system

Abstract

The system according to this embodiment aims to quantify employees' skills and effectively formulate career plans and learning plans. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a quantification unit, a career plan generation unit, and a learning plan generation unit. The data collection unit collects data on employees' skills. The analysis unit analyzes the data collected by the data collection unit. The quantification unit quantifies skills based on the data analyzed by the analysis unit. The career plan generation unit generates a career plan based on the skills quantified by the quantification unit. The learning plan generation unit generates a learning plan based on the skills quantified by the quantification unit.

Description

Technical Field

[0004]

[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 it is difficult to appropriately grasp the skills of employees and effectively formulate career plans and learning plans.

[0005] The system according to the embodiment aims to quantify the skills of employees and effectively formulate career plans and learning plans.

Means for Solving the Problems

[0006] The system according to the embodiment comprises a data collection unit, an analysis unit, a quantification unit, a career plan generation unit, and a learning plan generation unit. The data collection unit collects data on employees' skills. The analysis unit analyzes the data collected by the data collection unit. The quantification unit quantifies skills based on the data analyzed by the analysis unit. The career plan generation unit generates a career plan based on the skills quantified by the quantification unit. The learning plan generation unit generates a learning plan based on the skills quantified by the quantification unit. [Effects of the Invention]

[0007] The system according to this embodiment can quantify employees' skills and effectively formulate career plans and learning plans. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The skill visualization system according to an embodiment of the present invention is a system that facilitates appropriate assignments and growth support by easily collecting and visualizing employee skills. The skill visualization system collects data on employee skills, and a generating AI analyzes it, quantifying the employee's skills for each set item. This makes the employee's skill set visible, which is useful when transferring or assigning tasks. The generating AI also generates career plans and learning plans to address weaknesses, supporting employee growth. For example, the skill visualization system constantly monitors company PCs and meeting tools to collect data on employee skills. For example, it monitors work content such as programming in Java®, creating ideas for new projects, and creating various management files. It also constantly monitors meeting tools to analyze skills such as facilitation skills, proposal skills, and problem identification skills. This allows learning data to be obtained from daily work without the need to prepare individual inputs. Next, the generating AI analyzes the collected data and quantifies the employee's skills for each set item. For example, skills such as Java programming skills, new project idea generation skills, facilitation skills, proposal skills, and problem identification skills are quantified. This makes employees' skill sets visible, which is useful when transferring or assigning tasks. Furthermore, the generating AI creates career plans and learning plans to address any shortcomings in employees. For example, it may suggest a career plan indicating that tasks like XX are a good fit for employee A's skills and that they are expected to excel in those roles. It may also generate a learning plan indicating that A needs to develop their △△ skills in order to perform their desired tasks. This supports employee growth. This system not only makes employees' skill sets visible, which is useful when transferring or assigning tasks, but also supports employee growth. For example, employees can find a career plan that suits them and efficiently learn the necessary skills. In addition, managers can quantitatively understand employees' skill sets and make appropriate assignments. This improves employee satisfaction and increases overall company productivity. In summary, the skill visualization system makes it easy to collect and visualize employee skills, enabling appropriate assignments and growth support.

[0029] The skill visualization system according to this embodiment comprises a data collection unit, an analysis unit, a quantification unit, a career plan generation unit, and a learning plan generation unit. The data collection unit collects data on employees' skills. The data collection unit can collect data on employees' skills by, for example, constantly monitoring company PCs and meeting tools. For example, the data collection unit monitors work content such as Java programming, idea generation for new projects, and creation of various management files. The data collection unit can also constantly monitor meeting tools and analyze skills such as facilitation skills, proposal skills, and problem identification skills. This makes it possible to obtain learning data from daily work without preparing individual inputs. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can analyze the collected data and quantify skills by, for example, using a generation AI. For example, the analysis unit uses a generation AI to quantify skills such as Java programming skills, new project idea generation skills, facilitation skills, proposal skills, and problem identification skills. The quantification unit quantifies skills based on the data analyzed by the analysis unit. The quantification unit can quantify the analyzed skills for each set item. For example, the quantification unit quantifies skills such as Java programming skills, new project idea generation skills, facilitation skills, proposal skills, and problem identification skills. The career plan generation unit generates a career plan based on the skills quantified by the quantification unit. For example, the career plan generation unit can generate an employee's career plan based on the quantified skills. For example, the career plan generation unit proposes a career plan stating that A's skills are suited to tasks like XX, and that they are expected to perform well. The learning plan generation unit generates a learning plan based on the skills quantified by the quantification unit. For example, the learning plan generation unit generates a learning plan stating that A needs to improve their △△ skills in order to perform their desired tasks. As a result, the skill visualization system according to the embodiment can easily collect and visualize employee skills, enabling appropriate assignments and growth support.

[0030] The data collection department collects data on employees' skills. For example, it can continuously monitor company PCs and meeting tools to collect data on employees' skills. Specifically, monitoring software installed on company PCs records the operation history of applications and files used by employees and sends this data to a database. For example, it monitors work content such as Java programming, idea generation for new projects, and creation of various management files. This allows the department to understand what skills employees possess and how often they use those skills. The data collection department can also continuously monitor meeting tools and analyze skills such as facilitation skills, proposal skills, and problem identification skills. Using the speech recognition and text analysis functions of meeting tools, it evaluates the content, frequency, and quality of contributions during meetings and collects this as skill data. This allows learning data to be obtained from daily work without preparing individual inputs. Furthermore, the data collection department can also collect employee self-assessments and supervisor evaluations. For example, it can import the results of regularly conducted evaluation questionnaires and feedback sessions into a database and utilize this as skill data. This allows the data collection unit to evaluate employees' skills from multiple perspectives and collect more accurate data.

[0031] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can use generative AI to analyze the collected data and quantify skills. Specifically, generative AI uses natural language processing techniques and machine learning algorithms to analyze collected text and audio data. For instance, generative AI can quantify skills such as Java programming skills, new project idea generation skills, facilitation skills, proposal skills, and problem identification skills. The generative AI evaluates the quality and efficiency of programming code and uses this to quantify programming skills. It also analyzes the content and frequency of contributions during meetings to evaluate facilitation and proposal skills. Furthermore, the generative AI can evaluate relative skill levels by comparing them with past data and data from other employees. For example, it can assess how well or poorly a particular skill is compared to other employees performing the same tasks. This allows the analysis unit to quickly and accurately analyze the collected data and quantify employee skills. Additionally, the analysis unit can analyze skill trends and fluctuations. For example, it can track how a particular skill changes over time and evaluate skill improvement or decline. This allows the analysis department to understand not only the current state of employees' skills but also how those skills might change in the future.

[0032] The quantification unit quantifies skills based on data analyzed by the analysis unit. For example, the quantification unit can quantify analyzed skills for each set item. Specifically, the quantification unit sets evaluation criteria for each skill and quantifies the skill based on these criteria. For example, it quantifies skills such as Java programming skills, new project idea generation skills, facilitation skills, proposal skills, and problem identification skills. For programming skills, the quantification unit uses criteria such as code quality, efficiency, and bug rate, and quantifies the skill based on these criteria. For idea generation skills, it uses criteria such as the number and quality of proposed ideas and their feasibility. Furthermore, for facilitation and proposal skills, it uses criteria such as the content and frequency of contributions during meetings and evaluations from other participants. This allows the quantification unit to objectively and quantitatively evaluate and quantify each skill. In addition, the quantification unit can visualize the skill evaluation results. For example, it can display the evaluation results for each skill as graphs or charts, making them easily understandable for employees and supervisors. This allows the quantification unit to present skill evaluation results in an easy-to-understand manner, which can be used to help employees improve their skills and develop career plans.

[0033] The Career Plan Generation Unit generates career plans based on skills quantified by the Quantification Unit. For example, the Career Plan Generation Unit can generate employee career plans based on quantified skills. Specifically, it analyzes employees' strengths and weaknesses based on quantified skill data and proposes the optimal career plan. For example, the Career Plan Generation Unit might propose a career plan stating that, given Employee A's skills, tasks like XX are a good fit and they are expected to perform well. The Career Plan Generation Unit considers not only employee skill data but also employee aspirations and goals, as well as company needs and strategies, when generating career plans. For example, if an employee wishes to take on leadership roles in the future, the unit will propose tasks and training to improve their leadership skills. Also, if the company plans to launch a new business, it will prioritize assigning employees with skills related to that business. In this way, the Career Plan Generation Unit can match employee skills with company needs and provide the optimal career plan. Furthermore, the Career Plan Generation Unit can monitor the progress of career plans and revise them as needed. For example, if an employee acquires new skills or if the company's strategy changes, the Career Plan Generation Unit will re-evaluate the career plan and make appropriate revisions. This allows the career plan generation unit to provide flexible career plans based on the latest information at all times, supporting employee growth and company development.

