system

Abstract

The system according to this embodiment aims to enable employees to understand their own skills and aptitudes and to formulate appropriate career plans. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a management unit. The data collection unit collects employees' past performance, skill data, and aspirations. The analysis unit analyzes the data collected by the data collection unit and determines the employees' skills and aptitudes. The proposal unit proposes an appropriate career plan based on the analysis results obtained by the analysis unit. The management unit sets goals based on the career plan proposed by the proposal unit and manages their progress.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult for employees to understand their own skills and aptitudes and formulate appropriate career plans.

[0005] The system according to the embodiment aims to enable employees to understand their own skills and aptitudes and formulate appropriate career plans.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a management unit. The data collection unit collects employees' past performance, skill data, and aspirations. The analysis unit analyzes the data collected by the data collection unit to determine the employees' skills and aptitudes. The proposal unit proposes an appropriate career plan based on the analysis results obtained by the analysis unit. The management unit sets goals based on the career plan proposed by the proposal unit and manages their progress. [Effects of the Invention]

[0007] The system according to this embodiment allows employees to understand their own skills and aptitudes and to formulate appropriate career 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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards 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 career plan proposal system according to an embodiment of the present invention is a system that uses a generating AI to propose a career plan tailored to each employee, and provides goal setting and growth support. This career plan proposal system collects the employee's past performance, skill data, and aspirations, and the generating AI analyzes this data to propose an optimal career plan. Furthermore, it sets goals based on the career plan proposed by the generating AI and manages their progress. This mechanism allows employees to be satisfied with their careers and improves the overall performance of the company. For example, the career plan proposal system collects the employee's past performance, skill data, and aspirations. At this time, it collects detailed data such as the achievements the employee has made so far, the skills they have acquired, and their future aspirations. For example, it collects data such as projects the employee has worked on in the past, qualifications they have acquired, and future career goals. This allows the system to understand the employee's current situation and aspirations. Next, the generating AI analyzes the collected data. Based on the collected data, the generating AI determines the employee's skills and aptitudes and proposes an optimal career plan. For example, it can propose a future career path based on projects the employee has successfully completed in the past and qualifications they have acquired. This provides a career plan tailored to the employee's skills and aptitudes. Furthermore, it sets goals based on the career plan proposed by the generating AI. Specifically, the system sets goals for employees to achieve and manages their progress. For example, employees can set goals for acquiring new skills, and their progress can be regularly monitored. This supports employee growth. This system allows employees to be satisfied with their careers and improves the overall performance of the company. When employees have a career plan that matches their skills and aptitudes, their motivation for work increases, and the productivity of the entire company improves. For example, it can be expected that the overall performance of the company will improve as employees strive towards their career goals. In this way, the career plan proposal system can improve employee career satisfaction and the overall performance of the company.

[0029] The career plan proposal system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a management unit. The data collection unit collects data on employees' past performance, skills, and aspirations. The data collection unit collects data such as projects employees have worked on in the past, qualifications they have acquired, and future career goals. For example, the data collection unit can collect data on projects employees have successfully completed in the past. The data collection unit can also collect data on qualifications employees have acquired. Furthermore, the data collection unit can also collect data on employees' future career goals. For example, the data collection unit collects data on skill acquisition goals that employees have set as future career goals. The analysis unit analyzes the data collected by the data collection unit to determine the employee's skills and aptitude. For example, the analysis unit can determine an employee's skills based on the collected data. Furthermore, the analysis unit can also determine an employee's aptitude based on the collected data. Furthermore, the analysis unit can comprehensively determine an employee's skills and aptitude based on the collected data. For example, the analysis unit can determine an employee's skills based on data from projects employees have successfully completed in the past. The proposal department proposes the optimal career plan based on the analysis results obtained by the analysis department. For example, the proposal department can propose future career paths based on the employee's past successful projects and acquired qualifications. The proposal department can also propose the optimal career plan based on the employee's skills and aptitudes. Furthermore, the proposal department can propose the optimal career plan based on the employee's wishes. For example, the proposal department proposes a career plan based on the skill acquisition goals that the employee has set as their future career goals. The management department sets goals based on the career plan proposed by the proposal department and manages their progress. For example, the management department can set goals that employees should achieve. The management department can also manage the progress of employees' goals. Furthermore, the management department can provide feedback on the employee's achievement of their goals. For example, the management department sets goals for employees to acquire new skills and regularly checks their progress. As a result, the career plan proposal system according to this embodiment can improve employee career satisfaction and the overall performance of the company.

[0030] The data collection department collects data on employees' past performance, skills, and aspirations. Specifically, it collects data on projects employees have worked on in the past, qualifications they have obtained, and future career goals. For example, it can collect data on projects employees have successfully completed in the past. This includes detailed information such as the project's scale, duration, results achieved, and the technologies and tools used. The data collection department can also collect data on qualifications employees have obtained. It collects information such as the type of qualification, acquisition date, and certifying body to understand the employee's expertise and skill level. Furthermore, the data collection department can collect data on employees' future career goals. For example, it collects data on skill acquisition goals that employees have set as their future career goals. This includes the type of job or position the employee desires, the skills and knowledge they want to acquire, and the goals they want to achieve. The data collection department centrally manages this data and stores it in a database. Data collection is done by combining self-reported data entered by employees and data automatically obtained from internal systems. For example, it utilizes self-evaluation sheets that employees update regularly and data from the internal project management system. This allows the data collection department to comprehensively collect diverse data related to employees' careers, making it available for use by the analysis and proposal departments.

[0031] The analysis department analyzes data collected by the data collection department to determine employees' skills and aptitudes. Specifically, it can determine employees' skills based on the collected data. For example, it can determine an employee's skills based on data from past successful projects. By analyzing project results, technologies used, and goals achieved, it identifies the employee's strengths and areas of expertise. The analysis department can also determine employees' aptitudes based on the collected data. For example, it can comprehensively analyze an employee's past performance, acquired qualifications, and future career goals to determine what types of jobs and positions the employee is suited for. Furthermore, the analysis department can comprehensively determine employees' skills and aptitudes based on the collected data. For example, it can comprehensively evaluate an employee's skills and aptitudes based on the skill acquisition goals they have set as future career goals, providing basic data to propose the optimal career plan. The analysis department uses AI to analyze data and determine employees' skills and aptitudes with high accuracy. The AI ​​predicts employees' skills and aptitudes based on past data and statistical information, and provides information to propose the optimal career plan. This allows the analysis unit to quickly and accurately analyze the collected data and comprehensively evaluate employees' skills and aptitudes.

[0032] The Proposal Department proposes optimal career plans based on the analysis results obtained by the Analysis Department. Specifically, it can propose future career paths based on the employee's past successful projects and acquired qualifications. For example, it can suggest participation in similar projects based on data from past successful projects. The Proposal Department can also propose optimal career plans based on the employee's skills and aptitudes. For example, it can suggest the next skills and qualifications an employee should acquire based on the qualifications and skills they have acquired. Furthermore, the Proposal Department can propose optimal career plans based on the employee's aspirations. For example, it can propose a career plan based on the skill acquisition goals an employee has set as their future career objectives. The Proposal Department uses AI to propose optimal career plans based on the analysis results. The AI ​​comprehensively analyzes the employee's past data, aspirations, skills, and aptitudes to propose the optimal career plan. This allows the Proposal Department to propose the optimal career plan for each employee, improving employee career satisfaction. Furthermore, the Proposal Department monitors the implementation status of the proposed career plans and revise or modify the plans as needed. This allows the Proposal Department to continuously support employees' career plans and assist them in achieving their optimal career paths.

