system

Abstract

The system according to this embodiment aims to analyze the skills and aptitudes of employees and propose the optimal personnel placement. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, and an optimization unit. The collection unit collects employee skill information and past performance data. The analysis unit analyzes the data collected by the collection unit and evaluates each employee's abilities from multiple perspectives. The proposal unit proposes appropriate personnel placement based on the evaluation results obtained by the analysis unit. The optimization unit evaluates the placement results proposed by the proposal unit and performs continuous optimization.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, due to inappropriate personnel allocation, work efficiency may decrease and motivation may decline.

[0005] The system according to the embodiment aims to analyze the skills and aptitudes of employees and propose optimal personnel allocation.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and an optimization unit. The data collection unit collects employee skill information and past performance data. The analysis unit analyzes the data collected by the data collection unit and evaluates each employee's capabilities from multiple perspectives. The proposal unit proposes appropriate personnel placement based on the evaluation results obtained by the analysis unit. The optimization unit evaluates the placement results proposed by the proposal unit and performs continuous optimization. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the skills and aptitudes of employees and propose the optimal personnel placement. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

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

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

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

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

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

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

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

[0028] (Example of form 1) The personnel placement system according to an embodiment of the present invention is a system that uses an AI agent to analyze the skills and aptitudes of employees and propose the optimal personnel placement. This personnel placement system collects employee skill information and past performance data, and the AI ​​agent analyzes the collected data to evaluate each employee's abilities from multiple perspectives. Based on the evaluation results, it proposes the optimal personnel placement for the needs of each team in real time. Furthermore, it provides a feedback loop to continuously optimize based on the placement results. This mechanism ensures that work proceeds smoothly, while simultaneously improving employee performance and increasing motivation. For example, the personnel placement system collects employee skill information and past performance data. At this time, it collects detailed data such as what skills the employee has, what tasks they have been in charge of in the past, and what results they have achieved. For example, it collects specific skill information of each employee, such as programming skills and project management ability. Next, the AI ​​agent analyzes the collected data. The AI ​​agent analyzes the collected data and evaluates each employee's abilities from multiple perspectives. For example, it can identify employees with high programming skills or employees with excellent project management ability. This makes it possible to understand the aptitude of each employee. Based on the evaluation results, it proposes the optimal personnel placement for the needs of each team in real time. For example, employees with strong programming skills can be assigned to the programming team, and employees with excellent project management skills can be assigned to the project management team. In this way, optimal personnel allocation can be made according to the needs of each team. Furthermore, a feedback loop can be established to continuously optimize based on the allocation results. For example, by evaluating the work performance after allocation and reviewing the allocation as needed, optimal personnel allocation can be continuously achieved. This leads to increased work efficiency and improved employee performance. This system ensures smooth operations, improved employee performance, and increased motivation. For example, appropriate personnel allocation ensures that the workload is evenly distributed, creating an environment where each employee can maximize their skills. In addition, data-driven decision-making allows for objective evaluation of employee suitability and optimal allocation.This improves employee motivation and dramatically increases work efficiency. Therefore, the staffing system can improve operational efficiency.

[0029] The personnel placement system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and an optimization unit. The collection unit collects employee skill information and past performance data. The collection unit collects skill information such as employees' technical skills, soft skills, and qualification information. The collection unit can also collect past performance data such as employee performance evaluations, project results, and feedback. For example, the collection unit retrieves employee skill information from a database and analyzes past performance data. The analysis unit analyzes the data collected by the collection unit and evaluates each employee's capabilities from multiple perspectives. For example, the analysis unit evaluates technical skills and soft skills based on employee skill information. The analysis unit can also evaluate performance evaluations and project results based on past performance data. For example, the analysis unit analyzes employee skill information and evaluates the level of technical skills. The proposal unit proposes appropriate personnel placement based on the evaluation results obtained by the analysis unit. For example, the proposal unit places employees with high programming skills in the programming team and employees with excellent project management abilities in the project management team. Furthermore, the proposal department can propose optimal personnel allocation in real time according to the needs of each team. For example, the proposal department analyzes the needs of each team and proposes the optimal personnel allocation. The optimization department evaluates the allocation results proposed by the proposal department and performs continuous optimization. For example, the optimization department evaluates the work results after allocation and revises the allocation as needed. The optimization department can also establish a feedback loop to continuously achieve optimal personnel allocation. For example, the optimization department evaluates the work results after allocation and revises the allocation based on the feedback. As a result, the personnel allocation system according to this embodiment can improve work efficiency by analyzing the skills and aptitudes of employees and proposing optimal personnel allocation.

[0030] The data collection department collects employee skill information and historical performance data. Specifically, it collects skill information such as employees' technical skills, soft skills, and certifications. Technical skills include proficiency in programming languages, database management, and network construction, while soft skills include communication skills, leadership, and problem-solving abilities. Certifications include industry-standard qualifications and certifications. This information is collected by integrating self-reported data entered by employees, information on completion of internal training programs, and data from external certification bodies. The data collection department can also collect historical performance data such as employee performance evaluations, project outcomes, and feedback. Performance evaluations include evaluations from supervisors and colleagues, project completion rates, and goal achievement rates, while project outcomes include project completion status, on-time delivery, and quality evaluations. Feedback includes 360-degree feedback and customer feedback. This data is automatically retrieved from internal HR systems and project management tools and stored in a central database. The data collection department manages this data centrally and can link it with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and proposal departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0031] The Analysis Department analyzes data collected by the Data Collection Department to comprehensively evaluate the capabilities of each employee. Specifically, it evaluates technical and soft skills based on employee skill information. Technical skills include proficiency in programming languages, practical experience, and project results, while soft skills include communication skills, leadership, and problem-solving abilities. These evaluations are performed using AI-powered natural language processing and machine learning algorithms. For example, it analyzes employee self-reported data and evaluation comments from supervisors to quantitatively assess skill levels and aptitudes. It can also perform performance evaluations and project outcome evaluations based on past performance data. Performance evaluations include goal achievement rates, project completion status, and quality evaluations, while project outcome evaluations include on-time delivery and customer satisfaction. This data is analyzed using statistical methods and machine learning algorithms to clarify each employee's strengths and weaknesses. Furthermore, the Analysis Department can evaluate employees' career paths and growth potential, which can be used to formulate future placement and training plans. As a result, the Analysis Department can quickly and accurately analyze collected data and comprehensively evaluate the capabilities of each employee.

