system

Abstract

The system according to this embodiment aims to recommend optimal training and qualifications based on employees' career goals and skill sets, and to provide comprehensive support for their learning. [Solution] The system according to the embodiment comprises a reception unit, a recommendation unit, an analysis unit, a support unit, and a feedback unit. The reception unit inputs the employee's career goals, current skill set, job duties, and available time. The recommendation unit analyzes the information entered by the reception unit and recommends appropriate training and qualifications. The analysis unit analyzes learning trends and strengths based on the training and qualifications recommended by the recommendation unit. The support unit provides suggestions for learning timing, progress management, motivation maintenance, and preparation for tests based on the results analyzed by the analysis unit. The feedback unit provides immediate feedback according to the progress of learning supported by the support unit.

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, and includes 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 in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that optimal training and qualification acquisition are not sufficiently recommended based on the career goals and skill sets of employees, and learning is not fully supported.

[0005] The system according to the embodiment aims to recommend optimal training and qualification acquisition based on the career goals and skill sets of employees and fully support learning.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a recommendation unit, an analysis unit, a support unit, and a feedback unit. The reception unit inputs the employee's career goals, current skill set, job duties, and available time. The recommendation unit analyzes the information entered by the reception unit and recommends appropriate training and certifications. The analysis unit analyzes learning trends and strengths based on the training and certifications recommended by the recommendation unit. The support unit provides suggestions for learning timing, progress management, motivation maintenance, and test preparation based on the results analyzed by the analysis unit. The feedback unit provides immediate feedback according to the learning progress supported by the support unit. [Effects of the Invention]

[0007] The system according to this embodiment can recommend optimal training and qualifications based on employees' career goals and skill sets, and provide comprehensive support for their learning. [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 tagged storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. 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 tagged communication I / F (Interface) is an interface that includes a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, a specific processing unit 第290 (see FIG. 2) acquires 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 generative AI agent system according to an embodiment of the present invention is a system that recommends optimal training and qualification acquisition considering an employee's career goals, current skill set, job duties, and available time. This generative AI agent system takes the employee's career goals, current skill set, job duties, and available time as input, and the generative AI analyzes this information to recommend optimal training and qualification acquisition. Furthermore, it analyzes the employee's learning tendencies and strengths based on their past learning data and personality traits, and proposes an individualized learning strategy. It provides total support for learning, from suggesting learning timing, managing progress and maintaining motivation, to preparing for tests. It also provides real-time feedback according to the learning progress, enabling effective learning. In addition, a personalized virtual mentor function by the AI ​​agent is added, providing regular feedback and advice to each employee to maintain motivation and propose individualized learning strategies. For example, an employee might input "I aspire to a career as a project manager" and input "project management, team leadership, and communication skills" as their current skill set. They might also input "project management, team leadership, and communication tasks" as their job duties and "10 hours per week" as their available time. Next, the generating AI analyzes the input information and recommends the most suitable training and certifications. The generating AI recommends the most suitable training and certifications based on the employee's career goals, current skill set, job responsibilities, and available time. For example, the generating AI might recommend "project management certification training" to an employee aiming for a career as a project manager. The generating AI also analyzes the employee's learning tendencies and strengths based on their past learning data and personality traits, and proposes an individualized learning strategy. For example, the generating AI might suggest a "learning strategy centered on online learning" to an employee who has achieved high results in online learning in the past. Furthermore, the generating AI provides comprehensive learning support, from suggesting learning timing and managing progress to maintaining motivation and preparing for tests. For example, the generating AI might suggest that an employee who allocates 10 hours of study time per week should study for 2 hours each on Monday and Wednesday evenings.Furthermore, the generating AI provides real-time feedback based on learning progress, enabling effective learning. For example, the generating AI can provide advice to employees who are falling behind in their learning to help them accelerate their learning pace. In addition, a personalized virtual mentor function by the AI ​​agent is added, providing regular feedback and advice to each employee to maintain motivation and propose individual learning strategies. For example, the AI ​​agent can provide feedback and advice to employees every Friday to maintain motivation and support effective learning. As a result, the generating AI agent system can recommend optimal training and certifications based on employees' career goals and skill sets, providing comprehensive support for learning.

[0029] The generation AI agent system according to the embodiment comprises a reception unit, a recommendation unit, an analysis unit, a support unit, and a feedback unit. The reception unit inputs the employee's career goals, current skill set, job duties, and available time. The employee's career goals include, but are not limited to, short-term goals, long-term goals, and promotion goals. The skill set includes, but is not limited to, technical skills, soft skills, and specialized knowledge. Job duties include, but are not limited to, project management, sales activities, and technology development. Available time includes, but is not limited to, a certain number of hours per week or a certain number of hours per month. The recommendation unit analyzes the information entered by the reception unit and recommends the most suitable training and qualifications. The recommendation unit recommends the most suitable training and qualifications based, for example, the employee's career goals, current skill set, job duties, and available time. For example, the recommendation unit recommends project management qualification training to an employee aiming for a career as a project manager. The Recommendation Department analyzes employees' learning tendencies and strengths based on their past learning data and personality traits, and proposes individualized learning strategies. The Analysis Department analyzes learning tendencies and strengths based on training and qualifications recommended by the Recommendation Department. For example, the Analysis Department analyzes learning tendencies and strengths based on employees' past learning data and personality traits. For example, the Analysis Department proposes a learning strategy centered on online learning to employees who have achieved high results in online learning in the past. The Support Department provides support such as suggesting learning timing, managing progress, maintaining motivation, and preparing for tests, based on the results analyzed by the Analysis Department. For example, the Support Department suggests that an employee who allocates 10 hours of study time per week study for 2 hours each on Monday and Wednesday evenings. The Feedback Department provides real-time feedback according to the progress of learning supported by the Support Department. For example, the Feedback Department provides advice to employees who are falling behind in their learning to help them pick up the pace.As a result, the AI ​​agent system according to this embodiment can recommend optimal training and qualifications based on employees' career goals and skill sets, providing comprehensive support for their learning.

[0030] The reception desk inputs employees' career goals, current skill sets, job responsibilities, and available time. Employee career goals include, but are not limited to, short-term goals, long-term goals, and promotion goals. Specifically, employees can input detailed information such as what positions they aspire to hold in the future, what skills they want to acquire, and what types of work they want to be involved in. Skill sets include, but are not limited to, technical skills, soft skills, and specialized knowledge. Technical skills include specific technologies such as programming languages, database management, and network construction, while soft skills include communication skills, leadership, and problem-solving abilities. Specialized knowledge includes deep knowledge of specific industries or fields. Job responsibilities include, but are not limited to, project management, sales activities, and technology development. Specifically, employees can input details of their current projects and tasks, their roles, and responsibilities. Available time includes, but is not limited to, how many hours per week or month. By inputting the specific amount of time employees can dedicate to learning and training, the system can suggest an optimal learning plan. This allows the reception department to collect detailed information about employees and provide the data that forms the foundation of the entire system.

[0031] The recommendation department analyzes the information entered by the reception department and recommends the most suitable training and certifications. For example, the recommendation department recommends the most suitable training and certifications based on an employee's career goals, current skill set, job responsibilities, and available time. Specifically, it proposes training programs and certification courses to acquire the necessary skills and knowledge according to the employee's career goals. For example, it would recommend project management certification training to an employee aiming for a career as a project manager. In addition, the recommendation department analyzes employees' learning tendencies and strengths based on their past learning data and personality traits data, and proposes individualized learning strategies. For example, it would propose a training program centered on online learning to an employee who has achieved high results in online learning in the past, and propose in-person training to an employee who is more effective in face-to-face learning. Furthermore, the recommendation department uses AI to analyze employees' learning history and performance data and select the most suitable learning methods and materials. This allows the recommendation department to provide each employee with an optimal learning plan and support efficient and effective skill development.

