysis Department can apply an analysis algorithm specifically designed to evaluate technical skills to employees in technical positions. Similarly, the Career Analysis Department can apply an analysis algorithm specifically designed to evaluate leadership and management skills to employees in management positions. Furthermore, the Career Analysis Department can apply an analysis algorithm specifically designed to evaluate creativity and design skills to employees in creative positions. By applying analysis algorithms according to employee categories, more appropriate analysis results can be provided. Some or all of the above processes in the Career Analysis Department may be performed using AI, or not. For example, the Career Analysis Department can input employee category data into an AI, which can then automatically apply different analysis algorithms.

[0095] The Career Analysis Department can estimate an employee's emotions and adjust the length of the analysis based on the estimated emotions. For example, if an employee is in a hurry, the Career Analysis Department can provide a short, concise analysis. For example, if an employee is in a hurry, the Career Analysis Department can provide a short, concise analysis. For example, if an employee is relaxed, the Career Analysis Department can provide a longer analysis with detailed explanations. For example, if an employee is relaxed, the Career Analysis Department can provide a longer analysis with detailed explanations. For example, if an employee is excited, the Career Analysis Department can provide an analysis with visually stimulating effects. For example, if an employee is excited, the Career Analysis Department can provide an analysis with visually stimulating effects. In this way, by adjusting the length of the analysis according to the employee's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processes described above in the Career Analysis Department may be performed using AI, for example, or without AI. For example, the Career Analysis Department can input employee emotional data into AI, which can then automatically adjust the length of the analysis.

[0096] The Career Analysis Department can prioritize the analysis of career information based on when the information was submitted. For example, the Career Analysis Department can prioritize the analysis of recently submitted information to reflect the latest career status. The Career Analysis Department can also prioritize the analysis of information related to important projects. Furthermore, the Career Analysis Department can re-analyze older information as needed to bring it up to date. This allows the department to reflect the latest career status by prioritizing the analysis based on when the information was submitted. Some or all of the above processes in the Career Analysis Department may be performed using AI, for example, or not. For example, the Career Analysis Department can input information submission date data into an AI, which can then automatically determine the analysis priority.

[0097] The Career Analysis Department can adjust the order of analysis based on the relevance of the information when analyzing career information. For example, the Career Analysis Department can prioritize the analysis of information related to important skills and experiences. For example, the Career Analysis Department can prioritize the analysis of information related to employees' career goals. For example, the Career Analysis Department can prioritize the analysis of information related to employees' career goals. Furthermore, the Career Analysis Department can prioritize the analysis of information directly related to project success. For example, the Career Analysis Department can prioritize the analysis of information directly related to project success. By adjusting the order of analysis based on the relevance of the information, more appropriate analysis results can be provided. Some or all of the above processes in the Career Analysis Department may be performed using AI, for example, or not using AI. For example, the Career Analysis Department can input information relevance data into AI, and the AI ​​can automatically adjust the order of analysis.

[0098] The organizational data collection unit can estimate the organization's sentiment and adjust the timing of information collection based on the estimated sentiment. For example, if the organization urgently needs personnel, the organizational data collection unit can collect information quickly. For example, if the organization urgently needs personnel, the organizational data collection unit can collect information quickly. For example, if the organization is relaxed, the organizational data collection unit can collect detailed information over time. For example, if the organization is relaxed, the organizational data collection unit can collect detailed information over time. Furthermore, if the organization is busy, the organizational data collection unit can adjust the timing to collect information in short bursts between tasks. For example, if the organization is busy, the organizational data collection unit can adjust the timing to collect information in short bursts between tasks. This allows for more appropriate information collection by adjusting the collection timing according to the organization's sentiment. Sentiment estimation is achieved using sentiment estimation functions, such as sentiment engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the organizational data collection unit may be performed using AI, for example, or without AI. For example, the organizational data collection unit can input organizational sentiment data into an AI, which can then automatically adjust the timing of data collection.

[0099] The Organizational Data Collection Department can analyze the organization's past project information and select the optimal data collection method. For example, the Organizational Data Collection Department can identify the skill sets the organization needs from past project information and prioritize the collection of information related to those skills. The Organizational Data Collection Department can also collect information on new technologies and trends that the organization might be interested in, based on past project information. Furthermore, the Organizational Data Collection Department can analyze past project information and collect information to supplement any skills or resources the organization lacks. This allows the Organizational Data Collection Department to select the optimal data collection method by analyzing past project information. Some or all of the above processes in the Organizational Data Collection Department may be performed using AI, or not. For example, the Organizational Data Collection Department can input past project information into an AI, which can automatically select the optimal data collection method.

