system

Abstract

The system according to this embodiment aims to provide a personalized growth experience based on the skills and experience of employees. [Solution] The system according to the embodiment comprises an analysis unit, a generation unit, a tracking unit, an individualization unit, and a personalization unit. The analysis unit analyzes the skills and experience of employees and the progress of their current work. The generation unit generates the next mission to be tackled based on the information analyzed by the analysis unit. The tracking unit tracks the progress of the missions generated by the generation unit. The individualization unit reflects the results tracked by the tracking unit in an individualized leaderboard. The personalization unit personalizes the skill tree to match the career path and goals of each employee.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to assign appropriate tasks based on the skills and experience of employees, and there is a problem that there is a lack of measures to reduce the monotony of tasks.

[0005] The system according to the embodiment aims to provide a personalized growth experience based on the skills and experience of employees.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a generation unit, a tracking unit, an individualization unit, and a personalization unit. The analysis unit analyzes the skills, experience, and current work progress of employees. The generation unit generates the next mission to be tackled based on the information analyzed by the analysis unit. The tracking unit tracks the progress of the missions generated by the generation unit. The individualization unit reflects the results tracked by the tracking unit in a personalized leaderboard. The personalization unit personalizes the skill tree to match the career path and goals of each employee. [Effects of the Invention]

[0007] The system according to this embodiment can provide a personalized growth experience based on the skills and experience of employees. [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 such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An embodiment of the present invention provides a next-generation AI challenge manager that gamifies work and offers a personalized growth experience tailored to the needs and skills of employees. This system analyzes employees' skills, experience, and current work progress to generate the next mission they should tackle. The generated mission is presented to the employee, who then undertakes the challenge. Mission progress is tracked in real time and reflected on a leaderboard. The leaderboard is personalized, allowing each employee to see their own achievements. Furthermore, a personalized skill tree is provided, tailored to each employee's career path and goals. The skill tree outlines which skills an employee should acquire and how they should develop. For example, the next-generation AI challenge manager uses AI to analyze employees' skills, experience, and current work progress. The AI ​​analyzes the employee's past work history and current project progress to generate the next mission. The generated mission is customized based on the employee's skill level and interests. For example, a novice employee might be presented with a mission to acquire basic skills, while intermediate and advanced employees might be presented with more advanced missions. Mission progress is tracked in real time and reflected on the leaderboard. The leaderboard displays individual employee performance and promotes self-growth. Furthermore, a personalized skills tree is provided, tailored to each employee's career path and goals. The skills tree is a roadmap showing which skills employees should acquire and how they should grow, improving employee motivation. As a result, the next-generation AI Challenge Manager can reduce the monotony of tasks and increase employee job satisfaction and a sense of accomplishment.

[0029] The next-generation AI challenge manager according to this embodiment comprises an analysis unit, a generation unit, a tracking unit, an individualization unit, and a personalization unit. The analysis unit analyzes the skills and experience of employees and the progress of their current tasks. For example, the analysis unit analyzes the employee's past work history and the progress of their current projects. The analysis unit evaluates the employee's skill set and experience and provides basic data to determine the next mission they should take on. The generation unit generates the next mission to take on based on the information analyzed by the analysis unit. For example, the generation unit customizes the mission based on the employee's skill level and interests. The generation unit presents specific tasks and projects for employees to acquire new skills and grow. The tracking unit tracks the progress of the missions generated by the generation unit in real time. For example, the tracking unit monitors how much of the mission an employee has completed and records the progress. The tracking unit evaluates the employee's performance and provides feedback as needed. The individualization unit reflects the results tracked by the tracking unit in an individualized leaderboard. For example, the individualization unit compares an employee's results with those of other employees and displays them in a ranking format. The individualization unit provides visual feedback to help employees feel a sense of self-growth and maintain motivation. The personalization unit personalizes the skill tree to match each employee's career path and goals. For example, the personalization unit provides a roadmap showing which skills an employee should acquire and how they should grow. The personalization unit proposes an optimal skill acquisition plan tailored to the employee's career goals. As a result, the next-generation AI Challenge Manager according to this embodiment can provide a personalized growth experience based on the employee's skills, experience, and work progress, thereby reducing the monotony of the work.

[0030] The analytics department analyzes employees' skills, experience, and current work progress. Specifically, it conducts a detailed analysis of employees' past work history and the progress of current projects. The analytics department extracts data from the database on past projects employees have participated in, the roles they have played, and their results and evaluations, and analyzes this data using statistical methods and machine learning algorithms. For example, it uses natural language processing technology to analyze past project reports and evaluation comments to quantify skill sets and the depth of experience. For ongoing projects, it obtains data in real time from project management tools and task management systems to understand their progress and challenges. This allows the analytics department to comprehensively evaluate employees' skills and experience and provide foundational data to determine the next missions they should take on. Furthermore, the analytics department identifies employee skill gaps, clarifying which skills are lacking and which areas require growth. This allows it to optimize employee career paths and provide concrete guidelines to promote individual growth.

[0031] The generation unit generates the next mission to tackle based on the information analyzed by the analysis unit. Specifically, it customizes missions based on employees' skill levels and interests. The generation unit presents employees with specific tasks and projects to help them acquire new skills and grow. For example, if an employee wants to improve their data analysis skills, the generation unit generates and provides them with data analysis-related projects and tasks. The generation unit uses AI to analyze employees' past performance and current skill sets and automatically generates the optimal mission. For example, it uses generation AI to generate project outlines and specific task lists based on the employee's skill set and presents them to the employee. This allows the generation unit to provide employees with a concrete path to efficiently acquire new skills and grow. Furthermore, the generation unit can continuously improve the content of missions based on employee feedback, providing a more effective growth experience.

[0032] The tracking unit tracks the progress of missions generated by the generation unit in real time. Specifically, it monitors how much of the missions employees have completed and records their progress. The tracking unit evaluates employee performance and provides feedback as needed. For example, it uses project management tools and task management systems to track employees' task completion status and progress in real time and issues alerts if progress is behind schedule. When employees complete a mission, it evaluates their achievement and performance and provides feedback. The tracking unit uses AI to analyze employee performance data and improve performance and identify issues. For example, it uses machine learning algorithms to analyze employee performance data and identify performance trends and patterns. This allows the tracking unit to continuously monitor employee performance and provide appropriate feedback as needed. Furthermore, based on employee performance data, the tracking unit can predict future performance and suggest improvement measures.

[0033] The Personalization Unit reflects the results tracked by the Tracking Unit in a personalized leaderboard. Specifically, it compares employee performance with other employees and displays it in a ranking format. The Personalization Unit provides visual feedback to help employees feel a sense of self-growth and maintain motivation. For example, it generates a leaderboard based on employee performance data and visually displays employee performance. By comparing employees' achievements and performance with other employees and displaying them in a ranking format, the leaderboard stimulates employee competitiveness and improves motivation. Furthermore, the Personalization Unit can continuously improve the content of the leaderboard based on employee feedback, providing more effective visual feedback. For example, employees can provide feedback on the display format and content of the leaderboard, and the design and content of the leaderboard can be improved based on that feedback. In this way, the Personalization Unit can provide effective visual feedback to maintain employee motivation and promote self-growth.

