system

Abstract

The system according to this embodiment aims to propose individual learning plans and provide coaching based on employee performance data. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a coaching unit. The data collection unit collects performance data from each employee. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes a learning plan based on the analysis results obtained by the analysis unit. The coaching unit provides coaching based on the learning plan proposed by the proposal unit.

Description

Technical Field

[0005]

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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] <​​​​​​​​​​​The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a coaching unit. The data collection unit collects performance data from each employee. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes a learning plan based on the analysis results obtained by the analysis unit. The coaching unit provides coaching based on the learning plan proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can propose individual learning plans and provide coaching based on employee performance data. [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, etc. The communication I / F manages communication between multiple 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 AI system according to an embodiment of the present invention is a system that creates and updates individual learning plans in real time based on each employee's performance data. This system collects each employee's performance data, the AI ​​analyzes it, suggests which skills the employee should acquire and which learning materials they should use, and autonomously provides coaching. For example, the AI ​​system collects each employee's performance data. In this process, it collects detailed data such as the employee's work performance, skill evaluations, and feedback. For example, it collects data such as which projects the employee participated in, what results they achieved, and which skills they lack. This allows for an accurate understanding of the employee's current situation. Next, the AI ​​analyzes the collected data. Based on the collected data, the AI ​​suggests which skills the employee should acquire and which learning materials they should use. For example, it can suggest the optimal learning materials and training programs for an employee to acquire a specific skill. This allows the employee to obtain a learning plan that is best suited to them. Furthermore, based on the suggested learning plan, the AI ​​autonomously provides coaching to the employee. The AI ​​monitors the employee's learning progress in real time and updates the learning plan as needed. For example, if an employee acquires a specific skill, the AI ​​can suggest a new skill as the next step. This allows employees to always learn based on the latest learning plan. This enables the provision of an optimal learning process for each employee. Employees can learn at their own pace and acquire skills efficiently. Furthermore, companies can effectively support employee development. For example, even large organizations can create training programs tailored to each employee. This is expected to improve employee skills and enhance the company's competitiveness. The AI ​​system can create and update individual learning plans in real time based on each employee's performance data and provide autonomous coaching.

[0029] The AI ​​system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a coaching unit. The data collection unit collects performance data for each employee. The data collection unit collects data such as employee work performance, skill evaluations, and feedback. The data collection unit collects data such as which projects an employee participated in, what results they achieved, and which skills they lack. The data collection unit records employee work performance in detail, performs skill evaluations, and collects feedback. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the collected data using AI to identify which skills an employee should acquire. The analysis unit analyzes the data using AI to identify an employee's skill gap. The analysis unit analyzes the data using a machine learning algorithm to determine the priority of the employee's skill acquisition. The proposal unit proposes a learning plan based on the analysis results obtained by the analysis unit. The proposal unit proposes optimal learning materials and training programs based on the identified skills. The proposal unit proposes optimal learning materials for an employee to acquire a specific skill. The proposal department proposes training programs tailored to each employee's skill level. The coaching department provides coaching based on the learning plan proposed by the proposal department. The coaching department monitors employees' learning progress in real time and updates the learning plan as needed. The coaching department suggests new skills as the next step when an employee has acquired a particular skill. The coaching department provides feedback and adjusts the learning plan according to the employee's learning progress. As a result, the AI ​​system according to the embodiment can create and update individual learning plans in real time based on each employee's performance data and provide coaching autonomously.

[0030] The data collection department collects performance data for each employee. Specifically, it collects data such as employee work performance, skill evaluations, and feedback. For example, it collects detailed data such as which projects employees participated in, what results they achieved, and which skills they lack. The data collection department meticulously records employee work performance, conducts skill evaluations, and collects feedback. This includes goals achieved by employees in their daily work, project progress, and evaluation comments from supervisors and colleagues. Furthermore, the data collection department also collects employee self-assessments and the results of periodic performance reviews. This allows the data collection department to build a comprehensive dataset on employee performance and provide it to the analysis department. The data collection department centrally manages this data and can integrate it with other systems and departments as needed. For example, the collected data is stored on a cloud server and made accessible to the analysis and proposal departments. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis department analyzes the data collected by the data collection department. Specifically, it uses AI to analyze the collected data and identify which skills employees should acquire. The analysis department uses AI to analyze the data and identify employee skill gaps. For example, it uses machine learning algorithms to analyze the data and determine the priority of employee skill acquisition. Based on employees' past performance data and skill evaluations, the AI ​​assesses their current skill level and predicts the skills they will need in the future. Furthermore, the analysis department proposes the optimal learning path, taking into account employees' learning history and feedback. This allows the analysis department to provide each employee with an optimal skill acquisition plan. The analysis department can also use historical data and statistical information to analyze long-term skill development trends and predict future skill needs. For example, it can predict fluctuations in skill demand in specific industries or job types and identify the skills employees will need in the future. In addition, the analysis department can use anomaly detection algorithms to detect unusual performance patterns and anomalous data, enabling early countermeasures. This allows the analysis department to not only grasp the situation in real time but also to handle long-term skill development and anomaly detection, improving the reliability and effectiveness of the entire system.

[0032] The Proposal Department proposes learning plans based on the analysis results obtained by the Analysis Department. Specifically, it proposes optimal learning materials and training programs based on identified skills. The Proposal Department proposes the most suitable learning materials for employees to acquire specific skills. For example, it proposes training programs tailored to the employee's skill level. The Proposal Department can propose a variety of learning methods, such as online courses, workshops, and on-the-job training, according to the employee's learning style and preferences. Furthermore, the Proposal Department monitors the employee's learning progress and updates the learning plan as needed. For example, if an employee acquires a particular skill, it proposes a new skill as the next step. The Proposal Department can also collect employee feedback and continuously improve the accuracy and effectiveness of the learning plan. This allows the Proposal Department to provide an optimal learning plan for each employee and maximize the efficiency of skill acquisition. The Proposal Department aims to always provide the best learning methods by incorporating the latest educational technologies and trends. For example, it can propose personalized learning using AI or learning programs incorporating gamification. This allows the Proposal Department to increase employee motivation and support effective skill acquisition.

