system

Abstract

The system according to the embodiment aims to match employees with suitable projects based on their skills and interests. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and a configuration unit. The collection unit collects employee information. The analysis unit analyzes the information collected by the collection unit. The proposal unit proposes appropriate projects to employees based on the analysis results obtained by the analysis unit. The configuration unit analyzes the needs of the projects proposed by the proposal unit and assembles an appropriate project team.

Description

[Technical Field]

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

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

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

[0004] Previous technology had the challenge of making it difficult to match employees with appropriate projects based on their skills and interests.

[0005] The system according to the embodiment aims to match employees with suitable projects based on their skills and interests. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, and a configuration unit. The collection unit collects employee information. The analysis unit analyzes the information collected by the collection unit. The proposal unit proposes appropriate projects to employees based on the analysis results obtained by the analysis unit. The configuration unit analyzes the needs of the projects proposed by the proposal unit and assembles an appropriate project team. [Effects of the Invention]

[0007] The system according to the embodiment can match employees to suitable projects based on their skills and interests. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A project matching system according to an embodiment of the present invention allows employees to participate in projects that match their skills and interests. This system collects and analyzes employee information, proposes optimal projects, and assembles optimal project teams. For example, employees input their backgrounds, skills, and interests. For example, employees enter details about their past project experience, expertise, and areas of interest. This information is then input into an AI. The AI ​​then analyzes the input information. The AI ​​then analyzes the employee's backgrounds, skills, and interests in detail to propose optimal projects for the employee. For example, the AI ​​analyzes the employee's programming skills and proposes projects that require those skills. Furthermore, the AI ​​considers the project's needs and the team members' abilities to assemble an optimal project team. For example, the AI ​​analyzes the project requirements and selects team members who meet those requirements. The team is then assembled based on the team members' skills, experience, and interests. This maximizes project results. This allows the project matching system to maximize employee skills and contribute to project success. Furthermore, by assigning the right team members to projects, companies can increase the success rate of their projects. For example, AI can select members who meet the project requirements, which will help the project progress smoothly and improve results.

[0029] A project matching system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and a configuration unit. The collection unit collects employee information. The employee information includes, but is not limited to, information about the employee's career history, skills, and interests. The collection unit provides, for example, an interface for the employee to input their career history, skills, and interests. The collection unit can also collect the employee's past project history and feedback. For example, the collection unit automatically collects information about projects in which the employee has previously participated and stores the information in a database. The analysis unit analyzes the information collected by the collection unit. The analysis can be performed using, for example, but not limited to, data mining, statistical analysis, or a machine learning algorithm. For example, the analysis unit can perform a detailed analysis of the employee's skills and interests to generate data for proposing optimal projects. The analysis unit can also evaluate the employee's capabilities based on the employee's past project history and feedback. The proposal unit proposes optimal projects for the employee based on the analysis results obtained by the analysis unit. The proposal can be based, for example, on the employee's skills and interests, but is not limited to, the example. For example, the proposal unit analyzes an employee's programming skills and proposes projects that require those skills. The proposal unit can also propose projects in areas of interest to the employee based on their interests. The configuration unit analyzes the needs of the project proposed by the proposal unit and assembles an optimal project team. The configuration is performed, for example, by selecting members who match the project requirements, but is not limited to this example. For example, the configuration unit analyzes the project's technical requirements and selects members with high technical skills. The configuration unit can also analyze the project's marketing requirements and select members with high marketing skills. In this way, the project matching system according to the embodiment can collect and analyze employee information, propose optimal projects, and assemble optimal project teams.

[0030] The project matching system includes a monitoring unit that monitors the progress of the project. The monitoring unit monitors the progress of the project. Monitoring is performed by, for example, but not limited to, methods such as progress reports, task completion status, and schedule adherence. For example, the monitoring unit monitors the progress of the project in real time and detects delays in progress. The monitoring unit can also periodically report the progress of the project and detect abnormalities. Furthermore, the monitoring unit can evaluate the progress of the project by comparing it with past data. For example, the monitoring unit compares it with past project progress and evaluates delays in progress. In this way, monitoring the progress of the project makes the project proceed smoothly and improves results.

[0031] The project matching system includes an evaluation unit that evaluates the results of a project. The evaluation unit evaluates the results of the project. The evaluation is performed based on criteria such as, but not limited to, the degree of goal achievement, quality, and cost efficiency. For example, the evaluation unit analyzes the results of the project in detail and sets evaluation criteria. The evaluation unit can also evaluate the progress of the project by comparing it with past data. Furthermore, the evaluation unit can improve the accuracy of the evaluation by referring to literature related to the project. For example, the evaluation unit can improve the accuracy of the evaluation by referring to academic papers and technical reports. In this way, the success rate of the project can be increased by evaluating the results of the project.

[0032] The collection unit can collect employee backgrounds, skills, and interests. The collection unit, for example, collects employee backgrounds. For example, the collection unit collects information such as employees' work history, educational background, and qualifications. The collection unit can also collect employee skills. For example, the collection unit collects information such as employees' technical skills, soft skills, and language abilities. The collection unit can also collect employee interests. For example, the collection unit collects information such as employees' areas of interest and past project history. In this way, by collecting employees' backgrounds, skills, and interests, it is possible to provide data for proposing projects that are best suited to employees.

[0033] The analysis unit can analyze an employee's background, skills, and interests. The analysis unit, for example, analyzes an employee's background. For example, the analysis unit evaluates an employee's capabilities based on information such as the employee's work history, educational background, and qualifications. The analysis unit can also analyze an employee's skills. For example, the analysis unit evaluates an employee's skills based on information such as the employee's technical skills, soft skills, and language abilities. The analysis unit can also analyze an employee's interests. For example, the analysis unit evaluates an employee's interests based on information such as the employee's areas of interest and past project history. In this way, by analyzing an employee's background, skills, and interests in detail, it is possible to provide data for proposing projects that are best suited to the employee.

[0034] The proposal department can propose optimal projects based on the skills and interests of employees. The proposal department can, for example, propose projects based on the skills of employees. For example, the proposal department can analyze the programming skills of employees and propose projects that require those skills. The proposal department can also propose projects based on the interests of employees. For example, the proposal department can propose projects in areas that interest employees. Furthermore, the proposal department can propose optimal projects for employees based on the employee's past project history. For example, the proposal department can propose similar projects based on information about projects that the employee has been successful in the past. In this way, by proposing optimal projects based on the employee's skills and interests, it is possible to make the most of the employee's skills and contribute to the success of the project.

[0035] The project composition department can form an appropriate project team by considering the needs of the project or the abilities of the members. The project composition department can form a team by considering the needs of the project. For example, the project composition department can analyze the technical requirements of the project and select members with high technical skills. The project composition department can also analyze the marketing requirements of the project and select members with high marketing skills. The project composition department can also form a team by considering the abilities of the members. For example, the project composition department can select the optimal members based on the members' technical skills, experience, past performance, etc. Furthermore, the project composition department can form a team by considering the balance of the members' skill sets. For example, the project composition department can form the optimal team by considering the balance of technical skills and soft skills. In this way, by forming the optimal project team by considering the needs of the project and the abilities of the members, the project results can be maximized.

[0036] The collection department can analyze the employee's past project history and select the optimal information collection method. The collection department, for example, analyzes the employee's past project history. For example, the collection department selects the optimal information collection method based on information about projects that the employee has previously succeeded in. The collection department can also avoid information about projects that the employee has previously failed in. Furthermore, the collection department can select the most efficient information collection method from the employee's past project history. For example, the collection department refers to the information collection method used in projects that the employee has previously succeeded in. In this way, by analyzing the employee's past project history and selecting the optimal information collection method, the efficiency of information collection can be improved.

[0037] When collecting information, the collection unit can filter the information based on the employee's current work situation and areas of interest. For example, the collection unit filters the information taking into account the employee's current work situation. For example, the collection unit prioritizes collecting information related to the work the employee is currently working on. The collection unit can also filter the information based on the employee's areas of interest. For example, the collection unit prioritizes collecting information related to the employee's areas of interest. Furthermore, the collection unit can collect only necessary information depending on the employee's work situation. For example, the collection unit prioritizes collecting information that is highly important depending on the employee's work situation. In this way, by filtering information based on the employee's current work situation and areas of interest, the efficiency of information collection can be improved.

[0038] When collecting information, the collection unit can select an appropriate collection means depending on the employee's input method. The collection unit collects information depending on, for example, the employee's input method. For example, if the employee prefers voice input, the collection unit collects information by voice. Also, if the employee prefers text input, the collection unit can collect information by text. Furthermore, if the employee prefers image input, the collection unit can collect information by image. For example, the collection unit provides an interface for the employee to upload images. This allows the efficiency of information collection to be improved by selecting the optimal collection means depending on the employee's input method.

[0039] When collecting information, the collection unit can prioritize collecting highly relevant information based on the employee's geographical location information. The collection unit, for example, collects information taking into account the employee's geographical location information. For example, if the employee is in a specific area, the collection unit can prioritize collecting information related to that area. Furthermore, if the employee is on a business trip, the collection unit can prioritize collecting information related to the business trip destination. Furthermore, if the employee is working remotely, the collection unit can prioritize collecting information around the employee's home. For example, the collection unit filters highly relevant information based on the employee's geographical location information. This can improve the efficiency of information collection by prioritizing the collection of highly relevant information taking into account the employee's geographical location information.