[0033] The Management Department sets goals and manages their progress based on the career plans proposed by the Proposal Department. Specifically, it can set goals that employees should achieve. For example, it can set goals for employees to acquire new skills and regularly check their progress. The Management Department can also manage the progress of employees' goals. For example, it can regularly check the progress of goals set by employees and provide support and feedback as needed. Furthermore, the Management Department can provide feedback on the achievement of employees' goals. For example, if an employee achieves a goal they set, it evaluates the results and provides feedback to set the next goal. The Management Department uses AI to manage goal progress and monitor the execution of employees' career plans. The AI ​​analyzes employee progress data and provides advice and support for achieving goals. This allows the Management Department to efficiently manage the progress of employees' career plans and support their growth. Furthermore, the Management Department collects employee feedback and improves and revises career plans. For example, if an employee is having difficulty achieving a goal, the Management Department identifies the cause and provides appropriate support. This allows the Management Department to continuously manage the progress of employees' career plans and support their growth.

[0034] The data collection unit can collect data on projects employees have worked on in the past, qualifications they have obtained, and future career goals. For example, the data collection unit can collect data on projects employees have worked on in the past. For example, the data collection unit can collect data on projects employees have successfully completed in the past. The data collection unit can also collect data on qualifications employees have obtained. For example, the data collection unit can collect data on technical qualifications employees have obtained. Furthermore, the data collection unit can also collect data on employees' future career goals. For example, the data collection unit can collect data on skill acquisition goals that employees have set as future career goals. This allows for a detailed understanding of employees' past performance, skills, and future aspirations. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input data on projects employees have worked on in the past into a generating AI and have the generating AI perform analysis of the project data.

[0035] The analysis unit can determine an employee's skills and aptitudes based on the collected data. For example, the analysis unit can determine an employee's skills based on the collected data. For example, the analysis unit can determine an employee's skills based on data from projects the employee has successfully completed in the past. The analysis unit can also determine an employee's aptitudes based on the collected data. For example, the analysis unit can determine an employee's aptitudes based on data from qualifications the employee has obtained. Furthermore, the analysis unit can comprehensively determine an employee's skills and aptitudes based on the collected data. For example, the analysis unit comprehensively determines an employee's skills and aptitudes based on data from projects the employee has successfully completed in the past and data from qualifications the employee has obtained. This allows the system to provide career plans based on the employee's skills and aptitudes. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the collected data into a generative AI and have the generative AI perform the determination of the employee's skills and aptitudes.

[0036] The proposal department can propose future career paths based on projects employees have completed in the past and qualifications they have acquired. For example, the proposal department can propose future career paths based on data of projects employees have successfully completed in the past. The proposal department can also propose future career paths based on data of qualifications employees have acquired. For example, the proposal department can propose future career paths based on data of technical qualifications employees have acquired. Furthermore, the proposal department can also propose future career paths based on employees' skills and aptitudes. For example, the proposal department can propose future career paths based on employees' skills and aptitudes. This allows for the proposal of career paths based on employees' past successes. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal department can input data of employees' past successes into a generative AI and have the generative AI propose future career paths.

[0037] The management department can set goals that employees should achieve and manage their progress. For example, the management department can set goals for employees to achieve. For example, the management department can set goals for employees to acquire new skills. The management department can also manage the progress of employees' goals. For example, the management department can periodically check the progress of employees' goals. Furthermore, the management department can provide feedback on the achievement status of employees' goals. For example, the management department can provide feedback on the achievement status of employees' goals. This can support employee growth and promote goal achievement. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input employee goal progress data into a generating AI and have the generating AI perform progress management.

[0038] The management department includes a feedback department that provides feedback to employees on the results of progress management. The feedback department provides feedback on employees' progress, for example. For example, the feedback department can provide periodic feedback on employees' progress. The feedback department can also provide feedback on areas for improvement based on employees' progress. For example, the feedback department can provide feedback on areas for improvement based on employees' progress. Furthermore, the feedback department can also suggest the next steps based on employees' progress. For example, the feedback department can suggest the next steps based on employees' progress. This allows employees to understand their progress and recognize areas for improvement. Some or all of the above processes in the feedback department may be performed using AI, for example, or without AI. For example, the feedback department can input employee progress data into a generating AI and have the generating AI generate the feedback.

[0039] The data collection unit can analyze employees' past performance and skill data and select the optimal data collection method. For example, the data collection unit can prioritize collecting data from projects that employees have successfully completed in the past. The data collection unit can also collect relevant data based on the qualifications that employees have obtained. For example, the data collection unit can collect relevant data based on the qualifications that employees have obtained. Furthermore, the data collection unit can collect detailed data according to the employee's skill level. For example, the data collection unit can collect detailed data according to the employee's skill level. This allows the optimal data collection method to be selected based on the employee's past performance and skill data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input employees' past performance data into a generating AI and have the generating AI select the optimal data collection method.

[0040] The data collection unit can filter data based on an employee's current projects and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to a project the employee is currently working on. The data collection unit can also collect relevant data based on an employee's areas of interest. For example, the data collection unit can collect relevant data based on an employee's areas of interest. Furthermore, the data collection unit can collect data related to an employee's future career goals. For example, the data collection unit can collect data related to an employee's future career goals. This allows the data collection unit to prioritize collecting relevant data based on an employee's current projects 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 using AI. For example, the data collection unit can input an employee's current project data into a generating AI and have the generating AI perform the filtering.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of employees during data collection. For example, the data collection unit can prioritize the collection of data related to the location where the employee is currently located. The data collection unit can also prioritize the collection of data related to places that employees frequently visit. For example, the data collection unit can collect data related to places that employees frequently visit. Furthermore, the data collection unit can also collect data related to the employee's workplace. For example, the data collection unit can collect data related to the employee's workplace. This allows for the priority collection of highly relevant data based on the employee's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the employee's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0042] The data collection unit can analyze employees' social media activities and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on information shared by employees on social media. The data collection unit can also collect data related to accounts that employees follow. Furthermore, the data collection unit can collect data related to groups and communities that employees participate in. This allows for the collection of relevant data based on employees' social media activities. 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 social media activity data into a generating AI and have the generating AI collect the relevant data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the data. By adjusting the level of detail of the analysis according to the importance of the data, more effective analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the importance of the data into the generative AI and have the generative AI adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a skill matching algorithm to skill data. The analysis unit can also apply a performance evaluation algorithm to performance data. Furthermore, the analysis unit can apply a career path prediction algorithm to desired data. This improves the accuracy of the analysis by applying the most suitable analysis algorithm according to the data category. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the data category into a generative AI and have the generative AI execute the application of the analysis algorithm.

[0045] The analysis unit can determine the priority of analysis based on the data submission date during the analysis. For example, the analysis unit can prioritize the analysis of recently submitted data. The analysis unit can also postpone the analysis of older data. Furthermore, the analysis unit can adjust the analysis schedule according to the submission date. This allows for more effective analysis by determining the priority of analysis based on the data submission date. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the data submission date into the generative AI and have the generative AI determine the priority of analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. The analysis unit can also postpone the analysis of less relevant data. Furthermore, the analysis unit can determine the order of analysis according to the relevance of the data. By adjusting the order of analysis based on the relevance of the data, more effective analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the relevance of the data into a generative AI and have the generative AI adjust the order of analysis.