[0033] The Coaching Department provides coaching based on the learning plans proposed by the Proposal Department. Specifically, it monitors employees' learning progress in real time and updates the learning plans as needed. When an employee acquires a particular skill, the Coaching Department suggests new skills as the next step. For example, it provides feedback and adjusts the learning plan according to the employee's learning progress. The Coaching Department meticulously records employees' learning progress and provides appropriate feedback according to their progress. This includes evaluations of the goals achieved and skills acquired, and suggestions for skills to be acquired next. Furthermore, the Coaching Department provides support to maintain motivation in order to enhance employees' desire to learn. For example, it can introduce regular coaching sessions and reward systems for achieving goals. In this way, the Coaching Department can increase employees' desire to learn and support effective skill acquisition. The Coaching Department can collect employee feedback and continuously improve the accuracy and effectiveness of coaching. For example, it reviews coaching methods and learning plans based on employee feedback. The Coaching Department also aims to always provide the optimal coaching methods by incorporating the latest coaching techniques and trends. This allows the coaching department to provide optimal coaching to each employee, maximizing the efficiency of skill acquisition.

[0034] The data collection unit can collect data such as employee work performance, skill evaluations, and feedback. For example, the data collection unit can record employee work performance in detail, conduct skill evaluations, and collect feedback. For example, the data collection unit can record which projects employees participated in and what results they achieved. For example, the data collection unit can conduct employee skill evaluations and collect feedback. This allows for the creation of accurate learning plans by collecting detailed employee performance data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input employee work performance data into AI, which can then analyze and collect the data.

[0035] The analysis unit can analyze the collected data using AI to identify which skills employees should acquire. For example, the analysis unit can analyze the collected data using AI to identify which skills employees should acquire. For example, the analysis unit can use AI to analyze the data and identify employee skill gaps. For example, the analysis unit can use machine learning algorithms to analyze the data and determine the priority of employee skill acquisition. This allows the AI ​​to identify the skills employees should acquire through data analysis. Some or all of the above processes in the analysis unit may be performed using, for example, generative AI, or not using generative AI. For example, the analysis unit can input the collected data into a generative AI, which can then analyze the data and identify skills.

[0036] The suggestion unit can propose optimal learning materials and training programs based on identified skills. For example, the suggestion unit proposes optimal learning materials and training programs based on identified skills. For example, the suggestion unit proposes optimal learning materials for an employee to acquire a specific skill. For example, the suggestion unit proposes training programs tailored to an employee's skill level. This supports efficient learning by proposing optimal learning materials and training programs to employees. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input optimal learning materials and training programs based on identified skills into a generative AI, which can then make optimal suggestions.

[0037] The coaching department can monitor employees' learning progress in real time and update learning plans as needed. For example, the coaching department can monitor employees' learning progress in real time and update learning plans as needed. For example, when an employee has acquired a particular skill, the coaching department can suggest a new skill as the next step. For example, the coaching department can provide feedback based on the employee's learning progress and adjust the learning plan accordingly. This ensures that the optimal learning process is always provided by monitoring employees' learning progress in real time and updating learning plans. Some or all of the above processes in the coaching department may be performed using AI, or not. For example, the coaching department can input employee learning progress data into AI, which can then analyze the data and update the learning plan.

[0038] The data collection unit can analyze employees' past work performance and select the optimal data collection method. For example, the data collection unit can refer to data collection methods used in projects where employees have achieved high results in the past and apply similar methods. For example, the data collection unit can improve data collection methods for tasks that employees have struggled with in the past and select more effective methods. For example, the data collection unit can identify a tendency for employees to perform well during specific time periods based on their past work performance and collect data during those times. This allows the optimal data collection method to be selected by analyzing past work performance. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input employees' past work performance data into AI, which can then analyze the data and select the optimal collection method.

[0039] The data collection unit can filter performance data based on an employee's current projects and areas of interest. For example, the unit can collect only data related to the employee's current projects and exclude irrelevant data. For example, the unit can prioritize collecting data on relevant skills and knowledge based on the employee's areas of interest. For example, the unit can collect data on areas in which an employee has recently become interested and incorporate it into their learning plan. This allows for the collection of highly relevant data by filtering the data based on current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input data on an employee's current projects and areas of interest into an AI, which can then filter and collect the data.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location of employees when collecting performance data. For example, if an employee is in the office, the data collection unit will prioritize the collection of performance data within the office. If an employee is working remotely, the data collection unit will prioritize the collection of performance data at their home. If an employee is on a business trip, the data collection unit will prioritize the collection of performance data at their business trip destination. This allows for the priority collection of highly relevant data by considering geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical location of employees into AI, which can then analyze the data and prioritize the collection of highly relevant data.

[0041] The data collection unit can analyze employees' social media activity and collect relevant data when collecting performance data. For example, the data collection unit can analyze work-related posts shared by employees on social media and collect relevant performance data. For example, the data collection unit can analyze industry trends that employees follow on social media and collect relevant data. For example, the data collection unit can analyze the activities of communities that employees participate in on social media and collect relevant data. In this way, relevant performance data can be collected by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input employee social media activity data into AI, which can analyze the data and collect relevant performance data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the performance data during the analysis. For example, the analysis unit will analyze data related to important projects in detail to provide thorough analysis results. For example, the analysis unit will analyze data related to routine operations concisely to provide results that get straight to the point. For example, the analysis unit will analyze urgent data quickly to enable immediate action. In this way, appropriate analysis results can be provided by adjusting the level of detail of the analysis based on the importance of the performance data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the importance of the performance data into the AI, and the AI ​​can analyze the data and adjust the level of detail.

[0043] The analysis unit can apply different analysis algorithms depending on the category of performance data during analysis. For example, the analysis unit applies a technical analysis algorithm to data related to technical skills. For example, the analysis unit applies a behavioral analysis algorithm to data related to soft skills. For example, the analysis unit applies a project management-specific analysis algorithm to data related to project management. By applying the appropriate analysis algorithm according to the category of performance data, it is possible to provide highly accurate analysis results. 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 categories of performance data into the AI, and the AI ​​can analyze the data and apply the appropriate algorithm.