[0032] The Analysis Department analyzes learning trends and strengths based on training and qualification acquisitions recommended by the Recommendation Department. For example, the Analysis Department analyzes learning trends and strengths based on employees' past learning data and personality trait data. Specifically, it analyzes in detail which learning methods are most effective for employees and in which areas they excel. For example, it proposes a learning strategy centered on online learning to an employee who has achieved high results in online learning in the past. It also analyzes employees' learning styles and how they maintain motivation based on personality trait data and provides the optimal learning environment. For example, it proposes a highly flexible program where employees who are good at self-directed learning can create their own learning plans, and provides regular feedback and support to employees who need it. Furthermore, the Analysis Department uses AI to analyze employees' learning data in real time and evaluate their learning progress and results. This allows the Analysis Department to accurately grasp employees' learning trends and strengths and propose the optimal learning strategy.

[0033] The Support Department provides support based on the analysis results from the Analysis Department, including suggesting optimal learning timings, managing progress, maintaining motivation, and preparing for tests. Specifically, the Support Department proposes optimal learning schedules to help employees learn efficiently. For example, for an employee who allocates 10 hours of study time per week, the Support Department might suggest studying for 2 hours each on Monday and Wednesday evenings. In addition, as part of progress management, the Support Department regularly checks employees' learning status and provides advice to help them achieve their goals. To maintain motivation, the Support Department creates an environment that makes it easy for employees to continue learning by sending regular feedback and encouraging messages. Furthermore, as part of test preparation, the Support Department supports reviewing key points and conducting mock exams. In this way, the Support Department can provide comprehensive support to help employees learn efficiently and effectively.

[0034] The Feedback Department provides real-time feedback based on the learning progress supported by the Support Department. For example, the Feedback Department provides advice to employees who are falling behind in their learning to help them pick up the pace. Specifically, it monitors employees' learning data in real time and provides feedback according to their progress. For example, if an employee's learning progress is behind schedule, it will provide specific advice on how to increase the pace or suggest a review of their learning methods. For employees who are progressing well, it will provide additional assignments to encourage further challenges or advice on how to move to the next step. Furthermore, the Feedback Department evaluates employees' learning outcomes and increases motivation by providing rewards and recognition based on their achievements. For example, employees who achieve specific goals will be issued badges or certificates to enhance their internal evaluation. In this way, the Feedback Department can support employees' learning in real time and provide effective feedback, thereby improving the quality of learning and motivation.

[0035] The feedback department can provide real-time feedback based on learning progress. For example, it can advise employees who are falling behind on their learning to pick up the pace. It can also advise employees who are progressing well on how to move on to the next step. Furthermore, the feedback department can adjust the learning content or provide additional learning resources based on learning progress. This enables effective learning through real-time feedback tailored to learning progress.

[0036] The support department can provide assistance with suggesting study timings, managing progress, maintaining motivation, and preparing for tests. For example, the support department can suggest study timings. For instance, it might suggest that an employee who allocates 10 hours of study time per week study for 2 hours each on Monday and Wednesday evenings. The support department also manages progress. For example, it monitors learning progress and provides advice to increase the pace if progress is falling behind. Furthermore, the support department helps maintain motivation. For example, it can maintain employee motivation by providing incentives based on learning progress. Finally, the support department supports test preparation. For example, it can help employees prepare effectively for tests by providing mock exams and review methods. In this way, the support department provides comprehensive support for learning by suggesting study timings, managing progress, maintaining motivation, and preparing for tests.

[0037] The analytics department can analyze employees' learning tendencies and strengths based on their past learning data and personality trait data. For example, the analytics department can analyze learning tendencies based on employees' past learning data. For instance, it can propose a learning strategy centered on online learning to an employee who has achieved high results in online learning in the past. The analytics department can also analyze employees' strengths based on their personality trait data. For example, it can identify an employee's strengths based on personality assessment results and behavioral patterns and propose a learning strategy based on those strengths. Furthermore, the analytics department can comprehensively analyze learning tendencies and strengths and propose individualized learning strategies. In this way, by analyzing learning tendencies and strengths based on past learning data and personality trait data, it proposes individualized learning strategies.

[0038] The recommendation department can recommend the most suitable training and certifications based on an employee's career goals, current skill set, job responsibilities, and available time. For example, the recommendation department might recommend training in project management certification to an employee aiming for a career as a project manager. It can also recommend training based on an employee's current skill set. For example, it might recommend training to further enhance project management, team leadership, and communication skills to an employee with these skills. Furthermore, the recommendation department can recommend training based on an employee's job responsibilities. For example, it might recommend training related to project management, team leadership, and communication to an employee responsible for these tasks. This ensures that the recommendation department recommends the most suitable training and certifications based on an employee's career goals and skill set.

[0039] The AI ​​agent system according to this embodiment includes a mentoring unit. The mentoring unit can provide regular feedback and advice to each employee. For example, the mentoring unit provides feedback and advice to employees every Friday. For example, the mentoring unit provides specific areas for improvement and advice on the next steps based on the employee's learning progress and achievements. The mentoring unit can also provide encouraging messages and incentives to maintain employee motivation. Furthermore, the mentoring unit can propose long-term learning strategies based on the employee's career goals and skill set. This helps maintain employee motivation and proposes individualized learning strategies through regular feedback and advice.

[0040] The reception desk can analyze past input history and suggest the optimal input method. For example, it can automatically display information that the user has frequently entered in the past as a suggestion. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest information that the user will use at specific times based on their past input history. In this way, by analyzing past input history, it can suggest the optimal input method and achieve efficient information input.

[0041] The reception system can filter data input based on the user's current work status and areas of interest. For example, it can prioritize inputting information related to projects the user is currently working on. It can also filter and display highly relevant information based on the user's areas of interest. Furthermore, it can efficiently collect data by allowing users to input only the necessary information according to their work status. This allows for efficient input of highly relevant information by filtering data based on the user's work status and areas of interest.

[0042] The reception system can prioritize inputting highly relevant information by considering the user's geographical location during data entry. For example, if a user is in a specific region, the reception system will prioritize inputting information related to that region. Furthermore, the reception system can filter and display highly relevant information based on the user's current location. In addition, the reception system can efficiently collect data by considering the user's geographical location and requiring only the necessary information to be entered. This allows for efficient input of highly relevant information by considering the user's geographical location.

[0043] The reception desk can analyze the user's social media activity during data entry and input relevant information. For example, the reception desk can prompt users to input relevant information based on what they have shared on social media. Furthermore, the reception desk can analyze the user's social media activity and prioritize inputting information of interest. In addition, based on the user's social media activity, the reception desk can efficiently collect data by prompting users to input only the necessary information. This allows for efficient input of relevant information by analyzing the user's social media activity.

[0044] The recommendation department can adjust the level of detail in recommendations based on the importance of the training or qualifications. For example, it can provide detailed information for highly important training or qualifications, and concise information for less important ones. Furthermore, the recommendation department can adjust the display order of recommendations according to their importance. This allows for the priority provision of important information by adjusting the level of detail in recommendations based on the importance of the training or qualifications.

[0045] The recommendation department can apply different recommendation algorithms depending on the category of training or qualification. For example, for technical training or qualifications, the recommendation department can apply a recommendation algorithm that emphasizes technical skills. Similarly, for management-related training or qualifications, the recommendation department can apply a recommendation algorithm that emphasizes leadership skills. Furthermore, the recommendation department can select the most appropriate recommendation algorithm for each category and provide recommendations accordingly. This ensures that appropriate recommendations are provided by applying the most suitable recommendation algorithm for each training or qualification category.

[0046] The recommendation department can prioritize recommendations based on the timing of training and qualification offerings. For example, it will prioritize recommendations for training and qualifications that are available soon. It can also postpone recommendations for those with later availability dates. Furthermore, the recommendation department can adjust the display order of recommendations according to their availability dates. This ensures that recommendations are made at the appropriate time by prioritizing recommendations based on the timing of training and qualification offerings.

[0047] The recommendation system can adjust the order of recommendations based on the relevance of training programs and qualifications. For example, the recommendation system will prioritize recommending training programs and qualifications that are most relevant to the user's career goals. It can also delay recommending less relevant training programs and qualifications. Furthermore, the recommendation system can adjust the display order of recommendations based on their relevance. This allows the system to prioritize the provision of highly relevant information by adjusting the order of recommendations based on the relevance of training programs and qualifications.