[0040] When collecting information, the collection unit can analyze the social media activities of employees and collect relevant information. The collection unit, for example, analyzes the social media activities of employees. For example, the collection unit collects relevant information based on information shared by employees on social media. The collection unit can also analyze the content of employees' social media activities and collect relevant information. Furthermore, the collection unit can collect relevant information by referring to the activities of the employees' friends on social media. For example, the collection unit analyzes the content of employees' social media posts and filters out highly relevant information. In this way, the efficiency of information collection can be improved by analyzing employees' social media activities and collecting relevant information.

[0041] When collecting information, the collection unit can customize the collection method by reflecting past employee feedback. The collection unit, for example, improves the information collection method based on past employee feedback. For example, the collection unit improves the information collection method based on feedback provided by employees in the past. The collection unit can also select the optimal information collection means from past employee feedback. Furthermore, the collection unit can improve the accuracy of information collection by reflecting employee feedback. For example, the collection unit improves the design of a questionnaire or the content of interview questions based on past employee feedback. In this way, by customizing the collection method by reflecting past employee feedback, the efficiency of information collection can be improved.

[0042] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of an employee's skills. The analysis unit, for example, evaluates the importance of an employee's skills. For example, the analysis unit evaluates the importance of skills based on the employee's technical skills and experience. The analysis unit adjusts the level of detail of the analysis based on the evaluated level of skill importance. For example, if an employee's skills are high, detailed analysis results can be provided. On the other hand, if an employee's skills are low, simple analysis results can be provided. Furthermore, the level of detail of the analysis can be adjusted according to the importance of an employee's skills. For example, the granularity of the data and the depth of the analysis can be adjusted according to the importance of an employee's skills. In this way, adjusting the level of detail of the analysis based on the importance of an employee's skills can facilitate understanding of the analysis results.

[0043] During analysis, the analysis unit can apply different analysis algorithms depending on the employee category. The analysis unit, for example, classifies employee categories. For example, the analysis unit classifies categories based on the employee's job type, department, project type, etc. The analysis unit applies different analysis algorithms depending on the classified category. For example, if the employee is an engineer, a technical analysis algorithm can be applied. Also, if the employee is in charge of marketing, an analysis algorithm specialized for marketing can be applied. Furthermore, the optimal analysis algorithm can be selected depending on the employee category. For example, the analysis unit applies analysis algorithms such as regression analysis, clustering, and deep learning depending on the employee category. In this way, by applying different analysis algorithms depending on the employee category, the accuracy of the analysis results can be improved.

[0044] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the employee's past analysis results. The analysis unit, for example, analyzes the employee's past analysis results. For example, the analysis unit improves the accuracy of the analysis based on the employee's past project results and feedback. The analysis unit can also select the optimal analysis method from the employee's past analysis results. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to the employee's past analysis results. For example, the analysis unit aims to minimize errors and improve the accuracy of the model based on the employee's past analysis results. In this way, by improving the accuracy of the analysis by referring to the employee's past analysis results, the reliability of the analysis results can be increased.

[0045] During analysis, the analysis unit can determine the priority of analysis based on the time when the employee's information is submitted. The analysis unit, for example, determines the priority of analysis taking into account the time when the employee's information is submitted. For example, if an employee submits information early, the analysis can be prioritized. Also, if an employee submits information late, the information can be postponed. Furthermore, the priority of analysis can be adjusted depending on the time when the employee's information is submitted. For example, the analysis unit determines the priority of analysis taking into account the importance and urgency of the work depending on the time when the employee's information is submitted. In this way, by determining the priority of analysis based on the time when the employee's information is submitted, the efficiency of analysis can be improved.

[0046] During analysis, the analysis unit can adjust the order of analysis based on the relevance of employees. The analysis unit, for example, evaluates the relevance of employees. For example, the analysis unit prioritizes analysis when an employee is directly involved in a project. Also, the analysis unit can postpone analysis when an employee is indirectly involved in a project. Furthermore, the order of analysis can be adjusted according to the relevance of employees. For example, the analysis unit determines the order of analysis taking into account the importance and urgency of work according to the relevance of employees. In this way, adjusting the order of analysis based on the relevance of employees can improve the efficiency of analysis.

[0047] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the employee's level of expertise. The analysis unit, for example, evaluates the employee's level of expertise. For example, the analysis unit evaluates the employee's level of expertise based on the employee's qualifications, experience, past performance, etc. The analysis unit adjusts the use of technical terms in the analysis based on the evaluated level of expertise. For example, if the employee's level of expertise is high, the analysis unit can use a lot of technical terms. On the other hand, if the employee's level of expertise is low, the analysis unit can avoid using technical terms. Furthermore, the use of technical terms in the analysis can be adjusted according to the employee's level of expertise. For example, the analysis unit adjusts the level of detail of term definitions and explanations according to the employee's level of expertise. In this way, adjusting the use of technical terms in the analysis according to the employee's level of expertise can facilitate understanding of the analysis results.

[0048] When making a proposal, the proposal department can adjust the level of detail in the proposal based on the importance of the project. The proposal department, for example, evaluates the importance of the project. For example, the proposal department evaluates the importance of the project based on the business impact of the project, the need for resources, the strictness of the deadline, etc. The proposal department adjusts the level of detail in the proposal based on the evaluated importance of the project. For example, if the importance of the project is high, a detailed proposal can be made. On the other hand, if the importance of the project is low, a simple proposal can be made. Furthermore, the level of detail in the proposal can be adjusted depending on the importance of the project. For example, the proposal department adjusts the number of pages in the proposal and the depth of the explanation depending on the importance of the project. In this way, adjusting the level of detail in the proposal based on the importance of the project can promote understanding of the proposal content.

[0049] When making a proposal, the proposal unit can apply different proposal algorithms depending on the project category. The proposal unit, for example, classifies project categories. For example, the proposal unit classifies categories based on the type of project, the content of the work, the department, etc. The proposal unit applies different proposal algorithms depending on the classified category. For example, if the project is technical, a technical proposal algorithm is applied. Also, if the project is marketing, a proposal algorithm specialized for marketing can be applied. Furthermore, the proposal unit can select the optimal proposal algorithm depending on the project category. For example, the proposal unit applies a recommendation system or a machine learning algorithm depending on the project category. In this way, by applying different proposal algorithms depending on the project category, the accuracy of the proposal content can be improved.

[0050] When making a proposal, the proposal unit can improve the accuracy of the proposal by referring to the employee's past proposal results. The proposal unit, for example, analyzes the employee's past proposal results. For example, the proposal unit improves the accuracy of the proposal based on the success rate, feedback, implementation results, etc. of the employee's past proposals. The proposal unit can also select the optimal proposal method from the employee's past proposal results. Furthermore, the proposal unit can improve the accuracy of the proposal by referring to the employee's past proposal results. For example, the proposal unit aims to minimize errors and improve the accuracy of the model based on the employee's past proposal results. In this way, by improving the accuracy of the proposal by referring to the employee's past proposal results, the reliability of the proposal content can be increased.

[0051] When making a proposal, the proposal department can determine the priority of the proposal based on the time of project submission. The proposal department, for example, determines the priority of the proposal taking into account the time of project submission. For example, if the project submission deadline is approaching, the proposal can be made first. Also, if the project submission deadline is far away, the proposal can be postponed. Furthermore, the priority of the proposal can be adjusted depending on the time of project submission. For example, the proposal department determines the priority of the proposal taking into account the importance and urgency of the work depending on the time of project submission. In this way, by determining the priority of the proposal based on the time of project submission, the efficiency of proposals can be improved.

[0052] When making a proposal, the proposal department can adjust the order of proposals based on the relevance of the projects. The proposal department, for example, evaluates the relevance of the projects. For example, the proposal department can prioritize proposals if the projects are directly related to the skills of the employees. Also, the proposal department can prioritize proposals if the projects are related to the interests of the employees. Furthermore, the order of proposals can be adjusted according to the relevance of the projects. For example, the proposal department determines the order of proposals by taking into account the importance and urgency of the work according to the relevance of the projects. In this way, by adjusting the order of proposals based on the relevance of the projects, it is possible to improve the efficiency of proposals.

[0053] When making a proposal, the suggestion unit can adjust the use of technical terms in the proposal according to the employee's level of expertise. The suggestion unit, for example, evaluates the employee's level of expertise. For example, the suggestion unit evaluates the employee's level of expertise based on the employee's qualifications, experience, past achievements, etc. The suggestion unit adjusts the use of technical terms in the proposal based on the evaluated level of expertise. For example, if the employee's level of expertise is high, the suggestion unit can use a lot of technical terms. On the other hand, if the employee's level of expertise is low, the suggestion unit can avoid using technical terms. Furthermore, the use of technical terms in the proposal can be adjusted according to the employee's level of expertise. For example, the suggestion unit adjusts the level of detail in the definitions and explanations of terms according to the employee's level of expertise. In this way, adjusting the use of technical terms in the proposal according to the employee's level of expertise can promote understanding of the proposal content.

[0054] When forming a team, the composition department can analyze the project requirements in detail and select the most suitable members. The composition department, for example, analyzes the project requirements in detail. For example, the composition department analyzes the technical requirements of the project and selects members with high technical skills. The composition department can also analyze the marketing requirements of the project and select members with high marketing skills. Furthermore, the composition department can select the most suitable members according to the project requirements. For example, the composition department selects members who have the necessary resources and skills based on the project requirements. In this way, by analyzing the project requirements in detail and selecting the most suitable members, the project results can be maximized.

[0055] When forming a team, the composition unit can select an appropriate combination based on the members' past project history. The composition unit, for example, analyzes the members' past project history. For example, the composition unit selects the optimal combination based on information about projects in which the members have been successful in the past. The composition unit can also avoid information about projects in which the members have failed in the past. Furthermore, the composition unit can select the optimal combination by taking into account the members' past project history. For example, the composition unit forms the optimal team by taking into account the balance of skill sets and the compatibility of the members based on the members' past project history. In this way, by selecting the optimal combination by taking into account the members' past project history, it is possible to improve team performance.