[0044] The analysis unit can prioritize analyses based on the timing of performance data submissions. For example, the analysis unit prioritizes analyzing urgent data to enable immediate action. For example, it analyzes regularly submitted data according to a schedule. For example, it analyzes historical data as needed to understand long-term trends. This allows for a rapid response by prioritizing analyses based on the timing of performance data submissions. Some or all of the above processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the timing of performance data submissions into the AI, which can then analyze the data and determine priorities.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the performance data during the analysis. For example, the analysis unit may prioritize the analysis of directly related data and provide results quickly. For example, the analysis unit may postpone the analysis of indirectly related data. For example, the analysis unit may analyze less relevant data as needed and provide it as supplementary information. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the performance data. 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 relevance of the performance data into the AI, which can then analyze the data and adjust the order.

[0046] The proposal department can adjust the level of detail in proposals based on the importance of the skills. For example, it may provide detailed proposals for important skills to ensure thorough understanding. For example, it may provide concise proposals for everyday skills to get straight to the point. For example, it may provide quick proposals for urgent skills to ensure immediate response. By adjusting the level of detail in proposals based on the importance of the skills, it can provide appropriate proposals. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input the importance of skills into the AI, which can then analyze the data and adjust the level of detail.

[0047] The proposal function can apply different proposal algorithms depending on the skill category when making a proposal. For example, the proposal function applies a technical proposal algorithm to proposals related to technical skills. For example, the proposal function applies a behavioral analysis algorithm to proposals related to soft skills. For example, the proposal function applies a proposal algorithm specifically for project management to proposals related to project management. This allows for the provision of highly accurate proposals by applying the appropriate proposal algorithm according to the skill category. Some or all of the above processing in the proposal function may be performed using AI, for example, or without AI. For example, the proposal function can input the skill category into AI, which can then analyze the data and apply the appropriate algorithm.

[0048] The proposal department can prioritize proposals based on the timing of skill submissions. For example, the proposal department can prioritize proposals for urgent skills to ensure immediate action. For example, it can process proposals for regularly submitted skills according to a schedule. For example, it can process proposals for past skills as needed to encourage long-term skill improvement. This allows for a quick response by prioritizing proposals based on the timing of skill submissions. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input the skill submission timings into an AI, which can then analyze the data to determine priorities.

[0049] The proposal department can adjust the order of proposals based on the relevance of skills during the proposal process. For example, the proposal department may prioritize directly related skills to enable a quick response. For example, it may postpone proposing indirectly related skills. For example, it may propose less relevant skills as needed and provide them as supplementary information. This allows for efficient proposals by adjusting the order of proposals based on the relevance of skills. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input the relevance of skills into an AI, which can then analyze the data and adjust the order.

[0050] The coaching department can analyze an employee's past learning progress during coaching sessions to select the most suitable coaching method. For example, the coaching department might apply a similar method to a learning method that the employee has previously found successful. For example, the coaching department might improve a learning method that the employee has previously struggled with and select a more effective method. For example, the coaching department might select a coaching method that is effective for a specific time period based on the employee's past learning progress. In this way, the coaching department can select the most suitable coaching method by analyzing past learning progress. Some or all of the above processes in the coaching department may be performed using AI, for example, or not. For example, the coaching department can input data on an employee's past learning progress into an AI, which can then analyze the data and select the most suitable coaching method.

[0051] The coaching department can customize coaching methods based on the employee's current learning status during coaching sessions. For example, the coaching department can provide coaching methods related to the learning content the employee is currently working on. For example, the coaching department can provide appropriate feedback and advice according to the employee's learning progress. For example, the coaching department can provide coaching methods related to areas the employee has recently become interested in. This allows for effective coaching by customizing coaching methods based on the employee's current learning status. Some or all of the above processes in the coaching department may be performed using AI, for example, or not using AI. For example, the coaching department can input data on the employee's current learning status into AI, which can then analyze the data to customize the coaching methods.

[0052] The coaching department can select the optimal coaching method during coaching sessions by considering the employee's geographical location. For example, if the employee is in the office, the coaching department can provide a coaching method within the office. If the employee is working remotely, the coaching department can provide a coaching method at home. If the employee is on a business trip, the coaching department can provide a coaching method at their business trip location. This allows the coaching department to provide the optimal coaching method by considering geographical location. Some or all of the above processes in the coaching department may be performed using AI, for example, or not. For example, the coaching department can input the employee's geographical location information into an AI, which can then analyze the data and select the optimal coaching method.

[0053] The coaching department can analyze employees' social media activity during coaching sessions to suggest coaching methods. For example, the coaching department can analyze work-related posts shared by employees on social media and suggest relevant coaching methods. For example, the coaching department can analyze industry trends followed by employees on social media and suggest relevant coaching methods. For example, the coaching department can analyze the activities of communities employees participate in on social media and suggest relevant coaching methods. In this way, relevant coaching methods can be suggested by analyzing social media activity. Some or all of the above processes in the coaching department may be performed using AI, for example, or not. For example, the coaching department can input employee social media activity data into an AI, which can then analyze the data and suggest relevant coaching methods.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The data collection unit can analyze employees' past performance data and select the optimal data collection method. For example, it can refer to data collection methods used in projects where employees have achieved high performance in the past and apply similar methods. It can also improve data collection methods for tasks that employees have struggled with in the past and select more effective methods. By identifying employee tendencies to perform well during specific time periods based on their past work performance, data can be collected during those times. This allows for the selection of the optimal data collection method by analyzing past performance data.

[0056] The analysis unit can filter performance data based on employees' current projects and areas of interest. For example, it can collect only data related to the employee's current project and exclude irrelevant data. It can also prioritize collecting data on relevant skills and knowledge based on the employee's areas of interest. Furthermore, it can collect data on areas where employees have recently developed an interest and incorporate it into their learning plans. This allows for the collection of highly relevant data by filtering it based on current projects and areas of interest.

[0057] The proposal department can adjust the level of detail in proposals based on the importance of the skills being proposed. For example, proposals for important skills should be detailed to ensure a thorough understanding. Proposals for routine skills should be concise and to the point. Proposals for urgent skills should be made quickly to allow for immediate action. By adjusting the level of detail in proposals based on the importance of the skills, the department can provide appropriate proposals.

[0058] The coaching department can analyze an employee's past learning progress during coaching sessions to select the most effective coaching method. For example, they can apply similar methods based on the employee's past successes, improve upon learning methods the employee struggled with in the past, and select more effective methods. They can also select coaching methods that are effective for specific time slots based on the employee's past learning progress. In this way, the optimal coaching method can be selected by analyzing past learning progress.