[0032] The proposal department proposes appropriate personnel placements based on evaluation results obtained by the analysis department. Specifically, it places employees with strong programming skills in the programming team and employees with excellent project management skills in the project management team. The proposal department can also propose optimal personnel placements in real time according to the needs of each team. For example, the proposal department analyzes each team's project requirements, schedule, and resource status to propose the optimal personnel placement. This utilizes an AI-powered optimization algorithm to calculate the optimal placement based on each employee's skills, aptitudes, and past performance data. Furthermore, the proposal department can also propose future placements and development plans, taking into account employees' career paths and growth potential. For example, it can plan to have employees with specific skills participate in development programs and place them in leadership positions in the future. In this way, the proposal department can propose optimal personnel placements that meet the needs of each team and improve operational efficiency.

[0033] The Optimization Department evaluates the deployment results proposed by the Proposal Department and performs continuous optimization. Specifically, it evaluates the work results after deployment and revises the deployment as needed. The evaluation of work results includes project progress, goal achievement rate, and quality evaluation, and the effectiveness of the deployment is evaluated based on this data. The Optimization Department can also establish a feedback loop to continuously achieve optimal personnel deployment. For example, it evaluates the work results after deployment and revises the deployment based on feedback. This includes a process of collecting feedback from employees and supervisors to clarify the effectiveness and problems of the deployment. Furthermore, the Optimization Department can utilize AI-based predictive models to optimize future deployments. For example, it can predict what results a particular deployment will bring in the future based on past data and propose the optimal deployment. In this way, the Optimization Department can improve the overall system performance by evaluating the work results after deployment and performing continuous optimization.

[0034] The data collection unit can analyze an employee's past project history and select the optimal data collection method. For example, the unit can collect detailed skill information from employees with a high project success rate. It can also collect simpler performance data from employees with a high project failure rate. Furthermore, the unit can apply different data collection methods depending on the type of project. This improves the accuracy of data collection by selecting the optimal data collection method based on the employee's past project history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input an employee's past project history into a generating AI and have the generating AI select the optimal data collection method.

[0035] The data collection unit can filter skill information and performance data based on an employee's current work situation and areas of interest. For example, it can collect only essential skill information from employees who are currently busy. It can also prioritize collecting data related to specific areas of interest for employees with clear areas of interest. Furthermore, it can collect detailed performance data from employees whose work situation is stable. This allows for the collection of more relevant data by filtering data based on an employee's work situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input data on employees' work situations and areas of interest into a generating AI and have the generating AI perform the data filtering.

[0036] The data collection unit can prioritize the collection of highly relevant data based on the employee's geographical location when collecting skill information and performance data. For example, if an employee is working remotely, the data collection unit will prioritize the collection of skill information related to remote work. It can also collect performance data relevant to an overseas business trip if the employee is on a business trip abroad. Furthermore, if the employee works at headquarters, the data collection unit can prioritize the collection of data related to headquarters operations. This allows for the collection of more relevant data by considering the employee's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the employee's geographical location information into a generating AI and have the generating AI collect highly relevant data.

[0037] The data collection unit can analyze employees' social media activity and collect relevant data when collecting skill information and performance data. For example, the data collection unit can collect information related to skills that employees have shared on social media. It can also collect data related to projects that employees have participated in on social media. Furthermore, the data collection unit can collect data related to areas of interest from employees' social media activity. This allows for more multifaceted data collection by analyzing and collecting data from employees' social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input employee social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0038] The analysis department can evaluate employees while considering their skill development during data analysis. For example, the analysis department can give high ratings to employees whose skills are rapidly improving. It can also point out areas for improvement to employees whose skills are stagnating. Furthermore, the analysis department can suggest retraining to employees whose skills are declining. By considering the skill development of employees during evaluation, a more accurate assessment becomes possible. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input employee skill development data into a generating AI and have the generating AI perform the evaluation.

[0039] The analysis department can improve the accuracy of evaluations by referring to past employee feedback during data analysis. For example, the analysis department can adjust evaluation criteria based on past feedback. The analysis department can also analyze the content of the feedback to improve the accuracy of evaluations. Furthermore, the analysis department can weight evaluations by considering the frequency of feedback. This improves the accuracy of evaluations by referring to past employee feedback. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input past employee feedback data into a generating AI and have the generating AI perform the evaluation accuracy improvement.

[0040] The analysis department can perform evaluations while considering the geographical distribution of employees during data analysis. For example, the analysis department can centralize the evaluation of geographically dispersed employees. The analysis department can also perform evaluations while considering the characteristics of work in each region. Furthermore, the analysis department can reflect differences in work efficiency due to geographical factors in the evaluation. This makes it possible to perform more accurate evaluations by considering the geographical distribution of employees. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input employee geographical distribution data into a generating AI and have the generating AI perform the evaluation.

[0041] The analysis department can improve the accuracy of evaluations by referring to relevant literature for employees during data analysis. For example, the analysis department can revise evaluation criteria based on relevant literature. The analysis department can also analyze the content of the literature to improve the accuracy of evaluations. Furthermore, the analysis department can weight evaluations by considering the frequency of citations in the literature. As a result, the accuracy of evaluations is improved by referring to relevant literature for employees. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input employee relevant literature data into a generating AI and have the generating AI perform the task of improving the accuracy of evaluations.

[0042] The proposal department can adjust the level of detail of a proposal based on the needs of each team. For example, if the team's needs are clear, the proposal department will provide a detailed proposal. If the team's needs are unclear, the proposal department can provide a simplified proposal. Furthermore, if the team's needs change, the proposal department can provide a flexible proposal. By adjusting the level of detail of a proposal based on the needs of each team, it becomes possible to provide more appropriate proposals. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the needs data of each team into a generating AI and have the generating AI perform the adjustment of the level of detail of the proposal.