[0034] The Personalization Department personalizes skill trees to match each employee's career path and goals. Specifically, it provides a roadmap showing which skills employees should acquire and how they should develop. The Personalization Department proposes an optimal skill acquisition plan tailored to each employee's career goals. For example, if an employee aspires to become a project manager in the future, it provides a specific plan for acquiring skills related to project management and leadership. The Personalization Department uses AI to analyze employees' career goals and current skill sets and automatically generates the optimal skill tree. For example, it uses generation AI to create a skill tree based on an employee's career goals and presents it to the employee. This allows the Personalization Department to provide employees with a concrete roadmap for efficiently acquiring skills and achieving their career goals. Furthermore, the Personalization Department can continuously improve the content of the skill tree based on employee feedback, providing a more effective skill acquisition plan. For example, employees provide feedback on the content and plan of the skill tree, and the design and content of the skill tree are improved based on that feedback. This allows the Personalization Department to provide an optimal skill acquisition plan tailored to each employee's career goals and support their growth.

[0035] The analysis unit can analyze an employee's past work history and select the optimal analysis method. For example, the analysis unit may select an analysis method by referring to the methodologies used in projects the employee has successfully completed in the past. For example, the analysis unit may avoid the methodologies used in projects the employee has failed in the past. For example, the analysis unit may select the most efficient analysis method from the employee's past work history. This improves the accuracy of the analysis by selecting the optimal analysis method based on the employee's past work history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the employee's past work history data into a generating AI and have the generating AI select the optimal analysis method.

[0036] The analysis unit can filter data based on an employee's current projects and areas of interest during analysis. For example, the analysis unit can analyze only data related to the project the employee is currently working on. For example, the analysis unit can prioritize the analysis of data related to the employee's areas of interest. For example, the analysis unit can filter the analysis data based on the progress of the employee's current project. This improves the efficiency of the analysis by filtering the data based on the employee's current projects and areas of interest. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input employee project data into a generating AI and have the generating AI perform the filtering.

[0037] The analysis unit can prioritize analyzing highly relevant data by considering the geographical location information of employees during analysis. For example, if an employee is in a specific region, the analysis unit will prioritize analyzing data related to that region. For example, if an employee is on a business trip, the analysis unit will prioritize analyzing data related to the business trip destination. For example, if an employee is working remotely, the analysis unit will prioritize analyzing data around their home. This improves the accuracy of the analysis by prioritizing the analysis of highly relevant data based on the employee's geographical location information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the employee's geographical location information into a generating AI and have the generating AI select highly relevant data.

[0038] The analysis unit can analyze employees' social media activities and analyze relevant data during the analysis process. For example, the analysis unit can select analysis data based on information shared by employees on social media. For example, the analysis unit can select analysis data based on the activities of employees' social media followers and friends. For example, the analysis unit can prioritize the analysis of data related to topics that employees have shown interest in on social media. This improves the accuracy of the analysis by analyzing relevant data based on employees' social media activities. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input employee social media data into a generating AI and have the generating AI select relevant data.

[0039] The generation unit can adjust the difficulty of missions based on the employee's skill level when generating missions. For example, the generation unit can generate easy missions for beginner employees to acquire basic skills. For example, the generation unit can generate missions of moderate difficulty for intermediate employees to improve their skills. For example, the generation unit can generate missions of high difficulty for advanced employees to utilize their specialized skills. In this way, by adjusting the difficulty of missions according to the employee's skill level, missions of appropriate difficulty can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input employee skill level data into a generation AI and have the generation AI perform the adjustment of mission difficulty.

[0040] The generation unit can generate the optimal mission by referring to the employee's past mission history when generating a mission. For example, the generation unit generates a new mission by referring to the methods used in missions that the employee has successfully completed in the past. For example, the generation unit avoids the methods used in missions that the employee has failed at in the past. For example, the generation unit generates the most efficient mission from the employee's past mission history. This improves the accuracy of the mission by generating the optimal mission based on the employee's past mission history. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the employee's past mission history data into a generation AI and have the generation AI perform the generation of the optimal mission.

[0041] The generation unit can determine the priority of missions based on the progress of employees' work when generating missions. For example, the generation unit can determine the priority of missions based on the progress of projects that employees are currently working on. For example, the generation unit can generate high-urgency missions first, depending on the progress of employees' work. For example, the generation unit can postpone long-term missions based on the progress of employees' work. This enables efficient mission management by determining the priority of missions based on the progress of employees' work. Some or all of the above processes in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input employee work progress data into a generation AI and have the generation AI perform the task of determining mission priorities.

[0042] The generation unit can generate missions by referring to the employee's relevant project data. For example, the generation unit can generate missions based on data from projects the employee is currently working on. For example, the generation unit can generate missions by referring to the employee's past project data. For example, the generation unit can analyze the employee's relevant project data to generate the optimal mission. This allows the generation unit to provide appropriate missions by generating missions based on the employee's relevant project data. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the employee's project data into a generation AI and have the generation AI generate the optimal mission.

[0043] The tracking unit can improve tracking accuracy by referring to the employee's past work history during tracking. For example, the tracking unit can improve tracking accuracy by referring to the methodologies of projects the employee has successfully completed in the past. For example, the tracking unit can avoid the methodologies of projects the employee has failed in the past. For example, the tracking unit can select the most efficient tracking method from the employee's past work history. This improves tracking accuracy based on the employee's past work history, enabling appropriate tracking. Some or all of the above processes in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the employee's past work history data into a generating AI and have the generating AI perform the task of improving tracking accuracy.

[0044] The tracking unit can customize the tracking method based on the employee's current work status during tracking. For example, the tracking unit can customize the tracking method based on the progress of a project the employee is currently working on. For example, the tracking unit can change the tracking method according to the progress of the employee's work. For example, the tracking unit can select the optimal tracking method based on the employee's current work status. This enables appropriate tracking by customizing the tracking method based on the employee's current work status. Some or all of the above processes in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input employee work status data into a generating AI and have the generating AI perform the customization of the tracking method.

[0045] The tracking unit can select the optimal tracking method by considering the employee's geographical location information during tracking. For example, if an employee is in a specific region, the tracking unit will prioritize tracking data related to that region. For example, if an employee is on a business trip, the tracking unit will prioritize tracking data related to the business trip destination. For example, if an employee is working remotely, the tracking unit will prioritize tracking data around their home. This enables appropriate tracking by selecting the optimal tracking method based on the employee's geographical location information. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the employee's geographical location information into a generating AI and have the generating AI select the optimal tracking method.

[0046] The tracking unit can analyze an employee's social media activity and suggest tracking methods during tracking. For example, the tracking unit may select tracking methods based on information shared by the employee on social media. For example, the tracking unit may select tracking methods based on the activity of the employee's social media followers and friends. For example, the tracking unit may prioritize tracking data related to topics the employee has shown interest in on social media. This enables appropriate tracking by suggesting the most suitable tracking method based on the employee's social media activity. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input employee social media data into a generating AI and have the generating AI suggest tracking methods.

[0047] The personalization unit can select the optimal display method by referring to an employee's past performance when displaying the leaderboard. For example, the personalization unit customizes the leaderboard display method based on the performance an employee has achieved in the past. For example, the personalization unit analyzes an employee's past performance and selects the display method with the highest visibility. For example, the personalization unit adjusts the display order of the leaderboard based on an employee's past performance. This improves visibility by selecting the optimal display method based on an employee's past performance. Some or all of the above processing in the personalization unit may be performed using AI, for example, or without AI. For example, the personalization unit can input employee past performance data into a generating AI and have the generating AI select the optimal display method.