[0100] The organizational information collection unit can filter organizational information based on current projects and areas of interest. For example, it can prioritize collecting skills and experience related to current projects. The organizational information collection unit can also filter and collect relevant information based on the organization's areas of interest. Furthermore, the organizational information collection unit can collect necessary information in a timely manner according to the progress of current projects. This allows for the collection of highly relevant information by filtering information based on current projects and areas of interest. Some or all of the above processing in the organizational information collection unit may be performed using AI, or not. For example, the organizational information collection unit can input information on current projects and areas of interest into an AI, which can then automatically perform the filtering.

[0101] The organizational data collection unit can estimate the sentiment of an organization and determine the priority of information to collect based on the estimated sentiment. For example, if the organization urgently needs personnel, the organizational data collection unit will prioritize information that can be collected quickly. For example, if the organization urgently needs personnel, the organizational data collection unit will prioritize information that can be collected quickly. For example, if the organization is relaxed, the organizational data collection unit will take its time to collect detailed information. For example, if the organization is busy, the organizational data collection unit will prioritize information that can be collected quickly between tasks. For example, if the organization is busy, the organizational data collection unit will prioritize information that can be collected quickly between tasks. This allows for more appropriate information collection by prioritizing information according to the sentiment of the organization. Sentiment estimation is achieved using sentiment estimation functions, such as sentiment engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the organizational data collection unit may be performed using AI, for example, or without AI. For example, the organizational data collection department can input organizational sentiment data into an AI, which can then automatically determine the priority of the information.

[0102] The Organization Information Collection Unit can prioritize the collection of highly relevant information by considering geographical location information when collecting organizational information. For example, the Organization Information Collection Unit can prioritize the collection of job postings in the area where the organization is currently located. The Organization Information Collection Unit can also prioritize the collection of information related to the work location desired by the organization. Furthermore, the Organization Information Collection Unit can prioritize the collection of career options within the commutable distance of the organization. This allows for the priority collection of highly relevant information by considering geographical location information. Some or all of the above processing in the Organization Information Collection Unit may be performed using AI, for example, or without AI. For example, the Organization Information Collection Unit can input the organization's geographical location information into AI, which can then automatically prioritize the collection of highly relevant information.

[0103] The Organization Data Collection Department can collect relevant information by analyzing social media activity when collecting organizational information. For example, the Organization Data Collection Department can collect information related to areas in which the organization shows interest on social media. The Organization Data Collection Department can also collect relevant information based on industry trends and news that the organization follows. Furthermore, the Organization Data Collection Department can also collect relevant information by analyzing the activities of online communities and groups in which the organization participates. This allows for the collection of highly relevant information by analyzing social media activity. Some or all of the above processes in the Organization Data Collection Department may be performed using AI, or not. For example, the Organization Data Collection Department can input the organization's social media activity data into AI, which can then automatically collect relevant information.

[0104] The organizational analysis unit can estimate the emotions of an organization and adjust the presentation of the analysis based on the estimated emotions. For example, if an organization urgently needs personnel, the organizational analysis unit can provide concise and to-the-point analysis results. For example, if an organization urgently needs personnel, the organizational analysis unit can provide concise and to-the-point analysis results. Furthermore, if an organization is relaxed, the organizational analysis unit can provide detailed analysis results to facilitate a deeper understanding. For example, if an organization is relaxed, the organizational analysis unit can provide detailed analysis results to facilitate a deeper understanding. Furthermore, if an organization is busy, the organizational analysis unit can provide simple and visually easy-to-understand analysis results. For example, if an organization is busy, the organizational analysis unit can provide simple and visually easy-to-understand analysis results. In this way, by adjusting the presentation of the analysis according to the emotions of the organization, more appropriate analysis results can be provided. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the organizational analysis department may be performed using AI, for example, or without AI. For example, the organizational analysis department can input organizational sentiment data into AI, which can then automatically adjust the way the analysis is presented.

[0105] The organizational analysis department can adjust the level of detail of its analysis based on the importance of the projects when analyzing organizational information. For example, the organizational analysis department can analyze information related to important projects in detail to clarify the organization's needs. The organizational analysis department can also analyze less important information concisely to maintain overall balance. Furthermore, the organizational analysis department can prioritize and analyze information related to the organization's goals in detail. For example, the organizational analysis department prioritizes and analyzes information related to the organization's goals in detail. By adjusting the level of detail of the analysis based on the importance of the projects, more appropriate analysis results can be provided. Some or all of the above processes in the organizational analysis department may be performed using AI, for example, or not. For example, the organizational analysis department can input project importance data into the AI, and the AI ​​can automatically adjust the level of detail of the analysis.

