system

Abstract

The system according to this embodiment aims to efficiently support employees' skill development and career growth. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a coaching unit, and a feedback unit. The reception unit receives input from employees regarding their goals and skill levels. The generation unit proposes optimal learning plans and career advice based on the information received by the reception unit. The coaching unit provides a peer coaching function among employees. The feedback unit tracks learning progress and provides feedback in real time.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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] In the conventional technology, there is a lack of an efficient system for individually supporting the skill improvement and career growth of employees, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently support the skill improvement and career growth of employees.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a coaching unit, and a feedback unit. The reception unit receives input from employees regarding their goals and skill levels. The generation unit proposes optimal learning plans and career advice based on the information received by the reception unit. The coaching unit provides a peer coaching function among employees. The feedback unit tracks learning progress and provides real-time feedback. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently support employees' skill development and career growth. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, 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) The individual coaching platform according to an embodiment of the present invention is a system that utilizes generative AI to support employees' skill development and career growth. In this system, employees input their goals and skill levels through an app, and the generative AI proposes optimal learning plans and career advice based on this input. It also incorporates a peer coaching function among employees to promote knowledge sharing and collaboration. Furthermore, the generative AI tracks learning progress and provides real-time feedback. This mechanism supports employee skill development and career growth, strengthening the company's competitiveness. It also deepens knowledge sharing and collaboration among employees, improving overall organizational productivity. For example, in today's world, where the demand for online learning and career support is rapidly increasing due to the advancement of remote work, this platform is extremely useful. Thus, the individual coaching platform can support employee skill development and career growth, strengthening the company's competitiveness.

[0029] The individual coaching platform according to this embodiment comprises a reception unit, a generation unit, a coaching unit, and a feedback unit. The reception unit receives input from employees regarding their goals and skill levels. Employee goals and skill levels include, but are not limited to, short-term goals, long-term goals, quantitative goals, and qualitative goals. The reception unit allows employees to input their goals and skill levels through an app, for example. The reception unit can also provide multiple input methods, such as voice input and text input. The generation unit uses a generation AI to propose optimal learning plans and career advice based on the information received by the reception unit. The generation unit creates personalized curricula based on employees' goals and skill levels, for example. The generation unit can also adjust the learning plan according to progress. For example, the generation AI tracks the employee's goal achievement and updates the learning plan as needed. The coaching unit provides peer coaching functionality among employees to promote knowledge sharing and collaboration. The coaching unit provides an interface that allows employees to share their skills and knowledge with other employees and coach each other, for example. Furthermore, the coaching department can set the frequency and evaluation methods of coaching. The feedback department tracks learning progress and provides feedback in real time. For example, the feedback department can monitor employees' learning progress in real time and provide appropriate feedback. The feedback department can also adjust the timing and content of feedback. For example, the feedback department can provide immediate feedback when an employee achieves a specific goal. In this way, the individual coaching platform according to the embodiment can support employee skill development and career growth, thereby strengthening the competitiveness of the company.

[0030] The reception desk accepts employee input regarding their goals and skill levels. These goals and skill levels may include, but are not limited to, short-term goals, long-term goals, quantitative goals, and qualitative goals. The reception desk allows employees to input their goals and skill levels through an app. Specifically, the app's user interface is designed to be intuitive and easy to use, allowing employees to easily input their goals and skill levels. For example, drop-down menus and checkboxes can be used to select goal types and skill levels. The reception desk can also provide multiple input methods, such as voice input and text input. With voice input, speech recognition technology is used to convert what the employee says into text and register it as their goals and skill levels. With text input, employees can freely enter text to describe their goals and skill levels in detail. Furthermore, the reception desk has a function to automatically categorize the input information and organize it into appropriate categories. For example, it can manage short-term and long-term goals separately, and store quantitative and qualitative goals separately. This allows the reception department to efficiently manage employees' goals and skill levels and accurately provide the information necessary for subsequent processing.

[0031] The generation unit uses generative AI to propose optimal learning plans and career advice based on information received by the reception unit. For example, the generation unit creates personalized curricula based on employees' goals and skill levels. Specifically, the generative AI analyzes employee input information and uses algorithms to generate optimal learning plans. For example, it uses natural language processing technology to understand employees' goals and skill levels and proposes appropriate learning resources and training programs based on that. The generation unit can also adjust learning plans according to progress. For example, the generative AI tracks employees' goal achievement and updates the learning plan as needed. Specifically, if an employee achieves a particular goal, the generative AI sets a new goal and restructures the learning plan accordingly. The generation unit can also collect employee feedback and further optimize the learning plan based on it. For example, if an employee provides positive feedback on a particular training program, the generative AI will recommend that program to other employees. In this way, the generation unit can provide each employee with an optimal learning plan and support efficient skill development.

[0032] The Coaching Department provides peer coaching functionality among employees, promoting knowledge sharing and collaboration. For example, it provides an interface that allows employees to share their skills and knowledge with others and coach each other. Specifically, the Coaching Department provides a platform for employees to share their expertise and experience with others. For instance, employees can exchange questions and advice through online forums and chat functions. The Coaching Department can also set the frequency and evaluation methods of coaching. For example, it can schedule regular coaching sessions to provide employees with opportunities to discuss their progress and challenges. Furthermore, the Coaching Department can set metrics to evaluate the effectiveness of coaching, quantitatively assessing employee growth. For example, it can collect feedback after coaching sessions and evaluate the effectiveness of the coaching based on the results. The Coaching Department also has a skill matching function, pairing the most suitable coach with the person being coached. This allows the Coaching Department to promote knowledge sharing and collaboration among employees and improve the overall skill level.