[0048] The personalization unit can customize the display method based on the employee's current work status when displaying the leaderboard. For example, the personalization unit can customize the leaderboard display method based on the progress of a project the employee is currently working on. For example, the personalization unit can change the leaderboard display method according to the progress of the employee's work. For example, the personalization unit can select the optimal leaderboard display method based on the employee's current work status. This improves visibility by customizing the display method based on the employee's current work status. Some or all of the above processing in the personalization unit may be performed using AI, for example, or without AI. For example, the personalization unit can input employee work status data into a generating AI and have the generating AI perform the customization of the display method.

[0049] The personalization unit can select the optimal display method when displaying the leaderboard, taking into account the employee's geographical location information. For example, if an employee is in a specific region, the personalization unit will prioritize displaying data related to that region on the leaderboard. For example, if an employee is on a business trip, the personalization unit will prioritize displaying data related to the business trip destination on the leaderboard. For example, if an employee is working remotely, the personalization unit will prioritize displaying data around their home on the leaderboard. This improves visibility by selecting the optimal display method based on the employee's geographical location information. Some or all of the above processing in the personalization unit may be performed using AI, for example, or without AI. For example, the personalization unit can input the employee's geographical location information into a generating AI and have the generating AI select the optimal display method.

[0050] The personalization unit can analyze an employee's social media activity and suggest a display method when displaying a leaderboard. For example, the personalization unit selects a leaderboard display method based on information shared by the employee on social media. For example, the personalization unit selects a leaderboard display method based on the activity of the employee's social media followers and friends. For example, the personalization unit prioritizes displaying data related to topics the employee has shown interest in on social media on the leaderboard. This improves visibility by suggesting the optimal display method based on the employee's social media activity. Some or all of the above processing in the personalization unit may be performed using AI, for example, or without AI. For example, the personalization unit can input employee social media data into a generating AI and have the generating AI suggest a display method.

[0051] The personalization unit can select the optimal display method when displaying the skill tree by referring to the employee's past skill acquisition history. For example, the personalization unit customizes the display method of the skill tree based on the skills the employee has acquired in the past. For example, the personalization unit analyzes the employee's past skill acquisition history and selects the display method with the highest visibility. For example, the personalization unit adjusts the display order of the skill tree based on the employee's past skill acquisition history. This improves visibility by selecting the optimal display method based on the employee's past skill acquisition history. Some or all of the above processing in the personalization unit may be performed using AI, for example, or without AI. For example, the personalization unit can input the employee's skill acquisition history data into a generating AI and have the generating AI select the optimal display method.

[0052] The personalization unit can customize the display method of the skill tree based on the employee's current career path when displaying it. For example, the personalization unit can customize how the skill tree is displayed based on the career path the employee is currently working on. For example, the personalization unit can change the display method of the skill tree according to the progress of the employee's career path. For example, the personalization unit can select the optimal display method of the skill tree based on the employee's current career path. This improves visibility by customizing the display method based on the employee's current career path. Some or all of the above processing in the personalization unit may be performed using AI, for example, or without AI. For example, the personalization unit can input employee career path data into a generating AI and have the generating AI perform the customization of the display method.

[0053] The personalization unit can select the optimal display method when displaying the skill tree, taking into account the employee's geographical location. For example, if an employee is in a specific region, the personalization unit will prioritize displaying skills related to that region in the skill tree. For example, if an employee is on a business trip, the personalization unit will prioritize displaying skills related to their destination in the skill tree. For example, if an employee is working remotely, the personalization unit will prioritize displaying skills around their home in the skill tree. This improves visibility by selecting the optimal display method based on the employee's geographical location. Some or all of the above processing in the personalization unit may be performed using AI, for example, or without AI. For example, the personalization unit can input the employee's geographical location information into a generating AI and have the generating AI select the optimal display method.

[0054] The personalization unit can analyze an employee's social media activity and suggest a display method when displaying the skill tree. For example, the personalization unit can select a display method for the skill tree based on information shared by the employee on social media. For example, the personalization unit can select a display method for the skill tree based on the activity of the employee's social media followers and friends. For example, the personalization unit can prioritize displaying skills related to topics the employee has shown interest in on social media in the skill tree. This improves visibility by suggesting the optimal display method based on the employee's social media activity. Some or all of the above processing in the personalization unit may be performed using AI, for example, or without AI. For example, the personalization unit can input employee social media data into a generating AI and have the generating AI suggest a display method.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The next-generation AI challenge manager can acquire employee health data and adjust the difficulty of missions based on their health status. For example, if an employee is feeling fatigued, it can present them with an easy mission. If an employee is healthy, it can present them with a mission of normal difficulty. If an employee is very healthy, it can present them with a challenging mission. This allows for providing an appropriate workload by adjusting the difficulty of missions according to the employee's health status. Health data can be acquired using wearable devices or health management apps. Mission difficulty adjustment can be performed using AI or without AI. For example, health data can be input into a generating AI, and the generating AI can then perform the mission difficulty adjustment.

[0057] The next-generation AI Challenge Manager can refer to employees' past project data, analyze the success factors of those projects, and reflect them in the next mission. For example, it can generate new missions based on the methodologies of employees' past successful projects. It can also help employees avoid the methodologies of projects that have failed in the past. It can generate the most efficient missions from employees' past project data. This improves the accuracy of missions by generating optimal missions based on employees' past project data. The analysis of project data may be performed using AI or not. For example, project data can be input into a generation AI, and the generation AI can be made to generate the optimal mission.

[0058] The next-generation AI Challenge Manager can analyze employees' skill sets, identify skill gaps, and propose training programs to bridge those gaps. For example, if an employee lacks a specific skill, it can propose a training program to acquire that skill. It can also propose training programs to strengthen skills that employees already possess. Furthermore, it can propose training programs to help employees acquire new skills. This allows for the promotion of skill improvement by proposing optimal training programs based on employees' skill sets. Skill set analysis may be performed using AI or without AI. For example, skill set data can be input into a generating AI, and the generating AI can then generate training program suggestions.

[0059] The next-generation AI Challenge Manager can generate region-specific missions by considering employees' geographical location information. For example, if an employee is in a specific region, it can present them with projects or tasks related to that region as missions. If an employee is on a business trip, it can generate missions related to their destination. If an employee is working remotely, it can generate missions based on data from their home area. This allows for addressing region-specific challenges by providing optimal missions based on employees' geographical location information. Geographical location information is obtained using GPS devices or location services. Mission generation may be performed using AI or not. For example, geographical location information can be input into a generation AI, and the generation AI can then perform mission generation.

[0060] The next-generation AI Challenge Manager can analyze employees' social media activity and generate missions to increase their influence on social media. For example, it can generate missions to increase influence based on information shared by employees on social media. It can generate missions to increase influence by referring to the activities of employees' social media followers and friends. It can generate missions related to topics that employees have shown interest in on social media. This allows for increased influence by providing employees with the most suitable missions based on their social media activity. The analysis of social media activity may be performed using AI or not. For example, social media data can be input into a generation AI, and the generation AI can be made to generate missions.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The analysis unit analyzes employees' skills, experience, and current project progress. For example, it analyzes employees' past work history and current project progress to evaluate their skill sets and experience. Step 2: The generation unit generates the next mission to tackle based on the information analyzed by the analysis unit. For example, it customizes the mission based on the employee's skill level and interests, presenting specific tasks and projects to help them acquire new skills and grow. Step 3: The tracking unit tracks the progress of missions generated by the generation unit in real time. For example, it monitors how much of the missions employees have completed and records their progress. It evaluates employee performance and provides feedback as needed. Step 4: The individualization unit reflects the results tracked by the tracking unit in a personalized leaderboard. For example, it compares an employee's performance with other employees and displays it in a ranking format. This provides visual feedback to help employees feel a sense of self-growth and maintain motivation. Step 5: The personalization department personalizes the skill tree to match each employee's career path and goals. For example, it provides a roadmap showing which skills employees should acquire and how they should grow, and proposes an optimal skill acquisition plan.