[0043] The proposal department can apply different proposal algorithms based on the project progress of each team when making proposals. For example, if the project is in its initial stages, the proposal department can make basic proposals. If the project is in the middle stages, the proposal department can also make detailed proposals. Furthermore, if the project is in its final stages, the proposal department can make optimized proposals. This allows for more appropriate proposals by applying proposal algorithms according to the project progress of each team. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input project progress data for each team into a generating AI and have the generating AI execute the application of the proposal algorithm.

[0044] The proposal department can make optimal proposals based on each team's geographical location information. For example, if a team is working remotely, the proposal department will make proposals related to remote work. It can also make proposals related to the region where the team is on a business trip abroad. Furthermore, if a team is working at headquarters, the proposal department can make proposals related to headquarters operations. This allows for more appropriate proposals by considering each team's geographical location information. Some or all of the above processing in the proposal department may be performed using AI, or not. For example, the proposal department can input each team's geographical location data into a generating AI and have the AI ​​generate the optimal proposal.

[0045] The proposal department can analyze each team's social media activity and make relevant suggestions when submitting proposals. For example, the proposal department can make suggestions based on information that teams have posted on social media. It can also make suggestions related to projects that teams have participated in on social media. Furthermore, the proposal department can make suggestions related to areas of interest based on the teams' social media activity. By analyzing each team's social media activity and making suggestions accordingly, more appropriate suggestions can be made. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input each team's social media activity data into a generating AI and have the generating AI execute relevant suggestions.

[0046] The optimization unit can evaluate the business results after deployment and adjust the optimization algorithm during the optimization process. For example, if the business results are high, the optimization unit will maintain the current optimization algorithm. If the business results are low, the optimization unit can also review the optimization algorithm and identify areas for improvement. Furthermore, the optimization unit can analyze fluctuations in business results and dynamically adjust the optimization algorithm. This enables more effective optimization by evaluating the business results after deployment and optimizing the optimization algorithm. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the business result data after deployment into a generating AI and have the generating AI perform the adjustment of the optimization algorithm.

[0047] The optimization unit can improve the accuracy of optimization based on employee feedback during the optimization process. For example, the optimization unit adjusts the optimization algorithm based on employee feedback. The optimization unit can also analyze the content of the feedback to improve the accuracy of optimization. Furthermore, the optimization unit can weight the optimization process by considering the frequency of feedback. This improves the accuracy of optimization by referencing employee feedback. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input employee feedback data into a generating AI and have the generating AI perform optimization accuracy improvements.

[0048] The optimization unit can perform optimal placement based on employees' geographical location information during optimization. For example, if an employee is working remotely, the optimization unit will perform placement suitable for remote work. Furthermore, if an employee is on a business trip abroad, the optimization unit can perform placement suitable for that region. Additionally, if an employee works at headquarters, the optimization unit can perform placement suitable for headquarters work. This allows for more appropriate placement by considering employees' geographical location information. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input employee geographical location data into a generating AI and have the generating AI perform the optimal placement.

[0049] The optimization unit can improve the accuracy of optimization based on relevant literature for employees during the optimization process. For example, the optimization unit can revise the optimization algorithm based on the relevant literature. The optimization unit can also analyze the content of the literature to improve the accuracy of optimization. Furthermore, the optimization unit can weight the optimization by considering the frequency of citations of the literature. As a result, the accuracy of optimization is improved by performing optimization while referring to the relevant literature for employees. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input employee relevant literature data into a generating AI and have the generating AI perform the optimization accuracy improvement.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The data collection unit can adjust the frequency of data collection based on the employee's health status when collecting employee skill information and performance data. For example, if an employee is in good health, data can be collected more frequently. Conversely, if an employee is in poor health, the frequency of data collection can be reduced. Furthermore, if an employee is ill, data collection can be temporarily suspended. This allows for more appropriate data collection by adjusting the frequency of data collection according to the employee's health status. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input employee health status data into a generating AI and have the generating AI adjust the frequency of data collection.

[0052] The suggestion department can adjust suggestions based on employees' career goals when collecting employee skill information and performance data. For example, if an employee aims for career advancement, it can make suggestions that will help improve their skills. If an employee wishes to maintain the status quo, it can make suggestions that are appropriate for their current situation. Furthermore, if an employee wishes to change careers, it can make suggestions related to new fields. By adjusting suggestions according to employees' career goals, more appropriate suggestions can be made. Some or all of the above processing in the suggestion department may be performed using AI, for example, or not. For example, the suggestion department can input employee career goal data into a generating AI and have the generating AI adjust the suggestions.

[0053] The data collection unit can adjust the timing of data collection based on the employee's working hours when collecting employee skill information and performance data. For example, data collection can be withheld when the employee is outside of working hours. Conversely, data can be collected immediately when the employee is working. Furthermore, data collection can be temporarily suspended when the employee is on vacation. This allows for more appropriate data collection by adjusting the timing of data collection according to the employee's working hours. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input employee working hours data into a generating AI and have the generating AI adjust the timing of data collection.

[0054] The proposal department can adjust the timing of proposals based on the progress of an employee's project when collecting employee skill information and performance data. For example, it can provide basic proposals when a project is in its initial stages, detailed proposals when the project is in the middle stages, and optimized proposals when the project is in its final stages. By adjusting the timing of proposals according to the project's progress, more appropriate proposals can be made. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input project progress data into a generating AI and have the generating AI adjust the timing of proposals.

[0055] The data collection unit can adjust its data collection method based on the employee's work style when collecting employee skill information and performance data. For example, if an employee works full-time, it can collect detailed data. If an employee works part-time, it can collect simpler data. Furthermore, if an employee works remotely, it can collect data related to remote work. By adjusting the data collection method according to the employee's work style, more appropriate data collection becomes possible. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input employee work style data into a generating AI and have the generating AI perform the adjustment of the data collection method.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The data collection unit collects employee skill information and historical performance data. For example, the data collection unit retrieves skill information such as employees' technical skills, soft skills, and qualifications from a database, and collects historical performance data such as performance evaluations, project results, and feedback. Step 2: The analysis department analyzes the data collected by the data collection department and evaluates each employee's capabilities from multiple perspectives. For example, the analysis department evaluates technical and soft skills based on employee skill information, and evaluates performance and project results based on past performance data. Step 3: The proposal department proposes appropriate personnel allocation based on the evaluation results obtained by the analysis department. For example, the proposal department would assign employees with strong programming skills to the programming team and employees with excellent project management skills to the project management team. The proposal department also proposes the optimal personnel allocation in real time according to the needs of each team. Step 4: The Optimization Department evaluates the deployment results proposed by the Proposal Department and performs continuous optimization. For example, the Optimization Department evaluates the work results after deployment and revises the deployment as needed. The Optimization Department also establishes a feedback loop to continuously achieve optimal personnel deployment.

