system

Abstract

The system according to the embodiment aims to utilize unstructured data of employees to provide career path simulations and personalized training plan proposals. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a calculation unit, and a proposal unit. The collection unit collects unstructured data on employees' company history, work history, and training history. The analysis unit analyzes and patterns the data collected by the collection unit. The calculation unit calculates the degree of match with multiple positions within the company based on the data patterned by the analysis unit. The proposal unit makes personalized proposals for career path simulations and training plans to employees based on the degree of match calculated by the calculation unit.

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult to effectively utilize unstructured employee data to propose career paths and training plans.

[0005] The system according to the embodiment aims to utilize unstructured data of employees to provide career path simulations and personalized training plan proposals. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a calculation unit, and a proposal unit. The collection unit collects unstructured data on employees' company history, work history, and training history. The analysis unit analyzes and patterns the data collected by the collection unit. The calculation unit calculates the degree of match with multiple positions within the company based on the data patterned by the analysis unit. The proposal unit makes personalized proposals for career path simulations and training plans to employees based on the degree of match calculated by the calculation unit. [Effects of the Invention]

[0007] The system according to the embodiment can utilize unstructured data of employees to provide career path simulations and personalized training plan suggestions. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A career support system according to an embodiment of the present invention collects unstructured data, such as an employee's company history, work history, and training history. AI analyzes and patterns the data, calculates the degree of match with various positions within the company, and proposes internal job transfers (transfers) tailored to the employee's individual abilities and aptitudes. The career support system collects unstructured data, such as an employee's company history, work history, and training history, and analyzes and patterns the data to clarify the employee's abilities and aptitudes. Next, the AI ​​calculates the degree of match with various positions within the company based on the patterned data. This identifies the position that best suits the employee's individual abilities and aptitudes. Furthermore, the AI ​​makes personalized suggestions to employees, such as career path simulations and training plans for acquiring necessary skills. For example, the career support system collects unstructured data, such as an employee's company history, work history, and training history. For example, the system collects data such as the projects the employee has worked on and the content of the training they have taken. Next, the AI ​​analyzes and patterns the collected data. The AI ​​analyzes the collected data to clarify the employee's abilities and aptitudes. For example, the career support system identifies an employee's strengths and weaknesses based on the results of projects the employee has worked on in the past and their training performance. Next, the AI ​​in the career support system calculates the degree of match with various positions within the company based on the patterned data. The AI ​​calculates the degree of match with each position within the company based on the employee's abilities and aptitudes. For example, it determines whether the employee's skill set and work experience are suitable for a particular position. Next, the career support system uses the AI ​​to make personalized suggestions to the employee, such as career path simulations and training plans for acquiring the necessary skills. The AI ​​simulates the optimal career path based on the employee's career goals and current skill set. For example, it suggests the skills and experience necessary for the employee to reach their desired position. In this way, the career support system can improve employee job satisfaction and corporate productivity. In this way, the career support system can support employees' career development and improve corporate productivity.For example, employees can have a clear vision for their own career development and actively work on their career development. In addition, by working in positions that match their abilities and aptitudes, work efficiency improves and the company's productivity increases.

