system

Abstract

The system according to this embodiment aims to analyze employee performance data and provide individualized career development plans. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, a provision unit, and a feedback unit. The collection unit collects employee performance data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates a career growth plan based on the analysis results obtained by the analysis unit. The provision unit provides the career growth plan generated by the generation unit. The feedback unit provides real-time feedback based on the career growth plan provided by the provision unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that programs for cultivating leadership in enterprises are insufficient, and the situation is that next-generation leaders cannot be cultivated.

[0005] The system according to the embodiment aims to analyze the performance data of employees and provide individual career growth plans.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a generation unit, a provision unit, and a feedback unit. The data collection unit collects employee performance data. The analysis unit analyzes the data collected by the data collection unit. The generation unit generates a career growth plan based on the analysis results obtained by the analysis unit. The provision unit provides the career growth plan generated by the generation unit. The feedback unit provides real-time feedback based on the career growth plan provided by the provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze employee performance data and provide individualized career development plans. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 leadership development system according to an embodiment of the present invention is an innovative solution for developing next-generation leaders in companies using an AI agent. This leadership development system collects and analyzes employee performance data and provides individualized career growth plans. The leadership development system provides an optimal career growth plan for each employee by collecting and analyzing employee performance data. This career growth plan is generated based on the employee's behavioral data and skills and is updated in real time. The leadership development system also provides employees with real-time feedback and presents growth opportunities. This allows employees to develop their leadership skills while experiencing their own growth. For example, the leadership development system collects data such as the employee's work performance, skill level, and behavioral patterns. For example, it collects data such as what kind of work the employee is doing, how long it takes them to do it, what skills they have, and what kind of actions they are taking. Next, the leadership development system analyzes the collected data. Generative AI is used for the analysis. The generative AI analyzes the employee's behavioral data and skill data and generates an optimal career growth plan for each employee. For example, it analyzes what kind of training is needed and what kind of work they should be assigned, depending on the employee's skill level. The generated career growth plan is provided to the employee. This plan includes specific training plans, work assignments, and growth goals. For example, it includes training plans to help employees acquire the skills necessary to demonstrate leadership, and work assignments designed to facilitate leadership development. Furthermore, the leadership development system provides real-time feedback to employees. For instance, it provides feedback on what employees are doing well and what areas need improvement when performing their tasks. It also updates growth plans in real time based on employee progress, ensuring employees are always growing according to the latest plan. This mechanism enhances the overall leadership capabilities of the company.Employees can develop their leadership skills while experiencing personal growth. Furthermore, companies can provide systematic development programs, optimizing overall organizational performance. For example, when employees demonstrate leadership, projects proceed more smoothly, strengthening the company's competitiveness. Thus, leadership development systems can improve the overall leadership capabilities of the company.

[0029] The leadership development system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a provision unit, and a feedback unit. The collection unit collects employee performance data. The collection unit collects data such as the employee's work performance status, skill level, and behavioral patterns. For example, the collection unit collects data such as what tasks the employee performs, how long it takes, what skills they possess, and what actions they take. Some or all of the above-described processing in the collection unit may be performed using AI, or without AI. For example, the collection unit can input the employee's work performance status into the AI, and the AI ​​can collect the data. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes employee behavioral data and skill data. For example, the analysis unit analyzes what kind of training is needed and what kind of tasks the employee should be assigned, depending on their skill level. Some or all of the above-described processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input employee behavioral data into the generation AI, and the generation AI can analyze the data. The generation unit generates a career growth plan based on the analysis results obtained by the analysis unit. For example, the generation unit generates what training is necessary and what tasks should be assigned to employees, depending on their skill level. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input employee skill data into the generation AI, and the generation AI can generate a career growth plan. The provision unit provides the career growth plan generated by the generation unit. For example, the provision unit provides the generated career growth plan to the employee. Some or all of the above processing in the provision unit may be performed using AI or not. For example, the provision unit can input the generated career growth plan into the AI, and the AI ​​can provide it to the employee. The feedback unit provides real-time feedback based on the career growth plan provided by the provision unit. For example, the feedback unit provides feedback on what the employee does well and what areas need improvement when performing their duties.The feedback unit updates the growth plan in real time, for example, according to the employee's progress. Some or all of the above-described processes in the feedback unit may be performed using AI or not. For example, the feedback unit can input the employee's work performance status into the AI, and the AI ​​can provide feedback. This allows the leadership development system according to the embodiment to efficiently collect and analyze employee performance data, generate and provide career growth plans, and provide feedback.

[0030] The data collection unit collects employee performance data. For example, it collects data on employees' work performance, skill levels, and behavioral patterns. Specifically, it collects data on what tasks employees perform, how long it takes them, what skills they possess, and what behaviors they exhibit. This includes log data from tools and systems employees use daily, task completion status from project management tools, and timesheet records. Furthermore, to evaluate employee skill levels, regular skill assessments and self-assessment questionnaires can be conducted, and the results stored in a database. Behavioral pattern data includes, for example, what communication methods employees use, how often they contact team members, and their meeting attendance. This data is important for a multifaceted evaluation of employee performance. Some or all of the above processing in the data collection unit may or may not be performed using AI. For example, the data collection unit can input employee work performance data into an AI, which can then collect the data. Using AI makes it possible to efficiently collect vast amounts of data and update it in real time. This allows the data collection unit to comprehensively and accurately collect employee performance data and provide the information necessary for the next analysis step.

[0031] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes employee behavioral data and skill data. Specifically, it analyzes what kind of training is needed and what tasks should be assigned to employees based on their skill levels. Some or all of the above processing in the analysis unit is performed using generative AI. For example, the analysis unit inputs employee behavioral data into the generative AI, which then analyzes the data. Based on the employee's past performance data and skill assessment results, the generative AI identifies the employee's strengths and weaknesses and proposes optimal training programs and work assignments. For example, the generative AI analyzes the employee's skill set and, if there is a deficiency in a particular skill, recommends a training course to improve that skill. It can also analyze employee behavioral patterns and provide specific action plans to encourage behavioral changes necessary to improve leadership skills. Furthermore, the analysis unit can analyze employee performance data over time to understand performance trends and fluctuations. This allows for continuous monitoring of employee growth and adjustments to training plans and work assignments as needed. The analysis department can utilize generative AI to quickly and accurately analyze employee performance data and provide the information necessary to generate optimal career growth plans.

[0032] The generation unit generates career growth plans based on the analysis results obtained by the analysis unit. For example, the generation unit generates what training is necessary and what tasks should be assigned to employees, depending on their skill level. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit can input employee skill data into the generation AI, which can then generate a career growth plan. The generation AI creates individual career growth plans based on the employee's skill set and performance data. For example, the generation AI compares the employee's current skill level with their target skill level to identify skill gaps. It then proposes specific training courses and work experience to bridge those gaps. The generation AI can also create plans that show future career paths, taking into account the employee's career goals and interests. For example, for an employee who wants to improve their leadership skills, it can propose leadership training and mentoring programs and provide opportunities to demonstrate leadership in actual projects. Furthermore, the generation unit can monitor the progress of the career growth plan based on employee performance data and update the plan as needed. This allows the generation unit to provide concrete and practical plans to support employee career growth and improve employee motivation and performance.

[0033] The delivery unit provides the career growth plan generated by the generation unit. For example, the delivery unit provides the generated career growth plan to the employee. Some or all of the above processes in the delivery unit may be performed using AI or not. For example, the delivery unit can input the generated career growth plan into AI, which can then provide it to the employee. Specifically, the delivery unit distributes the generated career growth plan to the employee's individual account, making it accessible to the employee at any time. The employee can review their career growth plan and understand the necessary training and work assignments. The delivery unit also has a function to update the progress of the career growth plan in real time and notify the employee. For example, when an employee completes a particular training course, that information is reflected in the career growth plan, and the next step is suggested. Furthermore, the delivery unit can collect employee feedback and provide information to improve the content of the career growth plan. For example, by providing feedback from employees on the content of training courses and work assignments, the delivery unit can adjust the career growth plan based on that information and provide a more effective plan. In this way, the delivery unit can provide employees with appropriate career growth plans and support their growth.