[0047] The proposal unit can adjust the level of detail in its proposals based on the importance of the career plans. For example, the proposal unit can provide detailed proposals for career plans of high importance. The proposal unit can also provide simplified proposals for career plans of low importance. Furthermore, the proposal unit can determine the priority of proposals according to the importance of the career plans. By adjusting the level of detail in proposals according to the importance of the career plans, more effective proposals can be made. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input the importance of the career plans into the generative AI and have the generative AI adjust the level of detail in the proposals.

[0048] The proposal unit can apply different proposal algorithms depending on the category of the career plan when making a proposal. For example, the proposal unit can apply a skill matching algorithm to a career plan related to skill development. For example, the proposal unit can apply a performance evaluation algorithm to a career plan related to promotion. For example, the proposal unit can apply a career path prediction algorithm to a career plan related to job change. For example, the proposal unit can apply a career path prediction algorithm to a career plan related to job change. By applying the most suitable proposal algorithm according to the category of the career plan, the accuracy of the proposal is improved. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal unit can input the category of the career plan into a generative AI and have the generative AI perform the application of the proposal algorithm.

[0049] The proposal department can determine the priority of proposals based on the submission timing of career plans. For example, the proposal department can prioritize recently submitted career plans. The proposal department can also postpone older career plans. Furthermore, the proposal department can adjust the proposal schedule according to the submission timing. This allows for more effective proposals by prioritizing proposals based on the submission timing of career plans. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or not. For example, the proposal department can input the submission timing of career plans into a generative AI and have the generative AI determine the priority of proposals.

[0050] The proposal unit can adjust the order of proposals based on the relevance of the career plans. For example, the proposal unit can prioritize proposing highly relevant career plans. The proposal unit can also postpone less relevant career plans. Furthermore, the proposal unit can determine the order of proposals according to the relevance of the career plans. By adjusting the order of proposals based on the relevance of the career plans, more effective proposals can be made. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input the relevance of the career plans into a generative AI and have the generative AI adjust the order of proposals.

[0051] The management department can set optimal goals by analyzing employees' past performance when setting targets. For example, the management department can set realistic targets based on the performance employees have achieved in the past. The management department can also analyze employees' past performance and set challenging targets. For example, the management department can analyze employees' past performance and set challenging targets. Furthermore, the management department can adjust the difficulty level of targets according to employees' past performance. For example, the management department can adjust the difficulty level of targets according to employees' past performance. This allows for the setting of realistic and challenging targets based on employees' past performance. Some or all of the above processes in the management department may be performed using AI, or not. For example, the management department can input employees' past performance data into a generating AI and have the generating AI set optimal targets.

[0052] The management department can customize goals based on employees' current skill levels when setting goals. For example, the management department can set achievable goals according to employees' current skill levels. The management department can also set challenging goals considering employees' skill levels. Furthermore, the management department can customize the content of goals based on employees' skill levels. This allows for the setting of achievable and challenging goals according to employees' current skill levels. Some or all of the above processes in the management department may be performed using AI, or not. For example, the management department can input employee skill level data into a generating AI and have the generating AI perform goal customization.

[0053] The management department can set optimal goals by considering the geographical location information of employees when setting goals. For example, the management department can set goals related to the location where the employee is currently located. The management department can also set goals related to places that employees frequently visit. Furthermore, the management department can set goals related to the location where the employee works. This allows for the setting of highly relevant goals based on the geographical location information of employees. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input the geographical location information of employees into a generating AI and have the generating AI set optimal goals.

[0054] The management department can analyze employees' social media activity and propose goals when setting targets. For example, the management department can set relevant goals based on information employees share on social media. The management department can also set goals related to accounts employees follow. Furthermore, the management department can set goals related to groups and communities employees participate in. This allows for the setting of highly relevant goals based on employees' social media activity. Some or all of the above processes in the management department may be performed using AI, or not. For example, the management department can input employee social media activity data into a generating AI and have the generating AI propose goals.

[0055] The feedback unit can analyze an employee's past performance to provide optimal feedback. For example, the feedback unit can provide specific feedback based on the employee's past achievements. Furthermore, the feedback unit can analyze an employee's past performance and identify areas for improvement. In addition, the feedback unit can adjust the content of the feedback according to the employee's past performance. This allows the feedback unit to provide specific feedback that includes areas for improvement based on the employee's past performance. Some or all of the above processing in the feedback unit may be performed using AI, or not. For example, the feedback unit can input employee past performance data into a generating AI and have the generating AI provide optimal feedback.

[0056] The feedback unit can customize feedback based on the employee's current skill level. For example, the feedback unit can provide specific feedback according to the employee's current skill level. The feedback unit can also point out areas for improvement, taking the employee's skill level into consideration. Furthermore, the feedback unit can customize the content of the feedback based on the employee's skill level. This allows for the provision of specific feedback, including areas for improvement, tailored to the employee's current skill level. Some or all of the above processing in the feedback unit may be performed using AI, or not. For example, the feedback unit can input employee skill level data into a generating AI and have the generating AI customize the feedback.

[0057] The feedback unit can provide optimal feedback by considering the employee's geographical location information. For example, the feedback unit can provide feedback related to the employee's current location. The feedback unit can also provide feedback related to places the employee frequently visits. Furthermore, the feedback unit can provide feedback related to the employee's workplace. This allows for the provision of highly relevant feedback based on the employee's geographical location information. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the employee's geographical location information into a generating AI and have the generating AI provide optimal feedback.

[0058] The feedback department can analyze an employee's social media activity and propose feedback when providing it. For example, the feedback department can provide relevant feedback based on information shared by the employee on social media. The feedback department can also provide feedback related to accounts followed by the employee. Furthermore, the feedback department can provide feedback related to groups and communities in which the employee participates. This allows for the provision of highly relevant feedback based on the employee's social media activity. Some or all of the above processing in the feedback department may be performed using AI, for example, or not. For example, the feedback department can input employee social media activity data into a generating AI and have the generating AI generate feedback suggestions.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The career plan proposal system collects employee health data and can adjust career plans based on their health status. For example, the data collection unit can collect employee health checkup results and daily health data. The analysis unit can determine the employee's health status based on the collected health data and propose a career plan appropriate to that status. The proposal unit can propose challenging career plans for employees with good health and more manageable plans for employees with unstable health. The management unit can set goals based on health status and manage their progress. This allows the system to provide career plans that take into account the health status of employees.

[0061] The career plan proposal system can collect employees' hobbies and interests and propose career plans based on them. For example, the data collection unit can collect data on employees' hobbies and areas of interest. The analysis unit can determine employees' personalities and aptitudes based on the collected data on hobbies and interests. The proposal unit can propose career plans that allow employees to grow while enjoying themselves, based on their hobbies and interests. The management unit can set goals related to hobbies and interests and manage their progress. This allows the system to provide career plans that reflect employees' personal interests.

[0062] The career plan proposal system can propose career plans that take into account employees' family structure and life stage. For example, the data collection unit can collect data on employees' family structure and life stage. The analysis unit can determine a career plan appropriate to the employee's life stage based on the collected data. The proposal unit can propose a career plan that allows employees to balance family and work, based on their family structure and life stage. The management unit can set goals appropriate to the life stage and manage their progress. This allows the system to provide career plans that take into account employees' family circumstances.

[0063] The career plan proposal system can analyze employees' learning styles and propose the most suitable learning methods. For example, the data collection unit can collect data on employees' past learning experiences and learning styles. The analysis unit can determine employees' learning styles based on the collected data. The proposal unit can propose career plans that allow employees to learn efficiently based on their learning styles. The management unit can set goals according to the learning style and manage their progress. This allows the system to provide career plans tailored to each employee's learning style.