[0059] The coaching department can select the most suitable coaching method during coaching sessions by considering the employee's geographical location. For example, if an employee is in the office, an office-based coaching method will be provided. If an employee is working remotely, a home-based coaching method will be provided. If an employee is on a business trip, a coaching method will be provided at their business trip location. This allows for the provision of the most suitable coaching method by considering geographical location.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The data collection department collects performance data for each employee. For example, it collects data such as employee work performance, skill evaluations, and feedback, and gathers detailed data such as which projects employees participated in, what results they achieved, and what skills they lack. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it uses AI to analyze the data, identify which skills employees should acquire, identify skill gaps, and use machine learning algorithms to determine the priority of skill acquisition. Step 3: The proposal department proposes a learning plan based on the analysis results obtained by the analysis department. For example, it proposes optimal learning materials and training programs based on identified skills, suggesting the best learning materials and training programs tailored to the skill level of the employee to acquire specific skills. Step 4: The coaching department provides coaching based on the learning plan proposed by the proposal department. For example, they monitor employees' learning progress in real time, update the learning plan as needed, suggest new skills as the next step when an employee acquires a particular skill, provide feedback according to learning progress, and adjust the learning plan.

[0062] (Example of form 2) An AI system according to an embodiment of the present invention is a system that creates and updates individual learning plans in real time based on each employee's performance data. This system collects each employee's performance data, the AI ​​analyzes it, suggests which skills the employee should acquire and which learning materials they should use, and autonomously provides coaching. For example, the AI ​​system collects each employee's performance data. In this process, it collects detailed data such as the employee's work performance, skill evaluations, and feedback. For example, it collects data such as which projects the employee participated in, what results they achieved, and which skills they lack. This allows for an accurate understanding of the employee's current situation. Next, the AI ​​analyzes the collected data. Based on the collected data, the AI ​​suggests which skills the employee should acquire and which learning materials they should use. For example, it can suggest the optimal learning materials and training programs for an employee to acquire a specific skill. This allows the employee to obtain a learning plan that is best suited to them. Furthermore, based on the suggested learning plan, the AI ​​autonomously provides coaching to the employee. The AI ​​monitors the employee's learning progress in real time and updates the learning plan as needed. For example, if an employee acquires a specific skill, the AI ​​can suggest a new skill as the next step. This allows employees to always learn based on the latest learning plan. This enables the provision of an optimal learning process for each employee. Employees can learn at their own pace and acquire skills efficiently. Furthermore, companies can effectively support employee development. For example, even large organizations can create training programs tailored to each employee. This is expected to improve employee skills and enhance the company's competitiveness. The AI ​​system can create and update individual learning plans in real time based on each employee's performance data and provide autonomous coaching.

[0063] The AI ​​system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a coaching unit. The data collection unit collects performance data for each employee. The data collection unit collects data such as employee work performance, skill evaluations, and feedback. The data collection unit collects data such as which projects an employee participated in, what results they achieved, and which skills they lack. The data collection unit records employee work performance in detail, performs skill evaluations, and collects feedback. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the collected data using AI to identify which skills an employee should acquire. The analysis unit analyzes the data using AI to identify an employee's skill gap. The analysis unit analyzes the data using a machine learning algorithm to determine the priority of the employee's skill acquisition. The proposal unit proposes a learning plan based on the analysis results obtained by the analysis unit. The proposal unit proposes optimal learning materials and training programs based on the identified skills. The proposal unit proposes optimal learning materials for an employee to acquire a specific skill. The proposal department proposes training programs tailored to each employee's skill level. The coaching department provides coaching based on the learning plan proposed by the proposal department. The coaching department monitors employees' learning progress in real time and updates the learning plan as needed. The coaching department suggests new skills as the next step when an employee has acquired a particular skill. The coaching department provides feedback and adjusts the learning plan according to the employee's learning progress. As a result, the AI ​​system according to the embodiment can create and update individual learning plans in real time based on each employee's performance data and provide coaching autonomously.

[0064] The data collection department collects performance data for each employee. Specifically, it collects data such as employee work performance, skill evaluations, and feedback. For example, it collects detailed data such as which projects employees participated in, what results they achieved, and which skills they lack. The data collection department meticulously records employee work performance, conducts skill evaluations, and collects feedback. This includes goals achieved by employees in their daily work, project progress, and evaluation comments from supervisors and colleagues. Furthermore, the data collection department also collects employee self-assessments and the results of periodic performance reviews. This allows the data collection department to build a comprehensive dataset on employee performance and provide it to the analysis department. The data collection department centrally manages this data and can integrate it with other systems and departments as needed. For example, the collected data is stored on a cloud server and made accessible to the analysis and proposal departments. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0065] The analysis department analyzes the data collected by the data collection department. Specifically, it uses AI to analyze the collected data and identify which skills employees should acquire. The analysis department uses AI to analyze the data and identify employee skill gaps. For example, it uses machine learning algorithms to analyze the data and determine the priority of employee skill acquisition. Based on employees' past performance data and skill evaluations, the AI ​​assesses their current skill level and predicts the skills they will need in the future. Furthermore, the analysis department proposes the optimal learning path, taking into account employees' learning history and feedback. This allows the analysis department to provide each employee with an optimal skill acquisition plan. The analysis department can also use historical data and statistical information to analyze long-term skill development trends and predict future skill needs. For example, it can predict fluctuations in skill demand in specific industries or job types and identify the skills employees will need in the future. In addition, the analysis department can use anomaly detection algorithms to detect unusual performance patterns and anomalous data, enabling early countermeasures. This allows the analysis department to not only grasp the situation in real time but also to handle long-term skill development and anomaly detection, improving the reliability and effectiveness of the entire system.

[0066] The Proposal Department proposes learning plans based on the analysis results obtained by the Analysis Department. Specifically, it proposes optimal learning materials and training programs based on identified skills. The Proposal Department proposes the most suitable learning materials for employees to acquire specific skills. For example, it proposes training programs tailored to the employee's skill level. The Proposal Department can propose a variety of learning methods, such as online courses, workshops, and on-the-job training, according to the employee's learning style and preferences. Furthermore, the Proposal Department monitors the employee's learning progress and updates the learning plan as needed. For example, if an employee acquires a particular skill, it proposes a new skill as the next step. The Proposal Department can also collect employee feedback and continuously improve the accuracy and effectiveness of the learning plan. This allows the Proposal Department to provide an optimal learning plan for each employee and maximize the efficiency of skill acquisition. The Proposal Department aims to always provide the best learning methods by incorporating the latest educational technologies and trends. For example, it can propose personalized learning using AI or learning programs incorporating gamification. This allows the Proposal Department to increase employee motivation and support effective skill acquisition.