[0034] The learning plan generation unit generates learning plans based on skills quantified by the quantification unit. For example, the learning plan generation unit can generate employee learning plans based on quantified skills. Specifically, the learning plan generation unit analyzes which skills an employee needs to strengthen based on quantified skill data and proposes an optimal learning plan. For example, the learning plan generation unit generates a learning plan that states that employee A needs to improve their △△ skill for the desired work. The learning plan generation unit generates learning plans considering not only the employee's skill data but also their learning style and learning history. For example, if an employee prefers online courses, it proposes a learning plan centered on online courses. It also evaluates the results of training and courses the employee has previously taken and proposes effective learning methods. In this way, the learning plan generation unit can provide an optimal learning plan that meets the individual needs of each employee. Furthermore, the learning plan generation unit can monitor the progress of the learning plan and modify the plan as needed. For example, if an employee acquires a new skill or if the learning plan is not effective, it re-evaluates the learning plan and makes appropriate modifications. This allows the learning plan generation unit to always provide flexible learning plans based on the latest information, supporting employees' skill development.

[0035] The data collection unit can continuously monitor company PCs and meeting tools to collect data on employees' skills. For example, the data collection unit can continuously monitor company PCs to track tasks such as Java programming, idea generation for new projects, and creation of various management files. It can also continuously monitor meeting tools to analyze skills such as facilitation, proposal skills, and problem identification skills. This allows for easy acquisition of skill data by automatically collecting data on employees' skills. Continuous monitoring can be achieved, for example, by installing specific software to monitor the usage of employees' PCs and meeting tools in real time. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the usage status of company PCs and meeting tools into an AI and have the AI ​​collect skill data.

[0036] The analysis unit uses a generative AI to analyze collected data and quantify skills. For example, the analysis unit uses a generative AI to analyze collected data and quantify skills such as Java programming skills, new project idea generation skills, facilitation skills, proposal skills, and problem identification skills. This improves the accuracy of skill quantification by using a generative AI. The generative AI can, for example, use natural language processing technology to analyze collected text data and quantify skills. It can also use image recognition technology to analyze collected image data and quantify skills. Furthermore, the generative AI can use predictive models to analyze collected data and quantify skills. For example, the generative AI uses a natural language processing model that takes text data as input and outputs the result of quantifying skills to quantify them. This allows the analysis unit to use a generative AI to analyze collected data and quantify skills.

[0037] The quantification unit can quantify the analyzed skills for each set item. For example, the quantification unit quantifies the analyzed skills for each set item, such as Java programming skills, new project idea generation skills, facilitation skills, proposal skills, and problem identification skills. This makes it possible to visualize the skill set by quantifying skills for each set item. The quantification unit quantifies the analyzed skills using, for example, a scoring method. The quantification unit can also quantify the analyzed skills based on evaluation criteria. Furthermore, the quantification unit can quantify the analyzed skills using weighting. For example, the quantification unit quantifies skills on a scale from 0 to 100 as a scoring method. Evaluation criteria are set, for example, based on the importance or difficulty of the skill. Weighting is set, for example, according to the importance of each skill. This allows the quantification unit to quantify the analyzed skills for each set item.

[0038] The career plan generation unit can generate career plans based on quantified skills. For example, the unit generates an employee's career plan based on quantified skills. For instance, it might suggest a career plan indicating that employee A's skills are suited to tasks like XX, and that they are expected to excel in those roles. This enables employee career support by generating career plans based on quantified skills. The career plan generation unit can generate career plans that include, for example, promotion plans and skill development plans. It can also generate career plans based on employee aspirations and goals. Furthermore, it can generate career plans based on industry trends and market demand. For example, the career plan generation unit might suggest a career plan aiming for promotion to the next position as a promotion plan. A skill development plan might include, for example, a learning plan to improve specific skills. Employee aspirations and goals are set based on, for example, future career goals and desired job content. This allows the career plan generation unit to generate career plans based on quantified skills.

[0039] The learning plan generation unit can generate learning plans based on quantified skills. For example, the learning plan generation unit generates employee learning plans based on quantified skills. For example, the learning plan generation unit generates a learning plan stating that employee A needs to improve their △△ skill for the desired work. This enables support for employee growth by generating learning plans based on quantified skills. The learning plan generation unit generates learning plans that include, for example, training programs and self-study plans. The learning plan generation unit can also identify employee skill gaps and generate learning plans to bridge those gaps. Furthermore, the learning plan generation unit can generate learning plans based on industry trends and market demand. For example, the learning plan generation unit proposes training courses to improve specific skills as training programs. Self-study plans include, for example, online courses and self-study using books. Skill gaps are identified, for example, based on the difference between the current skill level and the target skill level. This allows the learning plan generation unit to generate learning plans based on quantified skills.

[0040] The data collection unit can analyze an employee's past work history and select the optimal data collection method. For example, the data collection unit can analyze the content of projects an employee has worked on in the past and prioritize the collection of relevant skill data. The data collection unit can also extract tasks that require specific skills from an employee's past work history and collect data related to those skills. Furthermore, the data collection unit can select the most efficient data collection method based on an employee's past work history and collect skill data. This enables efficient data collection by analyzing past work history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input an employee's past work history data into a generating AI and have the generating AI select the optimal data collection method.

[0041] The data collection unit can filter skill data based on an employee's current projects and areas of interest. For example, the data collection unit can prioritize collecting skill data related to the project the employee is currently working on. The data collection unit can also filter and collect relevant skill data based on the employee's areas of interest. Furthermore, the data collection unit can collect data based on the skills required for the employee's current project. This allows for the collection of highly relevant skill data by filtering the data based on the current project and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input employee project data and areas of interest data into a generating AI and have the generating AI perform the filtering.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location of employees when collecting skill data. For example, if an employee works in a specific region, the data collection unit will prioritize the collection of skill data related to that region. Furthermore, if an employee is working remotely, the data collection unit can prioritize the collection of skill data related to remote work. Additionally, if an employee is on a business trip, the data collection unit can prioritize the collection of skill data related to their business trip destination. This allows for the collection of highly relevant skill data by considering geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input employee geographical location data into a generating AI and have the generating AI collect highly relevant data.

[0043] The data collection unit can analyze employees' social media activity and collect relevant data when collecting skill data. For example, the data collection unit can collect skill data based on projects and work content shared by employees on social media. The data collection unit can also identify areas of interest from employees' social media activity and collect relevant skill data. Furthermore, the data collection unit can collect relevant skill data from experts and groups that employees follow on social media. This allows for the collection of skill data based on employees' areas of interest by analyzing their social media activity. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input employee social media data into a generating AI and have the generating AI collect relevant data.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the skill data during the analysis. For example, the analysis unit will perform a detailed analysis on important skill data. It can also perform a basic analysis on general skill data. Furthermore, for skill data related to a specific project, the analysis unit can adjust the level of detail of the analysis according to the importance of the project. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the skill data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the skill data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the category of skill data during analysis. For example, for programming skills, the analysis unit can apply an algorithm that evaluates the quality and efficiency of the code. For facilitation skills, the analysis unit can also apply an algorithm that evaluates the progress of meetings and the reactions of participants. Furthermore, for proposal skills, the analysis unit can apply an algorithm that evaluates the originality and feasibility of the proposal. By applying analysis algorithms according to the category of skill data, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the categories of skill data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0046] The analysis unit can determine the priority of analysis based on the timing of skill data collection during the analysis process. For example, the analysis unit may prioritize the analysis of recently collected skill data. It can also prioritize the analysis of skill data collected during a specific project period. Furthermore, the analysis unit can determine the priority of analysis based on regularly collected skill data. This enables efficient analysis by determining the priority of analysis based on the timing of skill data collection. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the timing of skill data collection into a generating AI and have the generating AI determine the priority of analysis.

[0047] The analysis unit can adjust the order of analysis based on the relevance of skill data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant skill data. It can also prioritize the analysis of skill data related to a specific project. Furthermore, it can prioritize the analysis of skill data related to an employee's career plan. This allows for efficient analysis by adjusting the order of analysis based on the relevance of skill data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of skill data into a generating AI and have the generating AI adjust the order of analysis.