[0064] The career plan proposal system can analyze employees' workplace relationships and propose optimal team compositions. For example, the data collection unit can collect data on employees' workplace relationships. The analysis unit can determine employees' relationships based on the collected data. The proposal unit can propose team compositions that allow employees to perform at their best based on these relationships. The management unit can set goals according to the team composition and manage their progress. This allows the system to provide career plans that take employees' relationships into consideration.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The data collection department collects employees' past performance, skill data, and aspirations. For example, it collects data on projects employees have worked on in the past, qualifications they have obtained, and future career goals. The data collection department can collect data on successful projects employees have completed in the past, qualifications they have obtained, and skill acquisition goals they have set as future career goals. Step 2: The analysis unit analyzes the data collected by the collection unit to determine the skills and aptitudes of employees. For example, it determines the skills and aptitudes of employees based on the collected data and makes an overall evaluation. Step 3: The proposal department proposes the optimal career plan based on the analysis results obtained by the analysis department. For example, they might propose a career plan based on the employee's past successful projects, acquired qualifications, skills, aptitudes, and aspirations. Step 4: The management department sets goals based on the career plans proposed by the proposal department and manages their progress. For example, they set goals that employees should achieve, regularly check their progress, and provide feedback.

[0067] (Example of form 2) The career plan proposal system according to an embodiment of the present invention is a system that uses a generating AI to propose a career plan tailored to each employee, and provides goal setting and growth support. This career plan proposal system collects the employee's past performance, skill data, and aspirations, and the generating AI analyzes this data to propose an optimal career plan. Furthermore, it sets goals based on the career plan proposed by the generating AI and manages their progress. This mechanism allows employees to be satisfied with their careers and improves the overall performance of the company. For example, the career plan proposal system collects the employee's past performance, skill data, and aspirations. At this time, it collects detailed data such as the achievements the employee has made so far, the skills they have acquired, and their future aspirations. For example, it collects data such as projects the employee has worked on in the past, qualifications they have acquired, and future career goals. This allows the system to understand the employee's current situation and aspirations. Next, the generating AI analyzes the collected data. Based on the collected data, the generating AI determines the employee's skills and aptitudes and proposes an optimal career plan. For example, it can propose a future career path based on projects the employee has successfully completed in the past and qualifications they have acquired. This provides a career plan tailored to the employee's skills and aptitudes. Furthermore, it sets goals based on the career plan proposed by the generating AI. Specifically, the system sets goals for employees to achieve and manages their progress. For example, employees can set goals for acquiring new skills, and their progress can be regularly monitored. This supports employee growth. This system allows employees to be satisfied with their careers and improves the overall performance of the company. When employees have a career plan that matches their skills and aptitudes, their motivation for work increases, and the productivity of the entire company improves. For example, it can be expected that the overall performance of the company will improve as employees strive towards their career goals. In this way, the career plan proposal system can improve employee career satisfaction and the overall performance of the company.

[0068] The career plan proposal system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a management unit. The data collection unit collects data on employees' past performance, skills, and aspirations. The data collection unit collects data such as projects employees have worked on in the past, qualifications they have acquired, and future career goals. For example, the data collection unit can collect data on projects employees have successfully completed in the past. The data collection unit can also collect data on qualifications employees have acquired. Furthermore, the data collection unit can also collect data on employees' future career goals. For example, the data collection unit collects data on skill acquisition goals that employees have set as future career goals. The analysis unit analyzes the data collected by the data collection unit to determine the employee's skills and aptitude. For example, the analysis unit can determine an employee's skills based on the collected data. Furthermore, the analysis unit can also determine an employee's aptitude based on the collected data. Furthermore, the analysis unit can comprehensively determine an employee's skills and aptitude based on the collected data. For example, the analysis unit can determine an employee's skills based on data from projects employees have successfully completed in the past. The proposal department proposes the optimal career plan based on the analysis results obtained by the analysis department. For example, the proposal department can propose future career paths based on the employee's past successful projects and acquired qualifications. The proposal department can also propose the optimal career plan based on the employee's skills and aptitudes. Furthermore, the proposal department can propose the optimal career plan based on the employee's wishes. For example, the proposal department proposes a career plan based on the skill acquisition goals that the employee has set as their future career goals. The management department sets goals based on the career plan proposed by the proposal department and manages their progress. For example, the management department can set goals that employees should achieve. The management department can also manage the progress of employees' goals. Furthermore, the management department can provide feedback on the employee's achievement of their goals. For example, the management department sets goals for employees to acquire new skills and regularly checks their progress. As a result, the career plan proposal system according to this embodiment can improve employee career satisfaction and the overall performance of the company.

[0069] The data collection department collects data on employees' past performance, skills, and aspirations. Specifically, it collects data on projects employees have worked on in the past, qualifications they have obtained, and future career goals. For example, it can collect data on projects employees have successfully completed in the past. This includes detailed information such as the project's scale, duration, results achieved, and the technologies and tools used. The data collection department can also collect data on qualifications employees have obtained. It collects information such as the type of qualification, acquisition date, and certifying body to understand the employee's expertise and skill level. Furthermore, the data collection department can collect data on employees' future career goals. For example, it collects data on skill acquisition goals that employees have set as their future career goals. This includes the type of job or position the employee desires, the skills and knowledge they want to acquire, and the goals they want to achieve. The data collection department centrally manages this data and stores it in a database. Data collection is done by combining self-reported data entered by employees and data automatically obtained from internal systems. For example, it utilizes self-evaluation sheets that employees update regularly and data from the internal project management system. This allows the data collection department to comprehensively collect diverse data related to employees' careers, making it available for use by the analysis and proposal departments.

[0070] The analysis department analyzes data collected by the data collection department to determine employees' skills and aptitudes. Specifically, it can determine employees' skills based on the collected data. For example, it can determine an employee's skills based on data from past successful projects. By analyzing project results, technologies used, and goals achieved, it identifies the employee's strengths and areas of expertise. The analysis department can also determine employees' aptitudes based on the collected data. For example, it can comprehensively analyze an employee's past performance, acquired qualifications, and future career goals to determine what types of jobs and positions the employee is suited for. Furthermore, the analysis department can comprehensively determine employees' skills and aptitudes based on the collected data. For example, it can comprehensively evaluate an employee's skills and aptitudes based on the skill acquisition goals they have set as future career goals, providing basic data to propose the optimal career plan. The analysis department uses AI to analyze data and determine employees' skills and aptitudes with high accuracy. The AI ​​predicts employees' skills and aptitudes based on past data and statistical information, and provides information to propose the optimal career plan. This allows the analysis unit to quickly and accurately analyze the collected data and comprehensively evaluate employees' skills and aptitudes.

[0071] The Proposal Department proposes optimal career plans based on the analysis results obtained by the Analysis Department. Specifically, it can propose future career paths based on the employee's past successful projects and acquired qualifications. For example, it can suggest participation in similar projects based on data from past successful projects. The Proposal Department can also propose optimal career plans based on the employee's skills and aptitudes. For example, it can suggest the next skills and qualifications an employee should acquire based on the qualifications and skills they have acquired. Furthermore, the Proposal Department can propose optimal career plans based on the employee's aspirations. For example, it can propose a career plan based on the skill acquisition goals an employee has set as their future career objectives. The Proposal Department uses AI to propose optimal career plans based on the analysis results. The AI ​​comprehensively analyzes the employee's past data, aspirations, skills, and aptitudes to propose the optimal career plan. This allows the Proposal Department to propose the optimal career plan for each employee, improving employee career satisfaction. Furthermore, the Proposal Department monitors the implementation status of the proposed career plans and revise or modify the plans as needed. This allows the Proposal Department to continuously support employees' career plans and assist them in achieving their optimal career paths.