[0067] The Coaching Department provides coaching based on the learning plans proposed by the Proposal Department. Specifically, it monitors employees' learning progress in real time and updates the learning plans as needed. When an employee acquires a particular skill, the Coaching Department suggests new skills as the next step. For example, it provides feedback and adjusts the learning plan according to the employee's learning progress. The Coaching Department meticulously records employees' learning progress and provides appropriate feedback according to their progress. This includes evaluations of the goals achieved and skills acquired, and suggestions for skills to be acquired next. Furthermore, the Coaching Department provides support to maintain motivation in order to enhance employees' desire to learn. For example, it can introduce regular coaching sessions and reward systems for achieving goals. In this way, the Coaching Department can increase employees' desire to learn and support effective skill acquisition. The Coaching Department can collect employee feedback and continuously improve the accuracy and effectiveness of coaching. For example, it reviews coaching methods and learning plans based on employee feedback. The Coaching Department also aims to always provide the optimal coaching methods by incorporating the latest coaching techniques and trends. This allows the coaching department to provide optimal coaching to each employee, maximizing the efficiency of skill acquisition.

[0068] The data collection unit can collect data such as employee work performance, skill evaluations, and feedback. For example, the data collection unit can record employee work performance in detail, conduct skill evaluations, and collect feedback. For example, the data collection unit can record which projects employees participated in and what results they achieved. For example, the data collection unit can conduct employee skill evaluations and collect feedback. This allows for the creation of accurate learning plans by collecting detailed employee performance data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input employee work performance data into AI, which can then analyze and collect the data.

[0069] The analysis unit can analyze the collected data using AI to identify which skills employees should acquire. For example, the analysis unit can analyze the collected data using AI to identify which skills employees should acquire. For example, the analysis unit can use AI to analyze the data and identify employee skill gaps. For example, the analysis unit can use machine learning algorithms to analyze the data and determine the priority of employee skill acquisition. This allows the AI ​​to identify the skills employees should acquire through data analysis. Some or all of the above processes in the analysis unit may be performed using, for example, generative AI, or not using generative AI. For example, the analysis unit can input the collected data into a generative AI, which can then analyze the data and identify skills.

[0070] The suggestion unit can propose optimal learning materials and training programs based on identified skills. For example, the suggestion unit proposes optimal learning materials and training programs based on identified skills. For example, the suggestion unit proposes optimal learning materials for an employee to acquire a specific skill. For example, the suggestion unit proposes training programs tailored to an employee's skill level. This supports efficient learning by proposing optimal learning materials and training programs to employees. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input optimal learning materials and training programs based on identified skills into a generative AI, which can then make optimal suggestions.

[0071] The coaching department can monitor employees' learning progress in real time and update learning plans as needed. For example, the coaching department can monitor employees' learning progress in real time and update learning plans as needed. For example, when an employee has acquired a particular skill, the coaching department can suggest a new skill as the next step. For example, the coaching department can provide feedback based on the employee's learning progress and adjust the learning plan accordingly. This ensures that the optimal learning process is always provided by monitoring employees' learning progress in real time and updating learning plans. Some or all of the above processes in the coaching department may be performed using AI, or not. For example, the coaching department can input employee learning progress data into AI, which can then analyze the data and update the learning plan.

[0072] The data collection unit can estimate employees' emotions and adjust the timing of performance data collection based on the estimated emotions. For example, if an employee is stressed, the data collection unit can delay the collection timing to collect data when the employee is relaxed. For example, if an employee is highly motivated, the data collection unit can collect data immediately to capture data at the peak of performance. For example, if an employee is tired, the data collection unit can collect data after a break to obtain accurate performance data. This allows for accurate performance data to be obtained by adjusting the collection timing according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input employee emotion data into a generative AI, which can estimate the emotion and adjust the collection timing.

[0073] The data collection unit can analyze employees' past work performance and select the optimal data collection method. For example, the data collection unit can refer to data collection methods used in projects where employees have achieved high results in the past and apply similar methods. For example, the data collection unit can improve data collection methods for tasks that employees have struggled with in the past and select more effective methods. For example, the data collection unit can identify a tendency for employees to perform well during specific time periods based on their past work performance and collect data during those times. This allows the optimal data collection method to be selected by analyzing past work performance. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input employees' past work performance data into AI, which can then analyze the data and select the optimal collection method.

[0074] The data collection unit can filter performance data based on an employee's current projects and areas of interest. For example, the unit can collect only data related to the employee's current projects and exclude irrelevant data. For example, the unit can prioritize collecting data on relevant skills and knowledge based on the employee's areas of interest. For example, the unit can collect data on areas in which an employee has recently become interested and incorporate it into their learning plan. This allows for the collection of highly relevant data by filtering the data based on current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input data on an employee's current projects and areas of interest into an AI, which can then filter and collect the data.

[0075] The data collection unit can estimate employees' emotions and prioritize the data to collect based on the estimated emotions. For example, if an employee is stressed, the data collection unit will prioritize collecting data that helps reduce stress. For example, if an employee is highly motivated, the data collection unit will prioritize collecting data related to challenging tasks. For example, if an employee is tired, the data collection unit will prioritize collecting data related to relaxation and rest. This allows for more effective data collection by prioritizing data according to employees' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input employee emotion data into a generative AI, which can estimate emotions and determine data priorities.

[0076] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location of employees when collecting performance data. For example, if an employee is in the office, the data collection unit will prioritize the collection of performance data within the office. If an employee is working remotely, the data collection unit will prioritize the collection of performance data at their home. If an employee is on a business trip, the data collection unit will prioritize the collection of performance data at their business trip destination. This allows for the priority collection of highly relevant data by considering geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical location of employees into AI, which can then analyze the data and prioritize the collection of highly relevant data.

[0077] The data collection unit can analyze employees' social media activity and collect relevant data when collecting performance data. For example, the data collection unit can analyze work-related posts shared by employees on social media and collect relevant performance data. For example, the data collection unit can analyze industry trends that employees follow on social media and collect relevant data. For example, the data collection unit can analyze the activities of communities that employees participate in on social media and collect relevant data. In this way, relevant performance data can be collected by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input employee social media activity data into AI, which can analyze the data and collect relevant performance data.