[0048] The quantification unit can improve the accuracy of quantification by considering the interrelationships between skill data during the quantification process. For example, the quantification unit can perform quantification by considering the interrelationship between programming skills and project management skills. It can also perform quantification by considering the interrelationship between facilitation skills and proposal skills. Furthermore, it can perform quantification by considering the interrelationship between problem identification skills and problem-solving skills. This improves the accuracy of quantification by considering the interrelationships between skill data. Some or all of the above processing in the quantification unit may be performed using AI, for example, or without AI. For example, the quantification unit can input the interrelationships of skill data into a generating AI and have the generating AI perform the improvement of quantification accuracy.

[0049] The quantification unit can perform quantification while considering the attribute information of the submitter of the skill data. For example, the quantification unit can perform quantification while considering the submitter's job title and position. It can also perform quantification while considering the submitter's years of experience. Furthermore, the quantification unit can perform quantification while considering the submitter's field of expertise. This makes it possible to perform more appropriate quantification by considering the submitter's attribute information. Some or all of the above processing in the quantification unit may be performed using AI, for example, or without using AI. For example, the quantification unit can input the submitter's attribute information into a generating AI and have the generating AI perform the quantification.

[0050] The quantification unit can perform quantification while considering the geographical distribution of skill data. For example, the quantification unit can prioritize the quantification of skill data in a specific region. It can also prioritize the quantification of skill data related to remote work. Furthermore, it can prioritize the quantification of skill data related to business trip destinations. This allows for more appropriate quantification by considering geographical distribution. Some or all of the above processing in the quantification unit may be performed using AI, for example, or without AI. For example, the quantification unit can input the geographical distribution of skill data into a generating AI and have the generating AI perform the quantification.

[0051] The quantification unit can improve the accuracy of quantification by referring to relevant literature on skill data during the quantification process. For example, the quantification unit can perform quantification by referring to academic papers related to skill data. It can also perform quantification by referring to industry reports related to skill data. Furthermore, the quantification unit can perform quantification by referring to patent documents related to skill data. This improves the accuracy of quantification by referring to relevant literature. Some or all of the above processing in the quantification unit may be performed using AI, for example, or without AI. For example, the quantification unit can input relevant literature on skill data into a generating AI and have the generating AI perform the quantification accuracy improvement.

[0052] The career plan generation unit can predict the current career plan by referring to past career data when generating a career plan. For example, the career plan generation unit predicts the optimal career plan based on an employee's past career data. The career plan generation unit can also predict future career plans from an employee's past career data. Furthermore, the career plan generation unit can analyze an employee's past career data and propose the most efficient career plan. This allows for the prediction of a more appropriate career plan by referring to past career data. Some or all of the above-described processes in the career plan generation unit may be performed using AI, for example, or without AI. For example, the career plan generation unit can input past career data into a generation AI and have the generation AI perform career plan predictions.

[0053] The career plan generation unit can apply different career plan generation methods to each category of skill data when generating career plans. For example, the career plan generation unit can propose a technical career plan for programming skills. It can also propose a management career plan for facilitation skills. Furthermore, it can propose a sales or marketing career plan for proposal skills. By applying different methods to each category of skill data, a more appropriate career plan can be generated. Some or all of the above processing in the career plan generation unit may be performed using AI, for example, or without AI. For example, the career plan generation unit can input skill data categories into a generation AI and have the generation AI execute the application of career plan generation methods.

[0054] The career plan generation unit can analyze changes in the career plan based on the timing of skill data collection when generating the career plan. For example, the career plan generation unit can propose the latest career plan based on recently collected skill data. The career plan generation unit can also analyze changes in the career plan based on skill data collected during a specific project period. Furthermore, the career plan generation unit can predict changes in the career plan based on regularly collected skill data. This allows for the provision of a more appropriate career plan by analyzing changes in the career plan based on the timing of skill data collection. Some or all of the above-described processes in the career plan generation unit may be performed using AI, for example, or without AI. For example, the career plan generation unit can input the timing of skill data collection into the generation AI and have the generation AI perform the analysis of changes in the career plan.

[0055] The career plan generation unit can analyze career plans by referring to relevant market data for skill data when generating them. For example, the career plan generation unit proposes career plans based on market demand related to skill data. The career plan generation unit can also analyze career plans based on industry trends related to skill data. Furthermore, the career plan generation unit can propose career plans based on market growth forecasts related to skill data. This allows for the provision of more appropriate career plans by referring to relevant market data. Some or all of the above processing in the career plan generation unit may be performed using AI, for example, or without AI. For example, the career plan generation unit can input relevant market data into a generation AI and have the generation AI perform the career plan analysis.

[0056] The learning plan generation unit can improve the accuracy of the learning plan by considering the interrelationships of skill data during the planning process. For example, the learning plan generation unit can generate a learning plan by considering the interrelationships between programming skills and project management skills. It can also generate a learning plan by considering the interrelationships between facilitation skills and proposal skills. Furthermore, it can generate a learning plan by considering the interrelationships between problem identification skills and problem-solving skills. This improves the accuracy of the learning plan by considering the interrelationships of skill data. Some or all of the above processing in the learning plan generation unit may be performed using AI, for example, or without AI. For example, the learning plan generation unit can input the interrelationships of skill data into a generation AI and have the generation AI perform the task of improving the accuracy of the learning plan.

[0057] The learning plan generation unit can generate a learning plan while considering the attribute information of the skill data submitter. For example, the learning plan generation unit can generate a learning plan while considering the submitter's job title and position. It can also generate a learning plan while considering the submitter's years of experience. Furthermore, the learning plan generation unit can generate a learning plan while considering the submitter's field of expertise. This allows for the provision of a more appropriate learning plan by considering the submitter's attribute information. Some or all of the above processing in the learning plan generation unit may be performed using AI, for example, or without AI. For example, the learning plan generation unit can input the submitter's attribute information into a generation AI and have the generation AI execute the generation of the learning plan.

[0058] The learning plan generation unit can generate learning plans while considering the geographical distribution of skill data. For example, the learning plan generation unit can generate learning plans based on skill data in a specific region. It can also generate learning plans based on skill data related to remote work. Furthermore, it can generate learning plans based on skill data related to business trip destinations. This allows for the provision of more appropriate learning plans by considering geographical distribution. Some or all of the above processing in the learning plan generation unit may be performed using AI, for example, or without AI. For example, the learning plan generation unit can input the geographical distribution of skill data into a generation AI and have the generation AI perform the generation of learning plans.

[0059] The learning plan generation unit can improve the accuracy of the learning plan by referring to relevant literature related to the skill data during the planning process. For example, the learning plan generation unit can generate a learning plan by referring to academic papers related to the skill data. It can also generate a learning plan by referring to industry reports related to the skill data. Furthermore, the learning plan generation unit can generate a learning plan by referring to patent documents related to the skill data. This improves the accuracy of the learning plan by referring to relevant literature. Some or all of the above processing in the learning plan generation unit may be performed using AI, for example, or without AI. For example, the learning plan generation unit can input relevant literature related to the skill data into a generation AI and have the generation AI perform the task of improving the accuracy of the learning plan.

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

[0061] The data collection unit can analyze an employee's past work history and select the optimal data collection method. For example, the data collection unit can analyze the content of projects an employee has worked on in the past and prioritize the collection of relevant skill data. The data collection unit can also extract tasks that require specific skills from an employee's past work history and collect data related to those skills. Furthermore, the data collection unit can select the most efficient data collection method based on an employee's past work history and collect skill data. This enables efficient data collection by analyzing past work history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input an employee's past work history data into a generating AI and have the generating AI select the optimal data collection method.

[0062] The data collection unit can filter skill data based on an employee's current projects and areas of interest. For example, the data collection unit can prioritize collecting skill data related to the project the employee is currently working on. The data collection unit can also filter and collect relevant skill data based on the employee's areas of interest. Furthermore, the data collection unit can collect data based on the skills required for the employee's current project. This allows for the collection of highly relevant skill data by filtering the data based on the current project and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input employee project data and areas of interest data into a generating AI and have the generating AI perform the filtering.

[0063] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location of employees when collecting skill data. For example, if an employee works in a specific region, the data collection unit will prioritize the collection of skill data related to that region. Furthermore, if an employee is working remotely, the data collection unit can prioritize the collection of skill data related to remote work. Additionally, if an employee is on a business trip, the data collection unit can prioritize the collection of skill data related to their business trip destination. This allows for the collection of highly relevant skill data by considering geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input employee geographical location data into a generating AI and have the generating AI collect highly relevant data.