[0029] A career support system according to an embodiment includes a collection unit, an analysis unit, a calculation unit, and a proposal unit. The collection unit collects unstructured data on an employee's company history, work history, and training history. The unstructured data on an employee's company history, work history, and training history includes, but is not limited to, text data, image data, and audio data. The collection unit collects data on projects the employee has previously worked on and the content of training they have taken. For example, the collection unit collects data on the objectives, results, and technologies used of projects the employee has previously worked on. The collection unit can also collect data on the themes, learning content, and grades of training the employee has taken. For example, the collection unit collects detailed data on projects the employee has participated in to clarify the project results and technologies used. The collection unit collects the content and grades of training the employee has taken to confirm the improvement of the employee's skills and knowledge. The analysis unit analyzes the data collected by the collection unit and creates patterns. The analysis unit creates patterns from the collected data using techniques such as clustering and topic modeling. For example, the analysis unit may use clustering technology to group employee data and identify groups with common characteristics. The analysis unit may also use topic modeling technology to extract major topics from the employee data and clarify the employee's abilities and aptitude. For example, the analysis unit may use clustering technology to classify the employee data into multiple clusters and analyze the characteristics of each cluster. The analysis unit may use topic modeling technology to extract major topics from the employee data and identify the employee's strengths and weaknesses. The calculation unit may calculate the degree of match with multiple positions within the company based on the data patterned by the analysis unit. The calculation unit may calculate the degree of match using methods such as a scoring algorithm or weighting criteria. For example, the calculation unit may use a scoring algorithm to determine whether an employee's skill set and work experience are suitable for a specific position. The calculation unit may also use weighting criteria to calculate the degree of match based on the employee's abilities and aptitude. For example, the calculation unit may use a scoring algorithm to evaluate an employee's skill set and work experience and calculate the degree of match for each position.The calculation unit uses weighting criteria to calculate a match degree based on the employee's abilities and aptitudes and identify an optimal position. The proposal unit performs a career path simulation and personalized training plan proposals for the employee based on the match degree calculated by the calculation unit. The proposal unit simulates a career path using methods such as scenario-based simulation and Monte Carlo simulation. For example, the proposal unit uses scenario-based simulation to propose an optimal career path based on the employee's career goals and current skill set. The proposal unit can also use Monte Carlo simulation to simulate multiple scenarios for the employee's career path and propose an optimal career path. For example, the proposal unit uses scenario-based simulation to propose an optimal career path based on the employee's career goals. The proposal unit uses Monte Carlo simulation to simulate multiple scenarios for the employee's career path and propose an optimal career path. This allows the career support system according to the embodiment to support employees' career development and improve corporate productivity. For example, employees can have a clear vision for their career development and actively engage in career development. In addition, when employees work in positions that suit their abilities and aptitudes, work efficiency improves and the company's productivity increases.

[0030] The collection unit can collect data on projects that employees have worked on in the past and the content of training they have taken. The collection unit collects data such as the objectives, results, and technologies used for projects that employees have worked on in the past. For example, the collection unit collects detailed data on projects in which employees have participated to clarify the project results and technologies used. The collection unit can also collect data such as the themes, learning content, and grades of training that employees have taken. For example, the collection unit collects the content and grades of training that employees have taken to confirm the improvement of employees' skills and knowledge. This enables more accurate analysis by collecting detailed information on employees' past work experience and training content. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input data on projects that employees have worked on in the past and the content of training they have taken into a generation AI and have the generation AI collect the data.

[0031] The analysis unit can analyze the collected data and clarify employee abilities and aptitudes. The analysis unit can use techniques such as clustering and topic modeling to create patterns in the collected data. For example, the analysis unit can use clustering technology to group employee data and identify groups with common characteristics. The analysis unit can also use topic modeling technology to extract major topics from the employee data and clarify employee abilities and aptitudes. For example, the analysis unit can use clustering technology to classify employee data into multiple clusters and analyze the characteristics of each cluster. The analysis unit can use topic modeling technology to extract major topics from the employee data and identify employee strengths and weaknesses. This can clarify employee abilities and aptitudes, making it possible to propose optimal positions and career paths. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI analyze the data.

[0032] The calculation unit can determine whether an employee's skill set and work experience are suitable for a specific position. The calculation unit calculates the match degree using, for example, a scoring algorithm, weighting criteria, or other methods. For example, the calculation unit can use a scoring algorithm to determine whether an employee's skill set and work experience are suitable for a specific position. The calculation unit can also calculate the match degree based on the employee's abilities and aptitude using weighting criteria. For example, the calculation unit can use a scoring algorithm to evaluate the employee's skill set and work experience and calculate the match degree for each position. The calculation unit can use weighting criteria to calculate the match degree based on the employee's abilities and aptitude and identify the optimal position. This makes it possible to identify the optimal position based on the employee's skill set and work experience. Some or all of the above-described processing by the calculation unit can be performed using, for example, AI, or without AI. For example, the calculation unit can input data on the employee's skill set and work experience into a generation AI and cause the generation AI to calculate the match degree.

[0033] The proposal unit can simulate a career path based on the employee's career goals and current skill set. The proposal unit simulates the career path using methods such as scenario-based simulation and Monte Carlo simulation. For example, the proposal unit uses the scenario-based simulation to propose an optimal career path based on the employee's career goals and current skill set. The proposal unit can also use Monte Carlo simulation to simulate multiple scenarios of the employee's career path and propose the optimal career path. For example, the proposal unit uses the scenario-based simulation to propose an optimal career path based on the employee's career goals. The proposal unit uses Monte Carlo simulation to simulate multiple scenarios of the employee's career path and propose the optimal career path. This makes it possible to propose an optimal career path based on the employee's career goals. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input data on the employee's career goals and current skill set into the generation AI and cause the generation AI to execute a career path simulation.