[0058] (Example of form 2) The personnel placement system according to an embodiment of the present invention is a system that uses an AI agent to analyze the skills and aptitudes of employees and propose the optimal personnel placement. This personnel placement system collects employee skill information and past performance data, and the AI ​​agent analyzes the collected data to evaluate each employee's abilities from multiple perspectives. Based on the evaluation results, it proposes the optimal personnel placement for the needs of each team in real time. Furthermore, it provides a feedback loop to continuously optimize based on the placement results. This mechanism ensures that work proceeds smoothly, while simultaneously improving employee performance and increasing motivation. For example, the personnel placement system collects employee skill information and past performance data. At this time, it collects detailed data such as what skills the employee has, what tasks they have been in charge of in the past, and what results they have achieved. For example, it collects specific skill information of each employee, such as programming skills and project management ability. Next, the AI ​​agent analyzes the collected data. The AI ​​agent analyzes the collected data and evaluates each employee's abilities from multiple perspectives. For example, it can identify employees with high programming skills or employees with excellent project management ability. This makes it possible to understand the aptitude of each employee. Based on the evaluation results, it proposes the optimal personnel placement for the needs of each team in real time. For example, employees with strong programming skills can be assigned to the programming team, and employees with excellent project management skills can be assigned to the project management team. In this way, optimal personnel allocation can be made according to the needs of each team. Furthermore, a feedback loop can be established to continuously optimize based on the allocation results. For example, by evaluating the work performance after allocation and reviewing the allocation as needed, optimal personnel allocation can be continuously achieved. This leads to increased work efficiency and improved employee performance. This system ensures smooth operations, improved employee performance, and increased motivation. For example, appropriate personnel allocation ensures that the workload is evenly distributed, creating an environment where each employee can maximize their skills. In addition, data-driven decision-making allows for objective evaluation of employee suitability and optimal allocation.This improves employee motivation and dramatically increases work efficiency. Therefore, the staffing system can improve operational efficiency.

[0059] The personnel placement system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and an optimization unit. The collection unit collects employee skill information and past performance data. The collection unit collects skill information such as employees' technical skills, soft skills, and qualification information. The collection unit can also collect past performance data such as employee performance evaluations, project results, and feedback. For example, the collection unit retrieves employee skill information from a database and analyzes past performance data. The analysis unit analyzes the data collected by the collection unit and evaluates each employee's capabilities from multiple perspectives. For example, the analysis unit evaluates technical skills and soft skills based on employee skill information. The analysis unit can also evaluate performance evaluations and project results based on past performance data. For example, the analysis unit analyzes employee skill information and evaluates the level of technical skills. The proposal unit proposes appropriate personnel placement based on the evaluation results obtained by the analysis unit. For example, the proposal unit places employees with high programming skills in the programming team and employees with excellent project management abilities in the project management team. Furthermore, the proposal department can propose optimal personnel allocation in real time according to the needs of each team. For example, the proposal department analyzes the needs of each team and proposes the optimal personnel allocation. The optimization department evaluates the allocation results proposed by the proposal department and performs continuous optimization. For example, the optimization department evaluates the work results after allocation and revises the allocation as needed. The optimization department can also establish a feedback loop to continuously achieve optimal personnel allocation. For example, the optimization department evaluates the work results after allocation and revises the allocation based on the feedback. As a result, the personnel allocation system according to this embodiment can improve work efficiency by analyzing the skills and aptitudes of employees and proposing optimal personnel allocation.

[0060] The data collection department collects employee skill information and historical performance data. Specifically, it collects skill information such as employees' technical skills, soft skills, and certifications. Technical skills include proficiency in programming languages, database management, and network construction, while soft skills include communication skills, leadership, and problem-solving abilities. Certifications include industry-standard qualifications and certifications. This information is collected by integrating self-reported data entered by employees, information on completion of internal training programs, and data from external certification bodies. The data collection department can also collect historical performance data such as employee performance evaluations, project outcomes, and feedback. Performance evaluations include evaluations from supervisors and colleagues, project completion rates, and goal achievement rates, while project outcomes include project completion status, on-time delivery, and quality evaluations. Feedback includes 360-degree feedback and customer feedback. This data is automatically retrieved from internal HR systems and project management tools and stored in a central database. The data collection department manages this data centrally and can link it with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and proposal departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0061] The Analysis Department analyzes data collected by the Data Collection Department to comprehensively evaluate the capabilities of each employee. Specifically, it evaluates technical and soft skills based on employee skill information. Technical skills include proficiency in programming languages, practical experience, and project results, while soft skills include communication skills, leadership, and problem-solving abilities. These evaluations are performed using AI-powered natural language processing and machine learning algorithms. For example, it analyzes employee self-reported data and evaluation comments from supervisors to quantitatively assess skill levels and aptitudes. It can also perform performance evaluations and project outcome evaluations based on past performance data. Performance evaluations include goal achievement rates, project completion status, and quality evaluations, while project outcome evaluations include on-time delivery and customer satisfaction. This data is analyzed using statistical methods and machine learning algorithms to clarify each employee's strengths and weaknesses. Furthermore, the Analysis Department can evaluate employees' career paths and growth potential, which can be used to formulate future placement and training plans. As a result, the Analysis Department can quickly and accurately analyze collected data and comprehensively evaluate the capabilities of each employee.