[0064] The data collection unit can analyze employees' social media activity and collect relevant data when collecting skill data. For example, the data collection unit can collect skill data based on projects and work content shared by employees on social media. The data collection unit can also identify areas of interest from employees' social media activity and collect relevant skill data. Furthermore, the data collection unit can collect relevant skill data from experts and groups that employees follow on social media. This allows for the collection of skill data based on employees' areas of interest by analyzing their social media activity. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input employee social media data into a generating AI and have the generating AI collect relevant data.

[0065] The analysis unit can adjust the level of detail of the analysis based on the importance of the skill data during the analysis. For example, the analysis unit will perform a detailed analysis on important skill data. It can also perform a basic analysis on general skill data. Furthermore, for skill data related to a specific project, the analysis unit can adjust the level of detail of the analysis according to the importance of the project. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the skill data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the skill data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0066] The analysis unit can apply different analysis algorithms depending on the category of skill data during analysis. For example, for programming skills, the analysis unit can apply an algorithm that evaluates the quality and efficiency of the code. For facilitation skills, the analysis unit can also apply an algorithm that evaluates the progress of meetings and the reactions of participants. Furthermore, for proposal skills, the analysis unit can apply an algorithm that evaluates the originality and feasibility of the proposal. By applying analysis algorithms according to the category of skill data, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the categories of skill data into a generating AI and have the generating AI execute the application of the analysis algorithm.

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

[0068] Step 1: The data collection unit collects data on employees' skills. The data collection unit can, for example, continuously monitor company PCs and meeting tools to collect data on employees' skills. Specifically, it monitors work content such as Java programming, idea generation for new projects, and creation of various management files. It can also continuously monitor meeting tools to analyze skills such as facilitation skills, proposal skills, and problem identification skills. This allows learning data to be obtained from daily work without preparing individual inputs. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the collected data and quantify skills, for example, using a generative AI. Specifically, the generative AI quantifies skills such as Java programming skills, new project idea generation skills, facilitation skills, proposal skills, and problem identification skills. Step 3: The quantification unit quantifies skills based on the data analyzed by the analysis unit. The quantification unit can, for example, quantify the analyzed skills for each set item. Specifically, it quantifies skills such as Java programming skills, new project idea generation skills, facilitation skills, proposal skills, and problem identification skills. Step 4: The career plan generation unit generates a career plan based on the skills quantified by the quantification unit. For example, the career plan generation unit can generate an employee's career plan based on the quantified skills. Specifically, it might propose a career plan stating that, given Person A's skills, tasks like XX are a good fit and that they are expected to perform well. Step 5: The learning plan generation unit generates a learning plan based on the skills quantified by the quantification unit. For example, the learning plan generation unit can generate an employee's learning plan based on the quantified skills. Specifically, it can generate a learning plan that indicates that Person A needs to improve their △△ skill for the desired work.

[0069] (Example of form 2) The skill visualization system according to an embodiment of the present invention is a system that facilitates appropriate assignments and growth support by easily collecting and visualizing employee skills. The skill visualization system collects data on employee skills, and a generating AI analyzes it, quantifying the employee's skills for each set item. This makes the employee's skill set visible, which is useful when transferring or assigning tasks. The generating AI also generates career plans and learning plans to address weaknesses, supporting employee growth. For example, the skill visualization system constantly monitors company PCs and meeting tools to collect data on employee skills. For example, it monitors work content such as Java programming, idea generation for new projects, and creation of various management files. It also constantly monitors meeting tools to analyze skills such as facilitation skills, proposal skills, and problem identification skills. This allows learning data to be obtained from daily work without the need to prepare individual inputs. Next, the generating AI analyzes the collected data and quantifies the employee's skills for each set item. For example, skills such as Java programming skills, new project idea generation skills, facilitation skills, proposal skills, and problem identification skills are quantified. This makes employees' skill sets visible, which is useful when transferring or assigning tasks. Furthermore, the generating AI creates career plans and learning plans to address any shortcomings in employees. For example, it may suggest a career plan indicating that tasks like XX are a good fit for employee A's skills and that they are expected to excel in those roles. It may also generate a learning plan indicating that A needs to develop their △△ skills in order to perform their desired tasks. This supports employee growth. This system not only makes employees' skill sets visible, which is useful when transferring or assigning tasks, but also supports employee growth. For example, employees can find a career plan that suits them and efficiently learn the necessary skills. In addition, managers can quantitatively understand employees' skill sets and make appropriate assignments. This improves employee satisfaction and increases overall company productivity. In summary, the skill visualization system makes it easy to collect and visualize employee skills, enabling appropriate assignments and growth support.

[0070] The skill visualization system according to this embodiment comprises a data collection unit, an analysis unit, a quantification unit, a career plan generation unit, and a learning plan generation unit. The data collection unit collects data on employees' skills. The data collection unit can collect data on employees' skills by, for example, constantly monitoring company PCs and meeting tools. For example, the data collection unit monitors work content such as Java programming, idea generation for new projects, and creation of various management files. The data collection unit can also constantly monitor meeting tools and analyze skills such as facilitation skills, proposal skills, and problem identification skills. This makes it possible to obtain learning data from daily work without preparing individual inputs. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can analyze the collected data and quantify skills by, for example, using a generation AI. For example, the analysis unit uses a generation AI to quantify skills such as Java programming skills, new project idea generation skills, facilitation skills, proposal skills, and problem identification skills. The quantification unit quantifies skills based on the data analyzed by the analysis unit. The quantification unit can quantify the analyzed skills for each set item. For example, the quantification unit quantifies skills such as Java programming skills, new project idea generation skills, facilitation skills, proposal skills, and problem identification skills. The career plan generation unit generates a career plan based on the skills quantified by the quantification unit. For example, the career plan generation unit can generate an employee's career plan based on the quantified skills. For example, the career plan generation unit proposes a career plan stating that A's skills are suited to tasks like XX, and that they are expected to perform well. The learning plan generation unit generates a learning plan based on the skills quantified by the quantification unit. For example, the learning plan generation unit generates a learning plan stating that A needs to improve their △△ skills in order to perform their desired tasks. As a result, the skill visualization system according to the embodiment can easily collect and visualize employee skills, enabling appropriate assignments and growth support.

[0071] The data collection department collects data on employees' skills. For example, it can continuously monitor company PCs and meeting tools to collect data on employees' skills. Specifically, monitoring software installed on company PCs records the operation history of applications and files used by employees and sends this data to a database. For example, it monitors work content such as Java programming, idea generation for new projects, and creation of various management files. This allows the department to understand what skills employees possess and how often they use those skills. The data collection department can also continuously monitor meeting tools and analyze skills such as facilitation skills, proposal skills, and problem identification skills. Using the speech recognition and text analysis functions of meeting tools, it evaluates the content, frequency, and quality of contributions during meetings and collects this as skill data. This allows learning data to be obtained from daily work without preparing individual inputs. Furthermore, the data collection department can also collect employee self-assessments and supervisor evaluations. For example, it can import the results of regularly conducted evaluation questionnaires and feedback sessions into a database and utilize this as skill data. This allows the data collection unit to evaluate employees' skills from multiple perspectives and collect more accurate data.

[0072] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can use generative AI to analyze the collected data and quantify skills. Specifically, generative AI uses natural language processing techniques and machine learning algorithms to analyze collected text and audio data. For instance, generative AI can quantify skills such as Java programming skills, new project idea generation skills, facilitation skills, proposal skills, and problem identification skills. The generative AI evaluates the quality and efficiency of programming code and uses this to quantify programming skills. It also analyzes the content and frequency of contributions during meetings to evaluate facilitation and proposal skills. Furthermore, the generative AI can evaluate relative skill levels by comparing them with past data and data from other employees. For example, it can assess how well or poorly a particular skill is compared to other employees performing the same tasks. This allows the analysis unit to quickly and accurately analyze the collected data and quantify employee skills. Additionally, the analysis unit can analyze skill trends and fluctuations. For example, it can track how a particular skill changes over time and evaluate skill improvement or decline. This allows the analysis department to understand not only the current state of employees' skills but also how those skills might change in the future.