[0034] The suggestion unit can suggest the skills and experience required for the employee to reach their desired position. The suggestion unit suggests the required skills and experience based on, for example, the employee's career goals and current skill set. For example, the suggestion unit clarifies the skills and experience required for the employee to reach their desired position. The suggestion unit can also suggest the required skills and experience according to the employee's career goals. For example, the suggestion unit suggests specific technical skills or leadership experience based on the employee's career goals. This makes it possible to clarify the skills and experience required for the employee to reach their desired position. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input data on the employee's career goals and current skill set into the generation AI and cause the generation AI to suggest the required skills and experience.

[0035] The proposal unit can match an employee's career aspirations with the organization's needs. For example, the proposal unit makes proposals based on the employee's career goals and current skill set in order to align the employee's career aspirations with the organization's needs. For example, the proposal unit proposes a position that meets the organization's needs based on the employee's career aspirations. The proposal unit can also consider the employee's skill set and work experience in order to match the employee's career aspirations with the organization's needs. For example, the proposal unit proposes a position that meets the organization's needs based on the employee's career aspirations, supporting the employee's career development. This aligns the employee's career aspirations with the organization's needs, thereby creating benefits for both parties. Some or all of the above-described processing in the proposal unit may be performed using, or without, AI. For example, the proposal unit can input data on the employee's career aspirations and current skill set into the generation AI and cause the generation AI to match the career aspirations with the organization's needs.

[0036] The proposal unit can improve employee job satisfaction and corporate productivity. The proposal unit makes proposals for improving employee job satisfaction and corporate productivity, for example, based on the employee's career goals and current skill set. For example, the proposal unit proposes positions that will improve job satisfaction based on the employee's career goals. The proposal unit can also propose positions that will contribute to improving corporate productivity, taking into account the employee's skill set and work experience. For example, the proposal unit proposes positions that will improve job satisfaction based on the employee's career goals, thereby improving corporate productivity. This makes it possible to simultaneously improve employee job satisfaction and corporate productivity. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input data on the employee's career goals and current skill set into the generation AI, and cause the generation AI to execute proposals that will improve job satisfaction and corporate productivity.

[0037] The collection unit can evaluate the results of employees' past projects and training and select a data collection method. For example, the collection unit can prioritize collecting data on employees' past successful projects and analyze the factors behind their success. For example, the collection unit can prioritize collecting data on employees' past successful projects and analyze the factors behind their success. The collection unit can also collect related data based on the employees' performance in training and confirm skill improvement. For example, the collection unit can collect related data based on the employees' performance in training and confirm skill improvement. The collection unit can also evaluate employees' past work performance and collect data related to specific tasks. For example, the collection unit can evaluate employees' past work performance and collect data related to specific tasks. This enables efficient data collection by selecting the optimal data collection method based on the employees' past performance. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input data on the results of employees' past projects and training into the generation AI and have the generation AI select the optimal data collection method.

[0038] When collecting data, the collection unit can filter the data based on the employee's current work situation and areas of interest. For example, the collection unit prioritizes collecting data related to projects the employee is currently working on. For example, the collection unit prioritizes collecting data related to projects the employee is currently working on. The collection unit can also filter and collect relevant data based on the employee's areas of interest. For example, the collection unit filters and collects relevant data based on the employee's areas of interest. The collection unit can also collect work-related data by taking the employee's work situation into consideration. For example, the collection unit collects work-related data by taking the employee's work situation into consideration. In this way, highly relevant data can be collected by filtering the data based on the employee's work situation and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input data on the employee's current work situation and areas of interest into the generation AI and have the generation AI filter the data.

[0039] When collecting data, the collection unit can select the optimal collection means depending on the employee's input method. For example, if the employee prefers voice input, the collection unit prioritizes collecting voice data. For example, if the employee prefers voice input, the collection unit prioritizes collecting voice data. Also, if the employee prefers text input, the collection unit can prioritize collecting text data. For example, if the employee prefers text input, the collection unit prioritizes collecting text data. Also, if the employee prefers image input, the collection unit can prioritize collecting image data. For example, if the employee prefers image input, the collection unit prioritizes collecting image data. This enables efficient data collection by selecting the optimal collection means depending on the employee's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the employee's input method into the generation AI and have the generation AI select the optimal collection means.