[0063] (Example of form 2) An embodiment of the present invention provides a next-generation AI challenge manager that gamifies work and offers a personalized growth experience tailored to the needs and skills of employees. This system analyzes employees' skills, experience, and current work progress to generate the next mission they should tackle. The generated mission is presented to the employee, who then undertakes the challenge. Mission progress is tracked in real time and reflected on a leaderboard. The leaderboard is personalized, allowing each employee to see their own achievements. Furthermore, a personalized skill tree is provided, tailored to each employee's career path and goals. The skill tree outlines which skills an employee should acquire and how they should develop. For example, the next-generation AI challenge manager uses AI to analyze employees' skills, experience, and current work progress. The AI ​​analyzes the employee's past work history and current project progress to generate the next mission. The generated mission is customized based on the employee's skill level and interests. For example, a novice employee might be presented with a mission to acquire basic skills, while intermediate and advanced employees might be presented with more advanced missions. Mission progress is tracked in real time and reflected on the leaderboard. The leaderboard displays individual employee performance and promotes self-growth. Furthermore, a personalized skills tree is provided, tailored to each employee's career path and goals. The skills tree is a roadmap showing which skills employees should acquire and how they should grow, improving employee motivation. As a result, the next-generation AI Challenge Manager can reduce the monotony of tasks and increase employee job satisfaction and a sense of accomplishment.

[0064] The next-generation AI challenge manager according to this embodiment comprises an analysis unit, a generation unit, a tracking unit, an individualization unit, and a personalization unit. The analysis unit analyzes the skills and experience of employees and the progress of their current tasks. For example, the analysis unit analyzes the employee's past work history and the progress of their current projects. The analysis unit evaluates the employee's skill set and experience and provides basic data to determine the next mission they should take on. The generation unit generates the next mission to take on based on the information analyzed by the analysis unit. For example, the generation unit customizes the mission based on the employee's skill level and interests. The generation unit presents specific tasks and projects for employees to acquire new skills and grow. The tracking unit tracks the progress of the missions generated by the generation unit in real time. For example, the tracking unit monitors how much of the mission an employee has completed and records the progress. The tracking unit evaluates the employee's performance and provides feedback as needed. The individualization unit reflects the results tracked by the tracking unit in an individualized leaderboard. For example, the individualization unit compares an employee's results with those of other employees and displays them in a ranking format. The individualization unit provides visual feedback to help employees feel a sense of self-growth and maintain motivation. The personalization unit personalizes the skill tree to match each employee's career path and goals. For example, the personalization unit provides a roadmap showing which skills an employee should acquire and how they should grow. The personalization unit proposes an optimal skill acquisition plan tailored to the employee's career goals. As a result, the next-generation AI Challenge Manager according to this embodiment can provide a personalized growth experience based on the employee's skills, experience, and work progress, thereby reducing the monotony of the work.

[0065] The analytics department analyzes employees' skills, experience, and current work progress. Specifically, it conducts a detailed analysis of employees' past work history and the progress of current projects. The analytics department extracts data from the database on past projects employees have participated in, the roles they have played, and their results and evaluations, and analyzes this data using statistical methods and machine learning algorithms. For example, it uses natural language processing technology to analyze past project reports and evaluation comments to quantify skill sets and the depth of experience. For ongoing projects, it obtains data in real time from project management tools and task management systems to understand their progress and challenges. This allows the analytics department to comprehensively evaluate employees' skills and experience and provide foundational data to determine the next missions they should take on. Furthermore, the analytics department identifies employee skill gaps, clarifying which skills are lacking and which areas require growth. This allows it to optimize employee career paths and provide concrete guidelines to promote individual growth.

[0066] The generation unit generates the next mission to tackle based on the information analyzed by the analysis unit. Specifically, it customizes missions based on employees' skill levels and interests. The generation unit presents employees with specific tasks and projects to help them acquire new skills and grow. For example, if an employee wants to improve their data analysis skills, the generation unit generates and provides them with data analysis-related projects and tasks. The generation unit uses AI to analyze employees' past performance and current skill sets and automatically generates the optimal mission. For example, it uses generation AI to generate project outlines and specific task lists based on the employee's skill set and presents them to the employee. This allows the generation unit to provide employees with a concrete path to efficiently acquire new skills and grow. Furthermore, the generation unit can continuously improve the content of missions based on employee feedback, providing a more effective growth experience.

[0067] The tracking unit tracks the progress of missions generated by the generation unit in real time. Specifically, it monitors how much of the missions employees have completed and records their progress. The tracking unit evaluates employee performance and provides feedback as needed. For example, it uses project management tools and task management systems to track employees' task completion status and progress in real time and issues alerts if progress is behind schedule. When employees complete a mission, it evaluates their achievement and performance and provides feedback. The tracking unit uses AI to analyze employee performance data and improve performance and identify issues. For example, it uses machine learning algorithms to analyze employee performance data and identify performance trends and patterns. This allows the tracking unit to continuously monitor employee performance and provide appropriate feedback as needed. Furthermore, based on employee performance data, the tracking unit can predict future performance and suggest improvement measures.

[0068] The Personalization Unit reflects the results tracked by the Tracking Unit in a personalized leaderboard. Specifically, it compares employee performance with other employees and displays it in a ranking format. The Personalization Unit provides visual feedback to help employees feel a sense of self-growth and maintain motivation. For example, it generates a leaderboard based on employee performance data and visually displays employee performance. By comparing employees' achievements and performance with other employees and displaying them in a ranking format, the leaderboard stimulates employee competitiveness and improves motivation. Furthermore, the Personalization Unit can continuously improve the content of the leaderboard based on employee feedback, providing more effective visual feedback. For example, employees can provide feedback on the display format and content of the leaderboard, and the design and content of the leaderboard can be improved based on that feedback. In this way, the Personalization Unit can provide effective visual feedback to maintain employee motivation and promote self-growth.

[0069] The Personalization Department personalizes skill trees to match each employee's career path and goals. Specifically, it provides a roadmap showing which skills employees should acquire and how they should develop. The Personalization Department proposes an optimal skill acquisition plan tailored to each employee's career goals. For example, if an employee aspires to become a project manager in the future, it provides a specific plan for acquiring skills related to project management and leadership. The Personalization Department uses AI to analyze employees' career goals and current skill sets and automatically generates the optimal skill tree. For example, it uses generation AI to create a skill tree based on an employee's career goals and presents it to the employee. This allows the Personalization Department to provide employees with a concrete roadmap for efficiently acquiring skills and achieving their career goals. Furthermore, the Personalization Department can continuously improve the content of the skill tree based on employee feedback, providing a more effective skill acquisition plan. For example, employees provide feedback on the content and plan of the skill tree, and the design and content of the skill tree are improved based on that feedback. This allows the Personalization Department to provide an optimal skill acquisition plan tailored to each employee's career goals and support their growth.