[0034] The Feedback Department provides real-time feedback based on the career growth plan provided by the Service Provider. For example, the Feedback Department provides feedback on what employees do well and what areas need improvement when performing their tasks. Specifically, the Feedback Department monitors employees' work performance and provides real-time feedback. For instance, when an employee performs a specific task, AI can evaluate the progress and results of that task and provide immediate feedback. The Feedback Department updates the growth plan in real time according to the employee's progress. For example, if an employee acquires a new skill, that information is reflected in the career growth plan, and the next steps are suggested. Some or all of the above processes in the Feedback Department may be performed using AI or not. For example, the Feedback Department can input employee work performance data into AI, which can then provide feedback. Using AI makes it possible to quickly and accurately evaluate employee performance and provide appropriate feedback. Furthermore, the Feedback Department can collect employee feedback and provide information to improve the content of the career growth plan. This allows the Feedback Department to continuously support employee growth and maximize the effectiveness of the career growth plan.

[0035] The data collection unit can collect data such as employees' work performance, skill levels, and behavioral patterns. For example, the data collection unit can collect data on employees' work performance, such as task completion status and the quality of deliverables. The data collection unit can also collect data on employees' skill levels, such as technical skills and communication skills. Furthermore, the data collection unit can collect data on employees' behavioral patterns, such as working hours and work processes. By collecting data on employees' work performance, skill levels, and behavioral patterns, the data collection unit can obtain more detailed performance data. 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 work performance data into AI, which can then collect the data.

[0036] The analysis unit can analyze employee behavioral data and skill data to generate an optimal career growth plan for each employee. For example, the analysis unit can analyze employee behavioral data. For example, the analysis unit can analyze data such as work logs and behavioral history. The analysis unit can also analyze employee skill data. For example, the analysis unit can analyze data such as skill evaluation results and training history. Furthermore, the analysis unit can generate an optimal career growth plan for each employee. For example, the analysis unit can generate individual training plans and career path suggestions. In this way, the analysis unit can generate an optimal career growth plan for each employee by analyzing employee behavioral data and skill data. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs employee behavioral data into the generation AI, and the generation AI can analyze the data.

[0037] The generation unit can generate information about the training required and the tasks that employees should be assigned, based on their skill levels. For example, the generation unit can generate information about the training required based on an employee's skill level. For example, the generation unit can generate training plans for technical skills, communication skills, etc. The generation unit can also generate information about the tasks that employees should be assigned, based on their skill levels. For example, the generation unit can generate tasks such as project assignments and task assignments. In this way, the generation unit can support efficient career growth by generating training and task assignments that are appropriate for each employee's skill level. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input employee skill data into the generation AI, which can then generate a career growth plan.

[0038] The service provider can provide the generated career growth plan to the employee. The service provider can, for example, provide the generated career growth plan to the employee. The service provider can provide the career growth plan, for example, training content and job assignments. In this way, the service provider can promote employee growth by providing the generated career growth plan to the employee. Some or all of the above processes in the service provider may be performed using AI or not using AI. For example, the service provider may perform the generated career growth plan using AI or not using AI. For example, the service provider can input the generated career growth plan into AI, and the AI ​​can provide it to the employee.

[0039] The feedback department can provide employees with real-time feedback and update their growth plans in real time. For example, the feedback department can provide feedback on what employees do well and what areas need improvement when performing their tasks. The feedback department can provide real-time feedback, such as immediate comments and notifications. Furthermore, the feedback department can update growth plans in real time according to the employee's progress. This can be done through methods such as feedback-based revisions and periodic reviews. In this way, the feedback department can continuously support employee growth by providing real-time feedback and updating growth plans in real time. Some or all of the above processes in the feedback department may be performed using AI or not. For example, the feedback department can input employee work performance data into AI, which can then provide feedback.

[0040] The data collection unit can analyze employees' past performance data and select the optimal data collection method. For example, the data collection unit can identify the time periods in which employees perform best from past data and collect data during those times. For example, the data collection unit can analyze employees' past behavioral patterns and select the most efficient data collection method. The data collection unit can also optimize the frequency of data collection based on employees' past performance data. In this way, the data collection unit can select the optimal data collection method and perform efficient data collection by analyzing employees' past performance data. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input employees' past performance data into AI, and the AI ​​can select the optimal data collection method.

[0041] The data collection unit can filter performance data based on an employee's current project and role. For example, the unit can collect only relevant data based on the progress of the current project. For example, the unit can select and collect necessary data based on an employee's role. The unit can also adjust the importance of the data collected based on project priorities. This allows the unit to collect highly relevant data by filtering it based on an employee's current project and role. Some or all of the above processing in the data collection unit may or may not be performed using AI. For example, the data collection unit can input data on an employee's current project and role into an AI, which can then perform the filtering.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location of employees when collecting performance data. For example, if an employee is working remotely, the data collection unit will prioritize the collection of data related to remote work. For example, if an employee is on a business trip, the data collection unit can prioritize the collection of data related to work at the business trip destination. Furthermore, if an employee is in the office, the data collection unit can prioritize the collection of data related to work at the office. In this way, the data collection unit can prioritize the collection of highly relevant data by considering the geographical location of employees. 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 the geographical location of employees into the AI, which can then prioritize the collection of highly relevant data.

[0043] The data collection unit can analyze employees' social media activity and collect relevant data when collecting performance data. For example, the data collection unit can identify work-related topics from employees' social media activity and collect that data. For example, the data collection unit can analyze employees' statements on social media and collect work-related data. The data collection unit can also analyze employees' networks on social media and collect work-related data. In this way, the data collection unit can collect work-related data by analyzing employees' social media activity. 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 social media activity data into AI, and the AI ​​can collect relevant data.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the employee. For example, the analysis unit can provide detailed analysis results to employees involved in important projects. For example, it can provide simple analysis results to employees performing general tasks. It can also provide basic analysis results to new employees. In this way, the analysis unit can provide the necessary information at the appropriate level by adjusting the level of detail of the analysis based on the importance of the employee. Some or all of the above processing in the analysis unit is performed using a generating AI. For example, the analysis unit inputs employee importance data into the generating AI, and the generating AI can adjust the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the employee's category during analysis. For example, the analysis unit can apply an analysis algorithm related to leadership skills to managers. For example, it can apply an analysis algorithm related to technical skills to engineers. It can also apply an analysis algorithm related to communication skills to sales representatives. In this way, the analysis unit can provide more appropriate analysis results by applying an analysis algorithm according to the employee's category. Some or all of the above processing in the analysis unit is performed using a generating AI. For example, the analysis unit inputs employee category data into the generating AI, and the generating AI can apply different analysis algorithms.

[0046] The analysis unit can determine the priority of analysis based on the employee's submission date. For example, the analysis unit can prioritize the analysis of submissions with approaching deadlines. For example, it can quickly analyze submissions submitted early. It can also postpone the analysis of submissions submitted late. In this way, the analysis unit can prioritize the analysis of important submissions by determining the priority of analysis based on the employee's submission date. Some or all of the above processing in the analysis unit is performed using a generating AI. For example, the analysis unit inputs employee submission date data into the generating AI, and the generating AI can determine the priority of analysis.