[0040] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the employee's geographical location information. For example, if an employee is on a business trip, the collection unit prioritizes collecting data related to the business trip destination. For example, if an employee is on a business trip, the collection unit prioritizes collecting data related to the business trip destination. Furthermore, if an employee works in a specific region, the collection unit can prioritize collecting data related to that region. For example, if an employee works in a specific region, the collection unit prioritizes collecting data related to that region. Furthermore, if an employee works remotely, the collection unit can prioritize collecting data related to remote work. For example, if an employee works remotely, the collection unit prioritizes collecting data related to remote work. This allows for efficient collection of highly relevant data by taking into account the employee's geographical location information. Some or all of the above-described processing by the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input data on the employee's geographical location information into the generation AI and cause the generation AI to prioritize collection of highly relevant data.

[0041] The collection unit can analyze employees' social media activities and collect related data when collecting data. The collection unit, for example, collects work-related information shared by employees on social media. For example, the collection unit collects work-related information shared by employees on social media. The collection unit can also analyze employees' social media activities and collect work-related data. For example, the collection unit analyzes employees' social media activities and collects work-related data. The collection unit can also collect related data based on the content of employees' social media posts. For example, the collection unit collects related data based on the content of employees' social media posts. In this way, work-related data can be efficiently collected by analyzing employees' social media activities. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input data on employees' social media activities to the generation AI and cause the generation AI to collect related data.

[0042] The collection unit can customize the collection method by reflecting employees' past feedback when collecting data. For example, the collection unit adjusts the data collection method based on feedback provided by employees in the past. For example, the collection unit adjusts the data collection method based on feedback provided by employees in the past. The collection unit can also customize the type of data to be collected by reflecting employees' past feedback. For example, the collection unit customizes the type of data to be collected by reflecting employees' past feedback. The collection unit can also adjust the frequency of data collection based on employees' feedback. For example, the collection unit adjusts the frequency of data collection based on employees' feedback. This enables more effective data collection by reflecting employees' past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input employees' past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0043] The analysis unit can apply a new algorithm for detailed evaluation of an employee's abilities and aptitudes during data analysis. For example, the analysis unit applies a new algorithm for evaluating abilities and aptitudes based on an employee's work results. For example, the analysis unit applies a new algorithm for evaluating abilities and aptitudes based on an employee's work results. The analysis unit can also apply a new algorithm for evaluating skill improvement based on an employee's training results. For example, the analysis unit applies a new algorithm for evaluating skill improvement based on an employee's training results. The analysis unit can also apply a new algorithm for evaluating aptitudes based on an employee's project participation history. For example, the analysis unit applies a new algorithm for evaluating aptitudes based on an employee's project participation history. In this way, by applying the new algorithm, an employee's abilities and aptitudes can be evaluated more accurately. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input data on an employee's work results and training results into a generation AI and have the generation AI apply the new algorithm.

[0044] During data analysis, the analysis unit can identify strengths and weaknesses based on an employee's past work performance and training results. For example, the analysis unit can analyze an employee's past work performance and identify strengths and weaknesses. For example, the analysis unit can analyze an employee's past work performance and identify strengths and weaknesses. The analysis unit can also identify skill strengths and weaknesses based on an employee's training performance. For example, the analysis unit can identify skill strengths and weaknesses based on an employee's training performance. The analysis unit can also analyze an employee's project participation history and identify strengths and weaknesses. For example, the analysis unit can analyze an employee's project participation history and identify strengths and weaknesses. This makes it possible to identify strengths and weaknesses based on an employee's past work performance and training results, thereby proposing a more appropriate career path. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input data on an employee's past work performance and training results into a generation AI and have the generation AI identify strengths and weaknesses.

[0045] When analyzing data, the analysis unit can apply different analysis methods depending on the employee's career goals. For example, the analysis unit applies an analysis method for identifying skill gaps depending on the employee's career goals. For example, the analysis unit applies an analysis method for identifying skill gaps depending on the employee's career goals. The analysis unit can also apply an analysis method for acquiring necessary skills based on the employee's career goals. For example, the analysis unit applies an analysis method for acquiring necessary skills based on the employee's career goals. The analysis unit can also apply an analysis method for proposing an optimal career path depending on the employee's career goals. For example, the analysis unit applies an analysis method for proposing an optimal career path depending on the employee's career goals. In this way, by applying an analysis method depending on the employee's career goals, it is possible to propose a more appropriate career path. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the employee's career goals into the generation AI and cause the generation AI to apply different analysis methods.