[0070] The analysis unit can estimate the employee's emotions and adjust the timing of the analysis based on the estimated emotions. For example, if the employee is stressed, the analysis unit may temporarily delay the analysis. For example, if the employee is relaxed, the analysis unit may perform the analysis immediately. For example, if the employee is focused, the analysis unit may continue without interruption. This allows for more appropriate analysis by adjusting the timing of the analysis according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit may input employee emotion data into a generative AI and have the generative AI perform emotion estimation.

[0071] The analysis unit can analyze an employee's past work history and select the optimal analysis method. For example, the analysis unit may select an analysis method by referring to the methodologies used in projects the employee has successfully completed in the past. For example, the analysis unit may avoid the methodologies used in projects the employee has failed in the past. For example, the analysis unit may select the most efficient analysis method from the employee's past work history. This improves the accuracy of the analysis by selecting the optimal analysis method based on the employee's past work history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the employee's past work history data into a generating AI and have the generating AI select the optimal analysis method.

[0072] The analysis unit can filter data based on an employee's current projects and areas of interest during analysis. For example, the analysis unit can analyze only data related to the project the employee is currently working on. For example, the analysis unit can prioritize the analysis of data related to the employee's areas of interest. For example, the analysis unit can filter the analysis data based on the progress of the employee's current project. This improves the efficiency of the analysis by filtering the data based on the employee's current projects and areas of interest. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input employee project data into a generating AI and have the generating AI perform the filtering.

[0073] The analysis unit can estimate employees' emotions and determine the priority of data to analyze based on the estimated emotions. For example, if an employee is stressed, the analysis unit will prioritize analyzing data of lower importance. For example, if an employee is relaxed, the analysis unit will prioritize analyzing data of higher importance. For example, if an employee is focused, the analysis unit will continue without changing the analysis priority. This improves the efficiency of the analysis by prioritizing data according to 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 may be performed using AI, for example, or not using AI. For example, the analysis unit can input employee emotion data into a generative AI and have the generative AI determine the data priority.

[0074] The analysis unit can prioritize analyzing highly relevant data by considering the geographical location information of employees during analysis. For example, if an employee is in a specific region, the analysis unit will prioritize analyzing data related to that region. For example, if an employee is on a business trip, the analysis unit will prioritize analyzing data related to the business trip destination. For example, if an employee is working remotely, the analysis unit will prioritize analyzing data around their home. This improves the accuracy of the analysis by prioritizing the analysis of highly relevant data based on the employee's geographical location information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the employee's geographical location information into a generating AI and have the generating AI select highly relevant data.

[0075] The analysis unit can analyze employees' social media activities and analyze relevant data during the analysis process. For example, the analysis unit can select analysis data based on information shared by employees on social media. For example, the analysis unit can select analysis data based on the activities of employees' social media followers and friends. For example, the analysis unit can prioritize the analysis of data related to topics that employees have shown interest in on social media. This improves the accuracy of the analysis by analyzing relevant data based on employees' social media activities. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input employee social media data into a generating AI and have the generating AI select relevant data.

[0076] The generation unit can estimate an employee's emotions and adjust the way the mission is presented based on the estimated emotions. For example, if an employee is stressed, the generation unit will present a simple and easy-to-understand mission. If an employee is relaxed, the generation unit will present a mission with detailed explanations. If an employee is excited, the generation unit will present a challenging mission. This improves understanding of the mission by adjusting its presentation according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input employee emotion data into the generation AI and have the generation AI adjust the way the mission is presented.

[0077] The generation unit can adjust the difficulty of missions based on the employee's skill level when generating missions. For example, the generation unit can generate easy missions for beginner employees to acquire basic skills. For example, the generation unit can generate missions of moderate difficulty for intermediate employees to improve their skills. For example, the generation unit can generate missions of high difficulty for advanced employees to utilize their specialized skills. In this way, by adjusting the difficulty of missions according to the employee's skill level, missions of appropriate difficulty can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input employee skill level data into a generation AI and have the generation AI perform the adjustment of mission difficulty.

[0078] The generation unit can generate the optimal mission by referring to the employee's past mission history when generating a mission. For example, the generation unit generates a new mission by referring to the methods used in missions that the employee has successfully completed in the past. For example, the generation unit avoids the methods used in missions that the employee has failed at in the past. For example, the generation unit generates the most efficient mission from the employee's past mission history. This improves the accuracy of the mission by generating the optimal mission based on the employee's past mission history. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the employee's past mission history data into a generation AI and have the generation AI perform the generation of the optimal mission.

[0079] The generation unit can estimate an employee's emotions and adjust the mission length based on the estimated emotions. For example, if an employee is stressed, the generation unit will generate a shorter mission. For example, if an employee is relaxed, the generation unit will generate a longer mission. For example, if an employee is focused, the generation unit will continue the mission without changing its length. This allows for the provision of missions of appropriate length by adjusting the mission length according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input employee emotion data into a generation AI and have the generation AI adjust the mission length.

[0080] The generation unit can determine the priority of missions based on the progress of employees' work when generating missions. For example, the generation unit can determine the priority of missions based on the progress of projects that employees are currently working on. For example, the generation unit can generate high-urgency missions first, depending on the progress of employees' work. For example, the generation unit can postpone long-term missions based on the progress of employees' work. This enables efficient mission management by determining the priority of missions based on the progress of employees' work. Some or all of the above processes in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input employee work progress data into a generation AI and have the generation AI perform the task of determining mission priorities.

[0081] The generation unit can generate missions by referring to the employee's relevant project data. For example, the generation unit can generate missions based on data from projects the employee is currently working on. For example, the generation unit can generate missions by referring to the employee's past project data. For example, the generation unit can analyze the employee's relevant project data to generate the optimal mission. This allows the generation unit to provide appropriate missions by generating missions based on the employee's relevant project data. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the employee's project data into a generation AI and have the generation AI generate the optimal mission.

[0082] The tracking unit can estimate the employee's emotions and adjust the tracking method based on the estimated emotions. For example, if the employee is stressed, the tracking unit may reduce the tracking frequency. For example, if the employee is relaxed, the tracking unit may increase the tracking frequency. For example, if the employee is focused, the tracking unit may continue without changing the tracking method. This allows for appropriate tracking by adjusting the tracking method according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the tracking unit may be performed using AI or not using AI. For example, the tracking unit may input employee emotion data into a generative AI and have the generative AI adjust the tracking method.

[0083] The tracking unit can improve tracking accuracy by referring to the employee's past work history during tracking. For example, the tracking unit can improve tracking accuracy by referring to the methodologies of projects the employee has successfully completed in the past. For example, the tracking unit can avoid the methodologies of projects the employee has failed in the past. For example, the tracking unit can select the most efficient tracking method from the employee's past work history. This improves tracking accuracy based on the employee's past work history, enabling appropriate tracking. Some or all of the above processes in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the employee's past work history data into a generating AI and have the generating AI perform the task of improving tracking accuracy.

[0084] The tracking unit can customize the tracking method based on the employee's current work status during tracking. For example, the tracking unit can customize the tracking method based on the progress of a project the employee is currently working on. For example, the tracking unit can change the tracking method according to the progress of the employee's work. For example, the tracking unit can select the optimal tracking method based on the employee's current work status. This enables appropriate tracking by customizing the tracking method based on the employee's current work status. Some or all of the above processes in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input employee work status data into a generating AI and have the generating AI perform the customization of the tracking method.