[0062] The proposal department proposes appropriate personnel placements based on evaluation results obtained by the analysis department. Specifically, it places employees with strong programming skills in the programming team and employees with excellent project management skills in the project management team. The proposal department can also propose optimal personnel placements in real time according to the needs of each team. For example, the proposal department analyzes each team's project requirements, schedule, and resource status to propose the optimal personnel placement. This utilizes an AI-powered optimization algorithm to calculate the optimal placement based on each employee's skills, aptitudes, and past performance data. Furthermore, the proposal department can also propose future placements and development plans, taking into account employees' career paths and growth potential. For example, it can plan to have employees with specific skills participate in development programs and place them in leadership positions in the future. In this way, the proposal department can propose optimal personnel placements that meet the needs of each team and improve operational efficiency.

[0063] The Optimization Department evaluates the deployment results proposed by the Proposal Department and performs continuous optimization. Specifically, it evaluates the work results after deployment and revises the deployment as needed. The evaluation of work results includes project progress, goal achievement rate, and quality evaluation, and the effectiveness of the deployment is evaluated based on this data. The Optimization Department can also establish a feedback loop to continuously achieve optimal personnel deployment. For example, it evaluates the work results after deployment and revises the deployment based on feedback. This includes a process of collecting feedback from employees and supervisors to clarify the effectiveness and problems of the deployment. Furthermore, the Optimization Department can utilize AI-based predictive models to optimize future deployments. For example, it can predict what results a particular deployment will bring in the future based on past data and propose the optimal deployment. In this way, the Optimization Department can improve the overall system performance by evaluating the work results after deployment and performing continuous optimization.

[0064] The data collection unit can estimate an employee's emotions and adjust the timing of skill and performance data collection based on the estimated emotions. For example, if an employee is stressed, the data collection unit can collect skill information when the employee is relaxed. It can also immediately collect performance data if the employee is highly motivated. Furthermore, if an employee is tired, the data collection unit can collect data after a break. This allows for more appropriate data collection by adjusting the timing of data collection according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input employee emotion data into a generative AI and have the generative AI perform emotion estimation.

[0065] The data collection unit can analyze an employee's past project history and select the optimal data collection method. For example, the unit can collect detailed skill information from employees with a high project success rate. It can also collect simpler performance data from employees with a high project failure rate. Furthermore, the unit can apply different data collection methods depending on the type of project. This improves the accuracy of data collection by selecting the optimal data collection method based on the employee's past project history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input an employee's past project history into a generating AI and have the generating AI select the optimal data collection method.

[0066] The data collection unit can filter skill information and performance data based on an employee's current work situation and areas of interest. For example, it can collect only essential skill information from employees who are currently busy. It can also prioritize collecting data related to specific areas of interest for employees with clear areas of interest. Furthermore, it can collect detailed performance data from employees whose work situation is stable. This allows for the collection of more relevant data by filtering data based on an employee's work situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input data on employees' work situations and areas of interest into a generating AI and have the generating AI perform the data filtering.

[0067] The data collection unit can estimate employees' emotions and prioritize the data to be collected based on the estimated emotions. For example, if an employee is stressed, the data collection unit may postpone collecting less important data. Conversely, if an employee is relaxed, the data collection unit may prioritize collecting detailed data. Furthermore, if an employee is in a hurry, the data collection unit may prioritize collecting the most important data. This ensures that important data is collected preferentially by prioritizing data according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input employee emotion data into a generative AI and have the generative AI determine the data prioritization.

[0068] The data collection unit can prioritize the collection of highly relevant data based on the employee's geographical location when collecting skill information and performance data. For example, if an employee is working remotely, the data collection unit will prioritize the collection of skill information related to remote work. It can also collect performance data relevant to an overseas business trip if the employee is on a business trip abroad. Furthermore, if the employee works at headquarters, the data collection unit can prioritize the collection of data related to headquarters operations. This allows for the collection of more relevant data by considering the employee's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the employee's geographical location information into a generating AI and have the generating AI collect highly relevant data.

[0069] The data collection unit can analyze employees' social media activity and collect relevant data when collecting skill information and performance data. For example, the data collection unit can collect information related to skills that employees have shared on social media. It can also collect data related to projects that employees have participated in on social media. Furthermore, the data collection unit can collect data related to areas of interest from employees' social media activity. This allows for more multifaceted data collection by analyzing and collecting data from employees' social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input employee social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0070] The analysis unit can estimate employees' emotions and adjust the data analysis method based on the estimated emotions. For example, if an employee is stressed, the analysis unit can apply a simplified analysis method. If an employee is relaxed, the analysis unit can apply a more detailed analysis method. Furthermore, if an employee is in a hurry, the analysis unit can apply a rapid analysis method. This allows for more appropriate data analysis by adjusting the data analysis method according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input employee emotion data into a generative AI and have the generative AI adjust the data analysis method.

[0071] The analysis department can evaluate employees while considering their skill development during data analysis. For example, the analysis department can give high ratings to employees whose skills are rapidly improving. It can also point out areas for improvement to employees whose skills are stagnating. Furthermore, the analysis department can suggest retraining to employees whose skills are declining. By considering the skill development of employees during evaluation, a more accurate assessment becomes possible. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input employee skill development data into a generating AI and have the generating AI perform the evaluation.

[0072] The analysis department can improve the accuracy of evaluations by referring to past employee feedback during data analysis. For example, the analysis department can adjust evaluation criteria based on past feedback. The analysis department can also analyze the content of the feedback to improve the accuracy of evaluations. Furthermore, the analysis department can weight evaluations by considering the frequency of feedback. This improves the accuracy of evaluations by referring to past employee feedback. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input past employee feedback data into a generating AI and have the generating AI perform the evaluation accuracy improvement.

[0073] The analysis department can estimate employees' emotions and adjust how evaluation results are displayed based on the estimated emotions. For example, if an employee is stressed, the analysis department can provide a simple display. If an employee is relaxed, the analysis department can also provide a detailed display. Furthermore, if an employee is in a hurry, the analysis department can provide a concise display. By adjusting how evaluation results are displayed according to employees' emotions, it becomes possible to present more appropriate evaluation results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis department may be performed using AI or not using AI. For example, the analysis department can input employee emotion data into a generative AI and have the generative AI adjust how evaluation results are displayed.