[0048] The analysis unit can improve the accuracy of its analysis by referring to past training data during the analysis process. For example, the analysis unit can set optimal analysis criteria based on the user's past training data. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to past training data. In addition, the analysis unit can analyze the user's learning trends and apply optimal analysis criteria. This improves the accuracy of the analysis by referring to past training data.

[0049] The analysis department can perform analyses while considering employee attribute information. For example, the analysis department can set optimal analysis criteria based on employee attribute information such as age and gender. Furthermore, the analysis department can improve the accuracy of the analysis by considering employees' work experience and skill sets. In addition, the analysis department can apply optimal analysis criteria based on employee attribute information. This allows the analysis department to provide appropriate results by considering employee attribute information.

[0050] The analysis department can perform analyses while considering the geographical distribution of employees. For example, the analysis department can set optimal analysis criteria based on the geographical distribution of employees. Furthermore, the analysis department can improve the accuracy of the analysis by considering geographical distribution. In addition, the analysis department can apply optimal analysis criteria based on the geographical distribution of employees. This allows for the provision of appropriate analysis results by considering the geographical distribution of employees.

[0051] The analysis unit can improve the accuracy of its analysis by referring to relevant literature during the analysis process. For example, the analysis unit can set optimal analysis criteria based on relevant literature. Furthermore, the analysis unit can improve the accuracy of its analysis by referring to relevant literature. In addition, the analysis unit can apply optimal analysis criteria based on relevant literature. This improves the accuracy of the analysis by referring to relevant literature.

[0052] The support department can select the optimal support method by referring to past support data during support. For example, the support department can select the optimal support method based on the user's past support data. Furthermore, the support department can improve the accuracy of support by referring to past support data. In addition, the support department can analyze the user's support history and provide the optimal support method. Thus, by referring to past support data, the optimal support method is selected.

[0053] The support department can customize the support methods provided based on the employee's current learning progress. For example, the support department can provide the most suitable support methods according to the employee's learning progress. Furthermore, the support department can monitor the employee's learning progress in real time and provide appropriate support. In addition, the support department can provide customized support methods based on the employee's learning progress. This allows for the provision of appropriate support by customizing support methods based on the employee's learning progress.

[0054] The support department can select the optimal support method by considering the employee's geographical location during support. For example, the support department can provide the optimal support method based on the employee's current location. Furthermore, the support department can provide highly relevant support by considering geographical location. In addition, the support department can select an efficient support method based on the employee's geographical location. This allows the support department to provide the optimal support method by considering the employee's geographical location.

[0055] The support department can analyze employees' social media activity during support sessions and propose appropriate support methods. For example, the support department can suggest the most suitable support method based on an employee's social media activity. Furthermore, the support department can analyze social media activity and provide support tailored to the employee's interests. In addition, the support department can propose customized support methods based on an employee's social media activity. This allows the support department to propose the most suitable support method by analyzing employees' social media activity.

[0056] The feedback unit can select the optimal feedback method by referring to past feedback data during the feedback process. For example, the feedback unit selects the optimal feedback method based on the user's past feedback data. Furthermore, the feedback unit can improve the accuracy of feedback by referring to past feedback data. In addition, the feedback unit can analyze the user's feedback history and provide the optimal feedback method. Thus, by referring to past feedback data, the optimal feedback method is selected.

[0057] The feedback unit can customize the feedback method based on the employee's current learning status. For example, the feedback unit can provide the optimal feedback method according to the employee's learning progress. Furthermore, the feedback unit can monitor the employee's learning status in real time and provide appropriate feedback. In addition, the feedback unit can provide customized feedback methods based on the employee's learning status. This ensures that appropriate feedback is provided by customizing the feedback method based on the employee's learning status.

[0058] The feedback department can select the optimal feedback method when providing feedback, taking into account the employee's geographical location. For example, the feedback department can provide the optimal feedback method based on the employee's current location. Furthermore, the feedback department can provide highly relevant feedback by considering geographical location information. In addition, the feedback department can select an efficient feedback method based on the employee's geographical location information. This allows the department to provide the optimal feedback method by considering the employee's geographical location information.

[0059] The feedback department can analyze employees' social media activity and propose feedback methods during the feedback process. For example, the feedback department can suggest the most suitable feedback method based on employees' social media activity. Furthermore, the feedback department can analyze social media activity and provide feedback content of interest. In addition, the feedback department can propose customized feedback methods based on employees' social media activity. This allows the department to propose the most suitable feedback method by analyzing employees' social media activity.

[0060] The mentoring unit can select the optimal mentoring method by referring to past mentoring data during mentoring sessions. For example, the mentoring unit can select the optimal mentoring method based on the user's past mentoring data. Furthermore, the mentoring unit can improve the accuracy of mentoring by referring to past mentoring data. In addition, the mentoring unit can analyze the user's mentoring history and provide the optimal mentoring method. Thus, by referring to past mentoring data, the optimal mentoring method is selected.

[0061] The mentoring department can customize mentoring methods based on the employee's current learning progress. For example, the mentoring department can provide the optimal mentoring method according to the employee's learning progress. Furthermore, the mentoring department can monitor the employee's learning progress in real time and provide appropriate mentoring. In addition, the mentoring department can provide customized mentoring methods based on the employee's learning progress. This allows for the provision of appropriate mentoring by customizing mentoring methods based on the employee's learning progress.

[0062] The mentoring department can select the optimal mentoring method by considering the employee's geographical location during mentoring sessions. For example, the mentoring department can provide the optimal mentoring method based on the employee's current location. Furthermore, the mentoring department can provide highly relevant mentoring by considering geographical location information. In addition, the mentoring department can select an efficient mentoring method based on the employee's geographical location information. This allows the department to provide the optimal mentoring method by considering the employee's geographical location information.

[0063] The mentoring department can analyze employees' social media activity during mentoring sessions and propose mentoring methods. For example, the mentoring department can propose the most suitable mentoring method based on an employee's social media activity. Furthermore, the mentoring department can analyze social media activity and provide mentoring content tailored to the employee's interests. In addition, the mentoring department can propose customized mentoring methods based on an employee's social media activity. This allows the department to propose the most suitable mentoring method by analyzing employees' social media activity.

[0064] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0065] The AI-generated agent system can also include a health management department. This department can monitor employees' health status and propose appropriate health management plans. For example, it can analyze employees' exercise habits and dietary content and provide advice for maintaining good health. It can also measure employees' stress levels and suggest relaxation methods to reduce stress. Furthermore, it can monitor employees' sleep patterns and provide advice to ensure high-quality sleep. This allows for comprehensive management of employees' health status and support for maintaining good health.

[0066] The AI ​​agent generation system can also be equipped with a communications department. This department can provide support to facilitate communication among employees. For example, it can analyze employees' communication styles and suggest effective communication methods. It can also plan workshops and training sessions for team building to strengthen collaboration among employees. Furthermore, it can collect employee feedback and suggest areas for improvement, thereby improving the workplace environment. This can lead to smoother communication among employees and an improved work environment.

[0067] The AI-generated agent system can also include a career coaching department. This department can provide coaching to support employees' career development. For example, it can propose specific career plans based on employees' career goals. It can also identify employees' skill gaps and provide training plans to acquire necessary skills. Furthermore, it can provide advice on employees' career paths and clarify their career direction. This supports employees' career development and helps them achieve their career goals.

[0068] The AI ​​agent generation system can also include a performance evaluation unit. This unit can evaluate employees' work performance and provide feedback. For example, it can quantitatively evaluate employees' work results and suggest specific areas for improvement. It can also analyze employees' strengths and weaknesses and provide advice for performance improvement. Furthermore, it can create regular evaluation reports to support employee growth. This allows for the evaluation of employee work performance and support for performance improvement.

[0069] The AI ​​agent generation system can also include a project management department. This department can manage the progress of projects assigned to employees and support efficient project operation. For example, it can monitor project progress in real time and quickly propose countermeasures if delays occur. It can also optimize project resource allocation to achieve efficient project operation. Furthermore, it can assess project risks and propose measures to mitigate them. This allows for effective project management and supports efficient project operation.

[0070] The following briefly describes the processing flow for example form 1.