[0033] The Feedback Department tracks learning progress and provides real-time feedback. For example, it monitors employees' learning progress in real time and provides appropriate feedback. Specifically, it monitors whether employees are progressing according to their learning plans and provides feedback as needed. For instance, it provides immediate feedback when an employee achieves a specific goal. The Feedback Department can also adjust the timing and content of feedback. For example, if an employee falls behind their learning plan, the Feedback Department analyzes the cause and provides appropriate advice. Furthermore, the Feedback Department can analyze employee learning data and make specific suggestions to help improve performance. For example, if an employee struggles with a particular skill, it can suggest additional training to strengthen that skill. The Feedback Department can also collect employee feedback and use it to improve the overall system. For example, it can analyze employee feedback to identify areas for improvement in learning plans and coaching methods. This allows the Feedback Department to effectively support employee learning progress and promote skill development and career growth.

[0034] The generation unit can propose optimal learning plans and career advice based on employees' goals and skill levels. For example, the generation unit can create individualized curricula based on employees' goals and skill levels. The generation unit can also adjust learning plans according to progress. For example, the generation unit can track employees' progress toward their goals and update learning plans as needed. This allows the generation unit to provide optimal learning plans and career advice based on employees' goals and skill levels. Some or all of the above processes in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input employees' goals and skill levels into a generation AI and have the generation AI generate optimal learning plans and career advice.

[0035] The coaching department can provide peer coaching functionality among employees, promoting knowledge sharing and collaboration. For example, the coaching department can provide an interface that allows employees to share their skills and knowledge with other employees and coach each other. The coaching department can also, for example, set the frequency and evaluation methods of coaching. The coaching department can, for example, schedule coaching sessions for employees to improve specific skills. This can promote knowledge sharing and collaboration among employees. Some or all of the processes described above in the coaching department may be performed using AI or not. For example, the coaching department can input employee skills and knowledge into AI and have the AI ​​suggest the best coaching pairs.

[0036] The feedback unit can track learning progress and provide feedback in real time. For example, the feedback unit can monitor an employee's learning progress in real time and provide appropriate feedback. The feedback unit can also adjust the timing and content of the feedback. For example, the feedback unit can provide immediate feedback when an employee achieves a specific goal. This allows for tracking learning progress and providing feedback in real time. Some or all of the above processes in the feedback unit may be performed using AI or not. For example, the feedback unit can input employee learning progress data into a generating AI and have the generating AI generate feedback in real time.

[0037] The reception desk can analyze an employee's past goal achievement history and suggest the optimal input format. For example, the reception desk can automatically display similar goals as candidates based on goals the employee has achieved in the past. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the employee has used in the past. For example, the reception desk can predict and suggest goals to be used during a specific time period based on the employee's past goal achievement history. This allows the reception desk to suggest the optimal input format based on the employee's past goal achievement history. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input the employee's past goal achievement data into a generating AI and have the generating AI suggest the optimal input format.

[0038] The reception desk can filter employee data based on their current projects and work content when they input goals and skill levels. For example, the reception desk may prioritize displaying goals related to the projects the employee is currently working on. For example, the reception desk may suggest highly relevant skill levels based on the employee's work content. For example, the reception desk may suggest appropriate goals considering the progress of the employee's current projects. This allows for filtering of goals and skill levels based on the employee's current projects and work content. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk may input employee project data into a generating AI and have the generating AI perform the filtering of goals and skill levels.

[0039] The reception desk can prioritize inputting highly relevant information by considering the employee's geographical location when they input goals and skill levels. For example, if an employee works in a specific region, the reception desk will prioritize suggesting skills and goals related to that region. For example, if an employee is working remotely, the reception desk will prioritize suggesting goals that can be achieved online. For example, if an employee is on a business trip, the reception desk will prioritize suggesting skills and goals related to their destination. This allows for the priority input of highly relevant information based on the employee's geographical location. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the employee's geographical location data into a generating AI and have the generating AI suggest highly relevant information.

[0040] The reception desk can analyze an employee's social media activity and input relevant information when they input their goals and skill levels. For example, the reception desk can suggest relevant goals based on the interests and passions the employee has shared on social media. For example, the reception desk can suggest relevant skills based on the experts and influencers the employee follows on social media. For example, the reception desk can analyze an employee's social media activity history and suggest relevant goals and skills. This allows the reception desk to input relevant information based on the employee's social media activity. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input employee social media data into a generating AI and have the generating AI suggest relevant information.

[0041] The generation unit can adjust the level of detail based on the employee's importance when generating learning plans and career advice. For example, a learning plan for an important skill might include detailed explanations and step-by-step guidance. A learning plan for a less important skill might include only concise explanations and key points. The generation unit can also provide detailed or concise advice depending on the importance of the career advice. This allows the level of detail in learning plans and career advice to be adjusted based on the employee's importance. Some or all of the above processing in the generation unit may be performed using the generation AI or not. For example, the generation unit can input employee importance data into the generation AI and have the generation AI perform the level of detail adjustment.

[0042] The generation unit can apply different algorithms depending on the employee's category when generating learning plans and career advice. For example, the generation unit can apply an algorithm specialized in improving technical skills to employees in technical positions. For example, the generation unit can apply an algorithm specialized in improving leadership skills to employees in management positions. For example, the generation unit can apply an algorithm specialized in improving basic skills to new employees. This allows different algorithms to be applied depending on the employee's category. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input employee category data into a generation AI and have the generation AI select the algorithm to apply.

[0043] The generation unit can prioritize learning plans and career advice based on employee submission deadlines. For example, it might prioritize generating learning plans for submissions with approaching deadlines. For example, it might generate long-term learning plans for submissions with distant deadlines. For example, it might generate learning plans that balance short-term and long-term goals based on submission deadlines. This allows for prioritizing learning plans and career advice based on employee submission deadlines. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input employee submission data into a generation AI and have the generation AI perform the priority determination.

[0044] The generation unit can adjust the order of learning plans and career advice based on employee relevance when generating them. For example, the generation unit might first suggest learning plans related to important skills and goals. For example, it might postpone learning plans related to less relevant skills and goals. For example, it might prioritize suggesting learning plans related to the employee's current job responsibilities. This allows the order of learning plans and career advice to be adjusted based on employee relevance. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input employee relevance data into a generation AI and have the generation AI perform the order adjustment.