[0072] The Management Department sets goals and manages their progress based on the career plans proposed by the Proposal Department. Specifically, it can set goals that employees should achieve. For example, it can set goals for employees to acquire new skills and regularly check their progress. The Management Department can also manage the progress of employees' goals. For example, it can regularly check the progress of goals set by employees and provide support and feedback as needed. Furthermore, the Management Department can provide feedback on the achievement of employees' goals. For example, if an employee achieves a goal they set, it evaluates the results and provides feedback to set the next goal. The Management Department uses AI to manage goal progress and monitor the execution of employees' career plans. The AI ​​analyzes employee progress data and provides advice and support for achieving goals. This allows the Management Department to efficiently manage the progress of employees' career plans and support their growth. Furthermore, the Management Department collects employee feedback and improves and revises career plans. For example, if an employee is having difficulty achieving a goal, the Management Department identifies the cause and provides appropriate support. This allows the Management Department to continuously manage the progress of employees' career plans and support their growth.

[0073] The data collection unit can collect data on projects employees have worked on in the past, qualifications they have obtained, and future career goals. For example, the data collection unit can collect data on projects employees have worked on in the past. For example, the data collection unit can collect data on projects employees have successfully completed in the past. The data collection unit can also collect data on qualifications employees have obtained. For example, the data collection unit can collect data on technical qualifications employees have obtained. Furthermore, the data collection unit can also collect data on employees' future career goals. For example, the data collection unit can collect data on skill acquisition goals that employees have set as future career goals. This allows for a detailed understanding of employees' past performance, skills, and future aspirations. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input data on projects employees have worked on in the past into a generating AI and have the generating AI perform analysis of the project data.

[0074] The analysis unit can determine an employee's skills and aptitudes based on the collected data. For example, the analysis unit can determine an employee's skills based on the collected data. For example, the analysis unit can determine an employee's skills based on data from projects the employee has successfully completed in the past. The analysis unit can also determine an employee's aptitudes based on the collected data. For example, the analysis unit can determine an employee's aptitudes based on data from qualifications the employee has obtained. Furthermore, the analysis unit can comprehensively determine an employee's skills and aptitudes based on the collected data. For example, the analysis unit comprehensively determines an employee's skills and aptitudes based on data from projects the employee has successfully completed in the past and data from qualifications the employee has obtained. This allows the system to provide career plans based on the employee's skills and aptitudes. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the collected data into a generative AI and have the generative AI perform the determination of the employee's skills and aptitudes.

[0075] The proposal department can propose future career paths based on projects employees have completed in the past and qualifications they have acquired. For example, the proposal department can propose future career paths based on data of projects employees have successfully completed in the past. The proposal department can also propose future career paths based on data of qualifications employees have acquired. For example, the proposal department can propose future career paths based on data of technical qualifications employees have acquired. Furthermore, the proposal department can also propose future career paths based on employees' skills and aptitudes. For example, the proposal department can propose future career paths based on employees' skills and aptitudes. This allows for the proposal of career paths based on employees' past successes. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal department can input data of employees' past successes into a generative AI and have the generative AI propose future career paths.

[0076] The management department can set goals that employees should achieve and manage their progress. For example, the management department can set goals for employees to achieve. For example, the management department can set goals for employees to acquire new skills. The management department can also manage the progress of employees' goals. For example, the management department can periodically check the progress of employees' goals. Furthermore, the management department can provide feedback on the achievement status of employees' goals. For example, the management department can provide feedback on the achievement status of employees' goals. This can support employee growth and promote goal achievement. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input employee goal progress data into a generating AI and have the generating AI perform progress management.

[0077] The management department includes a feedback department that provides feedback to employees on the results of progress management. The feedback department provides feedback on employees' progress, for example. For example, the feedback department can provide periodic feedback on employees' progress. The feedback department can also provide feedback on areas for improvement based on employees' progress. For example, the feedback department can provide feedback on areas for improvement based on employees' progress. Furthermore, the feedback department can also suggest the next steps based on employees' progress. For example, the feedback department can suggest the next steps based on employees' progress. This allows employees to understand their progress and recognize areas for improvement. Some or all of the above processes in the feedback department may be performed using AI, for example, or without AI. For example, the feedback department can input employee progress data into a generating AI and have the generating AI generate the feedback.

[0078] The data collection unit can estimate employees' emotions and adjust the timing of data collection based on the estimated emotions. For example, if an employee is stressed, the data collection unit can delay data collection until the employee is relaxed. The data collection unit can also start collecting data immediately if the employee is highly motivated. Furthermore, if an employee is tired, the data collection unit can collect data after a break. By adjusting the timing of data collection according to employees' emotions, more appropriate data collection becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input employee emotional data into a generating AI and have the generating AI adjust the timing of data collection.

[0079] The data collection unit can analyze employees' past performance and skill data and select the optimal data collection method. For example, the data collection unit can prioritize collecting data from projects that employees have successfully completed in the past. The data collection unit can also collect relevant data based on the qualifications that employees have obtained. For example, the data collection unit can collect relevant data based on the qualifications that employees have obtained. Furthermore, the data collection unit can collect detailed data according to the employee's skill level. For example, the data collection unit can collect detailed data according to the employee's skill level. This allows the optimal data collection method to be selected based on the employee's past performance and skill data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input employees' past performance data into a generating AI and have the generating AI select the optimal data collection method.

[0080] The data collection unit can filter data based on an employee's current projects and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to a project the employee is currently working on. The data collection unit can also collect relevant data based on an employee's areas of interest. For example, the data collection unit can collect relevant data based on an employee's areas of interest. Furthermore, the data collection unit can collect data related to an employee's future career goals. For example, the data collection unit can collect data related to an employee's future career goals. This allows the data collection unit to prioritize collecting relevant data based on an employee's current projects 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 using AI. For example, the data collection unit can input an employee's current project data into a generating AI and have the generating AI perform the filtering.

[0081] The data collection unit can estimate employees' emotions and prioritize the data to collect based on those emotions. For example, if an employee is stressed, the data collection unit can postpone collecting less important data. For example, if an employee is relaxed, the data collection unit can prioritize collecting detailed data. For example, if an employee is relaxed, the data collection unit can prioritize collecting detailed data. For example, if an employee is in a hurry, the data collection unit can prioritize collecting the most important data. This allows for more effective data collection by prioritizing the data to collect according to employees' emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input employee emotional data into a generating AI and have the generating AI determine the priority of the data.

[0082] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of employees during data collection. For example, the data collection unit can prioritize the collection of data related to the location where the employee is currently located. The data collection unit can also prioritize the collection of data related to places that employees frequently visit. For example, the data collection unit can collect data related to places that employees frequently visit. Furthermore, the data collection unit can also collect data related to the employee's workplace. For example, the data collection unit can collect data related to the employee's workplace. This allows for the priority collection of highly relevant data based on the employee's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the employee's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0083] The data collection unit can analyze employees' social media activities and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on information shared by employees on social media. The data collection unit can also collect data related to accounts that employees follow. Furthermore, the data collection unit can collect data related to groups and communities that employees participate in. This allows for the collection of relevant data based on employees' social media activities. 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 social media activity data into a generating AI and have the generating AI collect the relevant data.