[0078] The analysis unit can estimate employees' emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if an employee is stressed, the analysis unit provides a simple and visually easy-to-understand analysis result. If an employee is relaxed, the analysis unit provides a detailed analysis result to facilitate deeper understanding. If an employee is in a hurry, the analysis unit provides a concise analysis result that gets straight to the point. In this way, by adjusting the presentation of the analysis according to the employee's emotions, it is possible to provide an easy-to-understand analysis result. 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, which can estimate emotions and adjust the presentation of the analysis.

[0079] The analysis unit can adjust the level of detail of the analysis based on the importance of the performance data during the analysis. For example, the analysis unit will analyze data related to important projects in detail to provide thorough analysis results. For example, the analysis unit will analyze data related to routine operations concisely to provide results that get straight to the point. For example, the analysis unit will analyze urgent data quickly to enable immediate action. In this way, appropriate analysis results can be provided by adjusting the level of detail of the analysis based on the importance of the performance data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the importance of the performance data into the AI, and the AI ​​can analyze the data and adjust the level of detail.

[0080] The analysis unit can apply different analysis algorithms depending on the category of performance data during analysis. For example, the analysis unit applies a technical analysis algorithm to data related to technical skills. For example, the analysis unit applies a behavioral analysis algorithm to data related to soft skills. For example, the analysis unit applies a project management-specific analysis algorithm to data related to project management. By applying the appropriate analysis algorithm according to the category of performance data, it is possible to provide highly accurate analysis results. 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 categories of performance data into the AI, and the AI ​​can analyze the data and apply the appropriate algorithm.

[0081] The analysis unit can estimate an employee's emotions and adjust the length of the analysis based on the estimated emotions. For example, if an employee is stressed, the analysis unit provides a short, concise analysis. For example, if an employee is relaxed, the analysis unit provides a detailed analysis to facilitate deeper understanding. For example, if an employee is in a hurry, the analysis unit provides a concise and quickly understandable analysis. By adjusting the length of the analysis according to the employee's emotions, the analysis unit can provide easily understandable results. 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 can input employee emotion data into a generative AI, which can estimate emotions and adjust the length of the analysis.

[0082] The analysis unit can prioritize analyses based on the timing of performance data submissions. For example, the analysis unit prioritizes analyzing urgent data to enable immediate action. For example, it analyzes regularly submitted data according to a schedule. For example, it analyzes historical data as needed to understand long-term trends. This allows for a rapid response by prioritizing analyses based on the timing of performance data submissions. Some or all of the above processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the timing of performance data submissions into the AI, which can then analyze the data and determine priorities.

[0083] The analysis unit can adjust the order of analysis based on the relevance of the performance data during the analysis. For example, the analysis unit may prioritize the analysis of directly related data and provide results quickly. For example, the analysis unit may postpone the analysis of indirectly related data. For example, the analysis unit may analyze less relevant data as needed and provide it as supplementary information. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the performance data. 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 relevance of the performance data into the AI, which can then analyze the data and adjust the order.

[0084] The suggestion department can estimate an employee's emotions and adjust the way suggestions are presented based on that estimation. For example, if an employee is stressed, the suggestion department will provide a simple and visually easy-to-understand suggestion. If an employee is relaxed, the suggestion department will provide a detailed suggestion to facilitate deeper understanding. If an employee is in a hurry, the suggestion department will provide a concise suggestion that gets straight to the point. By adjusting the presentation of suggestions according to the employee's emotions, the department can provide suggestions that are easy to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion department may be performed using AI or not. For example, the suggestion department can input employee emotion data into a generative AI, which can estimate the emotion and adjust the presentation of the suggestion.

[0085] The proposal department can adjust the level of detail in proposals based on the importance of the skills. For example, it may provide detailed proposals for important skills to ensure thorough understanding. For example, it may provide concise proposals for everyday skills to get straight to the point. For example, it may provide quick proposals for urgent skills to ensure immediate response. By adjusting the level of detail in proposals based on the importance of the skills, it can provide appropriate proposals. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input the importance of skills into the AI, which can then analyze the data and adjust the level of detail.

[0086] The proposal function can apply different proposal algorithms depending on the skill category when making a proposal. For example, the proposal function applies a technical proposal algorithm to proposals related to technical skills. For example, the proposal function applies a behavioral analysis algorithm to proposals related to soft skills. For example, the proposal function applies a proposal algorithm specifically for project management to proposals related to project management. This allows for the provision of highly accurate proposals by applying the appropriate proposal algorithm according to the skill category. Some or all of the above processing in the proposal function may be performed using AI, for example, or without AI. For example, the proposal function can input the skill category into AI, which can then analyze the data and apply the appropriate algorithm.

[0087] The suggestion function can estimate an employee's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if an employee is stressed, the suggestion function will provide a short, to-the-point suggestion. If an employee is relaxed, the suggestion function will provide a detailed suggestion to facilitate deeper understanding. If an employee is in a hurry, the suggestion function will provide a concise and quickly understandable suggestion. By adjusting the length of the suggestion according to the employee's emotions, it is possible to provide suggestions that are easy to understand. 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 suggestion function may be performed using AI or not. For example, the suggestion function can input employee emotion data into a generative AI, which can estimate the emotion and adjust the length of the suggestion.

[0088] The proposal department can prioritize proposals based on the timing of skill submissions. For example, the proposal department can prioritize proposals for urgent skills to ensure immediate action. For example, it can process proposals for regularly submitted skills according to a schedule. For example, it can process proposals for past skills as needed to encourage long-term skill improvement. This allows for a quick response by prioritizing proposals based on the timing of skill submissions. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input the skill submission timings into an AI, which can then analyze the data to determine priorities.

[0089] The proposal department can adjust the order of proposals based on the relevance of skills during the proposal process. For example, the proposal department may prioritize directly related skills to enable a quick response. For example, it may postpone proposing indirectly related skills. For example, it may propose less relevant skills as needed and provide them as supplementary information. This allows for efficient proposals by adjusting the order of proposals based on the relevance of skills. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input the relevance of skills into an AI, which can then analyze the data and adjust the order.