[0046] During data analysis, the analysis unit can prioritize data based on the time of employee submission. For example, the analysis unit prioritizes analysis of data recently submitted by employees. The analysis unit can also prioritize analysis of data of high importance based on the time of employee submission. For example, the analysis unit prioritizes analysis of data of high importance based on the time of employee submission. The analysis unit can also prioritize analysis of timely data by taking into account the time of employee submission. For example, the analysis unit prioritizes analysis of timely data by taking into account the time of employee submission. This enables timely data analysis by determining the priority of data based on the time of employee submission. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data submitted by employees to the generation AI and have the generation AI determine the priority of the data.

[0047] When analyzing data, the analysis unit can improve the accuracy of the analysis by referring to literature related to the employee. For example, the analysis unit improves the accuracy of the analysis based on related literature that the employee has referenced in the past. For example, the analysis unit improves the accuracy of the analysis based on related literature that the employee has referenced in the past. The analysis unit can also improve the accuracy of the analysis by referring to literature related to the employee's work. For example, the analysis unit improves the accuracy of the analysis by referring to literature related to the employee's work. The analysis unit can also improve the accuracy of the analysis by referring to literature related to the employee's training content. For example, the analysis unit improves the accuracy of the analysis by referring to literature related to the employee's training content. In this way, the accuracy of the analysis is improved by referring to the employee's related literature. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input data on the employee's related literature into the generation AI and have the generation AI improve the accuracy of the analysis.

[0048] When analyzing data, the analysis unit can adjust the level of detail of the analysis according to the employee's level of expertise. For example, if the employee's level of expertise is high, the analysis unit provides detailed analysis results. For example, if the employee's level of expertise is high, the analysis unit provides detailed analysis results. The analysis unit can also provide simple, easy-to-understand analysis results if the employee's level of expertise is low. For example, if the employee's level of expertise is low, the analysis unit provides simple, easy-to-understand analysis results. The analysis unit can also adjust the level of detail of the analysis results according to the employee's level of expertise. For example, the analysis unit adjusts the level of detail of the analysis results according to the employee's level of expertise. In this way, by adjusting the level of detail of the analysis according to the employee's level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the employee's level of expertise to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0049] The calculation unit can apply a new algorithm for evaluating an employee's skill set and work experience in detail when calculating the match degree. The calculation unit, for example, applies a new algorithm for evaluating the match degree in detail based on the employee's skill set. For example, the calculation unit applies a new algorithm for evaluating the match degree in detail based on the employee's skill set. The calculation unit can also apply a new algorithm for evaluating the match degree in detail based on the employee's work experience. For example, the calculation unit applies a new algorithm for evaluating the match degree in detail based on the employee's work experience. The calculation unit can also apply a new algorithm for evaluating the match degree in detail based on the employee's project participation history. For example, the calculation unit applies a new algorithm for evaluating the match degree in detail based on the employee's project participation history. In this way, by applying the new algorithm, the employee's skill set and work experience can be evaluated more accurately. Some or all of the above-described processing in the calculation unit may be performed using AI, for example, or may be performed without using AI. For example, the calculation unit can input data on the employee's skill set and work experience into the generation AI and cause the generation AI to apply the new algorithm.

[0050] When calculating the match degree, the calculation unit can identify the optimal position based on the employee's past work performance and training results. The calculation unit, for example, identifies the optimal position based on the employee's past work performance. For example, the calculation unit identifies the optimal position based on the employee's past work performance. The calculation unit can also identify the optimal position based on the employee's training performance. For example, the calculation unit identifies the optimal position based on the employee's training performance. The calculation unit can also identify the optimal position based on the employee's project participation history. For example, the calculation unit identifies the optimal position based on the employee's project participation history. In this way, by identifying the optimal position based on the employee's past work performance and training performance, it is possible to propose a more appropriate position. Some or all of the above-mentioned processing by the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input data on the employee's past work performance and training performance into the generation AI and cause the generation AI to identify the optimal position.