[0084] The analysis unit can estimate the emotions of employees 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 easy-to-understand analysis results. For example, if an employee is stressed, the analysis unit can provide simple and easy-to-understand analysis results. The analysis unit can also provide detailed analysis results if an employee is relaxed. For example, if an employee is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if an employee is in a hurry, the analysis unit can provide concise analysis results. For example, if an employee is in a hurry, the analysis unit can provide concise analysis results. By adjusting the presentation of the analysis according to the emotions of employees, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input employee emotional data into a generating AI and have the generating AI adjust the way the analysis is presented.

[0085] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the data. By adjusting the level of detail of the analysis according to the importance of the data, more effective analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the importance of the data into the generative AI and have the generative AI adjust the level of detail of the analysis.

[0086] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a skill matching algorithm to skill data. The analysis unit can also apply a performance evaluation algorithm to performance data. Furthermore, the analysis unit can apply a career path prediction algorithm to desired data. This improves the accuracy of the analysis by applying the most suitable analysis algorithm according to the data category. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the data category into a generative AI and have the generative AI execute the application of the analysis algorithm.

[0087] The analysis unit can estimate an employee's emotions and adjust the length of the analysis based on the estimated emotions. For example, if an employee is stressed, the analysis unit can provide a short, concise analysis result. For example, if an employee is stressed, the analysis unit can provide a short, concise analysis result. The analysis unit can also provide a detailed analysis result if an employee is relaxed. For example, if an employee is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if an employee is in a hurry, the analysis unit can provide an analysis result that can be quickly understood. For example, if an employee is in a hurry, the analysis unit can provide an analysis result that can be quickly understood. 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, an emotion engine or a 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, for example, or without AI. For example, the analysis unit can input employee emotional data into a generating AI and have the generating AI adjust the length of the analysis.

[0088] The analysis unit can determine the priority of analysis based on the data submission date during the analysis. For example, the analysis unit can prioritize the analysis of recently submitted data. The analysis unit can also postpone the analysis of older data. Furthermore, the analysis unit can adjust the analysis schedule according to the submission date. This allows for more effective analysis by determining the priority of analysis based on the data submission date. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the data submission date into the generative AI and have the generative AI determine the priority of analysis.

[0089] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. The analysis unit can also postpone the analysis of less relevant data. Furthermore, the analysis unit can determine the order of analysis according to the relevance of the data. By adjusting the order of analysis based on the relevance of the data, more effective analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the relevance of the data into a generative AI and have the generative AI adjust the order of analysis.

[0090] The suggestion department can estimate an employee's emotions and adjust the way suggestions are presented based on those emotions. For example, if an employee is stressed, the suggestion department can provide a simple and visually clear suggestion. For example, if an employee is stressed, the suggestion department can provide a simple and visually clear suggestion. For example, if an employee is relaxed, the suggestion department can provide a detailed suggestion. For example, if an employee is relaxed, the suggestion department can provide a detailed suggestion. Furthermore, if an employee is in a hurry, the suggestion department can provide a concise suggestion. For example, if an employee is in a hurry, the suggestion department can provide a concise suggestion. In this way, by adjusting the way suggestions are presented according to an employee's emotions, more appropriate suggestions can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion department may be performed using AI, for example, or without AI. For example, the proposal department can input employee emotional data into a generation AI and have the AI ​​adjust the way proposals are expressed.

[0091] The proposal unit can adjust the level of detail in its proposals based on the importance of the career plans. For example, the proposal unit can provide detailed proposals for career plans of high importance. The proposal unit can also provide simplified proposals for career plans of low importance. Furthermore, the proposal unit can determine the priority of proposals according to the importance of the career plans. By adjusting the level of detail in proposals according to the importance of the career plans, more effective proposals can be made. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input the importance of the career plans into the generative AI and have the generative AI adjust the level of detail in the proposals.

[0092] The proposal unit can apply different proposal algorithms depending on the category of the career plan when making a proposal. For example, the proposal unit can apply a skill matching algorithm to a career plan related to skill development. For example, the proposal unit can apply a performance evaluation algorithm to a career plan related to promotion. For example, the proposal unit can apply a career path prediction algorithm to a career plan related to job change. For example, the proposal unit can apply a career path prediction algorithm to a career plan related to job change. By applying the most suitable proposal algorithm according to the category of the career plan, the accuracy of the proposal is improved. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal unit can input the category of the career plan into a generative AI and have the generative AI perform the application of the proposal algorithm.

[0093] The suggestion department can estimate an employee's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if an employee is stressed, the suggestion department can provide a short, to-the-point suggestion. For example, if an employee is stressed, the suggestion department can provide a short, to-the-point suggestion. For example, if an employee is relaxed, the suggestion department can provide a detailed suggestion. For example, if an employee is in a hurry, the suggestion department can provide a suggestion that can be quickly understood. For example, if an employee is in a hurry, the suggestion department can provide a suggestion that can be quickly understood. In this way, by adjusting the length of the suggestion according to the employee's emotions, more appropriate suggestions can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion department may be performed using AI, for example, or not using AI. For example, the proposal department can input employee emotional data into a generation AI and have the AI ​​adjust the length of the proposals.

[0094] The proposal department can determine the priority of proposals based on the submission timing of career plans. For example, the proposal department can prioritize recently submitted career plans. The proposal department can also postpone older career plans. Furthermore, the proposal department can adjust the proposal schedule according to the submission timing. This allows for more effective proposals by prioritizing proposals based on the submission timing of career plans. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or not. For example, the proposal department can input the submission timing of career plans into a generative AI and have the generative AI determine the priority of proposals.

[0095] The proposal unit can adjust the order of proposals based on the relevance of the career plans. For example, the proposal unit can prioritize proposing highly relevant career plans. The proposal unit can also postpone less relevant career plans. Furthermore, the proposal unit can determine the order of proposals according to the relevance of the career plans. By adjusting the order of proposals based on the relevance of the career plans, more effective proposals can be made. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input the relevance of the career plans into a generative AI and have the generative AI adjust the order of proposals.

[0096] The management department can estimate employees' emotions and adjust goal-setting methods based on those estimated emotions. For example, if an employee is stressed, the management department can set simple and easily achievable goals. For example, if an employee is relaxed, the management department can set detailed and challenging goals. For example, if an employee is relaxed, the management department can set detailed and challenging goals. Furthermore, if an employee is in a hurry, the management department can set goals that can be achieved quickly. For example, if an employee is in a hurry, the management department can set goals that can be achieved quickly. This allows for the setting of more appropriate goals by adjusting goal-setting methods according to employees' emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management department may be performed using AI, for example, or not using AI. For example, the management department can input employee emotional data into a generating AI and have the AI ​​adjust the goal-setting method.

[0097] The management department can set optimal goals by analyzing employees' past performance when setting targets. For example, the management department can set realistic targets based on the performance employees have achieved in the past. The management department can also analyze employees' past performance and set challenging targets. For example, the management department can analyze employees' past performance and set challenging targets. Furthermore, the management department can adjust the difficulty level of targets according to employees' past performance. For example, the management department can adjust the difficulty level of targets according to employees' past performance. This allows for the setting of realistic and challenging targets based on employees' past performance. Some or all of the above processes in the management department may be performed using AI, or not. For example, the management department can input employees' past performance data into a generating AI and have the generating AI set optimal targets.