[0047] The analysis unit can adjust the order of analysis based on employee relevance during the analysis process. For example, the analysis unit can prioritize the analysis of data of employees involved in important projects. For example, the analysis unit can prioritize the analysis of data of team leaders. It can also postpone the analysis of data of new employees. In this way, the analysis unit can prioritize the analysis of important data by adjusting the order of analysis based on employee relevance. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit inputs employee relevance data into the generative AI, which can then adjust the order of analysis.

[0048] The generation unit can adjust the level of detail generated based on the employee's skill level when creating a career growth plan. For example, the generation unit can provide a detailed career growth plan to a highly skilled employee. For example, it can provide a basic career growth plan to a medium-skilled employee. It can also provide a simple career growth plan to a low-skilled employee. In this way, the generation unit can provide the most suitable career growth plan for each employee by adjusting the level of detail based on the employee's skill level. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs employee skill level data into the generation AI, which can then adjust the level of detail.

[0049] The generation unit can apply different generation algorithms depending on the employee's role when generating career growth plans. For example, the generation unit can generate a career growth plan for a manager that strengthens leadership skills. For example, it can generate a career growth plan for an engineer that strengthens technical skills. It can also generate a career growth plan for a sales representative that strengthens communication skills. In this way, the generation unit can provide more appropriate career growth plans by applying generation algorithms that are appropriate for the employee's role. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input employee role data into the generation AI, and the generation AI can apply different generation algorithms.

[0050] The generation unit can determine the generation priority based on the employee's submission timing when generating career growth plans. For example, the generation unit can prioritize generating career growth plans for submissions with approaching deadlines. For example, it can quickly generate career growth plans for submissions with early submission dates. It can also postpone generating career growth plans for submissions with late submission dates. In this way, the generation unit can prioritize providing important career growth plans by determining the generation priority based on the employee's submission timing. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input employee submission timing data into the generation AI, which can then determine the generation priority.

[0051] The generation unit can adjust the generation order based on employee relevance when generating career growth plans. For example, the generation unit can prioritize generating career growth plans for employees involved in important projects. For example, it can prioritize generating career growth plans for team leaders. It can also postpone the generation of career growth plans for new employees. In this way, the generation unit can prioritize providing important career growth plans by adjusting the generation order based on employee relevance. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs employee relevance data into the generation AI, which can then adjust the generation order.

[0052] The service provider can select the optimal delivery method when providing a career growth plan by referring to the employee's past feedback history. For example, the service provider can identify the delivery method preferred by the employee from the past feedback history and provide it accordingly. For example, the service provider can analyze the feedback history and select a delivery method that is easy for the employee to understand. The service provider can also select the optimal delivery method for the employee's growth based on the past feedback history. In this way, the service provider can select the optimal delivery method and support the employee's growth by referring to the employee's past feedback history. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input the employee's past feedback history into AI, and the AI ​​can select the optimal delivery method.

[0053] The service provider can apply different service algorithms depending on the employee's role when providing career growth plans. For example, the service provider might select a service method to enhance leadership skills for managers. For example, the service provider might select a service method to enhance technical skills for engineers. The service provider might also select a service method to enhance communication skills for sales representatives. By applying service algorithms tailored to each employee's role, the service provider can provide more appropriate career growth plans. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input employee role data into an AI, which can then apply different service algorithms.

[0054] The service provider can select the optimal delivery method when providing career growth plans, taking into account the employee's geographical location. For example, if an employee is working remotely, the service provider can select an online delivery method. If an employee is on a business trip, the service provider can select a delivery method utilizing a mobile device. Furthermore, if an employee is in the office, the service provider can select an in-person delivery method. This allows the service provider to select the optimal delivery method and support employee growth by considering the employee's geographical location. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the employee's geographical location information into AI, which can then select the optimal delivery method.

[0055] The service provider can analyze employees' social media activity and propose delivery methods when providing career growth plans. For example, the service provider can identify preferred delivery methods from employees' social media activity and provide them accordingly. For example, the service provider can analyze employees' statements on social media and propose the most suitable delivery methods. The service provider can also analyze employees' networks on social media and propose delivery methods. In this way, the service provider can support employee growth by proposing the most suitable delivery methods through analysis of employees' social media activity. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input employee social media activity data into AI, and the AI ​​can propose delivery methods.

[0056] The feedback department can provide optimal feedback by referring to an employee's past feedback history when providing feedback. For example, the feedback department can identify the employee's preferred feedback method from their past feedback history and provide feedback accordingly. For example, the feedback department can analyze the feedback history and select a feedback method that is easy for the employee to understand. The feedback department can also provide feedback that is optimal for the employee's growth based on their past feedback history. In this way, the feedback department can provide optimal feedback and support employee growth by referring to an employee's past feedback history. Some or all of the above processes in the feedback department may be performed using AI or not. For example, the feedback department can input an employee's past feedback history into an AI, which can then provide optimal feedback.

[0057] The feedback department can apply different feedback algorithms depending on the employee's role when providing feedback. For example, the feedback department can provide managers with feedback that enhances their leadership skills. For example, it can provide engineers with feedback that enhances their technical skills. It can also provide sales representatives with feedback that enhances their communication skills. In this way, the feedback department can provide more appropriate feedback by applying feedback algorithms tailored to the employee's role. Some or all of the above processes in the feedback department may be performed using AI or not. For example, the feedback department can input employee role data into the AI, and the AI ​​can apply different feedback algorithms.

[0058] The feedback department can provide optimal feedback by considering the employee's geographical location when providing feedback. For example, if an employee is working remotely, the feedback department can provide online feedback. For example, if an employee is on a business trip, the feedback department can provide feedback using a mobile device. Furthermore, if an employee is in the office, the feedback department can provide face-to-face feedback. In this way, the feedback department can provide optimal feedback by considering the employee's geographical location and support their growth. Some or all of the above processes in the feedback department may be performed using AI or not. For example, the feedback department can input the employee's geographical location into the AI, which can then provide optimal feedback.

[0059] The feedback department can analyze an employee's social media activity and suggest methods for providing feedback when offering it. For example, the feedback department can identify preferred methods of feedback from an employee's social media activity and provide feedback accordingly. For example, the feedback department can analyze an employee's statements on social media and suggest the most suitable method of feedback. The feedback department can also analyze an employee's social media network and suggest methods of feedback. In this way, the feedback department can support employee growth by suggesting the most suitable method of feedback through analysis of an employee's social media activity. Some or all of the above processes in the feedback department may be performed using AI or not. For example, the feedback department can input employee social media activity data into an AI, which can then suggest methods of feedback.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] Leadership development systems can collect employee health data in addition to performance data. For example, they can collect data such as employee heart rate, sleep patterns, and exercise levels, and analyze this data to understand employees' health status. Furthermore, based on health data, they can provide advice to optimize employee performance. For instance, if an employee is fatigued, they can be advised to rest. If an employee is stressed, relaxation activities can be suggested. This allows leadership development systems to provide career development plans that take employee health into consideration.

[0062] Leadership development systems can analyze employees' social media activity when collecting employee performance data. For example, they can analyze employees' statements and actions on social media to understand their interests and concerns. Furthermore, they can provide career development plans tailored to employees based on their social media activity. For instance, if an employee is interested in a particular field, training or work related to that field can be suggested. Also, if an employee demonstrates leadership on social media, a career development plan that leverages those skills can be provided. In this way, leadership development systems can provide career development plans that take employees' social media activity into consideration.

[0063] Leadership development systems can take employees' geographical location into consideration when collecting employee performance data. For example, if an employee is working remotely, data related to remote work can be prioritized. Similarly, if an employee is traveling for business, data related to their work at their destination can be prioritized. Furthermore, if an employee is in the office, data related to their work at the office can be prioritized. This allows leadership development systems to collect performance data that takes employees' geographical location into account.