[0045] The coaching department can improve the accuracy of peer coaching by considering the relationships between employees. For example, the coaching department can suggest the most suitable coaching pairs based on the past collaboration history of employees. For example, the coaching department can analyze the relationships between employees and set up compatible coaching pairs. For example, the coaching department can suggest effective coaching methods considering the relationships between employees. This allows the coaching department to improve the accuracy of coaching by considering the relationships between employees. 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 relationship data into a generating AI and have the generating AI suggest coaching pairs.

[0046] The coaching department can conduct peer coaching while taking employee attribute information into consideration. For example, the coaching department can propose appropriate coaching methods based on the employee's job title and position. For example, the coaching department can propose appropriate coaching content based on the employee's years of experience. For example, the coaching department can propose appropriate coaching methods based on the employee's skill level. This allows for appropriate coaching based on employee attribute information. 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 attribute information into a generating AI and have the generating AI generate coaching method suggestions.

[0047] The coaching department can conduct peer coaching while considering the geographical distribution of employees. For example, the coaching department can pair employees working remotely and propose online coaching. For example, the coaching department can pair employees in the same office and propose in-person coaching. For example, the coaching department can pair employees who are geographically close to each other and propose efficient coaching. This allows for appropriate coaching based on the geographical distribution of employees. 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 geographical distribution data into a generating AI and have the generating AI propose coaching pairs.

[0048] The coaching department can improve the accuracy of peer coaching by referring to relevant literature used by employees. For example, the coaching department can suggest relevant coaching content based on literature previously referenced by employees. For example, the coaching department can update coaching content by referring to the latest literature related to employees' areas of expertise. For example, the coaching department can suggest effective coaching methods by referring to literature that helps improve employees' skills. In this way, the accuracy of coaching can be improved by referring to relevant literature used by employees. 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 relevant literature data into a generating AI and have the generating AI generate coaching content suggestions.

[0049] The feedback unit can predict current feedback by referring to past feedback data when providing feedback. For example, the feedback unit predicts current feedback based on feedback an employee has received in the past. For example, the feedback unit analyzes past feedback data to predict the trend of current feedback. For example, the feedback unit customizes current feedback by referring to an employee's past feedback history. This allows the feedback unit to predict current feedback by referring to past feedback data. Some or all of the above processes in the feedback unit may be performed using AI or not. For example, the feedback unit can input past feedback data into a generating AI and have the generating AI perform a prediction of current feedback.

[0050] The feedback unit can apply different feedback analysis methods to each employee category during the feedback process. For example, the feedback unit can apply a feedback analysis method specifically focused on improving technical skills to technical employees. For example, the feedback unit can apply a feedback analysis method specifically focused on improving leadership skills to managerial employees. For example, the feedback unit can apply a feedback analysis method specifically focused on improving basic skills to new employees. This allows for the application of different feedback analysis methods depending on the employee category. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input employee category data into a generating AI and have the generating AI select a feedback analysis method.

[0051] The feedback unit can analyze changes in feedback based on the timing of employee submissions. For example, the feedback unit may prioritize analyzing feedback submitted sooner. For example, the feedback unit may postpone the analysis of feedback submitted later. For example, the feedback unit may predict and analyze changes in feedback based on the submission timing. This allows for the analysis of changes in feedback based on employee submission timing. Some or all of the above processes in the feedback unit may be performed using AI or not. For example, the feedback unit can input employee submission timing data into a generating AI and have the generating AI perform the analysis of changes in feedback.

[0052] The feedback unit can analyze feedback by referring to relevant market data of the employee during the feedback process. For example, the feedback unit can analyze feedback based on market data related to the employee's industry. For example, the feedback unit can analyze feedback based on market data related to the employee's job type. For example, the feedback unit can analyze feedback based on market data related to the employee's skills. This allows the feedback unit to analyze feedback by referring to relevant market data of the employee. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input relevant market data of the employee into a generating AI and have the generating AI perform the feedback analysis.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The reception department can analyze employees' past learning history and propose optimal learning plans. For example, it can suggest relevant new courses based on the employee's past course and training history. Furthermore, it can evaluate past learning outcomes and customize learning plans to help improve specific skills. It can also identify areas where employees have struggled in the past and provide learning plans tailored to those areas. This allows for the provision of more effective learning plans based on employees' past learning history.

[0055] The coaching department can analyze employees' past coaching history and suggest the most suitable coaching pairs. For example, it can suggest successful coaching pairs from the past again. It can also set up coaching pairs that are helpful for improving specific skills based on feedback from past coaching sessions. Furthermore, it can analyze employees' growth patterns from their past coaching history and suggest the most suitable coaching methods. This allows for more effective coaching based on employees' past coaching history.

[0056] The reception desk can adjust how employees input their goals and skill levels, taking into account their current health status. For example, if an employee is tired, a simple interface can be provided, minimizing the input process. If the employee is healthy and energetic, detailed input options can be offered, and customizable input methods can be suggested. Furthermore, if an employee is ill, voice input can be prioritized to allow for quick input of goals and skill levels. This allows for adjustments to how employees input their goals and skill levels according to their health condition.

[0057] The coaching department can analyze employees' past project histories and propose the most suitable coaching content. For example, it can suggest coaching content for similar projects based on the methodologies used in past successful projects. It can also analyze past project failures and provide coaching content that leverages those lessons. Furthermore, it can identify employees' areas of expertise from their past project histories and propose coaching content tailored to those areas. This allows for more effective coaching based on each employee's past project history.