[0071] Step 1: The reception desk enters the employee's career goals, current skill set, job responsibilities, and available hours. Employee career goals include short-term goals, long-term goals, and promotion targets; skill sets include technical skills, soft skills, and specialized knowledge; job responsibilities include project management, sales activities, and technical development; and available hours include the number of hours per week and per month. Step 2: The recommendation department analyzes the information entered by the reception department and recommends the most suitable training and certifications. For example, based on an employee's career goals, current skill set, job responsibilities, and available time, it recommends project management certification training to an employee aiming for a career as a project manager. It also analyzes learning tendencies and strengths based on the employee's past learning data and personality traits data, and proposes individualized learning strategies. Step 3: The analysis department analyzes learning trends and strengths based on training and qualifications recommended by the recommendation department. For example, based on employees' past learning data and personality traits, they propose learning strategies centered on online learning to employees who have achieved high results in online learning in the past. Step 4: Based on the results analyzed by the analysis department, the support department provides support such as suggesting learning timing, managing progress, maintaining motivation, and preparing for tests. For example, for an employee who allocates 10 hours of study time per week, they might suggest studying for 2 hours each on Monday and Wednesday evenings. Step 5: The feedback department provides real-time feedback based on the learning progress supported by the support department. For example, it provides advice to employees who are falling behind in their learning to help them pick up the pace.

[0072] (Example of form 2) The generative AI agent system according to an embodiment of the present invention is a system that recommends optimal training and qualification acquisition considering an employee's career goals, current skill set, job duties, and available time. This generative AI agent system takes the employee's career goals, current skill set, job duties, and available time as input, and the generative AI analyzes this information to recommend optimal training and qualification acquisition. Furthermore, it analyzes the employee's learning tendencies and strengths based on their past learning data and personality traits, and proposes an individualized learning strategy. It provides total support for learning, from suggesting learning timing, managing progress and maintaining motivation, to preparing for tests. It also provides real-time feedback according to the learning progress, enabling effective learning. In addition, a personalized virtual mentor function by the AI ​​agent is added, providing regular feedback and advice to each employee to maintain motivation and propose individualized learning strategies. For example, an employee might input "I aspire to a career as a project manager" and input "project management, team leadership, and communication skills" as their current skill set. They might also input "project management, team leadership, and communication tasks" as their job duties and "10 hours per week" as their available time. Next, the generating AI analyzes the input information and recommends the most suitable training and certifications. The generating AI recommends the most suitable training and certifications based on the employee's career goals, current skill set, job responsibilities, and available time. For example, the generating AI might recommend "project management certification training" to an employee aiming for a career as a project manager. The generating AI also analyzes the employee's learning tendencies and strengths based on their past learning data and personality traits, and proposes an individualized learning strategy. For example, the generating AI might suggest a "learning strategy centered on online learning" to an employee who has achieved high results in online learning in the past. Furthermore, the generating AI provides comprehensive learning support, from suggesting learning timing and managing progress to maintaining motivation and preparing for tests. For example, the generating AI might suggest that an employee who allocates 10 hours of study time per week should study for 2 hours each on Monday and Wednesday evenings.Furthermore, the generating AI provides real-time feedback based on learning progress, enabling effective learning. For example, the generating AI can provide advice to employees who are falling behind in their learning to help them accelerate their learning pace. In addition, a personalized virtual mentor function by the AI ​​agent is added, providing regular feedback and advice to each employee to maintain motivation and propose individual learning strategies. For example, the AI ​​agent can provide feedback and advice to employees every Friday to maintain motivation and support effective learning. As a result, the generating AI agent system can recommend optimal training and certifications based on employees' career goals and skill sets, providing comprehensive support for learning.

[0073] The generation AI agent system according to the embodiment comprises a reception unit, a recommendation unit, an analysis unit, a support unit, and a feedback unit. The reception unit inputs the employee's career goals, current skill set, job duties, and available time. The employee's career goals include, but are not limited to, short-term goals, long-term goals, and promotion goals. The skill set includes, but is not limited to, technical skills, soft skills, and specialized knowledge. Job duties include, but are not limited to, project management, sales activities, and technology development. Available time includes, but is not limited to, a certain number of hours per week or a certain number of hours per month. The recommendation unit analyzes the information entered by the reception unit and recommends the most suitable training and qualifications. The recommendation unit recommends the most suitable training and qualifications based, for example, the employee's career goals, current skill set, job duties, and available time. For example, the recommendation unit recommends project management qualification training to an employee aiming for a career as a project manager. The Recommendation Department analyzes employees' learning tendencies and strengths based on their past learning data and personality traits, and proposes individualized learning strategies. The Analysis Department analyzes learning tendencies and strengths based on training and qualifications recommended by the Recommendation Department. For example, the Analysis Department analyzes learning tendencies and strengths based on employees' past learning data and personality traits. For example, the Analysis Department proposes a learning strategy centered on online learning to employees who have achieved high results in online learning in the past. The Support Department provides support such as suggesting learning timing, managing progress, maintaining motivation, and preparing for tests, based on the results analyzed by the Analysis Department. For example, the Support Department suggests that an employee who allocates 10 hours of study time per week study for 2 hours each on Monday and Wednesday evenings. The Feedback Department provides real-time feedback according to the progress of learning supported by the Support Department. For example, the Feedback Department provides advice to employees who are falling behind in their learning to help them pick up the pace.As a result, the AI ​​agent system according to this embodiment can recommend optimal training and qualifications based on employees' career goals and skill sets, providing comprehensive support for their learning.

[0074] The reception desk inputs employees' career goals, current skill sets, job responsibilities, and available time. Employee career goals include, but are not limited to, short-term goals, long-term goals, and promotion goals. Specifically, employees can input detailed information such as what positions they aspire to hold in the future, what skills they want to acquire, and what types of work they want to be involved in. Skill sets include, but are not limited to, technical skills, soft skills, and specialized knowledge. Technical skills include specific technologies such as programming languages, database management, and network construction, while soft skills include communication skills, leadership, and problem-solving abilities. Specialized knowledge includes deep knowledge of specific industries or fields. Job responsibilities include, but are not limited to, project management, sales activities, and technology development. Specifically, employees can input details of their current projects and tasks, their roles, and responsibilities. Available time includes, but is not limited to, how many hours per week or month. By inputting the specific amount of time employees can dedicate to learning and training, the system can suggest an optimal learning plan. This allows the reception department to collect detailed information about employees and provide the data that forms the foundation of the entire system.

[0075] The recommendation department analyzes the information entered by the reception department and recommends the most suitable training and certifications. For example, the recommendation department recommends the most suitable training and certifications based on an employee's career goals, current skill set, job responsibilities, and available time. Specifically, it proposes training programs and certification courses to acquire the necessary skills and knowledge according to the employee's career goals. For example, it would recommend project management certification training to an employee aiming for a career as a project manager. In addition, the recommendation department analyzes employees' learning tendencies and strengths based on their past learning data and personality traits data, and proposes individualized learning strategies. For example, it would propose a training program centered on online learning to an employee who has achieved high results in online learning in the past, and propose in-person training to an employee who is more effective in face-to-face learning. Furthermore, the recommendation department uses AI to analyze employees' learning history and performance data and select the most suitable learning methods and materials. This allows the recommendation department to provide each employee with an optimal learning plan and support efficient and effective skill development.

[0076] The Analysis Department analyzes learning trends and strengths based on training and qualification acquisitions recommended by the Recommendation Department. For example, the Analysis Department analyzes learning trends and strengths based on employees' past learning data and personality trait data. Specifically, it analyzes in detail which learning methods are most effective for employees and in which areas they excel. For example, it proposes a learning strategy centered on online learning to an employee who has achieved high results in online learning in the past. It also analyzes employees' learning styles and how they maintain motivation based on personality trait data and provides the optimal learning environment. For example, it proposes a highly flexible program where employees who are good at self-directed learning can create their own learning plans, and provides regular feedback and support to employees who need it. Furthermore, the Analysis Department uses AI to analyze employees' learning data in real time and evaluate their learning progress and results. This allows the Analysis Department to accurately grasp employees' learning trends and strengths and propose the optimal learning strategy.