[0064] Leadership development systems can analyze employees' past performance data when collecting employee performance data. For example, they can identify the time periods when employees perform best and collect data during those times. They can also analyze employees' past behavioral patterns and select the most efficient data collection method. Furthermore, they can optimize the frequency of data collection based on employees' past performance data. In this way, leadership development systems can select the optimal collection method and collect data efficiently by analyzing employees' past performance data.

[0065] Leadership development systems can filter employee performance data based on their current projects and roles. For example, they can collect only relevant data based on the progress of their current projects. They can also select and collect only the necessary data based on the employee's role. Furthermore, they can adjust the importance of the data collected based on project priorities. This allows leadership development systems to collect highly relevant data by filtering it based on employees' current projects and roles.

[0066] Leadership development systems can analyze employees' social media activity when collecting employee performance data. For example, they can analyze employees' statements and actions on social media to understand their interests and concerns. Furthermore, they can provide career development plans tailored to employees based on their social media activity. For instance, if an employee is interested in a particular field, training or work related to that field can be suggested. Also, if an employee demonstrates leadership on social media, a career development plan that leverages those skills can be provided. In this way, leadership development systems can provide career development plans that take employees' social media activity into consideration.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The data collection unit collects employee performance data. For example, it collects data on employees' work performance, skill levels, and behavioral patterns. The data collection unit collects data on what tasks employees perform, how long it takes them, what skills they possess, and what behaviors they exhibit. These processes may also be performed using AI. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it analyzes employee behavior data and skill data to determine what kind of training is needed and what kind of tasks should be assigned to employees according to their skill level. These processes are performed using generative AI. Step 3: The generation unit generates a career growth plan based on the analysis results obtained by the analysis unit. For example, it generates what kind of training is necessary and what kind of tasks should be assigned to employees, depending on their skill level. These processes are performed using generation AI. Step 4: The providing unit provides the career growth plan generated by the generating unit. For example, it provides the generated career growth plan to the employee. These processes may also be performed using AI. Step 5: The Feedback Department provides real-time feedback based on the career growth plan provided by the Service Provider. For example, it provides feedback on what the employee does well and what areas need improvement when performing their duties. The growth plan is updated in real time according to the employee's progress. These processes may also be performed using AI.

[0069] (Example of form 2) The leadership development system according to an embodiment of the present invention is an innovative solution for developing next-generation leaders in companies using an AI agent. This leadership development system collects and analyzes employee performance data and provides individualized career growth plans. The leadership development system provides an optimal career growth plan for each employee by collecting and analyzing employee performance data. This career growth plan is generated based on the employee's behavioral data and skills and is updated in real time. The leadership development system also provides employees with real-time feedback and presents growth opportunities. This allows employees to develop their leadership skills while experiencing their own growth. For example, the leadership development system collects data such as the employee's work performance, skill level, and behavioral patterns. For example, it collects data such as what kind of work the employee is doing, how long it takes them to do it, what skills they have, and what kind of actions they are taking. Next, the leadership development system analyzes the collected data. Generative AI is used for the analysis. The generative AI analyzes the employee's behavioral data and skill data and generates an optimal career growth plan for each employee. For example, it analyzes what kind of training is needed and what kind of work they should be assigned, depending on the employee's skill level. The generated career growth plan is provided to the employee. This plan includes specific training plans, work assignments, and growth goals. For example, it includes training plans to help employees acquire the skills necessary to demonstrate leadership, and work assignments designed to facilitate leadership development. Furthermore, the leadership development system provides real-time feedback to employees. For instance, it provides feedback on what employees are doing well and what areas need improvement when performing their tasks. It also updates growth plans in real time based on employee progress, ensuring employees are always growing according to the latest plan. This mechanism enhances the overall leadership capabilities of the company.Employees can develop their leadership skills while experiencing personal growth. Furthermore, companies can provide systematic development programs, optimizing overall organizational performance. For example, when employees demonstrate leadership, projects proceed more smoothly, strengthening the company's competitiveness. Thus, leadership development systems can improve the overall leadership capabilities of the company.

[0070] The leadership development system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a provision unit, and a feedback unit. The collection unit collects employee performance data. The collection unit collects data such as the employee's work performance status, skill level, and behavioral patterns. For example, the collection unit collects data such as what tasks the employee performs, how long it takes, what skills they possess, and what actions they take. Some or all of the above-described processing in the collection unit may be performed using AI, or without AI. For example, the collection unit can input the employee's work performance status into the AI, and the AI ​​can collect the data. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes employee behavioral data and skill data. For example, the analysis unit analyzes what kind of training is needed and what kind of tasks the employee should be assigned, depending on their skill level. Some or all of the above-described processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input employee behavioral data into the generation AI, and the generation AI can analyze the data. The generation unit generates a career growth plan based on the analysis results obtained by the analysis unit. For example, the generation unit generates what training is necessary and what tasks should be assigned to employees, depending on their skill level. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input employee skill data into the generation AI, and the generation AI can generate a career growth plan. The provision unit provides the career growth plan generated by the generation unit. For example, the provision unit provides the generated career growth plan to the employee. Some or all of the above processing in the provision unit may be performed using AI or not. For example, the provision unit can input the generated career growth plan into the AI, and the AI ​​can provide it to the employee. The feedback unit provides real-time feedback based on the career growth plan provided by the provision unit. For example, the feedback unit provides feedback on what the employee does well and what areas need improvement when performing their duties.The feedback unit updates the growth plan in real time, for example, according to the employee's progress. Some or all of the above-described processes in the feedback unit may be performed using AI or not. For example, the feedback unit can input the employee's work performance status into the AI, and the AI ​​can provide feedback. This allows the leadership development system according to the embodiment to efficiently collect and analyze employee performance data, generate and provide career growth plans, and provide feedback.

[0071] The data collection unit collects employee performance data. For example, it collects data on employees' work performance, skill levels, and behavioral patterns. Specifically, it collects data on what tasks employees perform, how long it takes them, what skills they possess, and what behaviors they exhibit. This includes log data from tools and systems employees use daily, task completion status from project management tools, and timesheet records. Furthermore, to evaluate employee skill levels, regular skill assessments and self-assessment questionnaires can be conducted, and the results stored in a database. Behavioral pattern data includes, for example, what communication methods employees use, how often they contact team members, and their meeting attendance. This data is important for a multifaceted evaluation of employee performance. Some or all of the above processing in the data collection unit may or may not be performed using AI. For example, the data collection unit can input employee work performance data into an AI, which can then collect the data. Using AI makes it possible to efficiently collect vast amounts of data and update it in real time. This allows the data collection unit to comprehensively and accurately collect employee performance data and provide the information necessary for the next analysis step.

[0072] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes employee behavioral data and skill data. Specifically, it analyzes what kind of training is needed and what tasks should be assigned to employees based on their skill levels. Some or all of the above processing in the analysis unit is performed using generative AI. For example, the analysis unit inputs employee behavioral data into the generative AI, which then analyzes the data. Based on the employee's past performance data and skill assessment results, the generative AI identifies the employee's strengths and weaknesses and proposes optimal training programs and work assignments. For example, the generative AI analyzes the employee's skill set and, if there is a deficiency in a particular skill, recommends a training course to improve that skill. It can also analyze employee behavioral patterns and provide specific action plans to encourage behavioral changes necessary to improve leadership skills. Furthermore, the analysis unit can analyze employee performance data over time to understand performance trends and fluctuations. This allows for continuous monitoring of employee growth and adjustments to training plans and work assignments as needed. The analysis department can utilize generative AI to quickly and accurately analyze employee performance data and provide the information necessary to generate optimal career growth plans.