[0098] The management department can customize goals based on employees' current skill levels when setting goals. For example, the management department can set achievable goals according to employees' current skill levels. The management department can also set challenging goals considering employees' skill levels. Furthermore, the management department can customize the content of goals based on employees' skill levels. This allows for the setting of achievable and challenging goals according to employees' current skill levels. Some or all of the above processes in the management department may be performed using AI, or not. For example, the management department can input employee skill level data into a generating AI and have the generating AI perform goal customization.

[0099] The management department can estimate employees' emotions and determine the priority of goal setting based on those estimated emotions. For example, if an employee is stressed, the management department can postpone less important goals. For example, if an employee is relaxed, the management department can prioritize setting detailed goals. For example, if an employee is relaxed, the management department can prioritize setting detailed goals. For example, if an employee is in a hurry, the management department can prioritize setting the most important goals. For example, if an employee is in a hurry, the management department can prioritize setting the most important goals. This allows for more effective goal setting by determining the priority of goal setting according to employees' emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management department may be performed using AI, for example, or without AI. For example, the management department can input employee emotional data into a generating AI and have the AI ​​determine the priorities for goal setting.

[0100] The management department can set optimal goals by considering the geographical location information of employees when setting goals. For example, the management department can set goals related to the location where the employee is currently located. The management department can also set goals related to places that employees frequently visit. Furthermore, the management department can set goals related to the location where the employee works. This allows for the setting of highly relevant goals based on the geographical location information of employees. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input the geographical location information of employees into a generating AI and have the generating AI set optimal goals.

[0101] The management department can analyze employees' social media activity and propose goals when setting targets. For example, the management department can set relevant goals based on information employees share on social media. The management department can also set goals related to accounts employees follow. Furthermore, the management department can set goals related to groups and communities employees participate in. This allows for the setting of highly relevant goals based on employees' social media activity. Some or all of the above processes in the management department may be performed using AI, or not. For example, the management department can input employee social media activity data into a generating AI and have the generating AI propose goals.

[0102] The feedback unit can estimate an employee's emotions and adjust the way feedback is presented based on the estimated emotions. For example, if an employee is stressed, the feedback unit can prioritize positive feedback. For example, if an employee is stressed, the feedback unit can prioritize positive feedback. For example, if an employee is relaxed, the feedback unit can provide detailed feedback. For example, if an employee is relaxed, the feedback unit can provide detailed feedback. For example, if an employee is in a hurry, the feedback unit can provide concise feedback. For example, if an employee is in a hurry, the feedback unit can provide concise feedback. In this way, by adjusting the way feedback is presented according to the employee's emotions, more appropriate feedback can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input employee emotional data into a generating AI and have the AI ​​adjust how the feedback is expressed.

[0103] The feedback unit can analyze an employee's past performance to provide optimal feedback. For example, the feedback unit can provide specific feedback based on the employee's past achievements. Furthermore, the feedback unit can analyze an employee's past performance and identify areas for improvement. In addition, the feedback unit can adjust the content of the feedback according to the employee's past performance. This allows the feedback unit to provide specific feedback that includes areas for improvement based on the employee's past performance. Some or all of the above processing in the feedback unit may be performed using AI, or not. For example, the feedback unit can input employee past performance data into a generating AI and have the generating AI provide optimal feedback.

[0104] The feedback unit can customize feedback based on the employee's current skill level. For example, the feedback unit can provide specific feedback according to the employee's current skill level. The feedback unit can also point out areas for improvement, taking the employee's skill level into consideration. Furthermore, the feedback unit can customize the content of the feedback based on the employee's skill level. This allows for the provision of specific feedback, including areas for improvement, tailored to the employee's current skill level. Some or all of the above processing in the feedback unit may be performed using AI, or not. For example, the feedback unit can input employee skill level data into a generating AI and have the generating AI customize the feedback.

[0105] The feedback department can estimate an employee's emotions and prioritize feedback based on the estimated emotions. For example, if an employee is stressed, the feedback department can postpone less important feedback. For example, if an employee is relaxed, the feedback department can prioritize providing detailed feedback. For example, if an employee is relaxed, the feedback department can prioritize providing detailed feedback. For example, if an employee is in a hurry, the feedback department can prioritize providing the most important feedback. For example, if an employee is in a hurry, the feedback department can prioritize providing the most important feedback. This allows for more effective feedback by prioritizing feedback according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback department may be performed using AI, for example, or without AI. For example, the feedback unit can input employee emotional data into a generating AI and have the AI ​​determine the priority of feedback.

[0106] The feedback unit can provide optimal feedback by considering the employee's geographical location information. For example, the feedback unit can provide feedback related to the employee's current location. The feedback unit can also provide feedback related to places the employee frequently visits. Furthermore, the feedback unit can provide feedback related to the employee's workplace. This allows for the provision of highly relevant feedback based on the employee's geographical location information. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the employee's geographical location information into a generating AI and have the generating AI provide optimal feedback.

[0107] The feedback department can analyze an employee's social media activity and propose feedback when providing it. For example, the feedback department can provide relevant feedback based on information shared by the employee on social media. The feedback department can also provide feedback related to accounts followed by the employee. Furthermore, the feedback department can provide feedback related to groups and communities in which the employee participates. This allows for the provision of highly relevant feedback based on the employee's social media activity. Some or all of the above processing in the feedback department may be performed using AI, for example, or not. For example, the feedback department can input employee social media activity data into a generating AI and have the generating AI generate feedback suggestions.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] The career plan proposal system collects employee health data and can adjust career plans based on their health status. For example, the data collection unit can collect employee health checkup results and daily health data. The analysis unit can determine the employee's health status based on the collected health data and propose a career plan appropriate to that status. The proposal unit can propose challenging career plans for employees with good health and more manageable plans for employees with unstable health. The management unit can set goals based on health status and manage their progress. This allows the system to provide career plans that take into account the health status of employees.

[0110] The career plan proposal system can collect employees' hobbies and interests and propose career plans based on them. For example, the data collection unit can collect data on employees' hobbies and areas of interest. The analysis unit can determine employees' personalities and aptitudes based on the collected data on hobbies and interests. The proposal unit can propose career plans that allow employees to grow while enjoying themselves, based on their hobbies and interests. The management unit can set goals related to hobbies and interests and manage their progress. This allows the system to provide career plans that reflect employees' personal interests.

[0111] The career plan proposal system can propose career plans that take into account employees' family structure and life stage. For example, the data collection unit can collect data on employees' family structure and life stage. The analysis unit can determine a career plan appropriate to the employee's life stage based on the collected data. The proposal unit can propose a career plan that allows employees to balance family and work, based on their family structure and life stage. The management unit can set goals appropriate to the life stage and manage their progress. This allows the system to provide career plans that take into account employees' family circumstances.

[0112] The career plan proposal system can analyze employees' learning styles and propose the most suitable learning methods. For example, the data collection unit can collect data on employees' past learning experiences and learning styles. The analysis unit can determine employees' learning styles based on the collected data. The proposal unit can propose career plans that allow employees to learn efficiently based on their learning styles. The management unit can set goals according to the learning style and manage their progress. This allows the system to provide career plans tailored to each employee's learning style.

[0113] The career plan proposal system can analyze employees' workplace relationships and propose optimal team compositions. For example, the data collection unit can collect data on employees' workplace relationships. The analysis unit can determine employees' relationships based on the collected data. The proposal unit can propose team compositions that allow employees to perform at their best based on these relationships. The management unit can set goals according to the team composition and manage their progress. This allows the system to provide career plans that take employees' relationships into consideration.