[0077] The Support Department provides support based on the analysis results from the Analysis Department, including suggesting optimal learning timings, managing progress, maintaining motivation, and preparing for tests. Specifically, the Support Department proposes optimal learning schedules to help employees learn efficiently. For example, for an employee who allocates 10 hours of study time per week, the Support Department might suggest studying for 2 hours each on Monday and Wednesday evenings. In addition, as part of progress management, the Support Department regularly checks employees' learning status and provides advice to help them achieve their goals. To maintain motivation, the Support Department creates an environment that makes it easy for employees to continue learning by sending regular feedback and encouraging messages. Furthermore, as part of test preparation, the Support Department supports reviewing key points and conducting mock exams. In this way, the Support Department can provide comprehensive support to help employees learn efficiently and effectively.

[0078] The Feedback Department provides real-time feedback based on the learning progress supported by the Support Department. For example, the Feedback Department provides advice to employees who are falling behind in their learning to help them pick up the pace. Specifically, it monitors employees' learning data in real time and provides feedback according to their progress. For example, if an employee's learning progress is behind schedule, it will provide specific advice on how to increase the pace or suggest a review of their learning methods. For employees who are progressing well, it will provide additional assignments to encourage further challenges or advice on how to move to the next step. Furthermore, the Feedback Department evaluates employees' learning outcomes and increases motivation by providing rewards and recognition based on their achievements. For example, employees who achieve specific goals will be issued badges or certificates to enhance their internal evaluation. In this way, the Feedback Department can support employees' learning in real time and provide effective feedback, thereby improving the quality of learning and motivation.

[0079] The feedback department can provide real-time feedback based on learning progress. For example, it can advise employees who are falling behind on their learning to pick up the pace. It can also advise employees who are progressing well on how to move on to the next step. Furthermore, the feedback department can adjust the learning content or provide additional learning resources based on learning progress. This enables effective learning through real-time feedback tailored to learning progress.

[0080] The support department can provide assistance with suggesting study timings, managing progress, maintaining motivation, and preparing for tests. For example, the support department can suggest study timings. For instance, it might suggest that an employee who allocates 10 hours of study time per week study for 2 hours each on Monday and Wednesday evenings. The support department also manages progress. For example, it monitors learning progress and provides advice to increase the pace if progress is falling behind. Furthermore, the support department helps maintain motivation. For example, it can maintain employee motivation by providing incentives based on learning progress. Finally, the support department supports test preparation. For example, it can help employees prepare effectively for tests by providing mock exams and review methods. In this way, the support department provides comprehensive support for learning by suggesting study timings, managing progress, maintaining motivation, and preparing for tests.

[0081] The analytics department can analyze employees' learning tendencies and strengths based on their past learning data and personality trait data. For example, the analytics department can analyze learning tendencies based on employees' past learning data. For instance, it can propose a learning strategy centered on online learning to an employee who has achieved high results in online learning in the past. The analytics department can also analyze employees' strengths based on their personality trait data. For example, it can identify an employee's strengths based on personality assessment results and behavioral patterns and propose a learning strategy based on those strengths. Furthermore, the analytics department can comprehensively analyze learning tendencies and strengths and propose individualized learning strategies. In this way, by analyzing learning tendencies and strengths based on past learning data and personality trait data, it proposes individualized learning strategies.

[0082] The recommendation department can recommend the most suitable training and certifications based on an employee's career goals, current skill set, job responsibilities, and available time. For example, the recommendation department might recommend training in project management certification to an employee aiming for a career as a project manager. It can also recommend training based on an employee's current skill set. For example, it might recommend training to further enhance project management, team leadership, and communication skills to an employee with these skills. Furthermore, the recommendation department can recommend training based on an employee's job responsibilities. For example, it might recommend training related to project management, team leadership, and communication to an employee responsible for these tasks. This ensures that the recommendation department recommends the most suitable training and certifications based on an employee's career goals and skill set.

[0083] The AI ​​agent system according to this embodiment includes a mentoring unit. The mentoring unit can provide regular feedback and advice to each employee. For example, the mentoring unit provides feedback and advice to employees every Friday. For example, the mentoring unit provides specific areas for improvement and advice on the next steps based on the employee's learning progress and achievements. The mentoring unit can also provide encouraging messages and incentives to maintain employee motivation. Furthermore, the mentoring unit can propose long-term learning strategies based on the employee's career goals and skill set. This helps maintain employee motivation and proposes individualized learning strategies through regular feedback and advice.

[0084] The reception system can estimate the user's emotions and prioritize input information based on those emotions. For example, if the user is stressed, the reception system will prioritize inputting important information and postpone detailed information. Conversely, if the user is relaxed, the reception system can encourage input including detailed information to collect more accurate data. Furthermore, if the user is in a hurry, the reception system can prompt them to input only the most important information to expedite processing. This enables efficient information input by prioritizing input information based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The reception desk can analyze past input history and suggest the optimal input method. For example, it can automatically display information that the user has frequently entered in the past as a suggestion. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest information that the user will use at specific times based on their past input history. In this way, by analyzing past input history, it can suggest the optimal input method and achieve efficient information input.

[0086] The reception system can filter data input based on the user's current work status and areas of interest. For example, it can prioritize inputting information related to projects the user is currently working on. It can also filter and display highly relevant information based on the user's areas of interest. Furthermore, it can efficiently collect data by allowing users to input only the necessary information according to their work status. This allows for efficient input of highly relevant information by filtering data based on the user's work status and areas of interest.

[0087] The reception desk can estimate the user's emotions and adjust how input information is displayed based on those emotions. For example, if the user is nervous, the reception desk can provide a simple and easy-to-read display. If the user is relaxed, the reception desk can also provide a display that includes detailed information. Furthermore, if the user is in a hurry, the reception desk can provide a concise display. By adjusting how input information is displayed based on the user's emotions, a highly easy-to-read display is provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The reception system can prioritize inputting highly relevant information by considering the user's geographical location during data entry. For example, if a user is in a specific region, the reception system will prioritize inputting information related to that region. Furthermore, the reception system can filter and display highly relevant information based on the user's current location. In addition, the reception system can efficiently collect data by considering the user's geographical location and requiring only the necessary information to be entered. This allows for efficient input of highly relevant information by considering the user's geographical location.

[0089] The reception desk can analyze the user's social media activity during data entry and input relevant information. For example, the reception desk can prompt users to input relevant information based on what they have shared on social media. Furthermore, the reception desk can analyze the user's social media activity and prioritize inputting information of interest. In addition, based on the user's social media activity, the reception desk can efficiently collect data by prompting users to input only the necessary information. This allows for efficient input of relevant information by analyzing the user's social media activity.

[0090] The recommendation system can estimate the user's emotions and adjust the way recommendations are presented based on those emotions. For example, if the user is relaxed, the recommendation system can provide detailed recommendations. If the user is in a hurry, it can provide concise and to-the-point recommendations. Furthermore, if the user is excited, it can provide visually appealing recommendations. This means that by adjusting the way recommendations are presented based on the user's emotions, it can provide visually appealing recommendations. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The recommendation department can adjust the level of detail in recommendations based on the importance of the training or qualifications. For example, it can provide detailed information for highly important training or qualifications, and concise information for less important ones. Furthermore, the recommendation department can adjust the display order of recommendations according to their importance. This allows for the priority provision of important information by adjusting the level of detail in recommendations based on the importance of the training or qualifications.

[0092] The recommendation department can apply different recommendation algorithms depending on the category of training or qualification. For example, for technical training or qualifications, the recommendation department can apply a recommendation algorithm that emphasizes technical skills. Similarly, for management-related training or qualifications, the recommendation department can apply a recommendation algorithm that emphasizes leadership skills. Furthermore, the recommendation department can select the most appropriate recommendation algorithm for each category and provide recommendations accordingly. This ensures that appropriate recommendations are provided by applying the most suitable recommendation algorithm for each training or qualification category.