[0073] The generation unit generates career growth plans based on the analysis results obtained by the analysis unit. For example, the generation unit generates what training is necessary and what tasks should be assigned to employees, depending on their skill level. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit can input employee skill data into the generation AI, which can then generate a career growth plan. The generation AI creates individual career growth plans based on the employee's skill set and performance data. For example, the generation AI compares the employee's current skill level with their target skill level to identify skill gaps. It then proposes specific training courses and work experience to bridge those gaps. The generation AI can also create plans that show future career paths, taking into account the employee's career goals and interests. For example, for an employee who wants to improve their leadership skills, it can propose leadership training and mentoring programs and provide opportunities to demonstrate leadership in actual projects. Furthermore, the generation unit can monitor the progress of the career growth plan based on employee performance data and update the plan as needed. This allows the generation unit to provide concrete and practical plans to support employee career growth and improve employee motivation and performance.

[0074] The delivery unit provides the career growth plan generated by the generation unit. For example, the delivery unit provides the generated career growth plan to the employee. Some or all of the above processes in the delivery unit may be performed using AI or not. For example, the delivery unit can input the generated career growth plan into AI, which can then provide it to the employee. Specifically, the delivery unit distributes the generated career growth plan to the employee's individual account, making it accessible to the employee at any time. The employee can review their career growth plan and understand the necessary training and work assignments. The delivery unit also has a function to update the progress of the career growth plan in real time and notify the employee. For example, when an employee completes a particular training course, that information is reflected in the career growth plan, and the next step is suggested. Furthermore, the delivery unit can collect employee feedback and provide information to improve the content of the career growth plan. For example, by providing feedback from employees on the content of training courses and work assignments, the delivery unit can adjust the career growth plan based on that information and provide a more effective plan. In this way, the delivery unit can provide employees with appropriate career growth plans and support their growth.

[0075] The Feedback Department provides real-time feedback based on the career growth plan provided by the Service Provider. For example, the Feedback Department provides feedback on what employees do well and what areas need improvement when performing their tasks. Specifically, the Feedback Department monitors employees' work performance and provides real-time feedback. For instance, when an employee performs a specific task, AI can evaluate the progress and results of that task and provide immediate feedback. The Feedback Department updates the growth plan in real time according to the employee's progress. For example, if an employee acquires a new skill, that information is reflected in the career growth plan, and the next steps are suggested. Some or all of the above processes in the Feedback Department may be performed using AI or not. For example, the Feedback Department can input employee work performance data into AI, which can then provide feedback. Using AI makes it possible to quickly and accurately evaluate employee performance and provide appropriate feedback. Furthermore, the Feedback Department can collect employee feedback and provide information to improve the content of the career growth plan. This allows the Feedback Department to continuously support employee growth and maximize the effectiveness of the career growth plan.

[0076] The data collection unit can collect data such as employees' work performance, skill levels, and behavioral patterns. For example, the data collection unit can collect data on employees' work performance, such as task completion status and the quality of deliverables. The data collection unit can also collect data on employees' skill levels, such as technical skills and communication skills. Furthermore, the data collection unit can collect data on employees' behavioral patterns, such as working hours and work processes. By collecting data on employees' work performance, skill levels, and behavioral patterns, the data collection unit can obtain more detailed performance data. 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 work performance data into AI, which can then collect the data.

[0077] The analysis unit can analyze employee behavioral data and skill data to generate an optimal career growth plan for each employee. For example, the analysis unit can analyze employee behavioral data. For example, the analysis unit can analyze data such as work logs and behavioral history. The analysis unit can also analyze employee skill data. For example, the analysis unit can analyze data such as skill evaluation results and training history. Furthermore, the analysis unit can generate an optimal career growth plan for each employee. For example, the analysis unit can generate individual training plans and career path suggestions. In this way, the analysis unit can generate an optimal career growth plan for each employee by analyzing employee behavioral data and skill data. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs employee behavioral data into the generation AI, and the generation AI can analyze the data.

[0078] The generation unit can generate information about the training required and the tasks that employees should be assigned, based on their skill levels. For example, the generation unit can generate information about the training required based on an employee's skill level. For example, the generation unit can generate training plans for technical skills, communication skills, etc. The generation unit can also generate information about the tasks that employees should be assigned, based on their skill levels. For example, the generation unit can generate tasks such as project assignments and task assignments. In this way, the generation unit can support efficient career growth by generating training and task assignments that are appropriate for each employee's skill level. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input employee skill data into the generation AI, which can then generate a career growth plan.

[0079] The service provider can provide the generated career growth plan to the employee. The service provider can, for example, provide the generated career growth plan to the employee. The service provider can provide the career growth plan, for example, training content and job assignments. In this way, the service provider can promote employee growth by providing the generated career growth plan to the employee. Some or all of the above processes in the service provider may be performed using AI or not using AI. For example, the service provider may perform the generated career growth plan using AI or not using AI. For example, the service provider can input the generated career growth plan into AI, and the AI ​​can provide it to the employee.

[0080] The feedback department can provide employees with real-time feedback and update their growth plans in real time. For example, the feedback department can provide feedback on what employees do well and what areas need improvement when performing their tasks. The feedback department can provide real-time feedback, such as immediate comments and notifications. Furthermore, the feedback department can update growth plans in real time according to the employee's progress. This can be done through methods such as feedback-based revisions and periodic reviews. In this way, the feedback department can continuously support employee growth by providing real-time feedback and updating growth plans in real time. Some or all of the above processes in the feedback department may be performed using AI or not. For example, the feedback department can input employee work performance data into AI, which can then provide feedback.

[0081] The data collection unit can estimate employees' emotions and adjust the timing of performance data collection based on the estimated emotions. For example, if an employee is stressed, the data collection unit can delay the collection timing to collect data when the employee is relaxed. For example, if an employee is highly motivated, the data collection unit can collect data immediately to capture the peak of performance. The data collection unit can also adjust the data collection timing to collect data after a break if the employee is tired. This allows the data collection unit to collect more accurate data by adjusting the timing of performance data collection based on employees' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above 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 an AI, which can then adjust the collection timing.

[0082] The data collection unit can analyze employees' past performance data and select the optimal data collection method. For example, the data collection unit can identify the time periods in which employees perform best from past data and collect data during those times. For example, the data collection unit can analyze employees' past behavioral patterns and select the most efficient data collection method. The data collection unit can also optimize the frequency of data collection based on employees' past performance data. In this way, the data collection unit can select the optimal data collection method and perform efficient data collection by analyzing employees' past performance data. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input employees' past performance data into AI, and the AI ​​can select the optimal data collection method.

[0083] The data collection unit can filter performance data based on an employee's current project and role. For example, the unit can collect only relevant data based on the progress of the current project. For example, the unit can select and collect necessary data based on an employee's role. The unit can also adjust the importance of the data collected based on project priorities. This allows the unit to collect highly relevant data by filtering it based on an employee's current project and role. Some or all of the above processing in the data collection unit may or may not be performed using AI. For example, the data collection unit can input data on an employee's current project and role into an AI, which can then perform the filtering.

[0084] The data collection unit can estimate employees' emotions and prioritize the data to collect based on the estimated emotions. For example, if an employee is stressed, the data collection unit will prioritize collecting data related to the tasks causing the stress. For example, if an employee is highly motivated, the data collection unit can prioritize collecting data to maintain that motivation. Similarly, if an employee is tired, the data collection unit can prioritize collecting data related to the tasks causing fatigue. In this way, the data collection unit can prioritize the collection of important data by prioritizing the data to collect based on the employee's emotions. 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 data collection unit may be performed using AI or not. For example, the data collection unit can input employee emotion data into an AI, which can then determine the data prioritization.

[0085] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location of employees when collecting performance data. For example, if an employee is working remotely, the data collection unit will prioritize the collection of data related to remote work. For example, if an employee is on a business trip, the data collection unit can prioritize the collection of data related to work at the business trip destination. Furthermore, if an employee is in the office, the data collection unit can prioritize the collection of data related to work at the office. In this way, the data collection unit can prioritize the collection of highly relevant data by considering the geographical location of employees. 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 the geographical location of employees into the AI, which can then prioritize the collection of highly relevant data.