[0051] When calculating the match degree, the calculation unit can apply different calculation methods depending on the employee's career goals. For example, the calculation unit applies a calculation method for identifying a skill gap depending on the employee's career goals. For example, the calculation unit applies a calculation method for identifying a skill gap depending on the employee's career goals. The calculation unit can also apply a calculation method for acquiring necessary skills based on the employee's career goals. For example, the calculation unit applies a calculation method for acquiring necessary skills based on the employee's career goals. The calculation unit can also apply a calculation method for proposing an optimal career path depending on the employee's career goals. For example, the calculation unit applies a calculation method for proposing an optimal career path depending on the employee's career goals. As a result, by applying a calculation method depending on the employee's career goals, it is possible to propose a more appropriate career path. Some or all of the above-mentioned processing by the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input data on the employee's career goals into the generation AI and cause the generation AI to apply different calculation methods.

[0052] When calculating the degree of match, the calculation unit can determine the calculation priority based on the employee's submission time. For example, the calculation unit prioritizes data recently submitted by the employee in the calculation. For example, the calculation unit prioritizes data recently submitted by the employee in the calculation. The calculation unit can also prioritize data of high importance based on the employee's submission time. For example, the calculation unit prioritizes data of high importance based on the employee's submission time. The calculation unit can also prioritize data that is timely, taking into account the employee's submission time. For example, the calculation unit prioritizes data that is timely, taking into account the employee's submission time. In this way, determining the calculation priority based on the employee's submission time enables timely data calculation. Some or all of the above-described processing by the calculation unit may be performed using AI, or may be performed without AI. For example, the calculation unit can input data of the employee's submission time into the generation AI and have the generation AI determine the calculation priority.

[0053] When calculating the degree of match, the calculation unit can improve the accuracy of the calculation by referring to related literature of the employee. For example, the calculation unit improves the accuracy of the calculation based on related literature that the employee has referenced in the past. For example, the calculation unit improves the accuracy of the calculation based on related literature that the employee has referenced in the past. The calculation unit can also improve the accuracy of the calculation by referring to literature related to the employee's work. For example, the calculation unit improves the accuracy of the calculation by referring to literature related to the employee's work. The calculation unit can also improve the accuracy of the calculation by referring to literature related to the employee's training content. For example, the calculation unit improves the accuracy of the calculation by referring to literature related to the employee's training content. In this way, the accuracy of the calculation is improved by referring to the related literature of the employee. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input data on related literature of the employee into the generation AI and cause the generation AI to improve the accuracy of the calculation.

[0054] When calculating the match degree, the calculation unit can adjust the level of detail of the calculation according to the employee's level of expertise. For example, if the employee's level of expertise is high, the calculation unit provides a detailed calculation result. For example, if the employee's level of expertise is high, the calculation unit provides a detailed calculation result. Furthermore, if the employee's level of expertise is low, the calculation unit can provide a simple, easy-to-understand calculation result. For example, if the employee's level of expertise is low, the calculation unit can provide a simple, easy-to-understand calculation result. Furthermore, the calculation unit can adjust the level of detail of the calculation result according to the employee's level of expertise. For example, the calculation unit adjusts the level of detail of the calculation result according to the employee's level of expertise. In this way, by adjusting the level of detail of the calculation according to the employee's level of expertise, it is possible to provide a calculation result that is easier to understand. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input data on the employee's level of expertise to the generation AI and cause the generation AI to adjust the level of detail of the calculation.

[0055] When making a proposal, the proposal unit can simulate an optimal career path based on the employee's career goals and current skill set. The proposal unit, for example, proposes necessary skills and experience based on the employee's career goals. For example, the proposal unit proposes necessary skills and experience based on the employee's career goals. The proposal unit can also simulate an optimal career path based on the employee's current skill set. For example, the proposal unit simulates an optimal career path based on the employee's current skill set. The proposal unit can also simulate and compare different career paths according to the employee's career goals. For example, the proposal unit simulates and compares different career paths according to the employee's career goals. This makes it possible to propose an optimal career path based on the employee's career goals and skill set. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, AI. For example, the proposal unit can input data on the employee's career goals and current skill set into a generation AI and cause the generation AI to execute a career path simulation.