[0058] The reception desk can adjust the input method for goals and skill levels based on the employee's current workload. For example, if an employee is busy, a simple interface can be provided to minimize the input steps. If an employee has more time, detailed input options can be offered, and customizable input methods can be suggested. Furthermore, if an employee is under project deadline pressure, voice input can be prioritized to allow for quick input of goals and skill levels. This allows for adjustment of the goal and skill level input method according to the employee's workload.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The reception desk receives input from employees regarding their goals and skill levels. These goals and skill levels include short-term goals, long-term goals, quantitative goals, and qualitative goals. The reception desk enables employees to input their goals and skill levels through the app, providing multiple input methods such as voice input and text input. Step 2: The generation unit uses a generation AI to propose optimal learning plans and career advice based on the information received by the reception unit. The generation unit creates an individualized curriculum based on the employee's goals and skill level, and adjusts the learning plan as the employee progresses. For example, the generation AI tracks the employee's progress toward their goals and updates the learning plan as needed. Step 3: The Coaching Department provides a peer coaching function among employees to promote knowledge sharing and collaboration. The Coaching Department provides an interface that allows employees to share their skills and knowledge with other employees and coach each other. It also allows for setting the frequency and evaluation methods of coaching. Step 4: The Feedback Department tracks learning progress and provides real-time feedback. The Feedback Department monitors employees' learning progress in real time and provides appropriate feedback. They adjust the timing and content of feedback and provide immediate feedback when employees achieve specific goals.

[0061] (Example of form 2) The individual coaching platform according to an embodiment of the present invention is a system that utilizes generative AI to support employees' skill development and career growth. In this system, employees input their goals and skill levels through an app, and the generative AI proposes optimal learning plans and career advice based on this input. It also incorporates a peer coaching function among employees to promote knowledge sharing and collaboration. Furthermore, the generative AI tracks learning progress and provides real-time feedback. This mechanism supports employee skill development and career growth, strengthening the company's competitiveness. It also deepens knowledge sharing and collaboration among employees, improving overall organizational productivity. For example, in today's world, where the demand for online learning and career support is rapidly increasing due to the advancement of remote work, this platform is extremely useful. Thus, the individual coaching platform can support employee skill development and career growth, strengthening the company's competitiveness.

[0062] The individual coaching platform according to this embodiment comprises a reception unit, a generation unit, a coaching unit, and a feedback unit. The reception unit receives input from employees regarding their goals and skill levels. Employee goals and skill levels include, but are not limited to, short-term goals, long-term goals, quantitative goals, and qualitative goals. The reception unit allows employees to input their goals and skill levels through an app, for example. The reception unit can also provide multiple input methods, such as voice input and text input. The generation unit uses a generation AI to propose optimal learning plans and career advice based on the information received by the reception unit. The generation unit creates personalized curricula based on employees' goals and skill levels, for example. The generation unit can also adjust the learning plan according to progress. For example, the generation AI tracks the employee's goal achievement and updates the learning plan as needed. The coaching unit provides peer coaching functionality among employees to promote knowledge sharing and collaboration. The coaching unit provides an interface that allows employees to share their skills and knowledge with other employees and coach each other, for example. Furthermore, the coaching department can set the frequency and evaluation methods of coaching. The feedback department tracks learning progress and provides feedback in real time. For example, the feedback department can monitor employees' learning progress in real time and provide appropriate feedback. The feedback department can also adjust the timing and content of feedback. For example, the feedback department can provide immediate feedback when an employee achieves a specific goal. In this way, the individual coaching platform according to the embodiment can support employee skill development and career growth, thereby strengthening the competitiveness of the company.

[0063] The reception desk accepts employee input regarding their goals and skill levels. These goals and skill levels may include, but are not limited to, short-term goals, long-term goals, quantitative goals, and qualitative goals. The reception desk allows employees to input their goals and skill levels through an app. Specifically, the app's user interface is designed to be intuitive and easy to use, allowing employees to easily input their goals and skill levels. For example, drop-down menus and checkboxes can be used to select goal types and skill levels. The reception desk can also provide multiple input methods, such as voice input and text input. With voice input, speech recognition technology is used to convert what the employee says into text and register it as their goals and skill levels. With text input, employees can freely enter text to describe their goals and skill levels in detail. Furthermore, the reception desk has a function to automatically categorize the input information and organize it into appropriate categories. For example, it can manage short-term and long-term goals separately, and store quantitative and qualitative goals separately. This allows the reception department to efficiently manage employees' goals and skill levels and accurately provide the information necessary for subsequent processing.

[0064] The generation unit uses generative AI to propose optimal learning plans and career advice based on information received by the reception unit. For example, the generation unit creates personalized curricula based on employees' goals and skill levels. Specifically, the generative AI analyzes employee input information and uses algorithms to generate optimal learning plans. For example, it uses natural language processing technology to understand employees' goals and skill levels and proposes appropriate learning resources and training programs based on that. The generation unit can also adjust learning plans according to progress. For example, the generative AI tracks employees' goal achievement and updates the learning plan as needed. Specifically, if an employee achieves a particular goal, the generative AI sets a new goal and restructures the learning plan accordingly. The generation unit can also collect employee feedback and further optimize the learning plan based on it. For example, if an employee provides positive feedback on a particular training program, the generative AI will recommend that program to other employees. In this way, the generation unit can provide each employee with an optimal learning plan and support efficient skill development.

[0065] The Coaching Department provides peer coaching functionality among employees, promoting knowledge sharing and collaboration. For example, it provides an interface that allows employees to share their skills and knowledge with others and coach each other. Specifically, the Coaching Department provides a platform for employees to share their expertise and experience with others. For instance, employees can exchange questions and advice through online forums and chat functions. The Coaching Department can also set the frequency and evaluation methods of coaching. For example, it can schedule regular coaching sessions to provide employees with opportunities to discuss their progress and challenges. Furthermore, the Coaching Department can set metrics to evaluate the effectiveness of coaching, quantitatively assessing employee growth. For example, it can collect feedback after coaching sessions and evaluate the effectiveness of the coaching based on the results. The Coaching Department also has a skill matching function, pairing the most suitable coach with the person being coached. This allows the Coaching Department to promote knowledge sharing and collaboration among employees and improve the overall skill level.