[0073] The quantification unit quantifies skills based on data analyzed by the analysis unit. For example, the quantification unit can quantify analyzed skills for each set item. Specifically, the quantification unit sets evaluation criteria for each skill and quantifies the skill based on these criteria. For example, it quantifies skills such as Java programming skills, new project idea generation skills, facilitation skills, proposal skills, and problem identification skills. For programming skills, the quantification unit uses criteria such as code quality, efficiency, and bug rate, and quantifies the skill based on these criteria. For idea generation skills, it uses criteria such as the number and quality of proposed ideas and their feasibility. Furthermore, for facilitation and proposal skills, it uses criteria such as the content and frequency of contributions during meetings and evaluations from other participants. This allows the quantification unit to objectively and quantitatively evaluate and quantify each skill. In addition, the quantification unit can visualize the skill evaluation results. For example, it can display the evaluation results for each skill as graphs or charts, making them easily understandable for employees and supervisors. This allows the quantification unit to present skill evaluation results in an easy-to-understand manner, which can be used to help employees improve their skills and develop career plans.

[0074] The Career Plan Generation Unit generates career plans based on skills quantified by the Quantification Unit. For example, the Career Plan Generation Unit can generate employee career plans based on quantified skills. Specifically, it analyzes employees' strengths and weaknesses based on quantified skill data and proposes the optimal career plan. For example, the Career Plan Generation Unit might propose a career plan stating that, given Employee A's skills, tasks like XX are a good fit and they are expected to perform well. The Career Plan Generation Unit considers not only employee skill data but also employee aspirations and goals, as well as company needs and strategies, when generating career plans. For example, if an employee wishes to take on leadership roles in the future, the unit will propose tasks and training to improve their leadership skills. Also, if the company plans to launch a new business, it will prioritize assigning employees with skills related to that business. In this way, the Career Plan Generation Unit can match employee skills with company needs and provide the optimal career plan. Furthermore, the Career Plan Generation Unit can monitor the progress of career plans and revise them as needed. For example, if an employee acquires new skills or if the company's strategy changes, the Career Plan Generation Unit will re-evaluate the career plan and make appropriate revisions. This allows the career plan generation unit to provide flexible career plans based on the latest information at all times, supporting employee growth and company development.

[0075] The learning plan generation unit generates learning plans based on skills quantified by the quantification unit. For example, the learning plan generation unit can generate employee learning plans based on quantified skills. Specifically, the learning plan generation unit analyzes which skills an employee needs to strengthen based on quantified skill data and proposes an optimal learning plan. For example, the learning plan generation unit generates a learning plan that states that employee A needs to improve their △△ skill for the desired work. The learning plan generation unit generates learning plans considering not only the employee's skill data but also their learning style and learning history. For example, if an employee prefers online courses, it proposes a learning plan centered on online courses. It also evaluates the results of training and courses the employee has previously taken and proposes effective learning methods. In this way, the learning plan generation unit can provide an optimal learning plan that meets the individual needs of each employee. Furthermore, the learning plan generation unit can monitor the progress of the learning plan and modify the plan as needed. For example, if an employee acquires a new skill or if the learning plan is not effective, it re-evaluates the learning plan and makes appropriate modifications. This allows the learning plan generation unit to always provide flexible learning plans based on the latest information, supporting employees' skill development.

[0076] The data collection unit can continuously monitor company PCs and meeting tools to collect data on employees' skills. For example, the data collection unit can continuously monitor company PCs to track tasks such as Java programming, idea generation for new projects, and creation of various management files. It can also continuously monitor meeting tools to analyze skills such as facilitation, proposal skills, and problem identification skills. This allows for easy acquisition of skill data by automatically collecting data on employees' skills. Continuous monitoring can be achieved, for example, by installing specific software to monitor the usage of employees' PCs and meeting tools in real time. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the usage status of company PCs and meeting tools into an AI and have the AI ​​collect skill data.

[0077] The analysis unit uses a generative AI to analyze collected data and quantify skills. For example, the analysis unit uses a generative AI to analyze collected data and quantify skills such as Java programming skills, new project idea generation skills, facilitation skills, proposal skills, and problem identification skills. This improves the accuracy of skill quantification by using a generative AI. The generative AI can, for example, use natural language processing technology to analyze collected text data and quantify skills. It can also use image recognition technology to analyze collected image data and quantify skills. Furthermore, the generative AI can use predictive models to analyze collected data and quantify skills. For example, the generative AI uses a natural language processing model that takes text data as input and outputs the result of quantifying skills to quantify them. This allows the analysis unit to use a generative AI to analyze collected data and quantify skills.

[0078] The quantification unit can quantify the analyzed skills for each set item. For example, the quantification unit quantifies the analyzed skills for each set item, such as Java programming skills, new project idea generation skills, facilitation skills, proposal skills, and problem identification skills. This makes it possible to visualize the skill set by quantifying skills for each set item. The quantification unit quantifies the analyzed skills using, for example, a scoring method. The quantification unit can also quantify the analyzed skills based on evaluation criteria. Furthermore, the quantification unit can quantify the analyzed skills using weighting. For example, the quantification unit quantifies skills on a scale from 0 to 100 as a scoring method. Evaluation criteria are set, for example, based on the importance or difficulty of the skill. Weighting is set, for example, according to the importance of each skill. This allows the quantification unit to quantify the analyzed skills for each set item.

[0079] The career plan generation unit can generate career plans based on quantified skills. For example, the unit generates an employee's career plan based on quantified skills. For instance, it might suggest a career plan indicating that employee A's skills are suited to tasks like XX, and that they are expected to excel in those roles. This enables employee career support by generating career plans based on quantified skills. The career plan generation unit can generate career plans that include, for example, promotion plans and skill development plans. It can also generate career plans based on employee aspirations and goals. Furthermore, it can generate career plans based on industry trends and market demand. For example, the career plan generation unit might suggest a career plan aiming for promotion to the next position as a promotion plan. A skill development plan might include, for example, a learning plan to improve specific skills. Employee aspirations and goals are set based on, for example, future career goals and desired job content. This allows the career plan generation unit to generate career plans based on quantified skills.

[0080] The learning plan generation unit can generate learning plans based on quantified skills. For example, the learning plan generation unit generates employee learning plans based on quantified skills. For example, the learning plan generation unit generates a learning plan stating that employee A needs to improve their △△ skill for the desired work. This enables support for employee growth by generating learning plans based on quantified skills. The learning plan generation unit generates learning plans that include, for example, training programs and self-study plans. The learning plan generation unit can also identify employee skill gaps and generate learning plans to bridge those gaps. Furthermore, the learning plan generation unit can generate learning plans based on industry trends and market demand. For example, the learning plan generation unit proposes training courses to improve specific skills as training programs. Self-study plans include, for example, online courses and self-study using books. Skill gaps are identified, for example, based on the difference between the current skill level and the target skill level. This allows the learning plan generation unit to generate learning plans based on quantified skills.

[0081] The data collection unit can estimate employees' emotions and adjust the timing of skill data collection based on the estimated emotions. For example, if an employee is stressed, the data collection unit can delay the collection timing to collect data when the employee is relaxed. Alternatively, if an employee is focused, the data collection unit can collect skill data at that time to obtain more accurate data. Furthermore, if an employee is tired, the data collection unit can obtain accurate skill data by collecting data after a break. This allows for the collection of more accurate skill data by adjusting the collection timing according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input employee facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0082] The data collection unit can analyze an employee's past work history and select the optimal data collection method. For example, the data collection unit can analyze the content of projects an employee has worked on in the past and prioritize the collection of relevant skill data. The data collection unit can also extract tasks that require specific skills from an employee's past work history and collect data related to those skills. Furthermore, the data collection unit can select the most efficient data collection method based on an employee's past work history and collect skill data. This enables efficient data collection by analyzing past work history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input an employee's past work history data into a generating AI and have the generating AI select the optimal data collection method.

[0083] The data collection unit can filter skill data based on an employee's current projects and areas of interest. For example, the data collection unit can prioritize collecting skill data related to the project the employee is currently working on. The data collection unit can also filter and collect relevant skill data based on the employee's areas of interest. Furthermore, the data collection unit can collect data based on the skills required for the employee's current project. This allows for the collection of highly relevant skill data by filtering the data based on the current project and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input employee project data and areas of interest data into a generating AI and have the generating AI perform the filtering.

[0084] The data collection unit can estimate an employee's emotions and prioritize the skill data to collect based on the estimated emotions. For example, if an employee is stressed, the data collection unit may prioritize collecting simple skill data. It can also prioritize collecting complex skill data if the employee is relaxed. Furthermore, it can prioritize collecting important skill data if the employee is focused. This allows for efficient data collection by prioritizing skill data according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input employee facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0085] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location of employees when collecting skill data. For example, if an employee works in a specific region, the data collection unit will prioritize the collection of skill data related to that region. Furthermore, if an employee is working remotely, the data collection unit can prioritize the collection of skill data related to remote work. Additionally, if an employee is on a business trip, the data collection unit can prioritize the collection of skill data related to their business trip destination. This allows for the collection of highly relevant skill data by considering geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input employee geographical location data into a generating AI and have the generating AI collect highly relevant data.