[0056] When forming a team, the composition department can optimize the composition by taking into account the balance of the members' skill sets. For example, the composition department evaluates the balance of the members' skill sets. For example, the composition department evaluates the balance of the skill sets based on the members' technical skills, soft skills, diversity of experience, etc. The composition department optimizes the team based on the evaluated balance of the skill sets. For example, the composition department forms an optimal team by taking into account the balance of technical skills and soft skills. The composition department can also avoid imbalances in the members' skill sets. Furthermore, the composition department can form an optimal team by taking into account the balance of the skill sets. For example, the composition department adjusts the division of roles and the balance of skill sets based on the balance of the members' skill sets. In this way, by optimizing the composition by taking into account the balance of the members' skill sets, it is possible to improve team performance.

[0057] When forming a team, the composition department can select optimal members by taking into account the geographical requirements of the project. For example, the composition department selects members by taking into account the geographical requirements of the project. For example, if the project is carried out in a specific region, the composition department selects members who are close to that region. In addition, if the project is carried out remotely, the composition department can select members who are suitable for remote work. Furthermore, the composition department can select optimal members according to the geographical requirements of the project. For example, the composition department selects members based on the geographical requirements of the project by taking into account the work location, the need for business trips, the availability of remote work, etc. In this way, by selecting optimal members by taking into account the geographical requirements of the project, it is possible to maximize the results of the project.

[0058] When forming a team, the composition unit can analyze the social media activities of the members and select the optimal combination. The composition unit, for example, analyzes the social media activities of the members. For example, the composition unit analyzes the content of the members' social media activities and selects the optimal combination. The composition unit can also select the optimal combination by taking into account the members' friendships on social media. Furthermore, the composition unit can select the optimal combination based on the members' social media activity history. For example, the composition unit analyzes the content of the members' social media posts and selects members who are highly relevant. In this way, by analyzing the members' social media activities and selecting the optimal combination, it is possible to improve the team's performance.

[0059] When forming a team, the composition unit can customize the composition method by reflecting the members' past feedback. The composition unit, for example, improves the composition method based on the members' past feedback. For example, the composition unit improves the team composition method based on feedback provided by the members in the past. The composition unit can also select the optimal team composition method from the members' past feedback. Furthermore, the composition unit can improve the accuracy of the team composition by reflecting the members' feedback. For example, the composition unit adjusts the balance of roles and skill sets based on the members' past feedback. In this way, by customizing the composition method by reflecting the members' past feedback, it is possible to improve team performance.

[0060] During monitoring, the monitoring unit can analyze the progress of the project in real time and detect abnormalities. The monitoring unit, for example, analyzes the progress of the project in real time. For example, the monitoring unit monitors the progress of the project in real time and detects delays in progress. The monitoring unit can also analyze the progress of the project in real time and detect resource shortages. Furthermore, the monitoring unit can analyze the progress of the project in real time and detect abnormal patterns. For example, the monitoring unit analyzes the progress of the project in real time and sets thresholds for abnormal values ​​and recognizes patterns. In this way, the project can be progressed smoothly by analyzing the progress of the project in real time and detecting abnormalities.

[0061] During monitoring, the monitoring unit can compare the progress of the project with past data to evaluate the progress. The monitoring unit, for example, compares the progress of the project with past data. For example, the monitoring unit compares the progress of the project with past project progress to evaluate delays in progress. The monitoring unit can also compare the progress of the project with past data to evaluate resource usage. Furthermore, the monitoring unit can compare the progress of the project with past data to evaluate abnormal patterns. For example, the monitoring unit compares the progress of the project with past data to evaluate the degree of goal achievement and schedule adherence. In this way, by comparing the progress of the project with past data to evaluate the progress, it is possible to smooth the progress of the project.

[0062] The monitoring department can adjust the monitoring method during monitoring, taking into account the geographical requirements of the project. For example, the monitoring department adjusts the monitoring method, taking into account the geographical requirements of the project. For example, if the project is carried out in a specific region, the monitoring department can prioritize monitoring items related to that region. Also, if the project is carried out remotely, the monitoring department can prioritize monitoring items related to remote work. Furthermore, the monitoring department can adjust the monitoring method according to the geographical requirements of the project. For example, the monitoring department selects monitoring items based on the geographical requirements of the project, taking into account the work location, the need for business trips, the availability of remote work, etc. In this way, by adjusting the monitoring method, taking into account the geographical requirements of the project, the efficiency of monitoring can be improved.

[0063] During monitoring, the monitoring department can improve the accuracy of monitoring by referring to literature related to the project. The monitoring department, for example, refers to literature related to the project. For example, the monitoring department improves the accuracy of monitoring by referring to literature related to the project. The monitoring department can also select the optimal monitoring method from the literature related to the project. Furthermore, the monitoring department can also improve the accuracy of monitoring by referring to literature related to the project. For example, the monitoring department improves the accuracy of monitoring by referring to academic papers and technical reports. In this way, by improving the accuracy of monitoring by referring to literature related to the project, the reliability of monitoring can be increased.

[0064] During evaluation, the evaluation department can analyze the project results in detail and set evaluation criteria. The evaluation department, for example, analyzes the project results in detail. For example, the evaluation department sets evaluation criteria based on the project's goal achievement level, quality, cost efficiency, etc. The evaluation department can also analyze the project results in detail and select the most appropriate evaluation criteria. Furthermore, the evaluation department can analyze the project results in detail and customize the evaluation criteria. For example, the evaluation department sets KPIs, benchmarks, industry standards, etc. based on the project results. In this way, by analyzing the project results in detail and setting evaluation criteria, the accuracy of the evaluation can be improved.

[0065] During evaluation, the evaluation unit can compare the progress of the project with past data to evaluate it. The evaluation unit, for example, compares the progress of the project with past data. For example, the evaluation unit compares the progress of the project with past project progress to evaluate delays in progress. The evaluation unit can also compare the progress of the project with past data to evaluate resource usage. Furthermore, the evaluation unit can compare the progress of the project with past data to evaluate abnormal patterns. For example, the evaluation unit compares the progress of the project with past data to evaluate the degree of goal achievement and schedule adherence. In this way, by evaluating the progress of the project by comparing it with past data, the accuracy of the evaluation can be improved.

[0066] The evaluation department can adjust the evaluation method during evaluation, taking into account the geographical requirements of the project. For example, the evaluation department adjusts the evaluation method, taking into account the geographical requirements of the project. For example, if the project is carried out in a specific region, the evaluation department may prioritize evaluation of evaluation items related to that region. Furthermore, if the project is carried out remotely, the evaluation department may prioritize evaluation of evaluation items related to remote work. Furthermore, the evaluation department can adjust the evaluation method according to the geographical requirements of the project. For example, the evaluation department selects evaluation items based on the geographical requirements of the project, taking into account the work location, the need for business trips, the availability of remote work, etc. In this way, adjusting the evaluation method to take into account the geographical requirements of the project can improve the efficiency of evaluation.

[0067] The evaluation unit can improve the accuracy of the evaluation by referring to literature related to the project during the evaluation. The evaluation unit, for example, refers to literature related to the project. For example, the evaluation unit improves the accuracy of the evaluation by referring to literature related to the project. The evaluation unit can also select the optimal evaluation method from the literature related to the project. Furthermore, the evaluation unit can also improve the accuracy of the evaluation by referring to literature related to the project. For example, the evaluation unit improves the accuracy of the evaluation by referring to academic papers and technical reports. In this way, by improving the accuracy of the evaluation by referring to literature related to the project, the reliability of the evaluation can be increased.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The project matching system can further include a feedback collection unit. The feedback collection unit collects feedback from employees during and after the project is completed. For example, the feedback collection unit can collect real-time feedback regarding the progress of the project. The feedback collection unit can also collect feedback regarding employee satisfaction and areas for improvement after the project is completed. Furthermore, the feedback collection unit can provide the collected feedback to the analysis unit and use it for the next project matching. In this way, the quality of projects can be improved by collecting feedback from employees during and after the project is completed.

[0070] The project matching system can further include a skill improvement support section. The skill improvement support section provides functions to support employee skill improvement. For example, the skill improvement support section can identify employee skill gaps and suggest appropriate training programs. The skill improvement support section can also monitor the progress of training in which employees have participated and provide support as needed. Furthermore, the skill improvement support section can collect feedback on employee skill improvement and use it to improve the next training program. This makes it possible to maximize the results of the project by supporting employee skill improvement.

[0071] The project matching system can further include a resource management unit. The resource management unit provides a function for efficiently managing the resources required for a project. For example, the resource management unit can adjust resource allocation according to the progress of the project. The resource management unit can also monitor resource usage in real time and add or reallocate resources as needed. Furthermore, the resource management unit can collect feedback on resource usage and use it to improve resource management next time. As a result, the project results can be maximized by efficiently managing the resources required for the project.

[0072] The project matching system may further include a risk management department. The risk management department identifies risks associated with the project and provides measures to minimize the risks. For example, the risk management department can monitor the progress of the project and detect potential risks early. The risk management department can also propose countermeasures when a risk occurs. Furthermore, the risk management department can collect feedback on the risks and use it to improve risk management next time. In this way, the project results can be maximized by identifying and minimizing risks associated with the project.