[0085] The tracking unit can estimate an employee's emotions and determine tracking priorities based on the estimated emotions. For example, if an employee is stressed, the tracking unit will prioritize tracking low-priority tasks. For example, if an employee is relaxed, the tracking unit will prioritize tracking high-priority tasks. For example, if an employee is focused, the tracking unit will continue tracking without changing the tracking priorities. This enables appropriate tracking by determining tracking priorities according to 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 tracking unit may be performed using AI or not using AI. For example, the tracking unit can input employee emotion data into a generative AI and have the generative AI determine tracking priorities.

[0086] The tracking unit can select the optimal tracking method by considering the employee's geographical location information during tracking. For example, if an employee is in a specific region, the tracking unit will prioritize tracking data related to that region. For example, if an employee is on a business trip, the tracking unit will prioritize tracking data related to the business trip destination. For example, if an employee is working remotely, the tracking unit will prioritize tracking data around their home. This enables appropriate tracking by selecting the optimal tracking method based on the employee's geographical location information. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the employee's geographical location information into a generating AI and have the generating AI select the optimal tracking method.

[0087] The tracking unit can analyze an employee's social media activity and suggest tracking methods during tracking. For example, the tracking unit may select tracking methods based on information shared by the employee on social media. For example, the tracking unit may select tracking methods based on the activity of the employee's social media followers and friends. For example, the tracking unit may prioritize tracking data related to topics the employee has shown interest in on social media. This enables appropriate tracking by suggesting the most suitable tracking method based on the employee's social media activity. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input employee social media data into a generating AI and have the generating AI suggest tracking methods.

[0088] The personalization unit can estimate an employee's emotions and adjust the leaderboard display method based on the estimated emotions. For example, if an employee is stressed, the personalization unit displays a simple and highly visible leaderboard. For example, if an employee is relaxed, the personalization unit displays a leaderboard with detailed information. For example, if an employee is excited, the personalization unit displays a visually stimulating leaderboard. This improves visibility by adjusting the leaderboard display method according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 processing in the personalization unit may be performed using AI or not. For example, the personalization unit can input employee emotion data into the generative AI and have the generative AI adjust the leaderboard display method.

[0089] The personalization unit can select the optimal display method by referring to an employee's past performance when displaying the leaderboard. For example, the personalization unit customizes the leaderboard display method based on the performance an employee has achieved in the past. For example, the personalization unit analyzes an employee's past performance and selects the display method with the highest visibility. For example, the personalization unit adjusts the display order of the leaderboard based on an employee's past performance. This improves visibility by selecting the optimal display method based on an employee's past performance. Some or all of the above processing in the personalization unit may be performed using AI, for example, or without AI. For example, the personalization unit can input employee past performance data into a generating AI and have the generating AI select the optimal display method.

[0090] The personalization unit can customize the display method based on the employee's current work status when displaying the leaderboard. For example, the personalization unit can customize the leaderboard display method based on the progress of a project the employee is currently working on. For example, the personalization unit can change the leaderboard display method according to the progress of the employee's work. For example, the personalization unit can select the optimal leaderboard display method based on the employee's current work status. This improves visibility by customizing the display method based on the employee's current work status. Some or all of the above processing in the personalization unit may be performed using AI, for example, or without AI. For example, the personalization unit can input employee work status data into a generating AI and have the generating AI perform the customization of the display method.

[0091] The personalization unit can estimate an employee's emotions and determine leaderboard priorities based on the estimated emotions. For example, if an employee is stressed, the personalization unit will prioritize displaying low-priority tasks on the leaderboard. For example, if an employee is relaxed, the personalization unit will prioritize displaying high-priority tasks on the leaderboard. For example, if an employee is focused, the personalization unit will continue without changing the leaderboard priorities. This improves visibility by determining leaderboard priorities according to employee emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 personalization unit may be performed using AI or not. For example, the personalization unit can input employee emotion data into a generative AI and have the generative AI determine leaderboard priorities.

[0092] The personalization unit can select the optimal display method when displaying the leaderboard, taking into account the employee's geographical location information. For example, if an employee is in a specific region, the personalization unit will prioritize displaying data related to that region on the leaderboard. For example, if an employee is on a business trip, the personalization unit will prioritize displaying data related to the business trip destination on the leaderboard. For example, if an employee is working remotely, the personalization unit will prioritize displaying data around their home on the leaderboard. This improves visibility by selecting the optimal display method based on the employee's geographical location information. Some or all of the above processing in the personalization unit may be performed using AI, for example, or without AI. For example, the personalization unit can input the employee's geographical location information into a generating AI and have the generating AI select the optimal display method.

[0093] The personalization unit can analyze an employee's social media activity and suggest a display method when displaying a leaderboard. For example, the personalization unit selects a leaderboard display method based on information shared by the employee on social media. For example, the personalization unit selects a leaderboard display method based on the activity of the employee's social media followers and friends. For example, the personalization unit prioritizes displaying data related to topics the employee has shown interest in on social media on the leaderboard. This improves visibility by suggesting the optimal display method based on the employee's social media activity. Some or all of the above processing in the personalization unit may be performed using AI, for example, or without AI. For example, the personalization unit can input employee social media data into a generating AI and have the generating AI suggest a display method.

[0094] The personalization unit can estimate an employee's emotions and adjust how the skill tree is displayed based on the estimated emotions. For example, if an employee is stressed, the personalization unit displays a simple and highly visible skill tree. If an employee is relaxed, the personalization unit displays a skill tree with detailed information. If an employee is excited, the personalization unit displays a visually stimulating skill tree. This improves visibility by adjusting how the skill tree is displayed according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 personalization unit may be performed using AI or not. For example, the personalization unit can input employee emotion data into a generative AI and have the generative AI adjust how the skill tree is displayed.

[0095] The personalization unit can select the optimal display method when displaying the skill tree by referring to the employee's past skill acquisition history. For example, the personalization unit customizes the display method of the skill tree based on the skills the employee has acquired in the past. For example, the personalization unit analyzes the employee's past skill acquisition history and selects the display method with the highest visibility. For example, the personalization unit adjusts the display order of the skill tree based on the employee's past skill acquisition history. This improves visibility by selecting the optimal display method based on the employee's past skill acquisition history. Some or all of the above processing in the personalization unit may be performed using AI, for example, or without AI. For example, the personalization unit can input the employee's skill acquisition history data into a generating AI and have the generating AI select the optimal display method.

[0096] The personalization unit can customize the display method of the skill tree based on the employee's current career path when displaying it. For example, the personalization unit can customize how the skill tree is displayed based on the career path the employee is currently working on. For example, the personalization unit can change the display method of the skill tree according to the progress of the employee's career path. For example, the personalization unit can select the optimal display method of the skill tree based on the employee's current career path. This improves visibility by customizing the display method based on the employee's current career path. Some or all of the above processing in the personalization unit may be performed using AI, for example, or without AI. For example, the personalization unit can input employee career path data into a generating AI and have the generating AI perform the customization of the display method.

[0097] The personalization unit can estimate an employee's emotions and determine the priority of the skill tree based on the estimated emotions. For example, if an employee is stressed, the personalization unit will prioritize displaying lower-priority skills in the skill tree. For example, if an employee is relaxed, the personalization unit will prioritize displaying higher-priority skills in the skill tree. For example, if an employee is focused, the personalization unit will continue without changing the skill tree priorities. This improves visibility by determining the skill tree priorities according to 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 personalization unit may be performed using AI or not using AI. For example, the personalization unit can input employee emotion data into a generative AI and have the generative AI perform the skill tree priority determination.