[0066] The Feedback Department tracks learning progress and provides real-time feedback. For example, it monitors employees' learning progress in real time and provides appropriate feedback. Specifically, it monitors whether employees are progressing according to their learning plans and provides feedback as needed. For instance, it provides immediate feedback when an employee achieves a specific goal. The Feedback Department can also adjust the timing and content of feedback. For example, if an employee falls behind their learning plan, the Feedback Department analyzes the cause and provides appropriate advice. Furthermore, the Feedback Department can analyze employee learning data and make specific suggestions to help improve performance. For example, if an employee struggles with a particular skill, it can suggest additional training to strengthen that skill. The Feedback Department can also collect employee feedback and use it to improve the overall system. For example, it can analyze employee feedback to identify areas for improvement in learning plans and coaching methods. This allows the Feedback Department to effectively support employee learning progress and promote skill development and career growth.

[0067] The generation unit can propose optimal learning plans and career advice based on employees' goals and skill levels. For example, the generation unit can create individualized curricula based on employees' goals and skill levels. The generation unit can also adjust learning plans according to progress. For example, the generation unit can track employees' progress toward their goals and update learning plans as needed. This allows the generation unit to provide optimal learning plans and career advice based on employees' goals and skill levels. Some or all of the above processes in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input employees' goals and skill levels into a generation AI and have the generation AI generate optimal learning plans and career advice.

[0068] The coaching department can provide peer coaching functionality among employees, promoting knowledge sharing and collaboration. For example, the coaching department can provide an interface that allows employees to share their skills and knowledge with other employees and coach each other. The coaching department can also, for example, set the frequency and evaluation methods of coaching. The coaching department can, for example, schedule coaching sessions for employees to improve specific skills. This can promote knowledge sharing and collaboration among employees. Some or all of the processes described above in the coaching department may be performed using AI or not. For example, the coaching department can input employee skills and knowledge into AI and have the AI ​​suggest the best coaching pairs.

[0069] The feedback unit can track learning progress and provide feedback in real time. For example, the feedback unit can monitor an employee's learning progress in real time and provide appropriate feedback. The feedback unit can also adjust the timing and content of the feedback. For example, the feedback unit can provide immediate feedback when an employee achieves a specific goal. This allows for tracking learning progress and providing feedback in real time. Some or all of the above processes in the feedback unit may be performed using AI or not. For example, the feedback unit can input employee learning progress data into a generating AI and have the generating AI generate feedback in real time.

[0070] The reception desk can estimate an employee's emotions and adjust the input method for goals and skill levels based on the estimated emotions. For example, if an employee is stressed, the reception desk can provide a simple interface and minimize the input steps. If an employee is relaxed, for example, the reception desk can provide detailed input options and suggest a customizable input method. If an employee is in a hurry, for example, the reception desk can prioritize voice input to allow for quick input of goals and skill levels. This allows the input method for goals and skill levels to be adjusted according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input employee facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0071] The reception desk can analyze an employee's past goal achievement history and suggest the optimal input format. For example, the reception desk can automatically display similar goals as candidates based on goals the employee has achieved in the past. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the employee has used in the past. For example, the reception desk can predict and suggest goals to be used during a specific time period based on the employee's past goal achievement history. This allows the reception desk to suggest the optimal input format based on the employee's past goal achievement history. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input the employee's past goal achievement data into a generating AI and have the generating AI suggest the optimal input format.

[0072] The reception desk can filter employee data based on their current projects and work content when they input goals and skill levels. For example, the reception desk may prioritize displaying goals related to the projects the employee is currently working on. For example, the reception desk may suggest highly relevant skill levels based on the employee's work content. For example, the reception desk may suggest appropriate goals considering the progress of the employee's current projects. This allows for filtering of goals and skill levels based on the employee's current projects and work content. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk may input employee project data into a generating AI and have the generating AI perform the filtering of goals and skill levels.

[0073] The reception desk can estimate an employee's emotions and, based on the estimated emotions, determine the priority of goals and skill levels to be entered. For example, if an employee is stressed, the reception desk will prioritize suggesting easily achievable goals. For example, if an employee is relaxed, the reception desk will prioritize suggesting challenging goals. For example, if an employee is in a hurry, the reception desk will prioritize suggesting goals that can be achieved in a short period of time. This allows for the prioritization of goals and skill levels according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input employee facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0074] The reception desk can prioritize inputting highly relevant information by considering the employee's geographical location when they input goals and skill levels. For example, if an employee works in a specific region, the reception desk will prioritize suggesting skills and goals related to that region. For example, if an employee is working remotely, the reception desk will prioritize suggesting goals that can be achieved online. For example, if an employee is on a business trip, the reception desk will prioritize suggesting skills and goals related to their destination. This allows for the priority input of highly relevant information based on the employee's geographical location. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the employee's geographical location data into a generating AI and have the generating AI suggest highly relevant information.

[0075] The reception desk can analyze an employee's social media activity and input relevant information when they input their goals and skill levels. For example, the reception desk can suggest relevant goals based on the interests and passions the employee has shared on social media. For example, the reception desk can suggest relevant skills based on the experts and influencers the employee follows on social media. For example, the reception desk can analyze an employee's social media activity history and suggest relevant goals and skills. This allows the reception desk to input relevant information based on the employee's social media activity. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input employee social media data into a generating AI and have the generating AI suggest relevant information.

[0076] The generation unit can estimate an employee's emotions and adjust the presentation of learning plans and career advice based on the estimated emotions. For example, if an employee is relaxed, the generation AI will propose a learning plan with detailed explanations. If an employee is in a hurry, the generation AI will propose a concise learning plan that gets straight to the point. If an employee is excited, the generation AI will propose career advice with visually stimulating effects. This allows the presentation of learning plans and career advice to be adjusted according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using or without a generation AI. For example, the generation unit can input employee facial expression data into a generation AI and have the generation AI perform emotion estimation.

[0077] The generation unit can adjust the level of detail based on the employee's importance when generating learning plans and career advice. For example, a learning plan for an important skill might include detailed explanations and step-by-step guidance. A learning plan for a less important skill might include only concise explanations and key points. The generation unit can also provide detailed or concise advice depending on the importance of the career advice. This allows the level of detail in learning plans and career advice to be adjusted based on the employee's importance. Some or all of the above processing in the generation unit may be performed using the generation AI or not. For example, the generation unit can input employee importance data into the generation AI and have the generation AI perform the level of detail adjustment.