[0086] The data collection unit can analyze employees' social media activity and collect relevant data when collecting skill data. For example, the data collection unit can collect skill data based on projects and work content shared by employees on social media. The data collection unit can also identify areas of interest from employees' social media activity and collect relevant skill data. Furthermore, the data collection unit can collect relevant skill data from experts and groups that employees follow on social media. This allows for the collection of skill data based on employees' areas of interest by analyzing their social media activity. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input employee social media data into a generating AI and have the generating AI collect relevant data.

[0087] The analysis unit can estimate employees' emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if an employee is stressed, the analysis unit can provide simple and visually easy-to-understand analysis results. If an employee is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if an employee is focused, the analysis unit can provide complex analysis results. By adjusting the presentation of the analysis according to the employee's emotions, it is possible to provide more easily understandable analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input employee facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0088] The analysis unit can adjust the level of detail of the analysis based on the importance of the skill data during the analysis. For example, the analysis unit will perform a detailed analysis on important skill data. It can also perform a basic analysis on general skill data. Furthermore, for skill data related to a specific project, the analysis unit can adjust the level of detail of the analysis according to the importance of the project. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the skill data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the skill data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0089] The analysis unit can apply different analysis algorithms depending on the category of skill data during analysis. For example, for programming skills, the analysis unit can apply an algorithm that evaluates the quality and efficiency of the code. For facilitation skills, the analysis unit can also apply an algorithm that evaluates the progress of meetings and the reactions of participants. Furthermore, for proposal skills, the analysis unit can apply an algorithm that evaluates the originality and feasibility of the proposal. By applying analysis algorithms according to the category of skill data, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the categories of skill data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0090] The analysis unit can estimate the employee's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the employee is stressed, the analysis unit can provide a short, concise analysis result. If the employee is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the employee is focused, the analysis unit can provide a complex analysis result. By adjusting the length of the analysis according to the employee's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input employee facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0091] The analysis unit can determine the priority of analysis based on the timing of skill data collection during the analysis process. For example, the analysis unit may prioritize the analysis of recently collected skill data. It can also prioritize the analysis of skill data collected during a specific project period. Furthermore, the analysis unit can determine the priority of analysis based on regularly collected skill data. This enables efficient analysis by determining the priority of analysis based on the timing of skill data collection. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the timing of skill data collection into a generating AI and have the generating AI determine the priority of analysis.

[0092] The analysis unit can adjust the order of analysis based on the relevance of skill data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant skill data. It can also prioritize the analysis of skill data related to a specific project. Furthermore, it can prioritize the analysis of skill data related to an employee's career plan. This allows for efficient analysis by adjusting the order of analysis based on the relevance of skill data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of skill data into a generating AI and have the generating AI adjust the order of analysis.

[0093] The quantification unit can estimate an employee's emotions and adjust the quantification criteria based on the estimated emotions. For example, if an employee is stressed, the quantification unit can use simple criteria for quantification. If an employee is relaxed, the quantification unit can use detailed criteria for quantification. Furthermore, if an employee is focused, the quantification unit can use complex criteria for quantification. This allows for more appropriate quantification by adjusting the quantification criteria according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the quantification unit may be performed using AI, or not using AI. For example, the quantification unit can input employee facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0094] The quantification unit can improve the accuracy of quantification by considering the interrelationships between skill data during the quantification process. For example, the quantification unit can perform quantification by considering the interrelationship between programming skills and project management skills. It can also perform quantification by considering the interrelationship between facilitation skills and proposal skills. Furthermore, it can perform quantification by considering the interrelationship between problem identification skills and problem-solving skills. This improves the accuracy of quantification by considering the interrelationships between skill data. Some or all of the above processing in the quantification unit may be performed using AI, for example, or without AI. For example, the quantification unit can input the interrelationships of skill data into a generating AI and have the generating AI perform the improvement of quantification accuracy.

[0095] The quantification unit can perform quantification while considering the attribute information of the submitter of the skill data. For example, the quantification unit can perform quantification while considering the submitter's job title and position. It can also perform quantification while considering the submitter's years of experience. Furthermore, the quantification unit can perform quantification while considering the submitter's field of expertise. This makes it possible to perform more appropriate quantification by considering the submitter's attribute information. Some or all of the above processing in the quantification unit may be performed using AI, for example, or without using AI. For example, the quantification unit can input the submitter's attribute information into a generating AI and have the generating AI perform the quantification.

[0096] The quantification unit can estimate an employee's emotions and adjust the order in which the quantification results are displayed based on the estimated emotions. For example, if an employee is stressed, the quantification unit can display important skill data first. It can also display detailed skill data first if the employee is relaxed. Furthermore, if the employee is focused, it can display complex skill data first. This allows for more easily understood results by adjusting the order in which the quantification results are displayed according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the quantification unit may be performed using AI, or not. For example, the quantification unit can input employee facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0097] The quantification unit can perform quantification while considering the geographical distribution of skill data. For example, the quantification unit can prioritize the quantification of skill data in a specific region. It can also prioritize the quantification of skill data related to remote work. Furthermore, it can prioritize the quantification of skill data related to business trip destinations. This allows for more appropriate quantification by considering geographical distribution. Some or all of the above processing in the quantification unit may be performed using AI, for example, or without AI. For example, the quantification unit can input the geographical distribution of skill data into a generating AI and have the generating AI perform the quantification.

[0098] The quantification unit can improve the accuracy of quantification by referring to relevant literature on skill data during the quantification process. For example, the quantification unit can perform quantification by referring to academic papers related to skill data. It can also perform quantification by referring to industry reports related to skill data. Furthermore, the quantification unit can perform quantification by referring to patent documents related to skill data. This improves the accuracy of quantification by referring to relevant literature. Some or all of the above processing in the quantification unit may be performed using AI, for example, or without AI. For example, the quantification unit can input relevant literature on skill data into a generating AI and have the generating AI perform the quantification accuracy improvement.

[0099] The career plan generation unit can estimate an employee's emotions and adjust how the career plan is displayed based on the estimated emotions. For example, if an employee is stressed, the career plan generation unit can provide a simple and visually easy-to-understand career plan. If the employee is relaxed, the career plan generation unit can also provide a detailed career plan. Furthermore, if the employee is focused, the career plan generation unit can provide a complex career plan. By adjusting how the career plan is displayed according to the employee's emotions, a more easily understandable career plan can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the career plan generation unit may be performed using AI, or not using AI. For example, the career plan generation unit can input employee facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0100] The career plan generation unit can predict the current career plan by referring to past career data when generating a career plan. For example, the career plan generation unit predicts the optimal career plan based on an employee's past career data. The career plan generation unit can also predict future career plans from an employee's past career data. Furthermore, the career plan generation unit can analyze an employee's past career data and propose the most efficient career plan. This allows for the prediction of a more appropriate career plan by referring to past career data. Some or all of the above-described processes in the career plan generation unit may be performed using AI, for example, or without AI. For example, the career plan generation unit can input past career data into a generation AI and have the generation AI perform career plan predictions.

[0101] The career plan generation unit can apply different career plan generation methods to each category of skill data when generating career plans. For example, the career plan generation unit can propose a technical career plan for programming skills. It can also propose a management career plan for facilitation skills. Furthermore, it can propose a sales or marketing career plan for proposal skills. By applying different methods to each category of skill data, a more appropriate career plan can be generated. Some or all of the above processing in the career plan generation unit may be performed using AI, for example, or without AI. For example, the career plan generation unit can input skill data categories into a generation AI and have the generation AI execute the application of career plan generation methods.