[0073] The project matching system can further include a performance analysis unit. The performance analysis unit provides a function for analyzing the performance of the project team in detail. For example, the performance analysis unit can evaluate the performance of team members based on the progress and results of the project. The performance analysis unit can also make suggestions for performance improvement based on the skills and experience of the team members. Furthermore, the performance analysis unit can collect feedback on the performance of team members and use it to improve the next performance analysis. As a result, the project results can be maximized by analyzing the performance of the project team in detail.

[0074] The project matching system can further include a project evaluation unit. The project evaluation unit provides a function for comprehensively evaluating the results of a project. For example, the project evaluation unit can perform a comprehensive evaluation of the project based on the degree of goal achievement, quality, cost efficiency, etc. The project evaluation unit can also perform a detailed analysis of the project's progress and results and set evaluation criteria. Furthermore, the project evaluation unit can collect feedback on the project evaluation and use it to improve the next project evaluation. This makes it possible to increase the success rate of projects by comprehensively evaluating the results of the project.

[0075] The processing flow of the first embodiment will be briefly explained below.

[0076] Step 1: The collection unit collects employee information. Employee information includes, for example, career history, skills, and interests. The collection unit provides an interface for employees to enter their career history, skills, and interests. The collection unit can also collect employees' past project history and feedback. For example, the collection unit automatically collects information on projects that employees have participated in in the past and stores it in a database. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. The analysis unit performs a detailed analysis of employees' skills and interests and generates data to suggest optimal projects. The analysis unit can also evaluate employees' capabilities based on their past project history and feedback. Step 3: The proposal unit proposes the most suitable projects for employees based on the analysis results obtained by the analysis unit. Suggestions are made based on, for example, the employee's skills and interests. The proposal unit analyzes the employee's programming skills and proposes projects that require those skills. The proposal unit can also propose projects in areas of interest to the employee based on their interests. Step 4: The Composition Department analyzes the needs of the project proposed by the Proposal Department and assembles the optimal project team. This is done, for example, by selecting members who match the project requirements. The Composition Department analyzes the project's technical requirements and selects members with high technical skills. The Composition Department can also analyze the project's marketing requirements and select members with high marketing skills.

[0077] (Example 2) A project matching system according to an embodiment of the present invention allows employees to participate in projects that match their skills and interests. This system collects and analyzes employee information, proposes optimal projects, and assembles optimal project teams. For example, employees input their backgrounds, skills, and interests. For example, employees enter details about their past project experience, expertise, and areas of interest. This information is then input into an AI. The AI ​​then analyzes the input information. The AI ​​then analyzes the employee's backgrounds, skills, and interests in detail to propose optimal projects for the employee. For example, the AI ​​analyzes the employee's programming skills and proposes projects that require those skills. Furthermore, the AI ​​considers the project's needs and the team members' abilities to assemble an optimal project team. For example, the AI ​​analyzes the project requirements and selects team members who meet those requirements. The team is then assembled based on the team members' skills, experience, and interests. This maximizes project results. This allows the project matching system to maximize employee skills and contribute to project success. Furthermore, by assigning the right team members to projects, companies can increase the success rate of their projects. For example, AI can select members who meet the project requirements, which will help the project progress smoothly and improve results.

[0078] A project matching system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and a configuration unit. The collection unit collects employee information. The employee information includes, but is not limited to, information about the employee's career history, skills, and interests. The collection unit provides, for example, an interface for the employee to input their career history, skills, and interests. The collection unit can also collect the employee's past project history and feedback. For example, the collection unit automatically collects information about projects in which the employee has previously participated and stores the information in a database. The analysis unit analyzes the information collected by the collection unit. The analysis can be performed using, for example, but not limited to, data mining, statistical analysis, or a machine learning algorithm. For example, the analysis unit can perform a detailed analysis of the employee's skills and interests to generate data for proposing optimal projects. The analysis unit can also evaluate the employee's capabilities based on the employee's past project history and feedback. The proposal unit proposes optimal projects for the employee based on the analysis results obtained by the analysis unit. The proposal can be based, for example, on the employee's skills and interests, but is not limited to, the example. For example, the proposal unit analyzes an employee's programming skills and proposes projects that require those skills. The proposal unit can also propose projects in areas of interest to the employee based on their interests. The configuration unit analyzes the needs of the project proposed by the proposal unit and assembles an optimal project team. The configuration is performed, for example, by selecting members who match the project requirements, but is not limited to this example. For example, the configuration unit analyzes the project's technical requirements and selects members with high technical skills. The configuration unit can also analyze the project's marketing requirements and select members with high marketing skills. In this way, the project matching system according to the embodiment can collect and analyze employee information, propose optimal projects, and assemble optimal project teams.

[0079] The project matching system includes a monitoring unit that monitors the progress of the project. The monitoring unit monitors the progress of the project. Monitoring is performed by, for example, but not limited to, methods such as progress reports, task completion status, and schedule adherence. For example, the monitoring unit monitors the progress of the project in real time and detects delays in progress. The monitoring unit can also periodically report the progress of the project and detect abnormalities. Furthermore, the monitoring unit can evaluate the progress of the project by comparing it with past data. For example, the monitoring unit compares it with past project progress and evaluates delays in progress. In this way, monitoring the progress of the project makes the project proceed smoothly and improves results.

[0080] The project matching system includes an evaluation unit that evaluates the results of a project. The evaluation unit evaluates the results of the project. The evaluation is performed based on criteria such as, but not limited to, the degree of goal achievement, quality, and cost efficiency. For example, the evaluation unit analyzes the results of the project in detail and sets evaluation criteria. The evaluation unit can also evaluate the progress of the project by comparing it with past data. Furthermore, the evaluation unit can improve the accuracy of the evaluation by referring to literature related to the project. For example, the evaluation unit can improve the accuracy of the evaluation by referring to academic papers and technical reports. In this way, the success rate of the project can be increased by evaluating the results of the project.

[0081] The collection unit can collect employee backgrounds, skills, and interests. The collection unit, for example, collects employee backgrounds. For example, the collection unit collects information such as employees' work history, educational background, and qualifications. The collection unit can also collect employee skills. For example, the collection unit collects information such as employees' technical skills, soft skills, and language abilities. The collection unit can also collect employee interests. For example, the collection unit collects information such as employees' areas of interest and past project history. In this way, by collecting employees' backgrounds, skills, and interests, it is possible to provide data for proposing projects that are best suited to employees.

[0082] The analysis unit can analyze an employee's background, skills, and interests. The analysis unit, for example, analyzes an employee's background. For example, the analysis unit evaluates an employee's capabilities based on information such as the employee's work history, educational background, and qualifications. The analysis unit can also analyze an employee's skills. For example, the analysis unit evaluates an employee's skills based on information such as the employee's technical skills, soft skills, and language abilities. The analysis unit can also analyze an employee's interests. For example, the analysis unit evaluates an employee's interests based on information such as the employee's areas of interest and past project history. In this way, by analyzing an employee's background, skills, and interests in detail, it is possible to provide data for proposing projects that are best suited to the employee.

[0083] The proposal department can propose optimal projects based on the skills and interests of employees. The proposal department can, for example, propose projects based on the skills of employees. For example, the proposal department can analyze the programming skills of employees and propose projects that require those skills. The proposal department can also propose projects based on the interests of employees. For example, the proposal department can propose projects in areas that interest employees. Furthermore, the proposal department can propose optimal projects for employees based on the employee's past project history. For example, the proposal department can propose similar projects based on information about projects that the employee has been successful in the past. In this way, by proposing optimal projects based on the employee's skills and interests, it is possible to make the most of the employee's skills and contribute to the success of the project.

[0084] The project composition department can form an appropriate project team by considering the needs of the project or the abilities of the members. The project composition department can form a team by considering the needs of the project. For example, the project composition department can analyze the technical requirements of the project and select members with high technical skills. The project composition department can also analyze the marketing requirements of the project and select members with high marketing skills. The project composition department can also form a team by considering the abilities of the members. For example, the project composition department can select the optimal members based on the members' technical skills, experience, past performance, etc. Furthermore, the project composition department can form a team by considering the balance of the members' skill sets. For example, the project composition department can form the optimal team by considering the balance of technical skills and soft skills. In this way, by forming the optimal project team by considering the needs of the project and the abilities of the members, the project results can be maximized.

[0085] The collection unit can estimate the employee's emotions and adjust the timing of information collection based on the estimated employee's emotions. The collection unit, for example, estimates the employee's emotions. For example, the collection unit analyzes the employee's facial expressions to estimate the emotions. The collection unit can also analyze the employee's voice to estimate the emotions. Furthermore, the collection unit can analyze the employee's biometric data to estimate the emotions. For example, the collection unit analyzes the employee's heart rate or electrodermal activity to estimate the emotions. Based on the estimated emotions, the collection unit adjusts the timing of information collection. For example, if the employee is feeling stressed, information collection can be performed during a relaxing time. Also, if the employee is concentrating, detailed information can be collected at that time. Furthermore, if the employee is tired, information collection can be performed after a break. In this way, by adjusting the timing of information collection based on the employee's emotions, the efficiency of information collection can be improved.

[0086] The collection department can analyze the employee's past project history and select the optimal information collection method. The collection department, for example, analyzes the employee's past project history. For example, the collection department selects the optimal information collection method based on information about projects that the employee has previously succeeded in. The collection department can also avoid information about projects that the employee has previously failed in. Furthermore, the collection department can select the most efficient information collection method from the employee's past project history. For example, the collection department refers to the information collection method used in projects that the employee has previously succeeded in. In this way, by analyzing the employee's past project history and selecting the optimal information collection method, the efficiency of information collection can be improved.