[0074] The analysis department can perform evaluations while considering the geographical distribution of employees during data analysis. For example, the analysis department can centralize the evaluation of geographically dispersed employees. The analysis department can also perform evaluations while considering the characteristics of work in each region. Furthermore, the analysis department can reflect differences in work efficiency due to geographical factors in the evaluation. This makes it possible to perform more accurate evaluations by considering the geographical distribution of employees. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input employee geographical distribution data into a generating AI and have the generating AI perform the evaluation.

[0075] The analysis department can improve the accuracy of evaluations by referring to relevant literature for employees during data analysis. For example, the analysis department can revise evaluation criteria based on relevant literature. The analysis department can also analyze the content of the literature to improve the accuracy of evaluations. Furthermore, the analysis department can weight evaluations by considering the frequency of citations in the literature. As a result, the accuracy of evaluations is improved by referring to relevant literature for employees. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input employee relevant literature data into a generating AI and have the generating AI perform the task of improving the accuracy of evaluations.

[0076] The suggestion department can estimate an employee's emotions and adjust the way suggestions are presented based on those emotions. For example, if an employee is stressed, the suggestion department can offer a simple suggestion. If the employee is relaxed, it can offer a more detailed suggestion. Furthermore, if the employee is in a hurry, it can offer a quick suggestion. By adjusting the way suggestions are presented according to the employee's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion department may be performed using AI or not. For example, the suggestion department can input employee emotion data into a generative AI and have the generative AI adjust the way suggestions are presented.

[0077] The proposal department can adjust the level of detail of a proposal based on the needs of each team. For example, if the team's needs are clear, the proposal department will provide a detailed proposal. If the team's needs are unclear, the proposal department can provide a simplified proposal. Furthermore, if the team's needs change, the proposal department can provide a flexible proposal. By adjusting the level of detail of a proposal based on the needs of each team, it becomes possible to provide more appropriate proposals. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the needs data of each team into a generating AI and have the generating AI perform the adjustment of the level of detail of the proposal.

[0078] The proposal department can apply different proposal algorithms based on the project progress of each team when making proposals. For example, if the project is in its initial stages, the proposal department can make basic proposals. If the project is in the middle stages, the proposal department can also make detailed proposals. Furthermore, if the project is in its final stages, the proposal department can make optimized proposals. This allows for more appropriate proposals by applying proposal algorithms according to the project progress of each team. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input project progress data for each team into a generating AI and have the generating AI execute the application of the proposal algorithm.

[0079] The suggestion department can estimate employees' emotions and prioritize suggestions based on those emotions. For example, if an employee is stressed, the suggestion department might postpone less important suggestions. Conversely, if an employee is relaxed, it might prioritize more detailed suggestions. Furthermore, if an employee is in a hurry, it might prioritize the most important suggestions. This allows for prioritizing important suggestions based on employee emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion department may be performed using AI or not. For example, the suggestion department could input employee emotion data into a generative AI and have the generative AI determine the priority of suggestions.

[0080] The proposal department can make optimal proposals based on each team's geographical location information. For example, if a team is working remotely, the proposal department will make proposals related to remote work. It can also make proposals related to the region where the team is on a business trip abroad. Furthermore, if a team is working at headquarters, the proposal department can make proposals related to headquarters operations. This allows for more appropriate proposals by considering each team's geographical location information. Some or all of the above processing in the proposal department may be performed using AI, or not. For example, the proposal department can input each team's geographical location data into a generating AI and have the AI ​​generate the optimal proposal.

[0081] The proposal department can analyze each team's social media activity and make relevant suggestions when submitting proposals. For example, the proposal department can make suggestions based on information that teams have posted on social media. It can also make suggestions related to projects that teams have participated in on social media. Furthermore, the proposal department can make suggestions related to areas of interest based on the teams' social media activity. By analyzing each team's social media activity and making suggestions accordingly, more appropriate suggestions can be made. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input each team's social media activity data into a generating AI and have the generating AI execute relevant suggestions.

[0082] The optimization unit can estimate the employee's emotions and adjust the optimization method based on the estimated emotions. For example, if an employee is stressed, the optimization unit can apply a simple optimization method. It can also apply a more detailed optimization method if the employee is relaxed. Furthermore, if the employee is in a hurry, the optimization unit can apply a rapid optimization method. This allows for more appropriate optimization by adjusting the optimization method according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the optimization unit may be performed using AI, or not. For example, the optimization unit can input employee emotion data into a generative AI and have the generative AI adjust the optimization method.

[0083] The optimization unit can evaluate the business results after deployment and adjust the optimization algorithm during the optimization process. For example, if the business results are high, the optimization unit will maintain the current optimization algorithm. If the business results are low, the optimization unit can also review the optimization algorithm and identify areas for improvement. Furthermore, the optimization unit can analyze fluctuations in business results and dynamically adjust the optimization algorithm. This enables more effective optimization by evaluating the business results after deployment and optimizing the optimization algorithm. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the business result data after deployment into a generating AI and have the generating AI perform the adjustment of the optimization algorithm.

[0084] The optimization unit can improve the accuracy of optimization based on employee feedback during the optimization process. For example, the optimization unit adjusts the optimization algorithm based on employee feedback. The optimization unit can also analyze the content of the feedback to improve the accuracy of optimization. Furthermore, the optimization unit can weight the optimization process by considering the frequency of feedback. This improves the accuracy of optimization by referencing employee feedback. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input employee feedback data into a generating AI and have the generating AI perform optimization accuracy improvements.

[0085] The optimization unit can estimate the employee's emotions and adjust the optimization frequency based on the estimated emotions. For example, if an employee is stressed, the optimization unit can reduce the optimization frequency. Conversely, if an employee is relaxed, the optimization unit can increase the optimization frequency. Furthermore, if an employee is in a hurry, the optimization unit can adjust the optimization frequency. This allows for more appropriate optimization by adjusting the optimization frequency according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using AI or not. For example, the optimization unit can input employee emotion data into the generative AI and have the generative AI adjust the optimization frequency.