[0102] The career plan generation unit can estimate an employee's emotions and adjust the importance of the career plan based on the estimated emotions. For example, if an employee is stressed, the career plan generation unit may prioritize short-term career plans. Conversely, if an employee is relaxed, the career plan generation unit may prioritize long-term career plans. Furthermore, if an employee is focused, the career plan generation unit may provide a detailed career plan. This allows for the provision of more appropriate career plans by adjusting the importance of career plans according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the career plan generation unit may be performed using AI or not. For example, the career plan generation unit can input employee facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0103] The career plan generation unit can analyze changes in the career plan based on the timing of skill data collection when generating the career plan. For example, the career plan generation unit can propose the latest career plan based on recently collected skill data. The career plan generation unit can also analyze changes in the career plan based on skill data collected during a specific project period. Furthermore, the career plan generation unit can predict changes in the career plan based on regularly collected skill data. This allows for the provision of a more appropriate career plan by analyzing changes in the career plan based on the timing of skill data collection. Some or all of the above-described processes in the career plan generation unit may be performed using AI, for example, or without AI. For example, the career plan generation unit can input the timing of skill data collection into the generation AI and have the generation AI perform the analysis of changes in the career plan.

[0104] The career plan generation unit can analyze career plans by referring to relevant market data for skill data when generating them. For example, the career plan generation unit proposes career plans based on market demand related to skill data. The career plan generation unit can also analyze career plans based on industry trends related to skill data. Furthermore, the career plan generation unit can propose career plans based on market growth forecasts related to skill data. This allows for the provision of more appropriate career plans by referring to relevant market data. Some or all of the above processing in the career plan generation unit may be performed using AI, for example, or without AI. For example, the career plan generation unit can input relevant market data into a generation AI and have the generation AI perform the career plan analysis.

[0105] The learning plan generation unit can estimate an employee's emotions and determine the priority of learning plans based on the estimated emotions. For example, if an employee is stressed, the learning plan generation unit will prioritize a simple learning plan. It can also prioritize a detailed learning plan if the employee is relaxed. Furthermore, it can prioritize a complex learning plan if the employee is focused. This allows for the provision of more appropriate learning plans by prioritizing them according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the learning plan generation unit may be performed using AI, or not. For example, the learning plan generation unit can input employee facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0106] The learning plan generation unit can improve the accuracy of the learning plan by considering the interrelationships of skill data during the planning process. For example, the learning plan generation unit can generate a learning plan by considering the interrelationships between programming skills and project management skills. It can also generate a learning plan by considering the interrelationships between facilitation skills and proposal skills. Furthermore, it can generate a learning plan by considering the interrelationships between problem identification skills and problem-solving skills. This improves the accuracy of the learning plan by considering the interrelationships of skill data. Some or all of the above processing in the learning plan generation unit may be performed using AI, for example, or without AI. For example, the learning plan generation unit can input the interrelationships of skill data into a generation AI and have the generation AI perform the task of improving the accuracy of the learning plan.

[0107] The learning plan generation unit can generate a learning plan while considering the attribute information of the skill data submitter. For example, the learning plan generation unit can generate a learning plan while considering the submitter's job title and position. It can also generate a learning plan while considering the submitter's years of experience. Furthermore, the learning plan generation unit can generate a learning plan while considering the submitter's field of expertise. This allows for the provision of a more appropriate learning plan by considering the submitter's attribute information. Some or all of the above processing in the learning plan generation unit may be performed using AI, for example, or without AI. For example, the learning plan generation unit can input the submitter's attribute information into a generation AI and have the generation AI execute the generation of the learning plan.

[0108] The learning plan generation unit can estimate an employee's emotions and adjust how the learning plan is displayed based on the estimated emotions. For example, if an employee is stressed, the learning plan generation unit can provide a simple and visually easy-to-understand learning plan. If the employee is relaxed, the learning plan generation unit can also provide a detailed learning plan. Furthermore, if the employee is focused, the learning plan generation unit can provide a complex learning plan. By adjusting how the learning plan is displayed according to the employee's emotions, a more easily understandable learning plan can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the learning plan generation unit may be performed using AI, or not using AI. For example, the learning plan generation unit can input employee facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0109] The learning plan generation unit can generate learning plans while considering the geographical distribution of skill data. For example, the learning plan generation unit can generate learning plans based on skill data in a specific region. It can also generate learning plans based on skill data related to remote work. Furthermore, it can generate learning plans based on skill data related to business trip destinations. This allows for the provision of more appropriate learning plans by considering geographical distribution. Some or all of the above processing in the learning plan generation unit may be performed using AI, for example, or without AI. For example, the learning plan generation unit can input the geographical distribution of skill data into a generation AI and have the generation AI perform the generation of learning plans.

[0110] The learning plan generation unit can improve the accuracy of the learning plan by referring to relevant literature related to the skill data during the planning process. For example, the learning plan generation unit can generate a learning plan by referring to academic papers related to the skill data. It can also generate a learning plan by referring to industry reports related to the skill data. Furthermore, the learning plan generation unit can generate a learning plan by referring to patent documents related to the skill data. This improves the accuracy of the learning plan by referring to relevant literature. Some or all of the above processing in the learning plan generation unit may be performed using AI, for example, or without AI. For example, the learning plan generation unit can input relevant literature related to the skill data into a generation AI and have the generation AI perform the task of improving the accuracy of the learning plan.

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

[0112] The data collection unit can estimate employees' emotions and adjust the timing of skill data collection based on the estimated emotions. For example, if an employee is stressed, the data collection unit can delay the collection timing to collect data when the employee is relaxed. Alternatively, if an employee is focused, the data collection unit can collect skill data at that time to obtain more accurate data. Furthermore, if an employee is tired, the data collection unit can obtain accurate skill data by collecting data after a break. This allows for the collection of more accurate skill data by adjusting the collection timing according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input employee facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0113] The data collection unit can analyze an employee's past work history and select the optimal data collection method. For example, the data collection unit can analyze the content of projects an employee has worked on in the past and prioritize the collection of relevant skill data. The data collection unit can also extract tasks that require specific skills from an employee's past work history and collect data related to those skills. Furthermore, the data collection unit can select the most efficient data collection method based on an employee's past work history and collect skill data. This enables efficient data collection by analyzing past work history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input an employee's past work history data into a generating AI and have the generating AI select the optimal data collection method.

[0114] The data collection unit can filter skill data based on an employee's current projects and areas of interest. For example, the data collection unit can prioritize collecting skill data related to the project the employee is currently working on. The data collection unit can also filter and collect relevant skill data based on the employee's areas of interest. Furthermore, the data collection unit can collect data based on the skills required for the employee's current project. This allows for the collection of highly relevant skill data by filtering the data based on the current project and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input employee project data and areas of interest data into a generating AI and have the generating AI perform the filtering.

[0115] The data collection unit can estimate an employee's emotions and prioritize the skill data to collect based on the estimated emotions. For example, if an employee is stressed, the data collection unit may prioritize collecting simple skill data. It can also prioritize collecting complex skill data if the employee is relaxed. Furthermore, it can prioritize collecting important skill data if the employee is focused. This allows for efficient data collection by prioritizing skill data according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input employee facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0116] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location of employees when collecting skill data. For example, if an employee works in a specific region, the data collection unit will prioritize the collection of skill data related to that region. Furthermore, if an employee is working remotely, the data collection unit can prioritize the collection of skill data related to remote work. Additionally, if an employee is on a business trip, the data collection unit can prioritize the collection of skill data related to their business trip destination. This allows for the collection of highly relevant skill data by considering geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input employee geographical location data into a generating AI and have the generating AI collect highly relevant data.

[0117] The data collection unit can analyze employees' social media activity and collect relevant data when collecting skill data. For example, the data collection unit can collect skill data based on projects and work content shared by employees on social media. The data collection unit can also identify areas of interest from employees' social media activity and collect relevant skill data. Furthermore, the data collection unit can collect relevant skill data from experts and groups that employees follow on social media. This allows for the collection of skill data based on employees' areas of interest by analyzing their social media activity. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input employee social media data into a generating AI and have the generating AI collect relevant data.

[0118] The analysis unit can estimate employees' emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if an employee is stressed, the analysis unit can provide simple and visually easy-to-understand analysis results. If an employee is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if an employee is focused, the analysis unit can provide complex analysis results. By adjusting the presentation of the analysis according to the employee's emotions, it is possible to provide more easily understandable analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input employee facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0119] The analysis unit can adjust the level of detail of the analysis based on the importance of the skill data during the analysis. For example, the analysis unit will perform a detailed analysis on important skill data. It can also perform a basic analysis on general skill data. Furthermore, for skill data related to a specific project, the analysis unit can adjust the level of detail of the analysis according to the importance of the project. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the skill data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the skill data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0120] The analysis unit can apply different analysis algorithms depending on the category of skill data during analysis. For example, for programming skills, the analysis unit can apply an algorithm that evaluates the quality and efficiency of the code. For facilitation skills, the analysis unit can also apply an algorithm that evaluates the progress of meetings and the reactions of participants. Furthermore, for proposal skills, the analysis unit can apply an algorithm that evaluates the originality and feasibility of the proposal. By applying analysis algorithms according to the category of skill data, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the categories of skill data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0121] The analysis unit can estimate the employee's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the employee is stressed, the analysis unit can provide a short, concise analysis result. If the employee is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the employee is focused, the analysis unit can provide a complex analysis result. By adjusting the length of the analysis according to the employee's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input employee facial expression data into the generative AI and have the generative AI perform emotion estimation.