[0090] The coaching department can estimate employees' emotions and adjust coaching methods based on those estimated emotions. For example, if an employee is stressed, the coaching department can provide a relaxing coaching method. For example, if an employee is highly motivated, the coaching department can provide a coaching method that includes challenging tasks. For example, if an employee is tired, the coaching department can provide a coaching method that incorporates rest. This allows for effective coaching by adjusting coaching methods 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 coaching department may be performed using AI or not. For example, the coaching department can input employee emotion data into a generative AI, which can estimate emotions and adjust coaching methods.

[0091] The coaching department can analyze an employee's past learning progress during coaching sessions to select the most suitable coaching method. For example, the coaching department might apply a similar method to a learning method that the employee has previously found successful. For example, the coaching department might improve a learning method that the employee has previously struggled with and select a more effective method. For example, the coaching department might select a coaching method that is effective for a specific time period based on the employee's past learning progress. In this way, the coaching department can select the most suitable coaching method by analyzing past learning progress. Some or all of the above processes in the coaching department may be performed using AI, for example, or not. For example, the coaching department can input data on an employee's past learning progress into an AI, which can then analyze the data and select the most suitable coaching method.

[0092] The coaching department can customize coaching methods based on the employee's current learning status during coaching sessions. For example, the coaching department can provide coaching methods related to the learning content the employee is currently working on. For example, the coaching department can provide appropriate feedback and advice according to the employee's learning progress. For example, the coaching department can provide coaching methods related to areas the employee has recently become interested in. This allows for effective coaching by customizing coaching methods based on the employee's current learning status. Some or all of the above processes in the coaching department may be performed using AI, for example, or not using AI. For example, the coaching department can input data on the employee's current learning status into AI, which can then analyze the data to customize the coaching methods.

[0093] The coaching department can estimate employees' emotions and prioritize coaching based on those estimated emotions. For example, if an employee is stressed, the coaching department will prioritize coaching that helps reduce stress. For example, if an employee is highly motivated, the coaching department will prioritize coaching that includes challenging tasks. For example, if an employee is tired, the coaching department will prioritize coaching that incorporates relaxation and rest. This allows for effective coaching by prioritizing coaching 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 coaching department may be performed using AI or not. For example, the coaching department can input employee emotion data into a generative AI, which can estimate emotions and determine coaching priorities.

[0094] The coaching department can select the optimal coaching method during coaching sessions by considering the employee's geographical location. For example, if the employee is in the office, the coaching department can provide a coaching method within the office. If the employee is working remotely, the coaching department can provide a coaching method at home. If the employee is on a business trip, the coaching department can provide a coaching method at their business trip location. This allows the coaching department to provide the optimal coaching method by considering geographical location. Some or all of the above processes in the coaching department may be performed using AI, for example, or not. For example, the coaching department can input the employee's geographical location information into an AI, which can then analyze the data and select the optimal coaching method.

[0095] The coaching department can analyze employees' social media activity during coaching sessions to suggest coaching methods. For example, the coaching department can analyze work-related posts shared by employees on social media and suggest relevant coaching methods. For example, the coaching department can analyze industry trends followed by employees on social media and suggest relevant coaching methods. For example, the coaching department can analyze the activities of communities employees participate in on social media and suggest relevant coaching methods. In this way, relevant coaching methods can be suggested by analyzing social media activity. Some or all of the above processes in the coaching department may be performed using AI, for example, or not. For example, the coaching department can input employee social media activity data into an AI, which can then analyze the data and suggest relevant coaching methods.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The analysis unit can estimate employees' emotions and adjust the timing of the analysis based on those estimates. For example, if an employee is stressed, the analysis can be delayed to allow them to relax. If an employee is highly motivated, the analysis can be performed immediately to capture data at their peak performance. If an employee is tired, the analysis can be performed after a break to obtain accurate results. In this way, by adjusting the timing of the analysis according to the employee's emotions, accurate analysis results can be provided.

[0098] The suggestion department can estimate employees' emotions and adjust the timing of suggestions based on those estimates. For example, if an employee is stressed, the suggestion can be delayed to allow them to relax. If an employee is highly motivated, the suggestion can be made immediately to coincide with their peak performance. If an employee is tired, the suggestion can be made after a break to provide a more accurate response. By adjusting the timing of suggestions according to employees' emotions, the department can deliver more effective suggestions.

[0099] The coaching department can estimate employees' emotions and adjust the timing of coaching based on those estimates. For example, if an employee is stressed, the coaching session can be delayed to allow for a more relaxed state. If an employee is highly motivated, coaching can be conducted immediately to optimize performance at its peak. If an employee is tired, coaching can be conducted after a break to provide effective coaching. In this way, by adjusting the timing of coaching according to the employee's emotions, effective coaching can be provided.

[0100] The data collection unit can estimate employees' emotions and determine the type of data to collect based on those estimates. For example, if an employee is stressed, it prioritizes collecting data that helps reduce stress. If an employee is highly motivated, it prioritizes collecting data related to challenging tasks. If an employee is tired, it prioritizes collecting data related to relaxation and rest. This allows for more effective data collection by determining the type of data to collect according to the employee's emotions.

[0101] The suggestion department can estimate employees' emotions and adjust the content of suggestions based on those estimates. For example, if an employee is stressed, it can offer suggestions that help reduce stress. If an employee is highly motivated, it can offer suggestions related to challenging tasks. If an employee is tired, it can offer suggestions related to relaxation and rest. By adjusting the content of suggestions according to the employee's emotions, the department can provide more effective suggestions.

[0102] The data collection unit can analyze employees' past performance data and select the optimal data collection method. For example, it can refer to data collection methods used in projects where employees have achieved high performance in the past and apply similar methods. It can also improve data collection methods for tasks that employees have struggled with in the past and select more effective methods. By identifying employee tendencies to perform well during specific time periods based on their past work performance, data can be collected during those times. This allows for the selection of the optimal data collection method by analyzing past performance data.

[0103] The analysis unit can filter performance data based on employees' current projects and areas of interest. For example, it can collect only data related to the employee's current project and exclude irrelevant data. It can also prioritize collecting data on relevant skills and knowledge based on the employee's areas of interest. Furthermore, it can collect data on areas where employees have recently developed an interest and incorporate it into their learning plans. This allows for the collection of highly relevant data by filtering it based on current projects and areas of interest.