[0093] The recommendation system can estimate the user's emotions and adjust the length of recommendations based on those emotions. For example, if the user is relaxed, the recommendation system will provide detailed recommendations. If the user is in a hurry, it can provide concise and to-the-point recommendations. Furthermore, if the user is excited, the recommendation system can provide visually appealing recommendations. By adjusting the length of recommendations based on the user's emotions, it can provide visually appealing recommendations. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The recommendation department can prioritize recommendations based on the timing of training and qualification offerings. For example, it will prioritize recommendations for training and qualifications that are available soon. It can also postpone recommendations for those with later availability dates. Furthermore, the recommendation department can adjust the display order of recommendations according to their availability dates. This ensures that recommendations are made at the appropriate time by prioritizing recommendations based on the timing of training and qualification offerings.

[0095] The recommendation system can adjust the order of recommendations based on the relevance of training programs and qualifications. For example, the recommendation system will prioritize recommending training programs and qualifications that are most relevant to the user's career goals. It can also delay recommending less relevant training programs and qualifications. Furthermore, the recommendation system can adjust the display order of recommendations based on their relevance. This allows the system to prioritize the provision of highly relevant information by adjusting the order of recommendations based on the relevance of training programs and qualifications.

[0096] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated emotions. For example, if the user is relaxed, the analysis unit can apply detailed analysis criteria. It can also apply concise analysis criteria if the user is in a hurry. Furthermore, if the user is excited, the analysis unit can apply visually appealing analysis criteria. This allows for appropriate analysis results by adjusting the analysis criteria based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The analysis unit can improve the accuracy of its analysis by referring to past training data during the analysis process. For example, the analysis unit can set optimal analysis criteria based on the user's past training data. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to past training data. In addition, the analysis unit can analyze the user's learning trends and apply optimal analysis criteria. This improves the accuracy of the analysis by referring to past training data.

[0098] The analysis department can perform analyses while considering employee attribute information. For example, the analysis department can set optimal analysis criteria based on employee attribute information such as age and gender. Furthermore, the analysis department can improve the accuracy of the analysis by considering employees' work experience and skill sets. In addition, the analysis department can apply optimal analysis criteria based on employee attribute information. This allows the analysis department to provide appropriate results by considering employee attribute information.

[0099] The analysis unit can estimate the user's emotions and adjust how the analysis results are displayed based on the estimated emotions. For example, if the user is relaxed, the analysis unit can display detailed results. If the user is in a hurry, the analysis unit can also display concise results. Furthermore, if the user is excited, the analysis unit can display visually appealing results. In this way, by adjusting how the analysis results are displayed based on the user's emotions, it provides visually appealing analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The analysis department can perform analyses while considering the geographical distribution of employees. For example, the analysis department can set optimal analysis criteria based on the geographical distribution of employees. Furthermore, the analysis department can improve the accuracy of the analysis by considering geographical distribution. In addition, the analysis department can apply optimal analysis criteria based on the geographical distribution of employees. This allows for the provision of appropriate analysis results by considering the geographical distribution of employees.

[0101] The analysis unit can improve the accuracy of its analysis by referring to relevant literature during the analysis process. For example, the analysis unit can set optimal analysis criteria based on relevant literature. Furthermore, the analysis unit can improve the accuracy of its analysis by referring to relevant literature. In addition, the analysis unit can apply optimal analysis criteria based on relevant literature. This improves the accuracy of the analysis by referring to relevant literature.

[0102] The support unit can estimate the user's emotions and adjust its support methods based on those emotions. For example, if the user is relaxed, the support unit can provide detailed support. If the user is in a hurry, the support unit can provide concise and to-the-point support. Furthermore, if the user is excited, the support unit can provide visually appealing support. This means that by adjusting the support methods based on the user's emotions, visually appealing support can be provided. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The support department can select the optimal support method by referring to past support data during support. For example, the support department can select the optimal support method based on the user's past support data. Furthermore, the support department can improve the accuracy of support by referring to past support data. In addition, the support department can analyze the user's support history and provide the optimal support method. Thus, by referring to past support data, the optimal support method is selected.

[0104] The support department can customize the support methods provided based on the employee's current learning progress. For example, the support department can provide the most suitable support methods according to the employee's learning progress. Furthermore, the support department can monitor the employee's learning progress in real time and provide appropriate support. In addition, the support department can provide customized support methods based on the employee's learning progress. This allows for the provision of appropriate support by customizing support methods based on the employee's learning progress.

[0105] The support unit can estimate the user's emotions and prioritize support based on those emotions. For example, if the user is stressed, the support unit will prioritize providing important support. If the user is relaxed, the support unit can also provide more detailed support. Furthermore, if the user is in a hurry, the support unit can prioritize providing support for a quick response. This ensures that important support is prioritized by prioritizing support based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0106] The support department can select the optimal support method by considering the employee's geographical location during support. For example, the support department can provide the optimal support method based on the employee's current location. Furthermore, the support department can provide highly relevant support by considering geographical location. In addition, the support department can select an efficient support method based on the employee's geographical location. This allows the support department to provide the optimal support method by considering the employee's geographical location.

[0107] The support department can analyze employees' social media activity during support sessions and propose appropriate support methods. For example, the support department can suggest the most suitable support method based on an employee's social media activity. Furthermore, the support department can analyze social media activity and provide support tailored to the employee's interests. In addition, the support department can propose customized support methods based on an employee's social media activity. This allows the support department to propose the most suitable support method by analyzing employees' social media activity.

[0108] The feedback unit can estimate the user's emotions and adjust the feedback method based on the estimated emotions. For example, if the user is relaxed, the feedback unit can provide detailed feedback. If the user is in a hurry, the feedback unit can provide concise and to-the-point feedback. Furthermore, if the user is excited, the feedback unit can provide visually appealing feedback. This means that by adjusting the feedback method based on the user's emotions, visually appealing feedback can be provided. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0109] The feedback unit can select the optimal feedback method by referring to past feedback data during the feedback process. For example, the feedback unit selects the optimal feedback method based on the user's past feedback data. Furthermore, the feedback unit can improve the accuracy of feedback by referring to past feedback data. In addition, the feedback unit can analyze the user's feedback history and provide the optimal feedback method. Thus, by referring to past feedback data, the optimal feedback method is selected.

[0110] The feedback unit can customize the feedback method based on the employee's current learning status. For example, the feedback unit can provide the optimal feedback method according to the employee's learning progress. Furthermore, the feedback unit can monitor the employee's learning status in real time and provide appropriate feedback. In addition, the feedback unit can provide customized feedback methods based on the employee's learning status. This ensures that appropriate feedback is provided by customizing the feedback method based on the employee's learning status.

[0111] The feedback unit can estimate the user's emotions and prioritize feedback based on those emotions. For example, if the user is stressed, the feedback unit will prioritize providing important feedback. It can also provide detailed feedback if the user is relaxed. Furthermore, if the user is in a hurry, the feedback unit can prioritize providing feedback for quick action. This ensures that important feedback is prioritized by prioritizing it based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0112] The feedback department can select the optimal feedback method when providing feedback, taking into account the employee's geographical location. For example, the feedback department can provide the optimal feedback method based on the employee's current location. Furthermore, the feedback department can provide highly relevant feedback by considering geographical location information. In addition, the feedback department can select an efficient feedback method based on the employee's geographical location information. This allows the department to provide the optimal feedback method by considering the employee's geographical location information.

[0113] The feedback department can analyze employees' social media activity and propose feedback methods during the feedback process. For example, the feedback department can suggest the most suitable feedback method based on employees' social media activity. Furthermore, the feedback department can analyze social media activity and provide feedback content of interest. In addition, the feedback department can propose customized feedback methods based on employees' social media activity. This allows the department to propose the most suitable feedback method by analyzing employees' social media activity.

[0114] The mentoring unit can estimate the user's emotions and adjust the mentoring method based on the estimated emotions. For example, if the user is relaxed, the mentoring unit can provide detailed mentoring content. If the user is in a hurry, the mentoring unit can provide concise and to-the-point mentoring content. Furthermore, if the user is excited, the mentoring unit can provide visually appealing mentoring content. In this way, by adjusting the mentoring method based on the user's emotions, visually appealing mentoring content can be provided. 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.