[0106] The Organizational Analysis Department can apply different analysis algorithms depending on the project category when analyzing organizational information. For example, the Organizational Analysis Department can apply an analysis algorithm specialized in evaluating technical skills to technical projects. For example, the Organizational Analysis Department can apply an analysis algorithm specialized in evaluating leadership and management skills to management projects. For example, the Organizational Analysis Department can apply an analysis algorithm specialized in evaluating leadership and management skills to management projects. Furthermore, the Organizational Analysis Department can apply an analysis algorithm specialized in evaluating creativity and design skills to creative projects. For example, the Organizational Analysis Department can apply an analysis algorithm specialized in evaluating creativity and design skills to creative projects. By applying an analysis algorithm according to the project category, more appropriate analysis results can be provided. Some or all of the above processing in the Organizational Analysis Department may be performed using AI, for example, or not using AI. For example, the Organizational Analysis Department can input project category data into AI, and the AI ​​can automatically apply different analysis algorithms.

[0107] The organizational analysis unit can estimate the emotions of an organization and adjust the length of the analysis based on the estimated emotions. For example, if the organization is in a hurry, the organizational analysis unit will provide a short, concise analysis. For example, if the organization is in a hurry, the organizational analysis unit will provide a short, concise analysis. The organizational analysis unit can also provide a longer analysis with detailed explanations if the organization is relaxed. For example, if the organization is relaxed, the organizational analysis unit will provide a longer analysis with detailed explanations. Furthermore, if the organization is excited, the organizational analysis unit can provide an analysis with visually stimulating effects. For example, if the organization is excited, the organizational analysis unit will provide an analysis with visually stimulating effects. This allows for more appropriate analysis results by adjusting the length of the analysis according to the emotions of the organization. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the organizational analysis department may be performed using AI, for example, or without AI. For example, the organizational analysis department can input organizational sentiment data into the AI, which can then automatically adjust the length of the analysis.

[0108] The organizational analysis department can prioritize the analysis of organizational information based on when the information was submitted. For example, the organizational analysis department can prioritize the analysis of recently submitted information to reflect the latest project status. The organizational analysis department can also prioritize the analysis of information related to important projects. Furthermore, the organizational analysis department can re-analyze older information as needed to bring it up to date. This allows the department to reflect the latest project status by prioritizing the analysis based on when the information was submitted. Some or all of the above processes in the organizational analysis department may be performed using AI, for example, or not. For example, the organizational analysis department can input information submission date data into an AI, which can then automatically determine the analysis priority.

[0109] The organizational analysis unit can adjust the order of analysis based on the relevance of the information when analyzing organizational information. For example, the organizational analysis unit can prioritize the analysis of information related to important skills and experience. For example, the organizational analysis unit can prioritize the analysis of information related to organizational goals. For example, the organizational analysis unit can prioritize the analysis of information related to organizational goals. Furthermore, the organizational analysis unit can prioritize the analysis of information that is directly related to the success of a project. For example, the organizational analysis unit can prioritize the analysis of information that is directly related to the success of a project. By adjusting the order of analysis based on the relevance of the information, more appropriate analysis results can be provided. Some or all of the above processes in the organizational analysis unit may be performed using AI, for example, or not using AI. For example, the organizational analysis unit can input information relevance data into AI, and the AI ​​can automatically adjust the order of analysis.

[0110] The matching unit can estimate the emotions of employees and organizations and adjust the matching criteria based on the estimated emotions. For example, if an employee is feeling stressed, the matching unit will prioritize matching them with organizations that provide a relaxing environment. The matching unit can also prioritize matching employees who can respond quickly if an organization urgently needs personnel. Furthermore, if the emotions of the employee and organization match, the matching unit can prioritize matches that are a good match. This allows for more appropriate matching by adjusting the matching criteria based on the emotions of employees and organizations. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input employee and organizational sentiment data into the AI, which can then automatically adjust the matching criteria.