[0087] When collecting information, the collection unit can filter the information based on the employee's current work situation and areas of interest. For example, the collection unit filters the information taking into account the employee's current work situation. For example, the collection unit prioritizes collecting information related to the work the employee is currently working on. The collection unit can also filter the information based on the employee's areas of interest. For example, the collection unit prioritizes collecting information related to the employee's areas of interest. Furthermore, the collection unit can collect only necessary information depending on the employee's work situation. For example, the collection unit prioritizes collecting information that is highly important depending on the employee's work situation. In this way, by filtering information based on the employee's current work situation and areas of interest, the efficiency of information collection can be improved.

[0088] When collecting information, the collection unit can select an appropriate collection means depending on the employee's input method. The collection unit collects information depending on, for example, the employee's input method. For example, if the employee prefers voice input, the collection unit collects information by voice. Also, if the employee prefers text input, the collection unit can collect information by text. Furthermore, if the employee prefers image input, the collection unit can collect information by image. For example, the collection unit provides an interface for the employee to upload images. This allows the efficiency of information collection to be improved by selecting the optimal collection means depending on the employee's input method.

[0089] The collection unit can estimate the employee's emotions and determine the priority of information to be collected based on the estimated employee's emotions. The collection unit, for example, estimates the employee's emotions. For example, the collection unit analyzes the employee's facial expressions to estimate the emotions. The collection unit can also analyze the employee's voice to estimate the emotions. The collection unit can also analyze the employee's biometric data to estimate the emotions. For example, the collection unit analyzes the employee's heart rate or electrodermal activity to estimate the emotions. The collection unit determines the priority of information to be collected based on the estimated emotions. For example, if the employee is feeling stressed, less important information can be postponed. Also, if the employee is relaxed, more important information can be collected preferentially. Furthermore, if the employee is in a hurry, the most important information can be collected preferentially. In this way, by determining the priority of information to be collected based on the employee's emotions, the efficiency of information collection can be improved.

[0090] When collecting information, the collection unit can prioritize collecting highly relevant information based on the employee's geographical location information. The collection unit, for example, collects information taking into account the employee's geographical location information. For example, if the employee is in a specific area, the collection unit can prioritize collecting information related to that area. Furthermore, if the employee is on a business trip, the collection unit can prioritize collecting information related to the business trip destination. Furthermore, if the employee is working remotely, the collection unit can prioritize collecting information around the employee's home. For example, the collection unit filters highly relevant information based on the employee's geographical location information. This can improve the efficiency of information collection by prioritizing the collection of highly relevant information taking into account the employee's geographical location information.

[0091] When collecting information, the collection unit can analyze the social media activities of employees and collect relevant information. The collection unit, for example, analyzes the social media activities of employees. For example, the collection unit collects relevant information based on information shared by employees on social media. The collection unit can also analyze the content of employees' social media activities and collect relevant information. Furthermore, the collection unit can collect relevant information by referring to the activities of the employees' friends on social media. For example, the collection unit analyzes the content of employees' social media posts and filters out highly relevant information. In this way, the efficiency of information collection can be improved by analyzing employees' social media activities and collecting relevant information.

[0092] When collecting information, the collection unit can customize the collection method by reflecting past employee feedback. The collection unit, for example, improves the information collection method based on past employee feedback. For example, the collection unit improves the information collection method based on feedback provided by employees in the past. The collection unit can also select the optimal information collection means from past employee feedback. Furthermore, the collection unit can improve the accuracy of information collection by reflecting employee feedback. For example, the collection unit improves the design of a questionnaire or the content of interview questions based on past employee feedback. In this way, by customizing the collection method by reflecting past employee feedback, the efficiency of information collection can be improved.

[0093] The analysis unit can estimate the employee's emotions and adjust the way the analysis is presented based on the estimated employee's emotions. The analysis unit, for example, estimates the employee's emotions. For example, the analysis unit analyzes the employee's facial expressions to estimate the emotions. The analysis unit can also analyze the employee's voice to estimate the emotions. The analysis unit can also analyze the employee's biometric data to estimate the emotions. For example, the analysis unit analyzes the employee's heart rate or electrodermal activity to estimate the emotions. The analysis unit adjusts the way the analysis is presented based on the estimated emotions. For example, if the employee is nervous, a simple and highly visible analysis result can be provided. If the employee is relaxed, a detailed analysis result can be provided. Furthermore, if the employee is in a hurry, a concise analysis result can be provided. In this way, adjusting the way the analysis is presented based on the employee's emotions can facilitate understanding of the analysis results.

[0094] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of an employee's skills. The analysis unit, for example, evaluates the importance of an employee's skills. For example, the analysis unit evaluates the importance of skills based on the employee's technical skills and experience. The analysis unit adjusts the level of detail of the analysis based on the evaluated level of skill importance. For example, if an employee's skills are high, detailed analysis results can be provided. On the other hand, if an employee's skills are low, simple analysis results can be provided. Furthermore, the level of detail of the analysis can be adjusted according to the importance of an employee's skills. For example, the granularity of the data and the depth of the analysis can be adjusted according to the importance of an employee's skills. In this way, adjusting the level of detail of the analysis based on the importance of an employee's skills can facilitate understanding of the analysis results.

[0095] During analysis, the analysis unit can apply different analysis algorithms depending on the employee category. The analysis unit, for example, classifies employee categories. For example, the analysis unit classifies categories based on the employee's job type, department, project type, etc. The analysis unit applies different analysis algorithms depending on the classified category. For example, if the employee is an engineer, a technical analysis algorithm can be applied. Also, if the employee is in charge of marketing, an analysis algorithm specialized for marketing can be applied. Furthermore, the optimal analysis algorithm can be selected depending on the employee category. For example, the analysis unit applies analysis algorithms such as regression analysis, clustering, and deep learning depending on the employee category. In this way, by applying different analysis algorithms depending on the employee category, the accuracy of the analysis results can be improved.

[0096] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the employee's past analysis results. The analysis unit, for example, analyzes the employee's past analysis results. For example, the analysis unit improves the accuracy of the analysis based on the employee's past project results and feedback. The analysis unit can also select the optimal analysis method from the employee's past analysis results. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to the employee's past analysis results. For example, the analysis unit aims to minimize errors and improve the accuracy of the model based on the employee's past analysis results. In this way, by improving the accuracy of the analysis by referring to the employee's past analysis results, the reliability of the analysis results can be increased.

[0097] The analysis unit can estimate the employee's emotions and adjust the length of the analysis based on the estimated employee's emotions. The analysis unit, for example, estimates the employee's emotions. For example, the analysis unit can analyze the employee's facial expressions to estimate the emotions. The analysis unit can also analyze the employee's voice to estimate the emotions. The analysis unit can also analyze the employee's biometric data to estimate the emotions. For example, the analysis unit can analyze the employee's heart rate or electrodermal activity to estimate the emotions. The analysis unit adjusts the length of the analysis based on the estimated emotions. For example, if the employee is in a hurry, the analysis unit can provide a short and concise analysis result. If the employee is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the employee is excited, the analysis unit can provide a visually stimulating analysis result. In this way, adjusting the length of the analysis based on the employee's emotions can facilitate understanding of the analysis results.

[0098] During analysis, the analysis unit can determine the priority of analysis based on the time when the employee's information is submitted. The analysis unit, for example, determines the priority of analysis taking into account the time when the employee's information is submitted. For example, if an employee submits information early, the analysis can be prioritized. Also, if an employee submits information late, the information can be postponed. Furthermore, the priority of analysis can be adjusted depending on the time when the employee's information is submitted. For example, the analysis unit determines the priority of analysis taking into account the importance and urgency of the work depending on the time when the employee's information is submitted. In this way, by determining the priority of analysis based on the time when the employee's information is submitted, the efficiency of analysis can be improved.

[0099] During analysis, the analysis unit can adjust the order of analysis based on the relevance of employees. The analysis unit, for example, evaluates the relevance of employees. For example, the analysis unit prioritizes analysis when an employee is directly involved in a project. Also, the analysis unit can postpone analysis when an employee is indirectly involved in a project. Furthermore, the order of analysis can be adjusted according to the relevance of employees. For example, the analysis unit determines the order of analysis taking into account the importance and urgency of work according to the relevance of employees. In this way, adjusting the order of analysis based on the relevance of employees can improve the efficiency of analysis.

[0100] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the employee's level of expertise. The analysis unit, for example, evaluates the employee's level of expertise. For example, the analysis unit evaluates the employee's level of expertise based on the employee's qualifications, experience, past performance, etc. The analysis unit adjusts the use of technical terms in the analysis based on the evaluated level of expertise. For example, if the employee's level of expertise is high, the analysis unit can use a lot of technical terms. On the other hand, if the employee's level of expertise is low, the analysis unit can avoid using technical terms. Furthermore, the use of technical terms in the analysis can be adjusted according to the employee's level of expertise. For example, the analysis unit adjusts the level of detail of term definitions and explanations according to the employee's level of expertise. In this way, adjusting the use of technical terms in the analysis according to the employee's level of expertise can facilitate understanding of the analysis results.

[0101] The suggestion unit can estimate the employee's emotions and adjust the way the suggestion is expressed based on the estimated employee's emotions. The suggestion unit, for example, estimates the employee's emotions. For example, the suggestion unit analyzes the employee's facial expressions to estimate the emotions. The suggestion unit can also analyze the employee's voice to estimate the emotions. Furthermore, the suggestion unit can analyze the employee's biometric data to estimate the emotions. For example, the suggestion unit analyzes the employee's heart rate or electrodermal activity to estimate the emotions. The suggestion unit adjusts the way the suggestion is expressed based on the estimated emotions. For example, if the employee is nervous, the suggestion unit can make a simple, highly visible suggestion. If the employee is relaxed, the suggestion unit can make a detailed suggestion. Furthermore, if the employee is in a hurry, the suggestion unit can make a suggestion that focuses on the main points. In this way, by adjusting the way the suggestion is expressed based on the employee's emotions, it is possible to promote understanding of the suggestion content.