[0086] The data collection unit can analyze employees' social media activity and collect relevant data when collecting performance data. For example, the data collection unit can identify work-related topics from employees' social media activity and collect that data. For example, the data collection unit can analyze employees' statements on social media and collect work-related data. The data collection unit can also analyze employees' networks on social media and collect work-related data. In this way, the data collection unit can collect work-related data by analyzing employees' social media activity. 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 social media activity data into AI, and the AI ​​can collect relevant data.

[0087] The analysis unit can estimate employees' emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if an employee is stressed, the analysis unit can provide a simple and easy-to-understand analysis result. If an employee is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if an employee is in a hurry, the analysis unit can provide a concise analysis result. In this way, the analysis unit can provide more easily understandable analysis results by adjusting the presentation of the analysis based on the employee's emotions. 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 unit is performed using generative AI. For example, the analysis unit inputs employee emotion data into the generative AI, and the generative AI can adjust the presentation of the analysis.

[0088] The analysis unit can adjust the level of detail of the analysis based on the importance of the employee. For example, the analysis unit can provide detailed analysis results to employees involved in important projects. For example, it can provide simple analysis results to employees performing general tasks. It can also provide basic analysis results to new employees. In this way, the analysis unit can provide the necessary information at the appropriate level by adjusting the level of detail of the analysis based on the importance of the employee. Some or all of the above processing in the analysis unit is performed using a generating AI. For example, the analysis unit inputs employee importance data into the generating AI, and the generating AI can adjust the level of detail of the analysis.

[0089] The analysis unit can apply different analysis algorithms depending on the employee's category during analysis. For example, the analysis unit can apply an analysis algorithm related to leadership skills to managers. For example, it can apply an analysis algorithm related to technical skills to engineers. It can also apply an analysis algorithm related to communication skills to sales representatives. In this way, the analysis unit can provide more appropriate analysis results by applying an analysis algorithm according to the employee's category. Some or all of the above processing in the analysis unit is performed using a generating AI. For example, the analysis unit inputs employee category data into the generating AI, and the generating AI can apply different analysis algorithms.

[0090] The analysis unit can estimate the employee's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the employee is in a hurry, the analysis unit can provide a short, concise analysis. For example, if the employee is relaxed, the analysis unit can provide a detailed analysis. Furthermore, if the employee is excited, the analysis unit can provide an analysis with visually stimulating effects. In this way, the analysis unit can provide analysis results tailored to the employee's situation by adjusting the length of the analysis based on the employee's emotions. 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 unit is performed using generative AI. For example, the analysis unit inputs employee emotion data into the generative AI, which can then adjust the length of the analysis.

[0091] The analysis unit can determine the priority of analysis based on the employee's submission date. For example, the analysis unit can prioritize the analysis of submissions with approaching deadlines. For example, it can quickly analyze submissions submitted early. It can also postpone the analysis of submissions submitted late. In this way, the analysis unit can prioritize the analysis of important submissions by determining the priority of analysis based on the employee's submission date. Some or all of the above processing in the analysis unit is performed using a generating AI. For example, the analysis unit inputs employee submission date data into the generating AI, and the generating AI can determine the priority of analysis.

[0092] The analysis unit can adjust the order of analysis based on employee relevance during the analysis process. For example, the analysis unit can prioritize the analysis of data of employees involved in important projects. For example, the analysis unit can prioritize the analysis of data of team leaders. It can also postpone the analysis of data of new employees. In this way, the analysis unit can prioritize the analysis of important data by adjusting the order of analysis based on employee relevance. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit inputs employee relevance data into the generative AI, which can then adjust the order of analysis.

[0093] The generation unit can estimate an employee's emotions and adjust the method of generating a career growth plan based on the estimated emotions. For example, if an employee is relaxed, the generation unit can generate a career growth plan that progresses at a relaxed pace. If an employee is in a hurry, the generation unit can generate a career growth plan that emphasizes the shortest route. Furthermore, if an employee is excited, the generation unit can generate a career growth plan with visually stimulating effects. This allows the generation unit to provide a more appropriate career growth plan by adjusting the generation method based on the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, 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 generation unit are performed using the generative AI. For example, the generation unit can input employee emotion data into the generative AI, which can then adjust the method of generating the career growth plan.

[0094] The generation unit can adjust the level of detail generated based on the employee's skill level when creating a career growth plan. For example, the generation unit can provide a detailed career growth plan to a highly skilled employee. For example, it can provide a basic career growth plan to a medium-skilled employee. It can also provide a simple career growth plan to a low-skilled employee. In this way, the generation unit can provide the most suitable career growth plan for each employee by adjusting the level of detail based on the employee's skill level. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs employee skill level data into the generation AI, which can then adjust the level of detail.

[0095] The generation unit can apply different generation algorithms depending on the employee's role when generating career growth plans. For example, the generation unit can generate a career growth plan for a manager that strengthens leadership skills. For example, it can generate a career growth plan for an engineer that strengthens technical skills. It can also generate a career growth plan for a sales representative that strengthens communication skills. In this way, the generation unit can provide more appropriate career growth plans by applying generation algorithms that are appropriate for the employee's role. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input employee role data into the generation AI, and the generation AI can apply different generation algorithms.

[0096] The generation unit can estimate an employee's emotions and adjust the length of the career growth plan based on the estimated emotions. For example, if an employee is in a hurry, the generation unit can generate a short, concise career growth plan. If an employee is relaxed, for example, the generation unit can generate a longer career growth plan that includes detailed explanations. Furthermore, if an employee is excited, the generation unit can generate a career growth plan with visually stimulating effects. This allows the generation unit to provide a career growth plan tailored to the employee's situation by adjusting its length based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit are performed using the generative AI. For example, the generation unit can input employee emotion data into the generative AI, which can then adjust the length of the career growth plan.

[0097] The generation unit can determine the generation priority based on the employee's submission timing when generating career growth plans. For example, the generation unit can prioritize generating career growth plans for submissions with approaching deadlines. For example, it can quickly generate career growth plans for submissions with early submission dates. It can also postpone generating career growth plans for submissions with late submission dates. In this way, the generation unit can prioritize providing important career growth plans by determining the generation priority based on the employee's submission timing. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input employee submission timing data into the generation AI, which can then determine the generation priority.

[0098] The generation unit can adjust the generation order based on employee relevance when generating career growth plans. For example, the generation unit can prioritize generating career growth plans for employees involved in important projects. For example, it can prioritize generating career growth plans for team leaders. It can also postpone the generation of career growth plans for new employees. In this way, the generation unit can prioritize providing important career growth plans by adjusting the generation order based on employee relevance. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs employee relevance data into the generation AI, which can then adjust the generation order.

[0099] The service provider can estimate an employee's emotions and adjust the way career development plans are delivered based on those estimated emotions. For example, if an employee is stressed, the service provider might select a simple and easy-to-understand delivery method. If an employee is relaxed, the service provider might select a delivery method that includes detailed explanations. If an employee is in a hurry, the service provider might select a delivery method that gets straight to the point. This allows the service provider to select a more appropriate delivery method by adjusting the way career development plans are delivered based on the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input employee emotion data into an AI, which can then adjust the delivery method.

[0100] The service provider can select the optimal delivery method when providing a career growth plan by referring to the employee's past feedback history. For example, the service provider can identify the delivery method preferred by the employee from the past feedback history and provide it accordingly. For example, the service provider can analyze the feedback history and select a delivery method that is easy for the employee to understand. The service provider can also select the optimal delivery method for the employee's growth based on the past feedback history. In this way, the service provider can select the optimal delivery method and support the employee's growth by referring to the employee's past feedback history. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input the employee's past feedback history into AI, and the AI ​​can select the optimal delivery method.