[0111] The matching unit can improve the accuracy of matching by considering the interrelationship between employees and organizations during the matching process. For example, the matching unit can perform matching that considers the interrelationship based on data from past successful matches. The matching unit can also analyze the mutual feedback between employees and organizations to perform highly accurate matching. Furthermore, the matching unit can consider the communication history between employees and organizations to perform compatible matching. This allows for more accurate matching by considering the interrelationship between employees and organizations. Some or all of the above processes in the matching unit may be performed using AI, or not. For example, the matching unit can input employee-organizational relationship data into AI, which can automatically improve the accuracy of matching.

[0112] The matching unit can perform matching by considering the attribute information of employees and organizations. For example, the matching unit can match an employee's skill set with the organization's needs. The matching unit can also match an employee's career goals with the organization's project goals. Furthermore, the matching unit can match an employee's preferred work location with the organization's work location. By considering the attribute information of employees and organizations, more appropriate matching becomes possible. Some or all of the above processes in the matching unit may be performed using AI, or not. For example, the matching unit can input employee and organization attribute information into AI, and the AI ​​can perform matching automatically.

[0113] The matching unit can estimate the emotions of employees and organizations and adjust the order in which matching results are displayed based on the estimated emotions. For example, if an employee is relaxed, the matching unit may prioritize displaying detailed matching results. The matching unit can also prioritize displaying matching results of employees who can respond quickly if the organization urgently needs personnel. Furthermore, if the emotions of the employee and the organization match, the matching unit may prioritize displaying matching results that are a good match. This allows for the provision of more appropriate matching results by adjusting the display order based on the emotions of employees and organizations. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input employee and organizational sentiment data into the AI, which can then automatically adjust the display order.

[0114] The matching unit can perform matching while considering geographical distribution. For example, the matching unit can match an employee's preferred work location with the organization's work location. The matching unit can also prioritize matching within an employee's commuting range. Furthermore, the matching unit can prioritize matching geographically close organizations with employees. This allows for more appropriate matching by considering geographical distribution. Some or all of the above processing in the matching unit may be performed using AI, or not. For example, the matching unit can input geographical distribution data of employees and organizations into AI, which can then automatically perform the matching.

[0115] The matching unit can improve the accuracy of matching by referring to relevant literature during the matching process. For example, the matching unit can refer to relevant research papers and apply the latest matching algorithms. The matching unit can also refer to industry best practices to perform highly accurate matching. Furthermore, the matching unit can refer to past success stories and perform matching under similar conditions. This allows for more accurate matching by referring to relevant literature. Some or all of the above processes in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input relevant literature data into AI, which can automatically improve the accuracy of matching.

[0116] The information delivery unit can estimate the emotions of employees and the organization and adjust the way information is presented based on the estimated emotions. For example, if employees are stressed, the information delivery unit can provide simple and visually easy-to-understand information. For example, if employees are stressed, the information delivery unit can provide simple and visually easy-to-understand information. For example, if employees are relaxed, the information delivery unit can provide detailed information to promote a deeper understanding. For example, if employees are relaxed, the information delivery unit can provide detailed information to promote a deeper understanding. For example, if employees are in a hurry, the information delivery unit can provide concise information to get straight to the point. For example, if employees are in a hurry, the information delivery unit can provide concise information to get straight to the point. This allows for more appropriate information delivery by adjusting the way information is presented based on the emotions of employees and the organization. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or not using AI. For example, the service provider can input employee and organizational sentiment data into the AI, which can then automatically adjust how the information is presented.

[0117] The information delivery department can select the optimal delivery method by referring to past feedback from employees and the organization at the time of delivery. For example, the information delivery department can identify the information delivery methods preferred by employees from past feedback and provide information based on those preferences. The information delivery department can also select the optimal information delivery method based on feedback from the organization. For example, the information delivery department can select the optimal information delivery method based on feedback from the organization. Furthermore, the information delivery department can analyze past feedback and select an information delivery method that suits the needs of employees and the organization. For example, the information delivery department can analyze past feedback and select an information delivery method that suits the needs of employees and the organization. This makes it possible to provide more appropriate information by referring to past feedback. Some or all of the above processes in the information delivery department may be performed using AI, for example, or not using AI. For example, the information delivery department can input past feedback data into AI, and the AI ​​can automatically select the optimal delivery method.