[0102] When making a proposal, the proposal department can adjust the level of detail in the proposal based on the importance of the project. The proposal department, for example, evaluates the importance of the project. For example, the proposal department evaluates the importance of the project based on the business impact of the project, the need for resources, the strictness of the deadline, etc. The proposal department adjusts the level of detail in the proposal based on the evaluated importance of the project. For example, if the importance of the project is high, a detailed proposal can be made. On the other hand, if the importance of the project is low, a simple proposal can be made. Furthermore, the level of detail in the proposal can be adjusted depending on the importance of the project. For example, the proposal department adjusts the number of pages in the proposal and the depth of the explanation depending on the importance of the project. In this way, adjusting the level of detail in the proposal based on the importance of the project can promote understanding of the proposal content.

[0103] When making a proposal, the proposal unit can apply different proposal algorithms depending on the project category. The proposal unit, for example, classifies project categories. For example, the proposal unit classifies categories based on the type of project, the content of the work, the department, etc. The proposal unit applies different proposal algorithms depending on the classified category. For example, if the project is technical, a technical proposal algorithm is applied. Also, if the project is marketing, a proposal algorithm specialized for marketing can be applied. Furthermore, the proposal unit can select the optimal proposal algorithm depending on the project category. For example, the proposal unit applies a recommendation system or a machine learning algorithm depending on the project category. In this way, by applying different proposal algorithms depending on the project category, the accuracy of the proposal content can be improved.

[0104] When making a proposal, the proposal unit can improve the accuracy of the proposal by referring to the employee's past proposal results. The proposal unit, for example, analyzes the employee's past proposal results. For example, the proposal unit improves the accuracy of the proposal based on the success rate, feedback, implementation results, etc. of the employee's past proposals. The proposal unit can also select the optimal proposal method from the employee's past proposal results. Furthermore, the proposal unit can improve the accuracy of the proposal by referring to the employee's past proposal results. For example, the proposal unit aims to minimize errors and improve the accuracy of the model based on the employee's past proposal results. In this way, by improving the accuracy of the proposal by referring to the employee's past proposal results, the reliability of the proposal content can be increased.

[0105] The suggestion unit can estimate the employee's emotions and adjust the length of the suggestion based on the estimated employee's emotions. The suggestion unit, for example, estimates the employee's emotions. For example, the suggestion unit analyzes the employee's facial expressions to estimate the emotions. The suggestion unit can also analyze the employee's voice to estimate the emotions. Furthermore, the suggestion unit can analyze the employee's biometric data to estimate the emotions. For example, the suggestion unit analyzes the employee's heart rate or electrodermal activity to estimate the emotions. The suggestion unit adjusts the length of the suggestion based on the estimated emotions. For example, if the employee is in a hurry, the suggestion unit can make a short and to-the-point suggestion. If the employee is relaxed, the suggestion unit can make a detailed suggestion. Furthermore, if the employee is excited, the suggestion unit can make a visually stimulating suggestion. In this way, adjusting the length of the suggestion based on the employee's emotions can promote understanding of the suggestion content.

[0106] When making a proposal, the proposal department can determine the priority of the proposal based on the time of project submission. The proposal department, for example, determines the priority of the proposal taking into account the time of project submission. For example, if the project submission deadline is approaching, the proposal can be made first. Also, if the project submission deadline is far away, the proposal can be postponed. Furthermore, the priority of the proposal can be adjusted depending on the time of project submission. For example, the proposal department determines the priority of the proposal taking into account the importance and urgency of the work depending on the time of project submission. In this way, by determining the priority of the proposal based on the time of project submission, the efficiency of proposals can be improved.

[0107] When making a proposal, the proposal department can adjust the order of proposals based on the relevance of the projects. The proposal department, for example, evaluates the relevance of the projects. For example, the proposal department can prioritize proposals if the projects are directly related to the skills of the employees. Also, the proposal department can prioritize proposals if the projects are related to the interests of the employees. Furthermore, the order of proposals can be adjusted according to the relevance of the projects. For example, the proposal department determines the order of proposals by taking into account the importance and urgency of the work according to the relevance of the projects. In this way, by adjusting the order of proposals based on the relevance of the projects, it is possible to improve the efficiency of proposals.

[0108] When making a proposal, the suggestion unit can adjust the use of technical terms in the proposal according to the employee's level of expertise. The suggestion unit, for example, evaluates the employee's level of expertise. For example, the suggestion unit evaluates the employee's level of expertise based on the employee's qualifications, experience, past achievements, etc. The suggestion unit adjusts the use of technical terms in the proposal based on the evaluated level of expertise. For example, if the employee's level of expertise is high, the suggestion unit can use a lot of technical terms. On the other hand, if the employee's level of expertise is low, the suggestion unit can avoid using technical terms. Furthermore, the use of technical terms in the proposal can be adjusted according to the employee's level of expertise. For example, the suggestion unit adjusts the level of detail in the definitions and explanations of terms according to the employee's level of expertise. In this way, adjusting the use of technical terms in the proposal according to the employee's level of expertise can promote understanding of the proposal content.

[0109] The composition unit can estimate the employee's emotions and adjust the team composition method based on the estimated employee's emotions. The composition unit, for example, estimates the employee's emotions. For example, the composition unit can analyze the employee's facial expressions to estimate the emotions. The composition unit can also analyze the employee's voice to estimate the emotions. Furthermore, the composition unit can analyze the employee's biometric data to estimate the emotions. For example, the composition unit can analyze the employee's heart rate or electrodermal activity to estimate the emotions. Based on the estimated emotions, the composition unit adjusts the team composition method. For example, if the employee is tense, the composition unit can form a team with members who can relax. Also, if the employee is relaxed, the composition unit can form a team with members who are challenging. Furthermore, if the employee is tired, the composition unit can form a team with members who provide a lot of support. In this way, by adjusting the team composition method based on the employee's emotions, it is possible to improve team performance.

[0110] When forming a team, the composition department can analyze the project requirements in detail and select the most suitable members. The composition department, for example, analyzes the project requirements in detail. For example, the composition department analyzes the technical requirements of the project and selects members with high technical skills. The composition department can also analyze the marketing requirements of the project and select members with high marketing skills. Furthermore, the composition department can select the most suitable members according to the project requirements. For example, the composition department selects members who have the necessary resources and skills based on the project requirements. In this way, by analyzing the project requirements in detail and selecting the most suitable members, the project results can be maximized.

[0111] When forming a team, the composition unit can select an appropriate combination based on the members' past project history. The composition unit, for example, analyzes the members' past project history. For example, the composition unit selects the optimal combination based on information about projects in which the members have been successful in the past. The composition unit can also avoid information about projects in which the members have failed in the past. Furthermore, the composition unit can select the optimal combination by taking into account the members' past project history. For example, the composition unit forms the optimal team by taking into account the balance of skill sets and the compatibility of the members based on the members' past project history. In this way, by selecting the optimal combination by taking into account the members' past project history, it is possible to improve team performance.

[0112] When forming a team, the composition department can optimize the composition by taking into account the balance of the members' skill sets. For example, the composition department evaluates the balance of the members' skill sets. For example, the composition department evaluates the balance of the skill sets based on the members' technical skills, soft skills, diversity of experience, etc. The composition department optimizes the team based on the evaluated balance of the skill sets. For example, the composition department forms an optimal team by taking into account the balance of technical skills and soft skills. The composition department can also avoid imbalances in the members' skill sets. Furthermore, the composition department can form an optimal team by taking into account the balance of the skill sets. For example, the composition department adjusts the division of roles and the balance of skill sets based on the balance of the members' skill sets. In this way, by optimizing the composition by taking into account the balance of the members' skill sets, it is possible to improve team performance.

[0113] The composition unit can estimate the emotions of employees and determine the priority of team composition based on the estimated emotions of employees. The composition unit, for example, estimates the emotions of employees. For example, the composition unit can analyze the facial expressions of employees to estimate their emotions. The composition unit can also analyze the voice of employees to estimate their emotions. Furthermore, the composition unit can analyze the biometric data of employees to estimate their emotions. For example, the composition unit can analyze the heart rate or electrodermal activity of employees to estimate their emotions. The composition unit determines the priority of team composition based on the estimated emotions. For example, if an employee is tense, the composition unit can prioritize team members who can relax. Also, if an employee is relaxed, the composition unit can prioritize team members who are challenging. Furthermore, if an employee is tired, the composition unit can prioritize team members who provide good support. In this way, by prioritizing team composition based on employees' emotions, team performance can be improved.

[0114] When forming a team, the composition department can select optimal members by taking into account the geographical requirements of the project. For example, the composition department selects members by taking into account the geographical requirements of the project. For example, if the project is carried out in a specific region, the composition department selects members who are close to that region. In addition, if the project is carried out remotely, the composition department can select members who are suitable for remote work. Furthermore, the composition department can select optimal members according to the geographical requirements of the project. For example, the composition department selects members based on the geographical requirements of the project by taking into account the work location, the need for business trips, the availability of remote work, etc. In this way, by selecting optimal members by taking into account the geographical requirements of the project, it is possible to maximize the results of the project.