[0078] The generation unit can apply different algorithms depending on the employee's category when generating learning plans and career advice. For example, the generation unit can apply an algorithm specialized in improving technical skills to employees in technical positions. For example, the generation unit can apply an algorithm specialized in improving leadership skills to employees in management positions. For example, the generation unit can apply an algorithm specialized in improving basic skills to new employees. This allows different algorithms to be applied depending on the employee's category. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input employee category data into a generation AI and have the generation AI select the algorithm to apply.

[0079] The generation unit can estimate an employee's emotions and adjust the length of learning plans and career advice based on the estimated emotions. For example, if an employee is in a hurry, the generation unit's AI can generate a short, concise learning plan. If an employee is relaxed, the generation unit's AI can generate a longer learning plan with detailed explanations. If an employee is excited, the generation unit's AI can generate career advice with visually stimulating effects. This allows the length of learning plans and career advice to be adjusted according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using or without a generation AI. For example, the generation unit can input employee facial expression data into a generation AI and have the generation AI perform emotion estimation.

[0080] The generation unit can prioritize learning plans and career advice based on employee submission deadlines. For example, it might prioritize generating learning plans for submissions with approaching deadlines. For example, it might generate long-term learning plans for submissions with distant deadlines. For example, it might generate learning plans that balance short-term and long-term goals based on submission deadlines. This allows for prioritizing learning plans and career advice based on employee submission deadlines. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input employee submission data into a generation AI and have the generation AI perform the priority determination.

[0081] The generation unit can adjust the order of learning plans and career advice based on employee relevance when generating them. For example, the generation unit might first suggest learning plans related to important skills and goals. For example, it might postpone learning plans related to less relevant skills and goals. For example, it might prioritize suggesting learning plans related to the employee's current job responsibilities. This allows the order of learning plans and career advice to be adjusted based on employee relevance. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input employee relevance data into a generation AI and have the generation AI perform the order adjustment.

[0082] The coaching department can estimate employees' emotions and adjust peer coaching standards based on those estimated emotions. For example, if an employee is tense, the coaching department can set coaching standards that promote relaxation. If an employee is relaxed, the coaching department can set challenging coaching standards. If an employee is stressed, the coaching department can set coaching standards aimed at stress reduction. This allows the coaching department to adjust peer coaching standards 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 facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0083] The coaching department can improve the accuracy of peer coaching by considering the relationships between employees. For example, the coaching department can suggest the most suitable coaching pairs based on the past collaboration history of employees. For example, the coaching department can analyze the relationships between employees and set up compatible coaching pairs. For example, the coaching department can suggest effective coaching methods considering the relationships between employees. This allows the coaching department to improve the accuracy of coaching by considering the relationships between employees. 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 relationship data into a generating AI and have the generating AI suggest coaching pairs.

[0084] The coaching department can conduct peer coaching while taking employee attribute information into consideration. For example, the coaching department can propose appropriate coaching methods based on the employee's job title and position. For example, the coaching department can propose appropriate coaching content based on the employee's years of experience. For example, the coaching department can propose appropriate coaching methods based on the employee's skill level. This allows for appropriate coaching based on employee attribute information. 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 attribute information into a generating AI and have the generating AI generate coaching method suggestions.

[0085] The coaching department can estimate an employee's emotions and adjust the order in which coaching results are displayed based on the estimated emotions. For example, if an employee is tense, the coaching department will display positive results first. If an employee is relaxed, the coaching department will display detailed results in a sequential manner. If an employee is in a hurry, the coaching department will display concise results first. This allows the order in which coaching results are displayed to be adjusted according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the coaching department may be performed using AI or not. For example, the coaching department can input employee facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0086] The coaching department can conduct peer coaching while considering the geographical distribution of employees. For example, the coaching department can pair employees working remotely and propose online coaching. For example, the coaching department can pair employees in the same office and propose in-person coaching. For example, the coaching department can pair employees who are geographically close to each other and propose efficient coaching. This allows for appropriate coaching based on the geographical distribution of employees. 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 geographical distribution data into a generating AI and have the generating AI propose coaching pairs.

[0087] The coaching department can improve the accuracy of peer coaching by referring to relevant literature used by employees. For example, the coaching department can suggest relevant coaching content based on literature previously referenced by employees. For example, the coaching department can update coaching content by referring to the latest literature related to employees' areas of expertise. For example, the coaching department can suggest effective coaching methods by referring to literature that helps improve employees' skills. In this way, the accuracy of coaching can be improved by referring to relevant literature used by employees. 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 relevant literature data into a generating AI and have the generating AI generate coaching content suggestions.

[0088] The feedback unit can estimate an employee's emotions and adjust how feedback is displayed based on the estimated emotions. For example, if an employee is nervous, the feedback unit will display positive feedback first. If an employee is relaxed, the feedback unit will display detailed feedback in a sequential manner. If an employee is in a hurry, the feedback unit will display concise feedback first. This allows the feedback display method to be adjusted according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input employee facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0089] The feedback unit can predict current feedback by referring to past feedback data when providing feedback. For example, the feedback unit predicts current feedback based on feedback an employee has received in the past. For example, the feedback unit analyzes past feedback data to predict the trend of current feedback. For example, the feedback unit customizes current feedback by referring to an employee's past feedback history. This allows the feedback unit to predict current feedback by referring to past feedback data. Some or all of the above processes in the feedback unit may be performed using AI or not. For example, the feedback unit can input past feedback data into a generating AI and have the generating AI perform a prediction of current feedback.

[0090] The feedback unit can apply different feedback analysis methods to each employee category during the feedback process. For example, the feedback unit can apply a feedback analysis method specifically focused on improving technical skills to technical employees. For example, the feedback unit can apply a feedback analysis method specifically focused on improving leadership skills to managerial employees. For example, the feedback unit can apply a feedback analysis method specifically focused on improving basic skills to new employees. This allows for the application of different feedback analysis methods depending on the employee category. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input employee category data into a generating AI and have the generating AI select a feedback analysis method.