[0086] The optimization unit can perform optimal placement based on employees' geographical location information during optimization. For example, if an employee is working remotely, the optimization unit will perform placement suitable for remote work. Furthermore, if an employee is on a business trip abroad, the optimization unit can perform placement suitable for that region. Additionally, if an employee works at headquarters, the optimization unit can perform placement suitable for headquarters work. This allows for more appropriate placement by considering employees' geographical location information. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input employee geographical location data into a generating AI and have the generating AI perform the optimal placement.

[0087] The optimization unit can improve the accuracy of optimization based on relevant literature for employees during the optimization process. For example, the optimization unit can revise the optimization algorithm based on the relevant literature. The optimization unit can also analyze the content of the literature to improve the accuracy of optimization. Furthermore, the optimization unit can weight the optimization by considering the frequency of citations of the literature. As a result, the accuracy of optimization is improved by performing optimization while referring to the relevant literature for employees. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input employee relevant literature data into a generating AI and have the generating AI perform the optimization accuracy improvement.

[0088] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0089] The suggestion department can estimate an employee's emotions and adjust the timing of suggestions based on those emotions. For example, if an employee is stressed, the suggestion may be postponed. Conversely, if an employee is relaxed, the suggestion may be made immediately. Furthermore, if an employee is in a hurry, the most important suggestions may be prioritized. This allows for more appropriate suggestions by adjusting the timing of suggestions according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion department may be performed using AI or not. For example, the suggestion department can input employee emotion data into a generative AI and have the generative AI adjust the timing of suggestions.

[0090] The data collection unit can adjust the frequency of data collection based on the employee's health status when collecting employee skill information and performance data. For example, if an employee is in good health, data can be collected more frequently. Conversely, if an employee is in poor health, the frequency of data collection can be reduced. Furthermore, if an employee is ill, data collection can be temporarily suspended. This allows for more appropriate data collection by adjusting the frequency of data collection according to the employee's health status. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input employee health status data into a generating AI and have the generating AI adjust the frequency of data collection.

[0091] The analysis department can estimate employees' emotions and adjust evaluation criteria based on those estimated emotions. For example, if an employee is stressed, the evaluation criteria can be relaxed. Conversely, if an employee is relaxed, the evaluation criteria can be made stricter. Furthermore, if an employee is in a hurry, a rapid evaluation criterion can be applied. This allows for more appropriate evaluations by adjusting evaluation criteria according to employees' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis department may be performed using AI or not. For example, the analysis department can input employee emotion data into a generative AI and have the generative AI adjust the evaluation criteria.

[0092] The suggestion department can adjust suggestions based on employees' career goals when collecting employee skill information and performance data. For example, if an employee aims for career advancement, it can make suggestions that will help improve their skills. If an employee wishes to maintain the status quo, it can make suggestions that are appropriate for their current situation. Furthermore, if an employee wishes to change careers, it can make suggestions related to new fields. By adjusting suggestions according to employees' career goals, more appropriate suggestions can be made. Some or all of the above processing in the suggestion department may be performed using AI, for example, or not. For example, the suggestion department can input employee career goal data into a generating AI and have the generating AI adjust the suggestions.

[0093] The optimization unit can estimate employees' emotions and determine optimization priorities based on those estimated emotions. For example, if an employee is stressed, less important optimizations can be postponed. Conversely, if an employee is relaxed, detailed optimizations can be prioritized. Furthermore, if an employee is in a hurry, the most important optimizations can be prioritized. This allows for prioritizing important optimizations based on employee emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using AI or not. For example, the optimization unit can input employee emotion data into a generative AI and have the generative AI determine the optimization priorities.

[0094] The data collection unit can adjust the timing of data collection based on the employee's working hours when collecting employee skill information and performance data. For example, data collection can be withheld when the employee is outside of working hours. Conversely, data can be collected immediately when the employee is working. Furthermore, data collection can be temporarily suspended when the employee is on vacation. This allows for more appropriate data collection by adjusting the timing of data collection according to the employee's working hours. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input employee working hours data into a generating AI and have the generating AI adjust the timing of data collection.

[0095] The analysis unit can estimate employees' emotions and adjust the accuracy of data analysis based on the estimated emotions. For example, if an employee is stressed, the analysis accuracy can be reduced. Conversely, if an employee is relaxed, the analysis accuracy can be increased. Furthermore, if an employee is in a hurry, a rapid analysis accuracy can be applied. This allows for more appropriate data analysis by adjusting the data analysis accuracy according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input employee emotion data into a generative AI and have the generative AI adjust the data analysis accuracy.

[0096] The proposal department can adjust the timing of proposals based on the progress of an employee's project when collecting employee skill information and performance data. For example, it can provide basic proposals when a project is in its initial stages, detailed proposals when the project is in the middle stages, and optimized proposals when the project is in its final stages. By adjusting the timing of proposals according to the project's progress, more appropriate proposals can be made. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input project progress data into a generating AI and have the generating AI adjust the timing of proposals.

[0097] The optimization unit can estimate the emotions of employees and adjust the optimization method based on the estimated emotions. For example, if an employee is stressed, a simple optimization method can be applied. If an employee is relaxed, a more detailed optimization method can be applied. Furthermore, if an employee is in a hurry, a rapid optimization method can be applied. This allows for more appropriate optimization by adjusting the optimization method according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using AI, or not using AI. For example, the optimization unit can input employee emotion data into a generative AI and have the generative AI adjust the optimization method.

[0098] The data collection unit can adjust its data collection method based on the employee's work style when collecting employee skill information and performance data. For example, if an employee works full-time, it can collect detailed data. If an employee works part-time, it can collect simpler data. Furthermore, if an employee works remotely, it can collect data related to remote work. By adjusting the data collection method according to the employee's work style, more appropriate data collection becomes possible. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input employee work style data into a generating AI and have the generating AI perform the adjustment of the data collection method.

[0099] The following briefly describes the processing flow for example form 2.