[0115] When forming a team, the composition unit can analyze the social media activities of the members and select the optimal combination. The composition unit, for example, analyzes the social media activities of the members. For example, the composition unit analyzes the content of the members' social media activities and selects the optimal combination. The composition unit can also select the optimal combination by taking into account the members' friendships on social media. Furthermore, the composition unit can select the optimal combination based on the members' social media activity history. For example, the composition unit analyzes the content of the members' social media posts and selects members who are highly relevant. In this way, by analyzing the members' social media activities and selecting the optimal combination, it is possible to improve the team's performance.

[0116] When forming a team, the composition unit can customize the composition method by reflecting the members' past feedback. The composition unit, for example, improves the composition method based on the members' past feedback. For example, the composition unit improves the team composition method based on feedback provided by the members in the past. The composition unit can also select the optimal team composition method from the members' past feedback. Furthermore, the composition unit can improve the accuracy of the team composition by reflecting the members' feedback. For example, the composition unit adjusts the balance of roles and skill sets based on the members' past feedback. In this way, by customizing the composition method by reflecting the members' past feedback, it is possible to improve team performance.

[0117] The monitoring unit can estimate the employee's emotions and adjust the monitoring method for the project progress based on the estimated employee's emotions. The monitoring unit, for example, estimates the employee's emotions. For example, the monitoring unit can analyze the employee's facial expressions to estimate the emotions. The monitoring unit can also analyze the employee's voice to estimate the emotions. The monitoring unit can also analyze the employee's biometric data to estimate the emotions. For example, the monitoring unit can analyze the employee's heart rate or electrodermal activity to estimate the emotions. Based on the estimated emotions, the monitoring unit adjusts the monitoring method for the project progress. For example, if the employee is tense, a simple and highly visible monitoring method can be provided. If the employee is relaxed, a detailed monitoring method can be provided. Furthermore, if the employee is in a hurry, a monitoring method that focuses on the main points can be provided. In this way, adjusting the monitoring method for the project progress based on the employee's emotions can facilitate understanding of the monitoring results.

[0118] During monitoring, the monitoring unit can analyze the progress of the project in real time and detect abnormalities. The monitoring unit, for example, analyzes the progress of the project in real time. For example, the monitoring unit monitors the progress of the project in real time and detects delays in progress. The monitoring unit can also analyze the progress of the project in real time and detect resource shortages. Furthermore, the monitoring unit can analyze the progress of the project in real time and detect abnormal patterns. For example, the monitoring unit analyzes the progress of the project in real time and sets thresholds for abnormal values ​​and recognizes patterns. In this way, the project can be progressed smoothly by analyzing the progress of the project in real time and detecting abnormalities.

[0119] During monitoring, the monitoring unit can compare the progress of the project with past data to evaluate the progress. The monitoring unit, for example, compares the progress of the project with past data. For example, the monitoring unit compares the progress of the project with past project progress to evaluate delays in progress. The monitoring unit can also compare the progress of the project with past data to evaluate resource usage. Furthermore, the monitoring unit can compare the progress of the project with past data to evaluate abnormal patterns. For example, the monitoring unit compares the progress of the project with past data to evaluate the degree of goal achievement and schedule adherence. In this way, by comparing the progress of the project with past data to evaluate the progress, it is possible to smooth the progress of the project.

[0120] The monitoring unit can estimate the employee's emotions and determine monitoring priorities based on the estimated employee's emotions. The monitoring unit, for example, estimates the employee's emotions. For example, the monitoring unit analyzes the employee's facial expressions to estimate the employee's emotions. The monitoring unit can also analyze the employee's voice to estimate the employee's emotions. Furthermore, the monitoring unit can analyze the employee's biometric data to estimate the employee's emotions. For example, the monitoring unit analyzes the employee's heart rate or electrodermal activity to estimate the employee's emotions. The monitoring unit determines monitoring priorities based on the estimated emotions. For example, if the employee is nervous, the monitoring unit can postpone monitoring items with lower importance. Also, if the employee is relaxed, the monitoring unit can prioritize monitoring items with higher importance. Furthermore, if the employee is in a hurry, the monitoring unit can prioritize monitoring items with higher importance. In this way, by determining monitoring priorities based on the employee's emotions, monitoring efficiency can be improved.

[0121] The monitoring department can adjust the monitoring method during monitoring, taking into account the geographical requirements of the project. For example, the monitoring department adjusts the monitoring method, taking into account the geographical requirements of the project. For example, if the project is carried out in a specific region, the monitoring department can prioritize monitoring items related to that region. Also, if the project is carried out remotely, the monitoring department can prioritize monitoring items related to remote work. Furthermore, the monitoring department can adjust the monitoring method according to the geographical requirements of the project. For example, the monitoring department selects monitoring items based on the geographical requirements of the project, taking into account the work location, the need for business trips, the availability of remote work, etc. In this way, by adjusting the monitoring method, taking into account the geographical requirements of the project, the efficiency of monitoring can be improved.

[0122] During monitoring, the monitoring department can improve the accuracy of monitoring by referring to literature related to the project. The monitoring department, for example, refers to literature related to the project. For example, the monitoring department improves the accuracy of monitoring by referring to literature related to the project. The monitoring department can also select the optimal monitoring method from the literature related to the project. Furthermore, the monitoring department can also improve the accuracy of monitoring by referring to literature related to the project. For example, the monitoring department improves the accuracy of monitoring by referring to academic papers and technical reports. In this way, by improving the accuracy of monitoring by referring to literature related to the project, the reliability of monitoring can be increased.

[0123] The evaluation unit can estimate the employee's emotions and adjust the project evaluation method based on the estimated employee's emotions. The evaluation unit, for example, estimates the employee's emotions. For example, the evaluation unit can analyze the employee's facial expressions to estimate the emotions. The evaluation unit can also analyze the employee's voice to estimate the emotions. Furthermore, the evaluation unit can analyze the employee's biometric data to estimate the emotions. For example, the evaluation unit can analyze the employee's heart rate or electrodermal activity to estimate the emotions. Based on the estimated emotions, the evaluation unit adjusts the project evaluation method. For example, if the employee is tense, a simple and highly visible evaluation method can be provided. If the employee is relaxed, a detailed evaluation method can be provided. Furthermore, if the employee is in a hurry, a more concise evaluation method can be provided. In this way, adjusting the project evaluation method based on the employee's emotions can facilitate understanding of the evaluation results.

[0124] During evaluation, the evaluation department can analyze the project results in detail and set evaluation criteria. The evaluation department, for example, analyzes the project results in detail. For example, the evaluation department sets evaluation criteria based on the project's goal achievement level, quality, cost efficiency, etc. The evaluation department can also analyze the project results in detail and select the most appropriate evaluation criteria. Furthermore, the evaluation department can analyze the project results in detail and customize the evaluation criteria. For example, the evaluation department sets KPIs, benchmarks, industry standards, etc. based on the project results. In this way, by analyzing the project results in detail and setting evaluation criteria, the accuracy of the evaluation can be improved.

[0125] During evaluation, the evaluation unit can compare the progress of the project with past data to evaluate it. The evaluation unit, for example, compares the progress of the project with past data. For example, the evaluation unit compares the progress of the project with past project progress to evaluate delays in progress. The evaluation unit can also compare the progress of the project with past data to evaluate resource usage. Furthermore, the evaluation unit can compare the progress of the project with past data to evaluate abnormal patterns. For example, the evaluation unit compares the progress of the project with past data to evaluate the degree of goal achievement and schedule adherence. In this way, by evaluating the progress of the project by comparing it with past data, the accuracy of the evaluation can be improved.

[0126] The evaluation unit can estimate the employee's emotions and determine the priority of evaluations based on the estimated employee's emotions. The evaluation unit, for example, estimates the employee's emotions. For example, the evaluation unit analyzes the employee's facial expressions to estimate the emotions. The evaluation unit can also analyze the employee's voice to estimate the emotions. Furthermore, the evaluation unit can analyze the employee's biometric data to estimate the emotions. For example, the evaluation unit analyzes the employee's heart rate or electrodermal activity to estimate the emotions. The evaluation unit determines the priority of evaluations based on the estimated emotions. For example, if the employee is nervous, the evaluation unit can postpone evaluation items with lower importance. Also, if the employee is relaxed, the evaluation unit can prioritize evaluation items with higher importance. Furthermore, if the employee is in a hurry, the evaluation unit can prioritize evaluation items with higher importance. In this way, by determining the priority of evaluations based on the employee's emotions, the efficiency of evaluations can be improved.

[0127] The evaluation department can adjust the evaluation method during evaluation, taking into account the geographical requirements of the project. For example, the evaluation department adjusts the evaluation method, taking into account the geographical requirements of the project. For example, if the project is carried out in a specific region, the evaluation department may prioritize evaluation of evaluation items related to that region. Furthermore, if the project is carried out remotely, the evaluation department may prioritize evaluation of evaluation items related to remote work. Furthermore, the evaluation department can adjust the evaluation method according to the geographical requirements of the project. For example, the evaluation department selects evaluation items based on the geographical requirements of the project, taking into account the work location, the need for business trips, the availability of remote work, etc. In this way, adjusting the evaluation method to take into account the geographical requirements of the project can improve the efficiency of evaluation.