[0091] The feedback unit can estimate an employee's emotions and adjust the importance of the feedback based on the estimated emotions. For example, if an employee is stressed, the feedback unit will display positive feedback with high importance. For example, if an employee is relaxed, the feedback unit will display detailed feedback with high importance. For example, if an employee is in a hurry, the feedback unit will display concise feedback with high importance. This allows the importance of feedback to be adjusted according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input employee facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0092] The feedback unit can analyze changes in feedback based on the timing of employee submissions. For example, the feedback unit may prioritize analyzing feedback submitted sooner. For example, the feedback unit may postpone the analysis of feedback submitted later. For example, the feedback unit may predict and analyze changes in feedback based on the submission timing. This allows for the analysis of changes in feedback based on employee submission timing. Some or all of the above processes in the feedback unit may be performed using AI or not. For example, the feedback unit can input employee submission timing data into a generating AI and have the generating AI perform the analysis of changes in feedback.

[0093] The feedback unit can analyze feedback by referring to relevant market data of the employee during the feedback process. For example, the feedback unit can analyze feedback based on market data related to the employee's industry. For example, the feedback unit can analyze feedback based on market data related to the employee's job type. For example, the feedback unit can analyze feedback based on market data related to the employee's skills. This allows the feedback unit to analyze feedback by referring to relevant market data of the employee. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input relevant market data of the employee into a generating AI and have the generating AI perform the feedback analysis.

[0094] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0095] The reception department can analyze employees' past learning history and propose optimal learning plans. For example, it can suggest relevant new courses based on the employee's past course and training history. Furthermore, it can evaluate past learning outcomes and customize learning plans to help improve specific skills. It can also identify areas where employees have struggled in the past and provide learning plans tailored to those areas. This allows for the provision of more effective learning plans based on employees' past learning history.

[0096] The generation unit can estimate an employee's emotions and adjust the difficulty level of the learning plan based on that estimation. For example, if an employee is stressed, it can suggest an easier learning plan. If an employee is relaxed, it can suggest a more difficult learning plan. Furthermore, if an employee is excited, it can provide a learning plan that includes challenging tasks. This allows the difficulty level of the learning plan to be adjusted according to the employee's emotions. Emotion estimation is achieved using an emotion engine or generative AI.

[0097] The coaching department can analyze employees' past coaching history and suggest the most suitable coaching pairs. For example, it can suggest successful coaching pairs from the past again. It can also set up coaching pairs that are helpful for improving specific skills based on feedback from past coaching sessions. Furthermore, it can analyze employees' growth patterns from their past coaching history and suggest the most suitable coaching methods. This allows for more effective coaching based on employees' past coaching history.

[0098] The feedback system can estimate an employee's emotions and adjust the content of the feedback based on that estimation. For example, if an employee is stressed, it will primarily provide positive feedback. If an employee is relaxed, it can provide more detailed feedback. If an employee is in a hurry, it can provide concise feedback that gets straight to the point. This allows the feedback to be adjusted according to the employee's emotions. Emotion estimation is achieved using an emotion engine or generative AI.

[0099] The reception desk can adjust how employees input their goals and skill levels, taking into account their current health status. For example, if an employee is tired, a simple interface can be provided, minimizing the input process. If the employee is healthy and energetic, detailed input options can be offered, and customizable input methods can be suggested. Furthermore, if an employee is ill, voice input can be prioritized to allow for quick input of goals and skill levels. This allows for adjustments to how employees input their goals and skill levels according to their health condition.

[0100] The generation unit can estimate an employee's emotions and adjust the frequency of progress reports on their learning plan based on those emotions. For example, if an employee is stressed, the frequency of progress reports can be reduced to alleviate pressure. If an employee is relaxed, the frequency of progress reports can be increased to provide more detailed feedback. If an employee is in a hurry, a concise progress report can be provided. This allows the frequency of progress reports on the learning plan to be adjusted according to the employee's emotions. Emotion estimation is achieved using an emotion engine or generative AI.

[0101] The coaching department can analyze employees' past project histories and propose the most suitable coaching content. For example, it can suggest coaching content for similar projects based on the methodologies used in past successful projects. It can also analyze past project failures and provide coaching content that leverages those lessons. Furthermore, it can identify employees' areas of expertise from their past project histories and propose coaching content tailored to those areas. This allows for more effective coaching based on each employee's past project history.

[0102] The feedback system can estimate an employee's emotions and adjust the format of the feedback based on that estimation. For example, if an employee is stressed, it can provide text-based feedback. If an employee is relaxed, it can provide video-based feedback. If an employee is in a hurry, it can provide audio-based feedback. This allows the feedback format to be adjusted according to the employee's emotions. Emotion estimation is achieved using an emotion engine or generative AI, among other methods.

[0103] The reception desk can adjust the input method for goals and skill levels based on the employee's current workload. For example, if an employee is busy, a simple interface can be provided to minimize the input steps. If an employee has more time, detailed input options can be offered, and customizable input methods can be suggested. Furthermore, if an employee is under project deadline pressure, voice input can be prioritized to allow for quick input of goals and skill levels. This allows for adjustment of the goal and skill level input method according to the employee's workload.

[0104] The generation unit can estimate employees' emotions and adjust the motivational elements of the learning plan based on those emotions. For example, if an employee is feeling stressed, it can suggest a learning plan that includes relaxing elements. If an employee is relaxed, it can suggest a learning plan that includes challenging elements. Furthermore, if an employee is excited, it can provide a learning plan that includes visually stimulating elements. This allows the motivational elements of the learning plan to be adjusted according to the employee's emotions. Emotion estimation is achieved using an emotion engine or generative AI.

[0105] The following briefly describes the processing flow for example form 2.