[0114] The career plan proposal system can estimate employees' emotions and adjust the timing of feedback based on those estimates. For example, the data collection unit can estimate an employee's emotions and delay feedback if they are feeling stressed. Conversely, if the employee is relaxed, feedback can be provided immediately. Furthermore, if the employee is highly motivated, detailed feedback can be provided. In this way, by adjusting the timing of feedback according to the employee's emotions, more effective feedback can be provided.

[0115] The career plan proposal system can estimate an employee's emotions and adjust the content of the career plan based on those emotions. For example, the data collection unit can estimate an employee's emotions and propose a less burdensome career plan if the employee is feeling stressed. Conversely, if the employee is relaxed, a more challenging career plan can be proposed. Furthermore, if the employee is highly motivated, a detailed career plan can be proposed. In this way, by adjusting the content of the career plan according to the employee's emotions, a more appropriate career plan can be provided.

[0116] The career plan proposal system can estimate employees' emotions and adjust the difficulty of goals based on those estimates. For example, the data collection unit can estimate an employee's emotions and set easily achievable goals if the employee is feeling stressed. Conversely, if the employee is relaxed, challenging goals can be set. Furthermore, if the employee is highly motivated, detailed goals can be set. In this way, by adjusting the difficulty of goals according to the employee's emotions, more appropriate goals can be set.

[0117] The career plan proposal system can estimate an employee's emotions and adjust the content of feedback based on those emotions. For example, the data collection unit can estimate an employee's emotions and prioritize positive feedback if the employee is feeling stressed. If the employee is relaxed, detailed feedback can be provided. Furthermore, if the employee is highly motivated, feedback including specific areas for improvement can be provided. This allows for more effective feedback by adjusting the content according to the employee's emotions.

[0118] The career plan proposal system can estimate employees' emotions and prioritize career plans based on those emotions. For example, the data collection department can estimate an employee's emotions and, if they are feeling stressed, can postpone less important career plans. Conversely, if an employee is relaxed, it can prioritize proposing detailed career plans. Furthermore, if an employee is highly motivated, it can prioritize proposing the most important career plans. In this way, by prioritizing career plans according to employees' emotions, a more effective career plan can be provided.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The data collection department collects employees' past performance, skill data, and aspirations. For example, it collects data on projects employees have worked on in the past, qualifications they have obtained, and future career goals. The data collection department can collect data on successful projects employees have completed in the past, qualifications they have obtained, and skill acquisition goals they have set as future career goals. Step 2: The analysis unit analyzes the data collected by the collection unit to determine the skills and aptitudes of employees. For example, it determines the skills and aptitudes of employees based on the collected data and makes an overall evaluation. Step 3: The proposal department proposes the optimal career plan based on the analysis results obtained by the analysis department. For example, they might propose a career plan based on the employee's past successful projects, acquired qualifications, skills, aptitudes, and aspirations. Step 4: The management department sets goals based on the career plans proposed by the proposal department and manages their progress. For example, they set goals that employees should achieve, regularly check their progress, and provide feedback.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, management unit, and feedback unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects employee data using the camera 42 and microphone 38B of the smart device 14 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes a career plan based on the analysis results. The management unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and sets goals based on the proposed career plan and manages its progress. The feedback unit is implemented, for example, by the control unit 46A of the smart device 14 and provides feedback on the employee's progress. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, management unit, and feedback unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects employee data using the camera 42 and microphone 238 of the smart glasses 214 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes a career plan based on the analysis results. The management unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and sets goals based on the proposed career plan and manages its progress. The feedback unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides feedback on the employee's progress. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, management unit, and feedback unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects employee data using the camera 42 and microphone 238 of the headset terminal 314 and transmits it to the data processing unit 12 by the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes a career plan based on the analysis results. The management unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and sets goals based on the proposed career plan and manages its progress. The feedback unit is implemented, for example, by the control unit 46A of the headset terminal 314 and provides feedback on the employee's progress. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, management unit, and feedback unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects employee data using the camera 42 and microphone 238 of the robot 414 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes a career plan based on the analysis results. The management unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and sets goals based on the proposed career plan and manages its progress. The feedback unit is implemented, for example, by the control unit 46A of the robot 414 and provides feedback on the employee's progress. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] (Note 1) The data collection department collects employees' past performance, skill data, and preferences. The data collected by the aforementioned collection unit is analyzed by an analysis unit to determine the skills and aptitudes of employees, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes an appropriate career plan. The system includes a management unit that sets goals based on the career plan proposed by the aforementioned proposal unit and manages their progress. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data on projects employees have worked on in the past, qualifications they have obtained, and their future career goals. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Based on the collected data, we will assess the skills and aptitudes of our employees. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We propose future career paths based on projects employees have completed in the past and qualifications they have obtained. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned management department, Set goals that employees should achieve and manage their progress. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned management department, The company has a feedback department that provides feedback to employees on the results of progress management. 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 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 performance and skill data to 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 data, filtering is performed 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 We estimate employees' emotions and prioritize the 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 data, the system prioritizes the collection of highly relevant data, taking into account the geographical location of employees. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, 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 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 data category. 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 analyses is determined based on the timing of data submission. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, We estimate the emotions of our employees and adjust the way we present proposals based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the career plan. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the career plan category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, Estimate employees' emotions and adjust the length of the proposal based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When submitting a proposal, prioritize the proposals based on when the career plan was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on their relevance to your career plan. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned management department, We estimate employees' emotions and adjust goal-setting methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned management department, When setting goals, analyze employees' past performance to determine the optimal goals. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned management department, When setting goals, customize them based on the employee's current skill level. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned management department, We estimate employees' emotions and determine the priority of goal setting based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned management department, When setting goals, consider the geographical location of employees to set optimal goals. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned management department, When setting goals, we analyze employees' social media activity and propose targets accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned feedback unit is We estimate employees' emotions and adjust the way feedback is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned feedback unit is When providing feedback, we analyze the employee's past performance to provide the most appropriate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned feedback unit is When providing feedback, customize the feedback based on the employee's current skill level. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned feedback unit is The system estimates employees' emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned feedback unit is When providing feedback, we take into account the employee's geographical location to provide the most appropriate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned feedback unit is When providing feedback, we analyze employees' social media activity and suggest appropriate feedback. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0193] 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 employees' past performance, skill data, and preferences. The data collected by the aforementioned collection unit is analyzed by an analysis unit to determine the skills and aptitudes of employees, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes an appropriate career plan. The system includes a management unit that sets goals based on the career plan proposed by the aforementioned proposal unit and manages their progress. A system characterized by the following features.

2. The aforementioned collection unit is We collect data on projects employees have worked on in the past, qualifications they have obtained, and their future career goals. The system according to feature 1.

3. The aforementioned analysis unit, Based on the collected data, we will assess the skills and aptitudes of our employees. The system according to feature 1.

4. The aforementioned proposal section is, We propose future career paths based on projects employees have completed in the past and qualifications they have obtained. The system according to feature 1.

5. The aforementioned management department, Set goals that employees should achieve and manage their progress. The system according to feature 1.

6. The aforementioned management department, The company has a feedback department that provides feedback to employees on the results of progress management. The system according to feature 1.

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

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

9. The aforementioned collection unit is When collecting data, filtering is performed based on employees' current projects and areas of interest. The system according to feature 1.

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