[0128] The evaluation unit can improve the accuracy of the evaluation by referring to literature related to the project during the evaluation. The evaluation unit, for example, refers to literature related to the project. For example, the evaluation unit improves the accuracy of the evaluation by referring to literature related to the project. The evaluation unit can also select the optimal evaluation method from the literature related to the project. Furthermore, the evaluation unit can also improve the accuracy of the evaluation by referring to literature related to the project. For example, the evaluation unit improves the accuracy of the evaluation by referring to academic papers and technical reports. In this way, by improving the accuracy of the evaluation by referring to literature related to the project, the reliability of the evaluation can be increased. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, proposal unit, configuration unit, monitoring unit, and evaluation unit described above is realized, for example, in at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects employee information via the control unit 46A of the smart device 14, and the information is analyzed by the specific processing unit 290 of the data processing device 12. The proposal unit proposes optimal projects for employees via the specific processing unit 290 of the data processing device 12, and the configuration unit assembles optimal project teams via the specific processing unit 290 of the data processing device 12. The monitoring unit monitors the progress of the project via the control unit 46A of the smart device 14, and the evaluation unit evaluates the results of the project via the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, proposal unit, configuration unit, monitoring unit, and evaluation unit described above is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects employee information via the control unit 46A of the smart glasses 214, and the information is analyzed by the specific processing unit 290 of the data processing device 12. The proposal unit proposes optimal projects for employees via the specific processing unit 290 of the data processing device 12, and the configuration unit assembles optimal project teams via the specific processing unit 290 of the data processing device 12. The monitoring unit monitors the progress of the project via the control unit 46A of the smart glasses 214, and the evaluation unit evaluates the results of the project via the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, proposal unit, configuration unit, monitoring unit, and evaluation unit described above is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects employee information via the control unit 46A of the headset type terminal 314, and the information is analyzed by the specific processing unit 290 of the data processing device 12. The proposal unit proposes optimal projects for employees via the specific processing unit 290 of the data processing device 12, and the configuration unit assembles optimal project teams via the specific processing unit 290 of the data processing device 12. The monitoring unit monitors the progress of the project via the control unit 46A of the headset type terminal 314, and the evaluation unit evaluates the results of the project via the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, proposal unit, configuration unit, monitoring unit, and evaluation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects employee information via the control unit 46A of the robot 414, and the information is analyzed by the specific processing unit 290 of the data processing device 12. The proposal unit proposes optimal projects for employees via the specific processing unit 290 of the data processing device 12, and the configuration unit assembles optimal project teams via the specific processing unit 290 of the data processing device 12. The monitoring unit monitors the progress of the project via the control unit 46A of the robot 414, and the evaluation unit evaluates the results of the project via the specific processing unit 290 of the data processing device 12.

[0129] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0130] The project matching system can further include a feedback collection unit. The feedback collection unit collects feedback from employees during and after the project is completed. For example, the feedback collection unit can collect real-time feedback regarding the progress of the project. The feedback collection unit can also collect feedback regarding employee satisfaction and areas for improvement after the project is completed. Furthermore, the feedback collection unit can provide the collected feedback to the analysis unit and use it for the next project matching. In this way, the quality of projects can be improved by collecting feedback from employees during and after the project is completed.

[0131] The project matching system can further include a skill improvement support section. The skill improvement support section provides functions to support employee skill improvement. For example, the skill improvement support section can identify employee skill gaps and suggest appropriate training programs. The skill improvement support section can also monitor the progress of training in which employees have participated and provide support as needed. Furthermore, the skill improvement support section can collect feedback on employee skill improvement and use it to improve the next training program. This makes it possible to maximize the results of the project by supporting employee skill improvement.

[0132] The project matching system can further include a motivation management unit. The motivation management unit provides functions for maintaining and improving employee motivation. For example, the motivation management unit can periodically evaluate employee motivation levels and propose measures to improve motivation as needed. The motivation management unit can also provide incentive programs to increase employee motivation. Furthermore, the motivation management unit can collect feedback on employee motivation and use it to improve the next motivation improvement measures. This makes it possible to maximize project results by maintaining and improving employee motivation.

[0133] The project matching system can further include a health management unit. The health management unit provides functions for monitoring employees' health conditions and supporting their health maintenance. For example, the health management unit can collect employees' health data and periodically evaluate their health conditions. The health management unit can also suggest appropriate health maintenance measures based on the employees' health conditions. Furthermore, the health management unit can collect feedback on employees' health and use it to improve the next health maintenance measure. This makes it possible to maximize the results of the project by monitoring employees' health conditions and supporting their health maintenance.

[0134] The project matching system can further include a communication support unit. The communication support unit provides functions for facilitating communication within the project team. For example, the communication support unit can manage communication history between team members and propose communication improvement measures as needed. The communication support unit can also provide tools and resources for promoting communication between team members. Furthermore, the communication support unit can collect feedback on team members' communication and use it to improve the next communication improvement measures. This can maximize the project results by facilitating communication within the project team.

[0135] The project matching system can further include a resource management unit. The resource management unit provides a function for efficiently managing the resources required for a project. For example, the resource management unit can adjust resource allocation according to the progress of the project. The resource management unit can also monitor resource usage in real time and add or reallocate resources as needed. Furthermore, the resource management unit can collect feedback on resource usage and use it to improve resource management next time. As a result, the project results can be maximized by efficiently managing the resources required for the project.

[0136] The project matching system may further include a risk management department. The risk management department identifies risks associated with the project and provides measures to minimize the risks. For example, the risk management department can monitor the progress of the project and detect potential risks early. The risk management department can also propose countermeasures when a risk occurs. Furthermore, the risk management department can collect feedback on the risks and use it to improve risk management next time. In this way, the project results can be maximized by identifying and minimizing risks associated with the project.

[0137] The project matching system can further include a career path suggestion unit. The career path suggestion unit provides a function for suggesting employee career paths. For example, the career path suggestion unit can suggest an optimal career path based on the employee's skills, interests, and past project history. The career path suggestion unit can also provide a plan for acquiring the necessary skills and experience according to the employee's career goals. Furthermore, the career path suggestion unit can collect feedback on the employee's career and use it to improve the next career path proposal. In this way, by proposing employee career paths, it is possible to improve employee motivation and maximize project results.

[0138] The project matching system can further include a performance analysis unit. The performance analysis unit provides a function for analyzing the performance of the project team in detail. For example, the performance analysis unit can evaluate the performance of team members based on the progress and results of the project. The performance analysis unit can also make suggestions for performance improvement based on the skills and experience of the team members. Furthermore, the performance analysis unit can collect feedback on the performance of team members and use it to improve the next performance analysis. As a result, the project results can be maximized by analyzing the performance of the project team in detail.

[0139] The project matching system can further include a project evaluation unit. The project evaluation unit provides a function for comprehensively evaluating the results of a project. For example, the project evaluation unit can perform a comprehensive evaluation of the project based on the degree of goal achievement, quality, cost efficiency, etc. The project evaluation unit can also perform a detailed analysis of the project's progress and results and set evaluation criteria. Furthermore, the project evaluation unit can collect feedback on the project evaluation and use it to improve the next project evaluation. This makes it possible to increase the success rate of projects by comprehensively evaluating the results of the project.

[0140] The processing flow of the second embodiment will be briefly explained below.

[0141] Step 1: The collection unit collects employee information. Employee information includes, for example, career history, skills, and interests. The collection unit provides an interface for employees to enter their career history, skills, and interests. The collection unit can also collect employees' past project history and feedback. For example, the collection unit automatically collects information on projects that employees have participated in in the past and stores it in a database. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. The analysis unit performs a detailed analysis of employees' skills and interests and generates data to suggest optimal projects. The analysis unit can also evaluate employees' capabilities based on their past project history and feedback. Step 3: The proposal unit proposes the most suitable projects for employees based on the analysis results obtained by the analysis unit. Suggestions are made based on, for example, the employee's skills and interests. The proposal unit analyzes the employee's programming skills and proposes projects that require those skills. The proposal unit can also propose projects in areas of interest to the employee based on their interests. Step 4: The Composition Department analyzes the needs of the project proposed by the Proposal Department and assembles the optimal project team. This is done, for example, by selecting members who match the project requirements. The Composition Department analyzes the project's technical requirements and selects members with high technical skills. The Composition Department can also analyze the project's marketing requirements and select members with high marketing skills.

[0142] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0144] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0147] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0148] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0149] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0150] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0152] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0153] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0154] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0155] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0156] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0158] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0160] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0161] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0162] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0163] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0164] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0165] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0166] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0167] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0168] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0169] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0170] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0171] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0172] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0173] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0174] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0175] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0176] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0177] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0178] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0179] 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.

[0180] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0181] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0182] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0183] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0184] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0185] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0186] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0187] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0188] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0189] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0190] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0191] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0192] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0193] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0194] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0195] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0196] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0197] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0198] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0199] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0200] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0201] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0202] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0203] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0204] 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.

[0205] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0206] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0207] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0208] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0209] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0210] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0211] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0212] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0213] [Explanation of symbols]

[0214] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection department that collects employee information; an analysis unit that analyzes the information collected by the collection unit; a proposal unit that proposes appropriate projects to employees based on the analysis results obtained by the analysis unit; a configuration unit that analyzes the needs of the project proposed by the proposal unit and configures an appropriate project team; Equipped with A system characterized by:

2. Have a monitoring department that monitors the progress of the project 2. The system of claim 1.

3. Equipping the project with an evaluation department to evaluate project results 2. The system of claim 1.

4. The collecting unit Collect employee background, skills, and interests 2. The system of claim 1.

5. The analysis unit Analyze employee backgrounds, skills, and interests 2. The system of claim 1.

6. The proposal unit Suggest the best projects for your employees based on their skills and interests 2. The system of claim 1.

7. The component comprises: Assemble an appropriate project team, taking into account the needs of the project and the capabilities of the members 2. The system of claim 1.

8. The collecting unit Estimate employee emotions and adjust the timing of information gathering based on the estimated employee emotions 2. The system of claim 1.

9. The collecting unit Analyze employees' past project history and select the best method of collecting information 2. The system of claim 1.

10. The collecting unit When collecting information, filter it based on the employee's current work situation and areas of interest.

2. The system of claim 1.

Images