[0101] The service provider can apply different service algorithms depending on the employee's role when providing career growth plans. For example, the service provider might select a service method to enhance leadership skills for managers. For example, the service provider might select a service method to enhance technical skills for engineers. The service provider might also select a service method to enhance communication skills for sales representatives. By applying service algorithms tailored to each employee's role, the service provider can provide more appropriate career growth plans. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input employee role data into an AI, which can then apply different service algorithms.

[0102] The service provider can estimate an employee's emotions and prioritize the provision of career development plans based on the estimated emotions. For example, if an employee is stressed, the service provider can prioritize providing career development plans that help reduce stress. For example, if an employee is highly motivated, the service provider can prioritize providing career development plans that help maintain motivation. Also, if an employee is tired, the service provider can prioritize providing career development plans that help with fatigue recovery. In this way, the service provider can prioritize providing important plans by prioritizing the provision of career development plans based on the employee's emotions. 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 service provider may be performed using AI or not. For example, the service provider can input employee emotion data into an AI, and the AI ​​can determine the priority of provision.

[0103] The service provider can select the optimal delivery method when providing career growth plans, taking into account the employee's geographical location. For example, if an employee is working remotely, the service provider can select an online delivery method. If an employee is on a business trip, the service provider can select a delivery method utilizing a mobile device. Furthermore, if an employee is in the office, the service provider can select an in-person delivery method. This allows the service provider to select the optimal delivery method and support employee growth by considering the employee's geographical location. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the employee's geographical location information into AI, which can then select the optimal delivery method.

[0104] The service provider can analyze employees' social media activity and propose delivery methods when providing career growth plans. For example, the service provider can identify preferred delivery methods from employees' social media activity and provide them accordingly. For example, the service provider can analyze employees' statements on social media and propose the most suitable delivery methods. The service provider can also analyze employees' networks on social media and propose delivery methods. In this way, the service provider can support employee growth by proposing the most suitable delivery methods through analysis of employees' social media activity. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input employee social media activity data into AI, and the AI ​​can propose delivery methods.

[0105] The feedback unit can estimate an employee's emotions and adjust the feedback method based on the estimated emotions. For example, if an employee is stressed, the feedback unit will prioritize providing positive feedback. If an employee is relaxed, the feedback unit can provide detailed feedback. Furthermore, if an employee is in a hurry, the feedback unit can provide concise feedback. This allows the feedback unit to provide more appropriate feedback by adjusting the feedback method based on the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input employee emotion data into an AI, which can then adjust the feedback method.

[0106] The feedback department can provide optimal feedback by referring to an employee's past feedback history when providing feedback. For example, the feedback department can identify the employee's preferred feedback method from their past feedback history and provide feedback accordingly. For example, the feedback department can analyze the feedback history and select a feedback method that is easy for the employee to understand. The feedback department can also provide feedback that is optimal for the employee's growth based on their past feedback history. In this way, the feedback department can provide optimal feedback and support employee growth by referring to an employee's past feedback history. Some or all of the above processes in the feedback department may be performed using AI or not. For example, the feedback department can input an employee's past feedback history into an AI, which can then provide optimal feedback.

[0107] The feedback department can apply different feedback algorithms depending on the employee's role when providing feedback. For example, the feedback department can provide managers with feedback that enhances their leadership skills. For example, it can provide engineers with feedback that enhances their technical skills. It can also provide sales representatives with feedback that enhances their communication skills. In this way, the feedback department can provide more appropriate feedback by applying feedback algorithms tailored to the employee's role. Some or all of the above processes in the feedback department may be performed using AI or not. For example, the feedback department can input employee role data into the AI, and the AI ​​can apply different feedback algorithms.

[0108] The feedback unit can estimate an employee's emotions and prioritize feedback based on the estimated emotions. For example, if an employee is stressed, the feedback unit can prioritize providing feedback that helps reduce stress. For example, if an employee is highly motivated, the feedback unit can prioritize providing feedback that helps maintain motivation. Also, if an employee is tired, the feedback unit can prioritize providing feedback that helps with fatigue recovery. In this way, the feedback unit can prioritize important feedback by prioritizing feedback based on the employee's emotions. 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 feedback unit may be performed using AI or not. For example, the feedback unit can input employee emotion data into an AI, and the AI ​​can determine the priority of feedback.

[0109] The feedback department can provide optimal feedback by considering the employee's geographical location when providing feedback. For example, if an employee is working remotely, the feedback department can provide online feedback. For example, if an employee is on a business trip, the feedback department can provide feedback using a mobile device. Furthermore, if an employee is in the office, the feedback department can provide face-to-face feedback. In this way, the feedback department can provide optimal feedback by considering the employee's geographical location and support their growth. Some or all of the above processes in the feedback department may be performed using AI or not. For example, the feedback department can input the employee's geographical location into the AI, which can then provide optimal feedback.

[0110] The feedback department can analyze an employee's social media activity and suggest methods for providing feedback when offering it. For example, the feedback department can identify preferred methods of feedback from an employee's social media activity and provide feedback accordingly. For example, the feedback department can analyze an employee's statements on social media and suggest the most suitable method of feedback. The feedback department can also analyze an employee's social media network and suggest methods of feedback. In this way, the feedback department can support employee growth by suggesting the most suitable method of feedback through analysis of an employee's social media activity. Some or all of the above processes in the feedback department may be performed using AI or not. For example, the feedback department can input employee social media activity data into an AI, which can then suggest methods of feedback.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] Leadership development systems can collect employee health data in addition to performance data. For example, they can collect data such as employee heart rate, sleep patterns, and exercise levels, and analyze this data to understand employees' health status. Furthermore, based on health data, they can provide advice to optimize employee performance. For instance, if an employee is fatigued, they can be advised to rest. If an employee is stressed, relaxation activities can be suggested. This allows leadership development systems to provide career development plans that take employee health into consideration.

[0113] Leadership development systems can analyze employees' social media activity when collecting employee performance data. For example, they can analyze employees' statements and actions on social media to understand their interests and concerns. Furthermore, they can provide career development plans tailored to employees based on their social media activity. For instance, if an employee is interested in a particular field, training or work related to that field can be suggested. Also, if an employee demonstrates leadership on social media, a career development plan that leverages those skills can be provided. In this way, leadership development systems can provide career development plans that take employees' social media activity into consideration.

[0114] A leadership development system can estimate an employee's emotions and adjust the content of feedback based on those emotions. For example, if an employee is stressed, positive feedback can be prioritized. If an employee is relaxed, detailed feedback can be provided. Furthermore, if an employee is in a hurry, concise feedback can be provided. In this way, the leadership development system can provide more effective feedback by adjusting the content of feedback based on the employee's emotions.

[0115] Leadership development systems can take employees' geographical location into consideration when collecting employee performance data. For example, if an employee is working remotely, data related to remote work can be prioritized. Similarly, if an employee is traveling for business, data related to their work at their destination can be prioritized. Furthermore, if an employee is in the office, data related to their work at the office can be prioritized. This allows leadership development systems to collect performance data that takes employees' geographical location into account.

[0116] A leadership development system can estimate employees' emotions and adjust the way career development plans are delivered based on those estimates. For example, if an employee is stressed, a simple and easy-to-understand delivery method can be selected. If an employee is relaxed, a delivery method including detailed explanations can be selected. Furthermore, if an employee is in a hurry, a delivery method that gets straight to the point can be selected. In this way, the leadership development system can select a more appropriate delivery method by adjusting the way career development plans are delivered based on employees' emotions.