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

[0123] Step 1: The data collection unit collects data on employees' skills. The data collection unit can, for example, continuously monitor company PCs and meeting tools to collect data on employees' skills. Specifically, it monitors work content such as Java programming, idea generation for new projects, and creation of various management files. It can also continuously monitor meeting tools to analyze skills such as facilitation skills, proposal skills, and problem identification skills. This allows learning data to be obtained from daily work without preparing individual inputs. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the collected data and quantify skills, for example, using a generative AI. Specifically, the generative AI quantifies skills such as Java programming skills, new project idea generation skills, facilitation skills, proposal skills, and problem identification skills. Step 3: The quantification unit quantifies skills based on the data analyzed by the analysis unit. The quantification unit can, for example, quantify the analyzed skills for each set item. Specifically, it quantifies skills such as Java programming skills, new project idea generation skills, facilitation skills, proposal skills, and problem identification skills. Step 4: The career plan generation unit generates a career plan based on the skills quantified by the quantification unit. For example, the career plan generation unit can generate an employee's career plan based on the quantified skills. Specifically, it might propose a career plan stating that, given Person A's skills, tasks like XX are a good fit and that they are expected to perform well. Step 5: The learning plan generation unit generates a learning plan based on the skills quantified by the quantification unit. For example, the learning plan generation unit can generate an employee's learning plan based on the quantified skills. Specifically, it can generate a learning plan that indicates that Person A needs to improve their △△ skill for the desired work.

[0124] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0126] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0127] Each of the multiple elements described above, including the data collection unit, analysis unit, quantification unit, career plan generation unit, and learning plan generation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects data on employees' skills using the computer 36 and camera 42 of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The quantification unit quantifies skills based on the data analyzed by the specific processing unit 290 of the data processing unit 12. The career plan generation unit generates a career plan based on the skills quantified by the specific processing unit 290 of the data processing unit 12. The learning plan generation unit generates a learning plan based on the skills quantified by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0129] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0130] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0131] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0132] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0134] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0135] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0136] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0137] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0138] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0140] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0142] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] Each of the multiple elements described above, including the data collection unit, analysis unit, digitization unit, career plan generation unit, and learning plan generation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects data on employees' skills using the computer 36 and camera 42 of the smart glasses 214. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The digitization unit digitizes skills based on the data analyzed by the specific processing unit 290 of the data processing unit 12. The career plan generation unit generates a career plan based on the skills digitized by the specific processing unit 290 of the data processing unit 12. The learning plan generation unit generates a learning plan based on the skills digitized by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0145] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0146] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0147] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0148] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0150] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0151] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

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

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

[0154] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0156] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0158] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] Each of the multiple elements described above, including the data collection unit, analysis unit, quantification unit, career plan generation unit, and learning plan generation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects data on employees' skills using the computer 36 and camera 42 of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The quantification unit quantifies skills based on the data analyzed by the specific processing unit 290 of the data processing unit 12. The career plan generation unit generates a career plan based on the skills quantified by the specific processing unit 290 of the data processing unit 12. The learning plan generation unit generates a learning plan based on the skills quantified by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0161] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0162] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0163] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0164] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0165] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0166] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0167] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0168] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0169] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0170] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0171] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0172] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0173] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0174] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0175] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0176] Each of the multiple elements described above, including the data collection unit, analysis unit, quantification unit, career plan generation unit, and learning plan generation unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects data on employees' skills using the robot 414's computer 36 and camera 42. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The quantification unit quantifies skills based on the data analyzed by the specific processing unit 290 of the data processing unit 12. The career plan generation unit generates a career plan based on the skills quantified by the specific processing unit 290 of the data processing unit 12. The learning plan generation unit generates a learning plan based on the skills quantified by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0177] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0178] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0179] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0180] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0181] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0182] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0183] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0184] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0185] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0186] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0187] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0188] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0189] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0190] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0191] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0192] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0193] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0194] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0195] (Note 1) The data collection department collects data on employee skills, An analysis unit analyzes the data collected by the aforementioned collection unit, A quantification unit that quantifies skills based on the data analyzed by the aforementioned analysis unit, A career plan generation unit generates a career plan based on the skills quantified by the aforementioned quantification unit, The system includes a learning plan generation unit that generates a learning plan based on the skills quantified by the aforementioned quantification unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We continuously monitor company PCs and meeting tools to collect data on employees' skills. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed by a generating AI, which then quantifies the skills. The system described in Appendix 1, characterized by the features described herein. (Note 4) The digitization unit is, The analyzed skills are quantified for each setting item. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned carrier plan generation unit, Generate a career plan based on quantified skills. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned learning plan generation unit, Generate learning plans based on quantified skills. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate employees' emotions and adjust the timing of skill data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze employees' past work history and select the most suitable data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting skills data, filter it based on employees' current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system estimates employees' emotions and prioritizes the skill data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting skill data, the system prioritizes collecting highly relevant data by considering the geographical location of employees. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting skills data, analyze employees' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, We estimate the emotions of our employees and adjust the representation of the analysis based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the skill data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of skill data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the emotions of employees and adjusts the length of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the skill data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the skill data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The digitization unit is, We estimate the emotions of our employees and adjust the quantification criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The digitization unit is, When quantifying, consider the interrelationships between skill data to improve the accuracy of the quantification. The system described in Appendix 1, characterized by the features described herein. (Note 21) The digitization unit is, When quantifying the data, the attribute information of the person submitting the skill data will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The digitization unit is, The system estimates employees' emotions and adjusts the order in which the numerical results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The digitization unit is, When quantifying the data, the geographical distribution of the skill data should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The digitization unit is, When quantifying skills, refer to relevant literature to improve the accuracy of the quantification. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned carrier plan generation unit, The system estimates employees' emotions and adjusts how career plans are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned carrier plan generation unit, When generating a career plan, past career data is referenced to predict the current career plan. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned carrier plan generation unit, When generating a career plan, different career plan generation methods are applied to each category of skill data. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned carrier plan generation unit, We estimate employees' emotions and adjust the importance of career plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned carrier plan generation unit, When generating a career plan, analyze how the career plan will change based on when skill data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned carrier plan generation unit, When generating a career plan, we analyze the plan by referring to relevant market data for skill data. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned learning plan generation unit, The system estimates employees' emotions and prioritizes learning plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned learning plan generation unit, When generating a learning plan, we improve the accuracy of the learning plan by considering the interrelationships of skill data. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned learning plan generation unit, When generating a learning plan, the system takes into account the attribute information of the person submitting the skill data. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned learning plan generation unit, The system estimates employees' emotions and adjusts how learning plans are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned learning plan generation unit, When generating a learning plan, the geographical distribution of skill data is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned learning plan generation unit, When generating a learning plan, we improve the accuracy of the learning plan by referring to relevant literature for the skill data. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

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

Claims

1. The data collection department collects data on employee skills, An analysis unit analyzes the data collected by the aforementioned collection unit, A quantification unit that quantifies skills based on the data analyzed by the aforementioned analysis unit, A career plan generation unit generates a career plan based on the skills quantified by the aforementioned quantification unit, The system includes a learning plan generation unit that generates a learning plan based on the skills quantified by the aforementioned quantification unit. A system characterized by the following features.

2. The aforementioned collection unit is We continuously monitor company PCs and meeting tools to collect data on employees' skills. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed by a generating AI, which then quantifies the skills. The system according to feature 1.

4. The digitization unit is, The analyzed skills are quantified for each setting item. The system according to feature 1.

5. The aforementioned carrier plan generation unit, Generate a career plan based on quantified skills. The system according to feature 1.

6. The aforementioned learning plan generation unit, Generate learning plans based on quantified skills. The system according to feature 1.

7. The aforementioned collection unit is We estimate employees' emotions and adjust the timing of skill data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze employees' past work history and select the most suitable data collection method. The system according to feature 1.

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