[0118] The information delivery unit can estimate the emotions of employees and the organization and prioritize the information to be delivered based on the estimated emotions. For example, if an employee is relaxed, the information delivery unit will prioritize providing detailed information. For example, if an organization urgently needs personnel, the information delivery unit will prioritize providing information that can be delivered quickly. For example, if an organization urgently needs personnel, the information delivery unit will prioritize providing information that can be delivered quickly. Furthermore, if the emotions of employees and the organization are aligned, the information delivery unit will prioritize providing information that is compatible with them. For example, if the emotions of employees and the organization are aligned, the information delivery unit will prioritize providing information that is compatible with them. This allows for more appropriate information delivery by prioritizing information based on the emotions of employees and the organization. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the service provider can input employee and organizational sentiment data into an AI, which can then automatically determine the priority of the information.

[0119] The information delivery unit can select the optimal delivery method by considering device information at the time of delivery. For example, if an employee is using a smartphone, the information delivery unit can provide an information delivery method that is adapted to the screen size. For example, if an organization is using tablets, the information delivery unit can provide an information delivery method that is adapted to the screen size. For example, if an organization is using tablets, the information delivery unit can provide an information delivery method that is adapted to the larger screen. For example, if an employee is using a smartwatch, the information delivery unit can provide a concise and highly visible information delivery method. For example, if an employee is using a smartwatch, the information delivery unit can provide a concise and highly visible information delivery method. This makes it possible to provide more appropriate information by considering device information. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or not using AI. For example, the information delivery unit can input device information into AI, and the AI ​​can automatically select the optimal delivery method.

[0120] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0121] The career matching system can also include a health management department that monitors employees' health status. This department collects and analyzes employee health data to understand their well-being. For example, it could collect data such as heart rate, sleep patterns, and exercise levels. It could also analyze employees' stress levels and fatigue to assess their health. Furthermore, the health management department could make appropriate career suggestions based on employees' health status. For instance, if an employee is experiencing stress, it could suggest a less stressful project. This allows for career suggestions that consider employees' health, enabling both health maintenance and career growth.

[0122] The career matching system can also include a "Hobby Collection Department" that gathers information about employees' hobbies and interests. This department collects and analyzes information about employees' hobbies and interests to understand their personalities. For example, it collects information about hobby clubs and events that employees participate in. It can also analyze the areas and activities that employees are interested in to assess their personalities. Furthermore, the Hobby Collection Department can make appropriate career suggestions based on employees' hobbies and interests. For instance, if an employee is interested in creative activities, the department can suggest creative projects. This allows for career suggestions that take employees' hobbies and interests into account, enabling both increased employee motivation and career growth.

[0123] The career matching system can also include a learning history collection unit that collects employees' learning histories. This unit collects and analyzes information about employees' past learning histories and acquired qualifications to understand their skill sets. For example, it collects information on training sessions and seminars employees have attended. It can also analyze information on acquired qualifications and certifications to evaluate their skill sets. Furthermore, the learning history collection unit can make appropriate career suggestions based on employees' learning histories. For instance, if an employee acquires a new skill, the unit can suggest projects that utilize that skill. This enables career suggestions that consider employees' learning histories, allowing for both skill development and career growth.

[0124] The career matching system can also include a communication analysis department that analyzes employees' communication styles. This department collects and analyzes information on employees' communication history and style to understand their communication characteristics. For example, it can collect employees' email and chat histories. It can also analyze employees' meeting and presentation histories to evaluate their communication styles. Furthermore, based on employees' communication styles, the communication analysis department can make appropriate career suggestions. For instance, if an employee has a leadership style, the department can suggest projects where they can demonstrate leadership. This enables career suggestions that consider employees' communication styles, supporting their career growth by leveraging their strengths.

[0125] The career matching system can further estimate an employee's emotions and adjust the content of career suggestions based on those emotions. For example, if an employee is feeling stressed, the career matching system can suggest a less stressful project. Conversely, if an employee is highly motivated, the system can suggest a more challenging project. Furthermore, if an employee is relaxed, the system can provide detailed career suggestions to promote a deeper understanding. By adjusting the content of career suggestions according to the employee's emotions, more appropriate career suggestions become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the career matching system may be performed using AI, or not. For example, the career matching system can input employee emotion data into an AI, which can then automatically adjust the content of career suggestions.

[0126] The career matching system can also include a career goal collection unit that gathers employees' career goals. This unit collects and analyzes information about employees' career goals to understand their future aspirations. For example, it collects both short-term and long-term career goals set by employees. It can also analyze information about desired career paths and positions and evaluate those goals. Furthermore, based on employees' career goals, the unit can provide appropriate career suggestions. For instance, if an employee wants to demonstrate leadership, the unit can suggest projects where they can do so. This enables career suggestions that consider employees' career goals, supporting their goal achievement and career growth.