[0098] The personalization unit can select the optimal display method when displaying the skill tree, taking into account the employee's geographical location. For example, if an employee is in a specific region, the personalization unit will prioritize displaying skills related to that region in the skill tree. For example, if an employee is on a business trip, the personalization unit will prioritize displaying skills related to their destination in the skill tree. For example, if an employee is working remotely, the personalization unit will prioritize displaying skills around their home in the skill tree. This improves visibility by selecting the optimal display method based on the employee's geographical location. Some or all of the above processing in the personalization unit may be performed using AI, for example, or without AI. For example, the personalization unit can input the employee's geographical location information into a generating AI and have the generating AI select the optimal display method.

[0099] The personalization unit can analyze an employee's social media activity and suggest a display method when displaying the skill tree. For example, the personalization unit can select a display method for the skill tree based on information shared by the employee on social media. For example, the personalization unit can select a display method for the skill tree based on the activity of the employee's social media followers and friends. For example, the personalization unit can prioritize displaying skills related to topics the employee has shown interest in on social media in the skill tree. This improves visibility by suggesting the optimal display method based on the employee's social media activity. Some or all of the above processing in the personalization unit may be performed using AI, for example, or without AI. For example, the personalization unit can input employee social media data into a generating AI and have the generating AI suggest a display method.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The next-generation AI Challenge Manager can estimate employee emotions and adjust mission rewards based on those emotions. For example, if an employee is stressed, the reward can be increased to boost their motivation. If an employee is relaxed, the reward can be set as usual. If an employee is excited, the reward can be reduced to maintain their willingness to take on challenges. This allows for the optimization of motivation by adjusting rewards according to employee emotions. Emotion estimation is achieved using an emotion engine or generative AI, such as text generation AI or multimodal generation AI, but is not limited to these examples. Reward adjustments may be performed using AI or not. For example, reward adjustments can be performed by generative AI.

[0102] The next-generation AI challenge manager can acquire employee health data and adjust the difficulty of missions based on their health status. For example, if an employee is feeling fatigued, it can present them with an easy mission. If an employee is healthy, it can present them with a mission of normal difficulty. If an employee is very healthy, it can present them with a challenging mission. This allows for providing an appropriate workload by adjusting the difficulty of missions according to the employee's health status. Health data can be acquired using wearable devices or health management apps. Mission difficulty adjustment can be performed using AI or without AI. For example, health data can be input into a generating AI, and the generating AI can then perform the mission difficulty adjustment.

[0103] The next-generation AI Challenge Manager 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 emphasized. If an employee is relaxed, constructive feedback can be provided. If an employee is excited, challenging feedback can be provided. This allows for the provision of effective feedback by adjusting the content according to the employee's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI. Feedback adjustment may be performed using AI or not. For example, the content of the feedback can be generated by a generative AI.

[0104] The next-generation AI Challenge Manager can refer to employees' past project data, analyze the success factors of those projects, and reflect them in the next mission. For example, it can generate new missions based on the methodologies of employees' past successful projects. It can also help employees avoid the methodologies of projects that have failed in the past. It can generate the most efficient missions from employees' past project data. This improves the accuracy of missions by generating optimal missions based on employees' past project data. The analysis of project data may be performed using AI or not. For example, project data can be input into a generation AI, and the generation AI can be made to generate the optimal mission.

[0105] The next-generation AI challenge manager can estimate employee emotions and adjust mission deadlines based on those emotions. For example, if an employee is stressed, the mission deadline can be extended. If an employee is relaxed, a normal deadline can be set. If an employee is excited, a shorter deadline can be set. This allows for appropriate pressure to be applied by adjusting mission deadlines according to employee emotions. Emotion estimation is achieved using an emotion engine or generative AI, such as text generation AI or multimodal generation AI, but is not limited to these examples. Mission deadline adjustment may be performed using AI or not. For example, mission deadlines can be set by a generative AI.

[0106] The next-generation AI Challenge Manager can analyze employees' skill sets, identify skill gaps, and propose training programs to bridge those gaps. For example, if an employee lacks a specific skill, it can propose a training program to acquire that skill. It can also propose training programs to strengthen skills that employees already possess. Furthermore, it can propose training programs to help employees acquire new skills. This allows for the promotion of skill improvement by proposing optimal training programs based on employees' skill sets. Skill set analysis may be performed using AI or without AI. For example, skill set data can be input into a generating AI, and the generating AI can then generate training program suggestions.

[0107] The next-generation AI Challenge Manager can estimate employee emotions and adjust mission notification methods based on those emotions. For example, if an employee is stressed, notifications can be less frequent. If an employee is relaxed, the normal notification method can be used. If an employee is excited, notifications can be sent more frequently. This allows for timely notifications by adjusting the notification method according to the employee's emotions. Emotion estimation is achieved using an emotion engine or generative AI, such as, but not limited to, text generation AI or multimodal generation AI. The adjustment of notification methods may be done using AI or not. For example, the generative AI can be used to adjust notification methods.

[0108] The next-generation AI Challenge Manager can generate region-specific missions by considering employees' geographical location information. For example, if an employee is in a specific region, it can present them with projects or tasks related to that region as missions. If an employee is on a business trip, it can generate missions related to their destination. If an employee is working remotely, it can generate missions based on data from their home area. This allows for addressing region-specific challenges by providing optimal missions based on employees' geographical location information. Geographical location information is obtained using GPS devices or location services. Mission generation may be performed using AI or not. For example, geographical location information can be input into a generation AI, and the generation AI can then perform mission generation.

[0109] The next-generation AI Challenge Manager can estimate employee emotions and adjust how mission progress is visualized based on those emotions. For example, if an employee is stressed, it can provide a simple and highly visible progress display. If an employee is relaxed, it can provide a detailed progress display. If an employee is excited, it can provide a visually stimulating progress display. This improves visibility by adjusting the progress visualization method according to the employee's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI is, but is not limited to, text generation AI or multimodal generation AI. The adjustment of the progress visualization method may be done using AI or not. For example, the adjustment of the progress visualization method can be performed by a generative AI.