[0115] The mentoring unit can select the optimal mentoring method by referring to past mentoring data during mentoring sessions. For example, the mentoring unit can select the optimal mentoring method based on the user's past mentoring data. Furthermore, the mentoring unit can improve the accuracy of mentoring by referring to past mentoring data. In addition, the mentoring unit can analyze the user's mentoring history and provide the optimal mentoring method. Thus, by referring to past mentoring data, the optimal mentoring method is selected.

[0116] The mentoring department can customize mentoring methods based on the employee's current learning progress. For example, the mentoring department can provide the optimal mentoring method according to the employee's learning progress. Furthermore, the mentoring department can monitor the employee's learning progress in real time and provide appropriate mentoring. In addition, the mentoring department can provide customized mentoring methods based on the employee's learning progress. This allows for the provision of appropriate mentoring by customizing mentoring methods based on the employee's learning progress.

[0117] The mentoring unit can estimate the user's emotions and prioritize mentoring based on those emotions. For example, if the user is stressed, the mentoring unit will prioritize important mentoring. It can also provide more detailed mentoring if the user is relaxed. Furthermore, if the user is in a hurry, the mentoring unit can prioritize mentoring for quick responses. This ensures that important mentoring is prioritized by determining mentoring priorities based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0118] The mentoring department can select the optimal mentoring method by considering the employee's geographical location during mentoring sessions. For example, the mentoring department can provide the optimal mentoring method based on the employee's current location. Furthermore, the mentoring department can provide highly relevant mentoring by considering geographical location information. In addition, the mentoring department can select an efficient mentoring method based on the employee's geographical location information. This allows the department to provide the optimal mentoring method by considering the employee's geographical location information.

[0119] The mentoring department can analyze employees' social media activity during mentoring sessions and propose mentoring methods. For example, the mentoring department can propose the most suitable mentoring method based on an employee's social media activity. Furthermore, the mentoring department can analyze social media activity and provide mentoring content tailored to the employee's interests. In addition, the mentoring department can propose customized mentoring methods based on an employee's social media activity. This allows the department to propose the most suitable mentoring method by analyzing employees' social media activity.

[0120] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0121] The AI-generated agent system can also include a health management department. This department can monitor employees' health status and propose appropriate health management plans. For example, it can analyze employees' exercise habits and dietary content and provide advice for maintaining good health. It can also measure employees' stress levels and suggest relaxation methods to reduce stress. Furthermore, it can monitor employees' sleep patterns and provide advice to ensure high-quality sleep. This allows for comprehensive management of employees' health status and support for maintaining good health.

[0122] The AI ​​agent generation system can also be equipped with a communications department. This department can provide support to facilitate communication among employees. For example, it can analyze employees' communication styles and suggest effective communication methods. It can also plan workshops and training sessions for team building to strengthen collaboration among employees. Furthermore, it can collect employee feedback and suggest areas for improvement, thereby improving the workplace environment. This can lead to smoother communication among employees and an improved work environment.

[0123] The AI-generated agent system can also include a career coaching department. This department can provide coaching to support employees' career development. For example, it can propose specific career plans based on employees' career goals. It can also identify employees' skill gaps and provide training plans to acquire necessary skills. Furthermore, it can provide advice on employees' career paths and clarify their career direction. This supports employees' career development and helps them achieve their career goals.

[0124] The AI ​​agent generation system can also include a performance evaluation unit. This unit can evaluate employees' work performance and provide feedback. For example, it can quantitatively evaluate employees' work results and suggest specific areas for improvement. It can also analyze employees' strengths and weaknesses and provide advice for performance improvement. Furthermore, it can create regular evaluation reports to support employee growth. This allows for the evaluation of employee work performance and support for performance improvement.

[0125] The AI ​​agent generation system can also include a project management department. This department can manage the progress of projects assigned to employees and support efficient project operation. For example, it can monitor project progress in real time and quickly propose countermeasures if delays occur. It can also optimize project resource allocation to achieve efficient project operation. Furthermore, it can assess project risks and propose measures to mitigate them. This allows for effective project management and supports efficient project operation.

[0126] The generating AI agent system can further adjust the learning environment based on employees' emotions using its emotion estimation function. For example, if an employee is feeling stressed, the system can provide a relaxing learning environment. It can also use the emotion estimation function to provide incentives to boost employee motivation. Furthermore, it can use the emotion estimation function to suggest a learning schedule that helps employees maintain concentration. In this way, the learning environment can be adjusted based on employees' emotions, supporting effective learning.

[0127] The generating AI agent system can further utilize emotion estimation capabilities to provide feedback based on employees' emotions. For example, using emotion estimation, it can provide detailed feedback when an employee is relaxed. It can also provide concise and to-the-point feedback when an employee is in a hurry. Furthermore, it can provide visually appealing feedback when an employee is excited. This enables the provision of emotion-based feedback, resulting in more effective feedback.

[0128] The generating AI agent system can further utilize emotion estimation capabilities to suggest learning strategies based on employees' emotions. For example, if an employee is feeling stressed, the system can suggest relaxing learning methods. It can also use emotion estimation to set learning goals that will boost employee motivation. Furthermore, it can suggest learning schedules that will help employees maintain focus. This allows the system to propose learning strategies based on employees' emotions, supporting effective learning.

[0129] The generating AI agent system can further utilize emotion estimation capabilities to provide support for maintaining employee motivation based on their emotions. For example, it can use emotion estimation to provide encouraging messages if an employee's motivation is low. It can also use emotion estimation to provide incentives to boost employee motivation. Furthermore, it can use emotion estimation to support employees in setting goals to maintain their motivation. This enables support for maintaining employee motivation based on their emotions and facilitates effective learning.

[0130] The generating AI agent system can further manage employees' learning progress based on their emotions by utilizing its emotion estimation function. For example, it can adjust the learning pace if an employee is feeling stressed. It can also suggest progress management methods to increase employee motivation and maintain concentration. This allows for learning progress management based on employees' emotions, supporting effective learning.

[0131] The following briefly describes the processing flow for example form 2.

[0132] Step 1: The reception desk enters the employee's career goals, current skill set, job responsibilities, and available hours. Employee career goals include short-term goals, long-term goals, and promotion targets; skill sets include technical skills, soft skills, and specialized knowledge; job responsibilities include project management, sales activities, and technical development; and available hours include the number of hours per week and per month. Step 2: The recommendation department analyzes the information entered by the reception department and recommends the most suitable training and certifications. For example, based on an employee's career goals, current skill set, job responsibilities, and available time, it recommends project management certification training to an employee aiming for a career as a project manager. It also analyzes learning tendencies and strengths based on the employee's past learning data and personality traits data, and proposes individualized learning strategies. Step 3: The analysis department analyzes learning trends and strengths based on training and qualifications recommended by the recommendation department. For example, based on employees' past learning data and personality traits, they propose learning strategies centered on online learning to employees who have achieved high results in online learning in the past. Step 4: Based on the results analyzed by the analysis department, the support department provides support such as suggesting learning timing, managing progress, maintaining motivation, and preparing for tests. For example, for an employee who allocates 10 hours of study time per week, they might suggest studying for 2 hours each on Monday and Wednesday evenings. Step 5: The feedback department provides real-time feedback based on the learning progress supported by the support department. For example, it provides advice to employees who are falling behind in their learning to help them pick up the pace.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements described above, including the reception, recommendation, analysis, support, feedback, and mentoring departments, is implemented by, for example, at least one of the smart device 14 and the data processing device 12. For example, the reception department is implemented by the control unit 46A of the smart device 14 and inputs the employee's career goals, current skill set, job duties, and available time. The recommendation department is implemented by, for example, the specific processing unit 290 of the data processing device 12 and analyzes the input information to recommend the most suitable training and qualifications. The analysis department is implemented by, for example, the specific processing unit 290 of the data processing device 12 and analyzes the employee's learning tendencies and strengths based on their past learning data and personality trait data. The support department is implemented by, for example, the control unit 46A of the smart device 14 and supports suggesting learning timing, progress management, motivation maintenance, and preparation for tests. The feedback department is implemented by, for example, the specific processing unit 290 of the data processing device 12 and provides real-time feedback according to the learning progress. The mentoring function is implemented, for example, by the control unit 46A of the smart device 14, which provides regular feedback and advice to each employee. The correspondence between each part and the device or control unit is not limited to the example described above and can be modified in various ways.