[0100] Step 1: The data collection unit collects employee skill information and historical performance data. For example, the data collection unit retrieves skill information such as employees' technical skills, soft skills, and qualifications from a database, and collects historical performance data such as performance evaluations, project results, and feedback. Step 2: The analysis department analyzes the data collected by the data collection department and evaluates each employee's capabilities from multiple perspectives. For example, the analysis department evaluates technical and soft skills based on employee skill information, and evaluates performance and project results based on past performance data. Step 3: The proposal department proposes appropriate personnel allocation based on the evaluation results obtained by the analysis department. For example, the proposal department would assign employees with strong programming skills to the programming team and employees with excellent project management skills to the project management team. The proposal department also proposes the optimal personnel allocation in real time according to the needs of each team. Step 4: The Optimization Department evaluates the deployment results proposed by the Proposal Department and performs continuous optimization. For example, the Optimization Department evaluates the work results after deployment and revises the deployment as needed. The Optimization Department also establishes a feedback loop to continuously achieve optimal personnel deployment.

[0101] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0103] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0104] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and optimization unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects employee skill information and past performance data using the camera 42 and microphone 38B of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to evaluate each employee's capabilities from multiple perspectives. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and proposes the optimal personnel placement based on the evaluation results. The optimization unit is implemented in the control unit 46A of the smart device 14, for example, and evaluates the placement results and performs continuous optimization. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0105] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0106] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0111] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0112] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0113] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0114] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0115] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and optimization unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects employee skill information and past performance data using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, which analyzes the collected data to evaluate each employee's capabilities from multiple perspectives. The proposal unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, which proposes the optimal personnel placement based on the evaluation results. The optimization unit is implemented, for example, in the control unit 46A of the smart glasses 214, which evaluates the placement results and performs continuous optimization. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0121] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0122] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0124] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0125] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0127] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0128] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0129] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0130] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0131] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and optimization unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects employee skill information and past performance data using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to evaluate each employee's abilities from multiple perspectives. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and proposes the optimal personnel placement based on the evaluation results. The optimization unit is implemented in the control unit 46A of the headset terminal 314, for example, and evaluates the placement results and performs continuous optimization. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0137] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0138] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

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

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

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

[0144] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0145] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0146] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0147] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0148] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0153] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and optimization unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects employee skill information and past performance data using the camera 42 and microphone 238 of the robot 414. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data to evaluate each employee's capabilities from multiple perspectives. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which proposes the optimal personnel placement based on the evaluation results. The optimization unit is implemented, for example, by the control unit 46A of the robot 414, which evaluates the placement results and performs continuous optimization. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0154] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0156] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0157] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0158] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0161] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0162] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0163] 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.

[0164] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0165] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0166] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0167] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0169] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0170] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0171] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0172] (Note 1) A data collection unit that collects employee skill information and past performance data, The data collected by the aforementioned collection unit is analyzed by the analysis unit, which evaluates the capabilities of each employee from multiple perspectives. Based on the evaluation results obtained by the aforementioned analysis unit, the proposal unit proposes appropriate personnel allocation, The system includes an optimization unit that evaluates the arrangement results proposed by the proposal unit and performs continuous optimization. A system characterized by the following features. (Note 2) The aforementioned collection unit is We estimate employee sentiment and adjust the timing of skill and performance data collection based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Analyze employees' past project history and select appropriate data collection methods. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is When collecting skills information and performance data, filter the data based on the employee's current work situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is We estimate employee sentiment and prioritize the data to collect based on the estimated employee sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is When collecting skill and performance data, prioritize the collection of relevant data based on the employee's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting skill information and performance data, analyze employees' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit is We estimate employee sentiment and adjust the data analysis method based on the estimated employee sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit is During data analysis, evaluations are conducted based on the degree of employee skill development. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit is When analyzing data, improve the accuracy of evaluations based on past employee feedback. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is The system estimates employee sentiment and adjusts how evaluation results are displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is During data analysis, evaluations are conducted based on the geographical distribution of employees. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is Improve the accuracy of evaluations based on relevant employee literature during data analysis. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, We estimate the employees' emotions and adjust the way we present proposals based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the needs of each team. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When making a proposal, a different proposal algorithm will be applied based on the project progress of each team. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, Estimate employee sentiment and prioritize proposals based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When making a proposal, we will provide the most suitable proposal based on the geographical location information of each team. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making a proposal, analyze each team's social media activity and make relevant suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The optimization unit, We estimate employee sentiment and adjust the optimization method based on the estimated employee sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The optimization unit, During optimization, the operational results after deployment are evaluated and the optimization algorithm is adjusted. The system described in Appendix 1, characterized by the features described herein. (Note 22) The optimization unit, During optimization, improve the accuracy of the optimization based on employee feedback. The system described in Appendix 1, characterized by the features described herein. (Note 23) The optimization unit, It estimates employee sentiment and adjusts the optimization frequency based on the estimated employee sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The optimization unit, During optimization, the optimal placement is determined based on the geographical location of employees. The system described in Appendix 1, characterized by the features described herein. (Note 25) The optimization unit, During optimization, improve the accuracy of optimization based on relevant literature from employees. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects employee skill information and past performance data, The data collected by the aforementioned collection unit is analyzed by the analysis unit, which evaluates the capabilities of each employee from multiple perspectives. Based on the evaluation results obtained by the aforementioned analysis unit, the proposal unit proposes appropriate personnel allocation, The system includes an optimization unit that evaluates the arrangement results proposed by the proposal unit and performs continuous optimization. A system characterized by the following features.

2. The aforementioned collection unit is We estimate employee sentiment and adjust the timing of skill and performance data collection based on the estimated sentiment. The system according to feature 1.

3. The aforementioned collection unit is Analyze employees' past project history and select appropriate data collection methods. The system according to feature 1.

4. The aforementioned collection unit is When collecting skills information and performance data, filter the data based on the employee's current work situation and areas of interest. The system according to feature 1.

5. The aforementioned collection unit is We estimate employee sentiment and prioritize the data to collect based on the estimated employee sentiment. The system according to feature 1.

6. The aforementioned collection unit is When collecting skill and performance data, prioritize the collection of relevant data based on the employee's geographical location. The system according to feature 1.

7. The aforementioned collection unit is When collecting skill information and performance data, analyze employees' social media activity and collect relevant data. The system according to feature 1.

8. The aforementioned analysis unit is We estimate employee sentiment and adjust the data analysis method based on the estimated employee sentiment. The system according to feature 1.

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