[0110] The next-generation AI Challenge Manager can analyze employees' social media activity and generate missions to increase their influence on social media. For example, it can generate missions to increase influence based on information shared by employees on social media. It can generate missions to increase influence by referring to the activities of employees' social media followers and friends. It can generate missions related to topics that employees have shown interest in on social media. This allows for increased influence by providing employees with the most suitable missions based on their social media activity. The analysis of social media activity may be performed using AI or not. For example, social media data can be input into a generation AI, and the generation AI can be made to generate missions.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The analysis unit analyzes employees' skills, experience, and current project progress. For example, it analyzes employees' past work history and current project progress to evaluate their skill sets and experience. Step 2: The generation unit generates the next mission to tackle based on the information analyzed by the analysis unit. For example, it customizes the mission based on the employee's skill level and interests, presenting specific tasks and projects to help them acquire new skills and grow. Step 3: The tracking unit tracks the progress of missions generated by the generation unit in real time. For example, it monitors how much of the missions employees have completed and records their progress. It evaluates employee performance and provides feedback as needed. Step 4: The individualization unit reflects the results tracked by the tracking unit in a personalized leaderboard. For example, it compares an employee's performance with other employees and displays it in a ranking format. This provides visual feedback to help employees feel a sense of self-growth and maintain motivation. Step 5: The personalization department personalizes the skill tree to match each employee's career path and goals. For example, it provides a roadmap showing which skills employees should acquire and how they should grow, and proposes an optimal skill acquisition plan.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the analysis unit, generation unit, tracking unit, individualization unit, and personalization unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the skills, experience, and current work progress of employees. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates the next mission to be tackled based on the analyzed information. The tracking unit is implemented by the control unit 46A of the smart device 14 and tracks the progress of the generated mission in real time. The individualization unit is implemented by the control unit 46A of the smart device 14 and reflects the tracked results in an individualized way on the leaderboard. The personalization unit is implemented by the specific processing unit 290 of the data processing unit 12 and personalizes the skill tree to match the career path and goals of each employee. 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.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the analysis unit, generation unit, tracking unit, individualization unit, and personalization unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the employee's skills, experience, and current work progress. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and generates the next mission to be tackled based on the analyzed information. The tracking unit is implemented, for example, by the control unit 46A of the smart glasses 214 and tracks the progress of the generated mission in real time. The individualization unit is implemented, for example, by the control unit 46A of the smart glasses 214 and reflects the tracked results in an individualized way on the leaderboard. The personalization unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and personalizes the skill tree to match each employee's career path and goals. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the analysis unit, generation unit, tracking unit, individualization unit, and personalization unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the employee's skills, experience, and current work progress. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates the next mission to be tackled based on the analyzed information. The tracking unit is implemented by the control unit 46A of the headset terminal 314 and tracks the progress of the generated mission in real time. The individualization unit is implemented by the control unit 46A of the headset terminal 314 and reflects the tracked results in an individualized way on the leaderboard. The personalization unit is implemented by the identification processing unit 290 of the data processing unit 12 and personalizes the skill tree to match each employee's career path and goals. 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.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] Each of the multiple elements described above, including the analysis unit, generation unit, tracking unit, individualization unit, and personalization unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the skills and experience of employees and the progress of their current work. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates the next mission to be attempted based on the analyzed information. The tracking unit is implemented by the control unit 46A of the robot 414 and tracks the progress of the generated mission in real time. The individualization unit is implemented by the control unit 46A of the robot 414 and reflects the tracked results in an individualized way on the leaderboard. The personalization unit is implemented by the specific processing unit 290 of the data processing unit 12 and personalizes the skill tree to match the career path and goals of each employee. 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] (Note 1) The analysis department analyzes employees' skills and experience, as well as the progress of their current work. A generation unit generates the next mission to be attempted based on the information analyzed by the analysis unit, A tracking unit that tracks the progress of the missions generated by the generation unit, A personalization unit that reflects the results tracked by the aforementioned tracking unit in a personalized manner on the leaderboard, It includes a personalization section that personalizes skill trees to match each employee's career path and goals. A system characterized by the following features. (Note 2) The aforementioned analysis unit, The system estimates employee emotions and adjusts the timing of the analysis based on the estimated employee emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyze employees' past work history and select the most suitable analysis method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, During analysis, filtering is performed based on the employee's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, We estimate employee sentiment and prioritize data to analyze based on the estimated employee sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, During analysis, the system prioritizes analyzing highly relevant data, taking into account the geographical location of employees. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, During the analysis, we analyze employees' social media activity and analyze related data. The system described in Appendix 1, characterized by the features described herein. (Note 8) The generating unit is We estimate employee sentiment and adjust how the mission is expressed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The generating unit is When generating missions, adjust the difficulty of the missions based on the skill levels of the employees. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is When generating a mission, the system references the employee's past mission history to generate the most suitable mission. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is Estimate employee emotions and adjust mission length based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is When generating missions, prioritize them based on the progress of employees' work. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When generating a mission, the mission is generated by referencing the employee's relevant project data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned tracking unit is We estimate employee sentiment and adjust tracking methods based on the estimated employee sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned tracking unit is During tracking, we improve tracking accuracy by referring to the employee's past work history. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned tracking unit is During tracking, customize the tracking method based on the employee's current work status. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned tracking unit is The system estimates employee sentiment and prioritizes tracking based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned tracking unit is During tracking, the optimal tracking method is selected, taking into account the employee's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned tracking unit is During tracking, we analyze employees' social media activity and suggest tracking methods. The system described in Appendix 1, characterized by the features described herein. (Note 20) The individualization unit is, The system estimates employee sentiment and adjusts how the leaderboard is displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The individualization unit is, When displaying the leaderboard, the system selects the optimal display method by referring to the employee's past performance. The system described in Appendix 1, characterized by the features described herein. (Note 22) The individualization unit is, When displaying the leaderboard, customize the display method based on the employee's current work status. The system described in Appendix 1, characterized by the features described herein. (Note 23) The individualization unit is, The system estimates employee sentiment and determines leaderboard priorities based on the estimated employee sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The individualization unit is, When displaying the leaderboard, the system selects the optimal display method by considering the geographical location information of the employees. The system described in Appendix 1, characterized by the features described herein. (Note 25) The individualization unit is, When displaying leaderboards, analyze employees' social media activity and suggest ways to display it. The system described in Appendix 1, characterized by the features described herein. (Note 26) The personalization unit described above is Estimate employee sentiment and adjust how the skill tree is displayed based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 27) The personalization unit described above is When displaying the skill tree, the system selects the optimal display method by referring to the employee's past skill acquisition history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The personalization unit described above is When displaying the skills tree, customize the display method based on the employee's current career path. The system described in Appendix 1, characterized by the features described herein. (Note 29) The personalization unit described above is Estimate employee sentiment and prioritize skills in the skill tree based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 30) The personalization unit described above is When displaying the skill tree, the system will select the optimal display method considering the employee's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The personalization unit described above is When displaying the skills tree, analyze employees' social media activity and suggest ways to display it. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0185] 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 analysis department analyzes employees' skills and experience, as well as the progress of their current work. A generation unit generates the next mission to be attempted based on the information analyzed by the analysis unit, A tracking unit that tracks the progress of the missions generated by the generation unit, A personalization unit that reflects the results tracked by the aforementioned tracking unit in a personalized manner on the leaderboard, It includes a personalization section that personalizes skill trees to match each employee's career path and goals. A system characterized by the following features.

2. The aforementioned analysis unit, The system estimates employee emotions and adjusts the timing of the analysis based on the estimated employee emotions. The system according to feature 1.

3. The aforementioned analysis unit, Analyze employees' past work history and select the most suitable analysis method. The system according to feature 1.

4. The aforementioned analysis unit, During analysis, filtering is performed based on the employee's current projects and areas of interest. The system according to feature 1.

5. The aforementioned analysis unit, We estimate employee sentiment and prioritize data to analyze based on the estimated employee sentiment. The system according to feature 1.

6. The aforementioned analysis unit, During analysis, the system prioritizes analyzing highly relevant data, taking into account the geographical location of employees. The system according to feature 1.

7. The aforementioned analysis unit, During the analysis, we analyze employees' social media activity and analyze related data. The system according to feature 1.

8. The generating unit is We estimate employee sentiment and adjust how the mission is expressed based on that estimated sentiment. The system according to feature 1.

9. The generating unit is When generating missions, adjust the difficulty of the missions based on the skill levels of the employees. The system according to feature 1.

10. The generating unit is When generating a mission, the system references the employee's past mission history to generate the most suitable mission. The system according to feature 1.

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