[0127] The career matching system can further estimate employees' emotions and adjust how career information is collected based on those estimated emotions. For example, if an employee is feeling stressed, the career matching system can adjust its collection methods to provide information in a relaxed state. Conversely, if an employee is highly motivated, the career matching system can actively collect career information and obtain detailed information. Furthermore, if an employee is busy, the career matching system can adjust its collection methods to allow them to collect information quickly during breaks in their work. This allows for more appropriate information collection by adjusting the collection method according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the career matching system may be performed using AI, or not. For example, the career matching system can input employee emotion data into an AI, which can then automatically adjust the collection method.

[0128] The career matching system may also include an anonymization unit that anonymizes employees' career information. The anonymization unit anonymizes employees' career information and protects their privacy. For example, the anonymization unit removes personal information such as employees' names and contact information. The anonymization unit can also treat employees' career information as statistical data, ensuring that individuals cannot be identified. Furthermore, the anonymization unit can use the anonymized career information to perform matching according to the needs of the organization or project. For example, the anonymization unit can anonymize employees' skill sets and experience to perform matching according to the organization's needs. This makes it possible to make appropriate career suggestions while protecting employee privacy. Some or all of the above processing in the anonymization unit may be performed using AI, for example, or not using AI. For example, the anonymization unit can input employee career information into AI, and the AI ​​can automatically perform the anonymization.

[0129] The career matching system may also include an update unit that periodically updates employees' career information. The update unit periodically collects employees' career information and updates it to the latest information. For example, if an employee acquires a new skill, the update unit collects that information and updates the career information. The update unit can also collect experience when an employee participates in a new project and update the career information. Furthermore, the update unit can periodically review employees' career information and update it to reflect the latest situation. For example, the update unit periodically reviews employees' career information and updates it to reflect the latest situation. This ensures that employees' career information is always up-to-date, enabling more appropriate career suggestions. Some or all of the above processes in the update unit may be performed using AI, for example, or not. For example, the update unit can input employee career information into an AI, which can then automatically perform the updates.

[0130] The career matching system can further estimate employees' emotions and prioritize career information based on those emotions. For example, if an employee is feeling stressed, the career matching system will prioritize collecting information that helps them relax. Similarly, if an employee is highly motivated, the system can prioritize collecting information related to challenging projects. Furthermore, if an employee is feeling tired, the system can prioritize collecting information related to less demanding career options. This allows for more appropriate information gathering by prioritizing information according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the career matching system may be performed using AI or not. For example, the career matching system can input employee emotion data into an AI, which can then automatically determine the priority of information.

[0131] The following briefly describes the processing flow for example form 2.

[0132] Step 1: The Career Information Department collects employee career information. For example, it collects information such as employees' past work experience, skills, qualifications, achievements, positions, responsibilities, project experience, technical skills, soft skills, language skills, professional qualifications, and industry certifications. Step 2: The Career Analysis Department analyzes the collected career information to identify employees' strengths and aptitudes. For example, it uses data mining techniques, statistical analysis techniques, and machine learning algorithms to analyze career information. Step 3: The Organizational Information Gathering Department collects information on organizations and projects that are experiencing personnel shortages. For example, they gather information on the personnel needs of the organization or project, the required skill sets, project details, available positions, project objectives and duration, necessary resources, and the organization's culture and values. Step 4: The organizational analysis department analyzes the collected information to identify the needs of the organization and the project. For example, they analyze organizational information using data mining techniques, statistical analysis techniques, and machine learning algorithms. Step 5: The Matching Department uses information obtained from the Career Analysis Department and the Organizational Analysis Department to perform optimal matching between employees and organizations or projects. For example, it uses skill matching technology, cultural fit technology, and AI to evaluate the compatibility between employees and organizations and perform optimal matching. Step 6: The provisioning department provides the matching results obtained by the matching department to the employees and organizations. For example, the matching results are provided via web applications, mobile applications, email, or paper media, and are updated in real time.