[0104] The proposal department can adjust the level of detail in proposals based on the importance of the skills being proposed. For example, proposals for important skills should be detailed to ensure a thorough understanding. Proposals for routine skills should be concise and to the point. Proposals for urgent skills should be made quickly to allow for immediate action. By adjusting the level of detail in proposals based on the importance of the skills, the department can provide appropriate proposals.

[0105] The coaching department can analyze an employee's past learning progress during coaching sessions to select the most effective coaching method. For example, they can apply similar methods based on the employee's past successes, improve upon learning methods the employee struggled with in the past, and select more effective methods. They can also select coaching methods that are effective for specific time slots based on the employee's past learning progress. In this way, the optimal coaching method can be selected by analyzing past learning progress.

[0106] The coaching department can select the most suitable coaching method during coaching sessions by considering the employee's geographical location. For example, if an employee is in the office, an office-based coaching method will be provided. If an employee is working remotely, a home-based coaching method will be provided. If an employee is on a business trip, a coaching method will be provided at their business trip location. This allows for the provision of the most suitable coaching method by considering geographical location.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The data collection department collects performance data for each employee. For example, it collects data such as employee work performance, skill evaluations, and feedback, and gathers detailed data such as which projects employees participated in, what results they achieved, and what skills they lack. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it uses AI to analyze the data, identify which skills employees should acquire, identify skill gaps, and use machine learning algorithms to determine the priority of skill acquisition. Step 3: The proposal department proposes a learning plan based on the analysis results obtained by the analysis department. For example, it proposes optimal learning materials and training programs based on identified skills, suggesting the best learning materials and training programs tailored to the skill level of the employee to acquire specific skills. Step 4: The coaching department provides coaching based on the learning plan proposed by the proposal department. For example, they monitor employees' learning progress in real time, update the learning plan as needed, suggest new skills as the next step when an employee acquires a particular skill, provide feedback according to learning progress, and adjust the learning plan.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and coaching unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects employee performance data using the camera 42 and microphone 38B of the smart device 14 and records the data with the control unit 46A. The analysis unit is implemented in the identification unit 290 of the data processing unit 12, for example, and analyzes the collected data to identify the employee's skill gaps. The proposal unit is implemented in the identification unit 290 of the data processing unit 12, for example, and proposes an optimal learning plan based on the analysis results. The coaching unit is implemented in the control unit 46A of the smart device 14, for example, and provides coaching to the employee based on the proposed learning plan. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and coaching unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects employee performance data using the camera 42 and microphone 238 of the smart glasses 214 and records the data with the control unit 46A. The analysis unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, which analyzes the collected data and identifies the employee's skill gaps. The proposal unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, which proposes an optimal learning plan based on the analysis results. The coaching unit is implemented, for example, in the control unit 46A of the smart glasses 214, which provides coaching to the employee based on the proposed learning plan. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and coaching unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects employee performance data using the camera 42 and microphone 238 of the headset terminal 314 and records the data with the control unit 46A. The analysis unit is implemented in the identification unit 290 of the data processing unit 12, for example, and analyzes the collected data to identify the employee's skill gaps. The proposal unit is implemented in the identification unit 290 of the data processing unit 12, for example, and proposes an optimal learning plan based on the analysis results. The coaching unit is implemented in the control unit 46A of the headset terminal 314, for example, and provides coaching to the employee based on the proposed learning plan. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and coaching unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects employee performance data using the camera 42 and microphone 238 of the robot 414 and records the data by the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data and identifies the employee's skill gaps. The proposal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which proposes an optimal learning plan based on the analysis results. The coaching unit is implemented, for example, by the control unit 46A of the robot 414, which provides coaching to the employee based on the proposed learning plan. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) A data collection unit that collects performance data for each employee, An analysis unit analyzes the data collected by the aforementioned collection unit, A proposal unit proposes a learning plan based on the analysis results obtained by the analysis unit, The system includes a coaching unit that provides coaching based on the learning plan proposed by the proposal unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect data such as employee work performance, skill evaluations, and feedback. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed using AI to identify which skills employees should acquire. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We propose the most suitable teaching materials and training programs based on identified skills. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned coaching department, Monitor employee learning progress in real time and update learning plans as needed. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We estimate employee sentiment and adjust the timing of performance data collection based on the estimated employee sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze employees' past work performance and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting performance data, filter it based on the employee's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is We estimate employee sentiment and prioritize the data to collect based on the estimated employee sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting performance data, the system prioritizes collecting highly relevant data by considering the geographical location of employees. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting performance data, analyze employees' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, We estimate the emotions of employees and adjust the representation of the analysis based on the estimated emotions of the employees. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the performance data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of performance data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates employee sentiment and adjusts the length of the analysis based on the estimated employee sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the analysis priority is determined based on when the performance data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the performance data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, We estimate the employees' emotions and adjust the way we present proposals based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the skills. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the skill category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, Estimate the employee's feelings and adjust the length of the suggestion based on those feelings. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When submitting a proposal, prioritize the proposals based on when the skills were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the skills. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned coaching department, Estimate the employee's emotions and adjust the coaching method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned coaching department, During coaching sessions, we analyze the employee's past learning progress to select the most suitable coaching method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned coaching department, During coaching sessions, customize the coaching methods based on the employee's current learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned coaching department, Estimate employees' emotions and determine coaching priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned coaching department, When coaching, the most suitable coaching method is selected by considering the employee's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned coaching department, During coaching sessions, we analyze employees' social media activity and propose coaching strategies. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects performance data for each employee, An analysis unit analyzes the data collected by the aforementioned collection unit, A proposal unit proposes a learning plan based on the analysis results obtained by the analysis unit, The system includes a coaching unit that provides coaching based on the learning plan proposed by the proposal unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect data such as employee work performance, skill evaluations, and feedback. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed using AI to identify which skills employees should acquire. The system according to feature 1.

4. The aforementioned proposal section is, We propose the most suitable teaching materials and training programs based on identified skills. The system according to feature 1.

5. The aforementioned coaching department, Monitor employee learning progress in real time and update learning plans as needed. The system according to feature 1.

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

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

8. The aforementioned collection unit is When collecting performance data, filter it based on the employee's current projects and areas of interest. The system according to feature 1.

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