[0137] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0138] 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.

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

[0140] The 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.

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

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (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).

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

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements mentioned above, including the reception, recommendation, analysis, support, feedback, and mentoring departments, is implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception department is implemented by the control unit 46A of the smart glasses 214, which inputs the employee's career goals, current skill set, job duties, and available time. The recommendation department is implemented, for example, by the specific processing unit 290 of the data processing device 12, which analyzes the input information and recommends the most suitable training and qualifications. The analysis department is implemented, for example, by the specific processing unit 290 of the data processing device 12, which analyzes the employee's learning tendencies and strengths based on their past learning data and personality trait data. The support department is implemented, for example, by the control unit 46A of the smart glasses 214, which supports suggesting learning timing, progress management, motivation maintenance, and preparation for tests. The feedback department is implemented, for example, by the specific processing unit 290 of the data processing device 12, which provides real-time feedback according to the learning progress. The mentoring function is implemented, for example, by the control unit 46A of the smart glasses 214, which provides regular feedback and advice to each employee. The correspondence between each part and the device or control unit is not limited to the example described above and can be modified in various ways.

[0153] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] Each of the multiple elements described above, including the reception, recommendation, analysis, support, feedback, and mentoring departments, is implemented by, for example, at least one of the headset terminal 314 and the data processing device 12. For example, the reception department is implemented by the control unit 46A of the headset terminal 314, which inputs the employee's career goals, current skill set, job duties, and available time. The recommendation department is implemented by, for example, the specific processing unit 290 of the data processing device 12, which analyzes the input information and recommends the most suitable training and qualifications. The analysis department is implemented by, for example, the specific processing unit 290 of the data processing device 12, which analyzes the employee's learning tendencies and strengths based on their past learning data and personality trait data. The support department is implemented by, for example, the control unit 46A of the headset terminal 314, which supports suggesting learning timing, progress management, motivation maintenance, and preparation for tests. The feedback department is implemented by, for example, the specific processing unit 290 of the data processing device 12, which provides real-time feedback according to the learning progress. The mentoring function is implemented, for example, by the control unit 46A of the headset terminal 314, which provides regular feedback and advice to each employee. The correspondence between each part and the device or control unit is not limited to the example described above and can be modified in various ways.

[0169] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.).

[0182] 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.

[0183] 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.

[0184] 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.

[0185] Each of the multiple elements described above, including the reception, recommendation, analysis, support, feedback, and mentoring departments, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception department is implemented by the control unit 46A of the robot 414 and inputs the employee's career goals, current skill set, job duties, and available time. The recommendation department is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the input information to recommend the most suitable training and qualifications. The analysis department is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the employee's learning tendencies and strengths based on their past learning data and personality trait data. The support department is implemented by, for example, the control unit 46A of the robot 414 and supports suggesting learning timing, progress management, motivation maintenance, and preparation for tests. The feedback department is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides real-time feedback according to the learning progress. The mentoring function is implemented, for example, by the control unit 46A of robot 414, which provides regular feedback and advice to each employee. The correspondence between each part and the device or control unit is not limited to the example described above and can be modified in various ways.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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."

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] (Note 1) The reception desk is where employees enter their career goals, current skill sets, job responsibilities, and available time. The reception department analyzes the information entered and recommends appropriate training and qualifications, The Analysis Department analyzes learning trends and strengths based on training and qualifications recommended by the aforementioned Recommendation Department, Based on the results of the analysis conducted by the aforementioned analysis department, the support department provides assistance with suggesting learning timing, managing progress, maintaining motivation, and preparing for tests immediately before they begin. The system includes a feedback unit that provides immediate feedback according to the progress of learning supported by the aforementioned support unit. A system characterized by the following features. (Note 2) The aforementioned feedback unit is Provides real-time feedback based on learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned support unit is We offer suggestions for learning timing, support for progress management and motivation maintenance, and assistance with last-minute test preparation. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit is Analyzes employees' learning tendencies and strengths based on their past learning data and personality traits. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned recommendation department, We recommend the most suitable training and certifications based on employees' career goals, current skill sets, job responsibilities, and available time. The system described in Appendix 1, characterized by the features described herein. (Note 6) We have a mentoring department that provides regular feedback and advice to each employee. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and prioritizes input information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is We analyze past input history and suggest the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When inputting data, filtering is performed based on the user's current work situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and adjusts how input information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When inputting data, the system prioritizes inputting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is During input, the system analyzes the user's social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned recommendation department, It estimates the user's emotions and adjusts the way recommendations are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned recommendation department, When making a recommendation, adjust the level of detail based on the importance of the training and qualifications. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned recommendation department, When making recommendations, different recommendation algorithms are applied depending on the category of training or qualification. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned recommendation department, It estimates the user's sentiment and adjusts the length of recommendations based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned recommendation department, When making recommendations, priority will be determined based on the timing of training and qualification provision. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned recommendation department, When making recommendations, we adjust the order of recommendations based on the relevance of training and qualifications. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is We estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit is During analysis, past training data is referenced to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit is When conducting the analysis, employee attribute information will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit is When conducting the analysis, the geographical distribution of employees will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit is During analysis, refer to relevant literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned support unit is It estimates the user's emotions and adjusts the support method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned support unit is During support, we refer to past support data to select the most suitable support method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned support unit is When providing support, customize the support methods based on the employee's current learning status. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned support unit is The system estimates the user's emotions and determines support priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned support unit is When providing support, the most suitable support method will be selected considering the geographical location of the employees. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned support unit is During support, we analyze employees' social media activity and propose support methods. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned feedback unit is It estimates the user's emotions and adjusts the feedback method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned feedback unit is When providing feedback, refer to past feedback data to select the most suitable feedback method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned feedback unit is When providing feedback, customize the feedback method based on the employee's current learning status. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned feedback unit is It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned feedback unit is When providing feedback, the most suitable feedback method will be selected, taking into account the employee's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned feedback unit is When providing feedback, we analyze employees' social media activity and propose methods for providing feedback. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned mentoring unit is, It estimates the user's emotions and adjusts the mentoring method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned mentoring unit is, During mentoring sessions, past mentoring data is referenced to select the most suitable mentoring method. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned mentoring unit is, During mentoring sessions, customize the mentoring methods based on the employee's current learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned mentoring unit is, The system estimates the user's emotions and determines mentoring priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned mentoring unit is, When mentoring, the most suitable mentoring method is selected by considering the employee's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned mentoring unit is, During mentoring sessions, we analyze employees' social media activity and propose mentoring strategies. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception desk is where employees enter their career goals, current skill sets, job responsibilities, and available time. The reception department analyzes the information entered and recommends appropriate training and qualifications, The Analysis Department analyzes learning trends and strengths based on training and qualifications recommended by the aforementioned Recommendation Department, Based on the results of the analysis conducted by the aforementioned analysis department, the support department provides assistance with suggesting learning timing, managing progress, maintaining motivation, and preparing for tests immediately before they begin. The system includes a feedback unit that provides immediate feedback according to the progress of learning supported by the aforementioned support unit. A system characterized by the following features.

2. The aforementioned feedback unit is It estimates the user's emotions and adjusts the feedback method based on the estimated user emotions. The system according to feature 1.

3. The aforementioned support unit is During support, we analyze employees' social media activity and propose support methods. The system according to feature 1.

4. The aforementioned analysis unit is Analyzes employees' learning tendencies and strengths based on their past learning data and personality traits. The system according to feature 1.

5. The aforementioned recommendation department, When making recommendations, priority will be determined based on the timing of training and qualification provision. The system according to feature 1.

6. We have a mentoring department that provides regular feedback and advice to each employee. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and prioritizes input information based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is We analyze past input history and suggest the optimal input method. The system according to feature 1.

9. The aforementioned reception unit is When inputting data, filtering is performed based on the user's current work situation and areas of interest. The system according to feature 1.

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