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0135] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the career collection unit, career analysis unit, organization collection unit, organization analysis unit, matching unit, and provision unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the career collection unit collects employee career information by the control unit 46A of the smart device 14. The career analysis unit analyzes the collected career information by, for example, the specific processing unit 290 of the data processing unit 12 to identify the employee's strengths and aptitudes. The organization collection unit collects information on organizations and projects with personnel shortages by, for example, the control unit 46A of the smart device 14. The organization analysis unit analyzes the collected information by, for example, the specific processing unit 290 of the data processing unit 12 to identify the needs of organizations and projects. The matching unit performs optimal matching between employees and organizations or projects by, for example, the specific processing unit 290 of the data processing unit 12. The provision unit provides the matching results to employees and organizations by, for example, the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0137] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0138] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the career collection unit, career analysis unit, organization collection unit, organization analysis unit, matching unit, and provision unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the career collection unit collects employee career information using the control unit 46A of the smart glasses 214. The career analysis unit analyzes the collected career information using the specific processing unit 290 of the data processing unit 12 to identify the employee's strengths and aptitudes. The organization collection unit collects information on organizations and projects with personnel shortages using the control unit 46A of the smart glasses 214. The organization analysis unit analyzes the collected information using the specific processing unit 290 of the data processing unit 12 to identify the needs of organizations and projects. The matching unit performs optimal matching between employees and organizations or projects using the specific processing unit 290 of the data processing unit 12. The provision unit provides the matching results to employees and organizations using the control unit 46A of the smart glasses 214. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0153] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0154] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0155] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0156] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0157] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0159] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0160] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0161] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0162] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0163] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0164] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0165] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0166] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0167] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0168] Each of the multiple elements described above, including the career collection unit, career analysis unit, organization collection unit, organization analysis unit, matching unit, and provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the career collection unit collects employee career information using the control unit 46A of the headset terminal 314. The career analysis unit analyzes the collected career information using, for example, the specific processing unit 290 of the data processing unit 12 to identify the employee's strengths and aptitudes. The organization collection unit collects information on organizations and projects with personnel shortages using, for example, the control unit 46A of the headset terminal 314. The organization analysis unit analyzes the collected information using, for example, the specific processing unit 290 of the data processing unit 12 to identify the needs of organizations and projects. The matching unit performs optimal matching between employees and organizations or projects using, for example, the specific processing unit 290 of the data processing unit 12. The provision unit provides the matching results to employees and organizations using, for example, the control unit 46A of the headset terminal 314. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0169] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0170] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0171] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0172] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0173] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0174] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0175] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0176] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0177] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0178] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0179] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0180] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0181] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0182] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0183] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0184] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0185] Each of the multiple elements described above, including the career collection unit, career analysis unit, organization collection unit, organization analysis unit, matching unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the career collection unit collects employee career information by the control unit 46A of the robot 414. The career analysis unit analyzes the collected career information by, for example, the specific processing unit 290 of the data processing unit 12 to identify the employee's strengths and aptitudes. The organization collection unit collects information on organizations and projects with personnel shortages by, for example, the control unit 46A of the robot 414. The organization analysis unit analyzes the collected information by, for example, the specific processing unit 290 of the data processing unit 12 to identify the needs of organizations and projects. The matching unit performs optimal matching between employees and organizations or projects by, for example, the specific processing unit 290 of the data processing unit 12. The provision unit provides the matching results to employees and organizations by, for example, the control unit 46A of the robot 414. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0186] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0187] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0188] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0189] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0190] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0191] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0192] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0193] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0194] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0195] 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.