[0106] Step 1: The reception desk receives input from employees regarding their goals and skill levels. These goals and skill levels include short-term goals, long-term goals, quantitative goals, and qualitative goals. The reception desk enables employees to input their goals and skill levels through the app, providing multiple input methods such as voice input and text input. Step 2: The generation unit uses a generation AI to propose optimal learning plans and career advice based on the information received by the reception unit. The generation unit creates an individualized curriculum based on the employee's goals and skill level, and adjusts the learning plan as the employee progresses. For example, the generation AI tracks the employee's progress toward their goals and updates the learning plan as needed. Step 3: The Coaching Department provides a peer coaching function among employees to promote knowledge sharing and collaboration. The Coaching Department provides an interface that allows employees to share their skills and knowledge with other employees and coach each other. It also allows for setting the frequency and evaluation methods of coaching. Step 4: The Feedback Department tracks learning progress and provides real-time feedback. The Feedback Department monitors employees' learning progress in real time and provides appropriate feedback. They adjust the timing and content of feedback and provide immediate feedback when employees achieve specific goals.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] Each of the multiple elements described above, including the reception unit, generation unit, coaching unit, and feedback unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, allowing employees to input their goals and skill levels through an app. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which uses a generation AI to propose optimal learning plans and career advice. The coaching unit is implemented by the control unit 46A of the smart device 14, which provides a peer coaching function among employees. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12, which tracks learning progress and provides real-time feedback. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0111] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the reception unit, generation unit, coaching unit, and feedback unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, allowing employees to input goals and skill levels via voice input. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which uses generation AI to propose optimal learning plans and career advice. The coaching unit is implemented, for example, by the control unit 46A of the smart glasses 214, which provides a peer coaching function among employees. The feedback unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which tracks learning progress and provides real-time feedback. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0127] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the reception unit, generation unit, coaching unit, and feedback unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, allowing employees to input goals and skill levels via voice input. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which uses generation AI to propose optimal learning plans and career advice. The coaching unit is implemented by, for example, the control unit 46A of the headset terminal 314, which provides a peer coaching function among employees. The feedback unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which tracks learning progress and provides real-time feedback. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0143] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In 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.

[0155] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0157] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] The data processing system 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.

[0159] Each of the multiple elements described above, including the reception unit, generation unit, coaching unit, and feedback unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414, allowing employees to input goals and skill levels via voice input. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which uses generated AI to propose optimal learning plans and career advice. The coaching unit is implemented by, for example, the control unit 46A of the robot 414, which provides a peer coaching function among employees. The feedback unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which tracks learning progress and provides real-time feedback. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] (Note 1) A reception desk that accepts input from employees regarding their goals and skill levels, A generation unit proposes optimal learning plans and career advice based on the information received by the reception unit, The Coaching Department provides a peer coaching function among employees, It includes a feedback unit that tracks learning progress and provides real-time feedback. A system characterized by the following features. (Note 2) The generating unit is We propose optimal learning plans and career advice based on employees' goals and skill levels. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned coaching department, We provide a peer coaching function among employees to promote knowledge sharing and collaboration. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned feedback unit is Track learning progress and provide real-time feedback. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is The system estimates employees' emotions and adjusts the input methods for goals and skill levels based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is We analyze employees' past goal achievement history and propose the optimal input format. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When employees enter their goals and skill levels, the system filters the data based on their current projects and responsibilities. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is The system estimates employees' emotions and determines the priority of input goals and skill levels based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When entering goals and skill levels, the system prioritizes inputting highly relevant information by considering the employee's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When employees enter their goals and skill levels, the system analyzes their social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is The system estimates employees' emotions and adjusts the way learning plans and career advice are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is When generating learning plans and career advice, adjust the level of detail based on the employee's importance. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When generating learning plans and career advice, different algorithms are applied depending on the employee's category. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is The system estimates employees' emotions and adjusts the length of learning plans and career advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating learning plans and career advice, prioritize them based on when employees submit them. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating learning plans and career advice, the order is adjusted based on employee relevance. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned coaching department, We estimate the emotions of our employees and adjust the criteria for peer coaching based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned coaching department, When conducting peer coaching, consider the interpersonal relationships among employees to improve the effectiveness of the coaching. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned coaching department, When conducting peer coaching, consider employee attribute information when providing coaching. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned coaching department, The system estimates the employee's emotions and adjusts the order in which coaching results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned coaching department, When conducting peer coaching, consider the geographical distribution of employees. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned coaching department, During peer coaching, employees can improve the accuracy of their coaching by referring to relevant literature. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned feedback unit is The system estimates employees' emotions and adjusts how feedback is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned feedback unit is When providing feedback, past feedback data is used to predict current feedback. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned feedback unit is When providing feedback, different feedback analysis methods are applied to each employee category. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned feedback unit is The system estimates employees' emotions and adjusts the importance of feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned feedback unit is When providing feedback, analyze how feedback changes based on when employees submit it. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned feedback unit is When providing feedback, we analyze the feedback by referring to relevant market data of the employees. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0179] 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 reception desk that accepts input from employees regarding their goals and skill levels, A generation unit proposes optimal learning plans and career advice based on the information received by the reception unit, The Coaching Department provides a peer coaching function among employees, It includes a feedback unit that tracks learning progress and provides real-time feedback. A system characterized by the following features.

2. The generating unit is We propose optimal learning plans and career advice based on employees' goals and skill levels. The system according to feature 1.

3. The aforementioned coaching department, We provide a peer coaching function among employees to promote knowledge sharing and collaboration. The system according to feature 1.

4. The aforementioned feedback unit is Track learning progress and provide real-time feedback. The system according to feature 1.

5. The aforementioned reception unit is The system estimates employees' emotions and adjusts the input methods for goals and skill levels based on those estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is We analyze employees' past goal achievement history and propose the optimal input format. The system according to feature 1.

7. The aforementioned reception unit is When employees enter their goals and skill levels, the system filters the data based on their current projects and responsibilities. The system according to feature 1.

8. The aforementioned reception unit is The system estimates employees' emotions and determines the priority of input goals and skill levels based on those estimated emotions. The system according to feature 1.

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