[0117] Leadership development systems can analyze employees' past performance data when collecting employee performance data. For example, they can identify the time periods when employees perform best and collect data during those times. They can also analyze employees' past behavioral patterns and select the most efficient data collection method. Furthermore, they can optimize the frequency of data collection based on employees' past performance data. In this way, leadership development systems can select the optimal collection method and collect data efficiently by analyzing employees' past performance data.

[0118] A leadership development system can estimate an employee's emotions and adjust the length of their career development plan based on those emotions. For example, if an employee is in a hurry, it can generate a short, to-the-point career development plan. If the employee is relaxed, it can generate a longer plan with more detailed explanations. Furthermore, if the employee is excited, it can generate a career development plan with visually stimulating effects. In this way, the leadership development system can provide an employee with a career development plan tailored to their situation by adjusting its length based on their emotions.

[0119] Leadership development systems can filter employee performance data based on their current projects and roles. For example, they can collect only relevant data based on the progress of their current projects. They can also select and collect only the necessary data based on the employee's role. Furthermore, they can adjust the importance of the data collected based on project priorities. This allows leadership development systems to collect highly relevant data by filtering it based on employees' current projects and roles.

[0120] A leadership development system can estimate an employee's emotions and prioritize feedback based on those emotions. For example, if an employee is stressed, feedback that helps reduce stress can be prioritized. If an employee is highly motivated, feedback that helps maintain that motivation can be prioritized. Furthermore, if an employee is fatigued, feedback that helps with recovery can be prioritized. In this way, a leadership development system can prioritize important feedback by determining its priority based on an employee's emotions.

[0121] Leadership development systems can analyze employees' social media activity when collecting employee performance data. For example, they can analyze employees' statements and actions on social media to understand their interests and concerns. Furthermore, they can provide career development plans tailored to employees based on their social media activity. For instance, if an employee is interested in a particular field, training or work related to that field can be suggested. Also, if an employee demonstrates leadership on social media, a career development plan that leverages those skills can be provided. In this way, leadership development systems can provide career development plans that take employees' social media activity into consideration.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The data collection unit collects employee performance data. For example, it collects data on employees' work performance, skill levels, and behavioral patterns. The data collection unit collects data on what tasks employees perform, how long it takes them, what skills they possess, and what behaviors they exhibit. These processes may also be performed using AI. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it analyzes employee behavior data and skill data to determine what kind of training is needed and what kind of tasks should be assigned to employees according to their skill level. These processes are performed using generative AI. Step 3: The generation unit generates a career growth plan based on the analysis results obtained by the analysis unit. For example, it generates what kind of training is necessary and what kind of tasks should be assigned to employees, depending on their skill level. These processes are performed using generation AI. Step 4: The providing unit provides the career growth plan generated by the generating unit. For example, it provides the generated career growth plan to the employee. These processes may also be performed using AI. Step 5: The Feedback Department provides real-time feedback based on the career growth plan provided by the Service Provider. For example, it provides feedback on what the employee does well and what areas need improvement when performing their duties. The growth plan is updated in real time according to the employee's progress. These processes may also be performed using AI.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, and feedback unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects employee performance data using the camera 42 and microphone 38B of the smart device 14 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates a career growth plan based on the analysis results. The provision unit is implemented in the specific processing unit 46A of the smart device 14 and provides the generated career growth plan to the employee. The feedback unit is implemented in the specific processing unit 46A of the smart device 14 and provides real-time feedback according to the employee's growth status. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, and feedback unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects employee performance data using the camera 42 and microphone 238 of the smart glasses 214 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates a career growth plan based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides the generated career growth plan to the employee. The feedback unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides real-time feedback according to the employee's growth status. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, and feedback unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects employee performance data using the camera 42 and microphone 238 of the headset terminal 314 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates a career growth plan based on the analysis results. The provision unit is implemented in the specific processing unit 46A of the headset terminal 314 and provides the generated career growth plan to the employee. The feedback unit is implemented in the specific processing unit 46A of the headset terminal 314 and provides real-time feedback according to the employee's growth status. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, and feedback unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects employee performance data using the camera 42 and microphone 238 of the robot 414 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates a career growth plan based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the robot 414 and provides the generated career growth plan to the employee. The feedback unit is implemented, for example, by the control unit 46A of the robot 414 and provides real-time feedback according to the employee's growth status. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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."

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] (Note 1) The data collection department collects employee performance data, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit generates a career growth plan based on the analysis results obtained by the analysis unit, A providing unit that provides the career growth plan generated by the generation unit, The system includes a feedback unit that provides real-time feedback based on the career growth plan provided by the aforementioned provision unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect data on employee work performance, skill levels, and behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, We analyze employee behavioral and skill data to generate optimal career growth plans for each employee. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Based on the skill level of each employee, it generates information on what training they need and what tasks they should be assigned. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Provide employees with the generated career growth plan. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned feedback unit is Provide employees with real-time feedback and update their growth plans in real time. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate employee sentiment and adjust the timing of performance data collection based on the estimated employee sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze employees' past performance data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting performance data, filter it based on the employee's current project and role. The system described in Appendix 1, characterized by the features described herein. (Note 10) 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 11) The aforementioned collection unit is When collecting performance data, the system prioritizes collecting highly relevant data by considering the geographical location of employees. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting performance data, analyze employees' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, We estimate the emotions of employees and adjust the representation of the analysis based on the estimated emotions of the employees. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of each employee. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the employee category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates employee sentiment and adjusts the length of the analysis based on the estimated employee sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the employee submitted their data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on employee relevance. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is We estimate employee sentiment and adjust the method of generating career growth plans based on the estimated employee sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating career growth plans, adjust the level of detail based on the employee's skill level. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating career growth plans, different generation algorithms are applied depending on the employee's role. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is Estimate employee sentiment and adjust the length of career growth plans based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating career growth plans, prioritize their creation based on when employees submit them. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating career growth plans, the generation order is adjusted based on employee relevance. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, We estimate employee sentiment and adjust how career development plans are delivered based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing career development plans, we select the optimal delivery method by referring to the employee's past feedback history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing career development plans, different delivery algorithms are applied depending on the employee's role. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, We estimate employee sentiment and prioritize the provision of career growth plans based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing career development plans, the optimal delivery method will be selected considering the geographical location information of employees. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing career development plans, we analyze employees' social media activity and propose methods for delivering those plans. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned feedback unit is Estimate employees' emotions and adjust feedback methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned feedback unit is When providing feedback, we refer to the employee's past feedback history to provide the most appropriate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned feedback unit is When providing feedback, different feedback algorithms are applied depending on the employee's role. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned feedback unit is Estimate employee emotions and prioritize feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned feedback unit is When providing feedback, we take into account the employee's geographical location to provide the most appropriate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned feedback unit is When providing feedback, we analyze employees' social media activity and suggest methods for providing feedback. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The data collection department collects employee performance data, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit generates a career growth plan based on the analysis results obtained by the analysis unit, A providing unit that provides the career growth plan generated by the generation unit, The system includes a feedback unit that provides real-time feedback based on the career growth plan provided by the aforementioned provision unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect data on employee work performance, skill levels, and behavioral patterns. The system according to feature 1.

3. The aforementioned analysis unit, We analyze employee behavioral and skill data to generate optimal career growth plans for each employee. The system according to feature 1.

4. The generating unit is Based on the skill level of each employee, it generates information on what training they need and what tasks they should be assigned. The system according to feature 1.

5. The aforementioned supply unit is, Provide employees with the generated career growth plan. The system according to feature 1.

6. The aforementioned feedback unit is Provide employees with real-time feedback and update their growth plans in real time. The system according to feature 1.

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

8. The aforementioned collection unit is Analyze employees' past performance data and select the optimal data collection method. The system according to feature 1.

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