[0196] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0197] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0198] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0199] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0200] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0201] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0202] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0203] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0204] (Note 1) The Career Information Collection Department collects employee career information, The career analysis unit analyzes the career information collected by the aforementioned career collection unit to identify the strengths and aptitudes of employees, The Organizational Information Gathering Department collects information on organizations and projects facing personnel shortages, The Organizational Analysis Department analyzes the information collected by the aforementioned Organizational Information Collection Department and identifies the needs of the organization and project, Based on the information obtained by the Career Analysis Department and the Organizational Analysis Department, a Matching Department is established to perform optimal matching between employees and organizations or projects. The system includes a providing unit that provides the matching results obtained by the matching unit. A system characterized by the following features. (Note 2) The aforementioned carrier collection unit is Collect information on employees' past work experience, skills, qualifications, and achievements. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned carrier analysis unit, We analyze the collected career information to identify employees' strengths and aptitudes. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned organization collection unit, Gather information such as the personnel needs of the organization or project, the required skill sets, and project details. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned tissue analysis unit, Analyze the collected information to identify the needs of the organization and project. The system described in Appendix 1, characterized by the features described herein. (Note 6) The matching unit is Based on information obtained by the Career Analysis Department and the Organizational Analysis Department, we will perform optimal matching between employees and organizations or projects. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, Providing matching results to employees and organizations The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned carrier collection unit is We estimate employees' emotions and adjust the timing of career information collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned carrier collection unit is Analyze employees' past career information and select the most suitable data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned carrier collection unit is When collecting career information, filter it based on the employee's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned carrier collection unit is We estimate employees' emotions and prioritize the career information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned carrier collection unit is When collecting career information, the system prioritizes collecting highly relevant information by considering the geographical location of employees. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned carrier collection unit is When collecting career information, we analyze employees' social media activity and gather relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned carrier analysis unit, We estimate the emotions of our employees and adjust the representation of the analysis based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned carrier analysis unit, When analyzing career information, the level of detail in the analysis is adjusted based on the importance of the employee. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned carrier analysis unit, When analyzing career information, different analysis algorithms are applied depending on the employee's category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned carrier analysis unit, The system estimates the emotions of employees and adjusts the length of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned carrier analysis unit, When analyzing career information, the priority of analysis is determined based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned carrier analysis unit, When analyzing carrier information, the order of analysis is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned organization collection unit, Estimate the organization's sentiment and adjust the timing of information collection based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned organization collection unit, Analyze the organization's past project information and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned organization collection unit, When collecting organizational information, filter it based on current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned organization collection unit, Estimate the organization's sentiment and prioritize the information to collect based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned organization collection unit, When collecting organizational information, prioritize the collection of highly relevant information, taking geographical location into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned organization collection unit, When collecting organizational information, analyze social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned tissue analysis unit, We estimate the organization's sentiment and adjust the representation of the analysis based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned tissue analysis unit, When analyzing organizational information, adjust the level of detail of the analysis based on the importance of the project. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned tissue analysis unit, When analyzing organizational information, different analysis algorithms are applied depending on the project category. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned tissue analysis unit, Estimate the organization's sentiment and adjust the length of the analysis based on the estimated organizational sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned tissue analysis unit, When analyzing organizational information, prioritize the analysis based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned tissue analysis unit, When analyzing organizational information, adjust the order of analysis based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 32) The matching unit is We estimate the sentiments of employees and the organization, and adjust the matching criteria based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 33) The matching unit is To improve the accuracy of matching, we consider the interrelationship between employees and organizations during the matching process. The system described in Appendix 1, characterized by the features described herein. (Note 34) The matching unit is During the matching process, the attribute information of both employees and organizations is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 35) The matching unit is It estimates the sentiments of employees and the organization, and adjusts the order in which matching results are displayed based on the estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 36) The matching unit is When matching, the geographical distribution is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 37) The matching unit is During the matching process, we refer to relevant literature to improve the accuracy of the matching. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned supply unit is, We estimate the sentiments of employees and the organization, and adjust the way information is presented based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned supply unit is, When providing the service, we will select the optimal delivery method by referring to past feedback from employees and the organization. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned supply unit is, We estimate the sentiments of employees and the organization, and prioritize the information we provide based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking device information into consideration. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The Career Information Collection Department collects employee career information, The career analysis unit analyzes the career information collected by the aforementioned career collection unit to identify the strengths and aptitudes of employees, The Organizational Information Gathering Department collects information on organizations and projects facing personnel shortages, The Organizational Analysis Department analyzes the information collected by the aforementioned Organizational Information Collection Department and identifies the needs of the organization and project, Based on the information obtained by the Career Analysis Department and the Organizational Analysis Department, a Matching Department is established to perform optimal matching between employees and organizations or projects. The system includes a providing unit that provides the matching results obtained by the matching unit. A system characterized by the following features.

2. The aforementioned carrier collection unit is Collect information on employees' past work experience, skills, qualifications, and achievements. The system according to feature 1.

3. The aforementioned carrier analysis unit, We analyze the collected career information to identify employees' strengths and aptitudes. The system according to feature 1.

4. The aforementioned organization collection unit, Gather information such as the personnel needs of the organization or project, the required skill sets, and project details. The system according to feature 1.

5. The aforementioned tissue analysis unit, Analyze the collected information to identify the needs of the organization and project. The system according to feature 1.

6. The matching unit is Based on the information obtained by the Career Analysis Department and the Organizational Analysis Department, the optimal matching of employees with organizations and projects is performed. The system according to feature 1.

7. The aforementioned supply unit is, Providing matching results to employees and organizations The system according to feature 1.

8. The aforementioned carrier collection unit is We estimate employees' emotions and adjust the timing of career information collection based on those estimated emotions. The system according to feature 1.

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