[0056] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the employee's past proposal results. The suggestion unit, for example, analyzes the employee's past proposal results and makes a new proposal based on successful proposals. For example, the suggestion unit analyzes the employee's past proposal results and makes a new proposal based on successful proposals. The suggestion unit can also make a proposal that reflects improvements by referring to the employee's past proposal results. For example, the suggestion unit makes a proposal that reflects improvements by referring to the employee's past proposal results. The suggestion unit can also make an optimal proposal based on the employee's past proposal results. For example, the suggestion unit makes an optimal proposal based on the employee's past proposal results. As a result, the accuracy of the proposal is improved by referring to the employee's past proposal results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the employee's past proposal results into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0057] When making a proposal, the proposal unit can apply different proposal algorithms depending on the employee's career goals. For example, the proposal unit applies a proposal algorithm that identifies skill gaps depending on the employee's career goals. For example, the proposal unit applies a proposal algorithm that identifies skill gaps depending on the employee's career goals. The proposal unit can also apply a proposal algorithm for acquiring necessary skills based on the employee's career goals. For example, the proposal unit applies a proposal algorithm for acquiring necessary skills based on the employee's career goals. The proposal unit can also apply a proposal algorithm for proposing an optimal career path depending on the employee's career goals. For example, the proposal unit applies a proposal algorithm for proposing an optimal career path depending on the employee's career goals. This makes it possible to propose a more appropriate career path by applying a proposal algorithm depending on the employee's career goals. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input data on the employee's career goals into the generation AI and cause the generation AI to apply different proposal algorithms.

[0058] When making a proposal, the suggestion unit can determine the priority of the proposal based on the employee's submission time. For example, the suggestion unit prioritizes proposals based on data recently submitted by the employee. For example, the suggestion unit prioritizes proposals based on data recently submitted by the employee. The suggestion unit can also prioritize proposals with higher importance based on the employee's submission time. For example, the suggestion unit prioritizes proposals with higher importance based on the employee's submission time. The suggestion unit can also prioritize timely proposals by taking into account the employee's submission time. For example, the suggestion unit prioritizes timely proposals by taking into account the employee's submission time. This enables timely proposals by determining the priority of proposals based on the employee's submission time. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input data on the employee's submission time into the generation AI and have the generation AI determine the priority of the proposals.

[0059] When making a proposal, the suggestion unit can adjust the order of the proposals based on the employee's relevance. For example, the suggestion unit prioritizes proposals related to the employee's current work. For example, the suggestion unit prioritizes proposals related to the employee's current work. The suggestion unit can also prioritize proposals related to the employee's career goals. For example, the suggestion unit prioritizes proposals related to the employee's career goals. The suggestion unit can also prioritize proposals related to the employee's skill set. For example, the suggestion unit prioritizes proposals related to the employee's skill set. This enables more effective proposals by adjusting the order of proposals based on the employee's relevance. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input data on the employee's relevance to the generation AI and cause the generation AI to adjust the order of the proposals.

[0060] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal depending on the employee's level of expertise. For example, if the employee's level of expertise is high, the suggestion unit makes a proposal that uses a lot of technical terminology. For example, if the employee's level of expertise is high, the suggestion unit makes a proposal that uses a lot of technical terminology. Furthermore, if the employee's level of expertise is low, the suggestion unit can make a proposal that is simple and easy to understand. For example, if the employee's level of expertise is low, the suggestion unit can make a proposal that is simple and easy to understand. Furthermore, the suggestion unit can adjust the use of technical terminology in the proposal depending on the employee's level of expertise. In this way, by adjusting the use of technical terminology depending on the employee's level of expertise, a more understandable proposal can be made. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the employee's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The career support system can also collect employee health data, which the analysis department analyzes to make career suggestions based on the employee's health status. For example, the collection department collects employees' health checkup results and fitness tracker data, which the analysis department analyzes. Next, the calculation department calculates positions that are less stressful for the employee and positions that are suitable for maintaining health based on their health status. Based on this data, the proposal department can propose career paths and training plans that take employee health into consideration. This makes it possible to balance employee health with career development.

[0063] The career support system can also collect data on employees' hobbies and interests, which the analysis department analyzes to make career suggestions based on the employee's interests. For example, the collection department collects survey data on employees' hobbies and interests, which the analysis department analyzes. Next, the calculation department calculates positions where employees can enjoy working based on their hobbies and interests. Based on this data, the proposal department can propose career paths and training plans that reflect the employee's interests. This can increase employee motivation.

[0064] The career support system can also collect employees' social media activities, which the analysis department analyzes to make career suggestions based on the employee's online presence. For example, the collection department collects employees' social media activity data, which the analysis department analyzes. Next, the calculation department calculates positions suitable for the employee in digital marketing or social media management based on their online presence. Based on this data, the proposal department can suggest career paths and training plans that make use of the employee's online activities. This allows the digital skills of employees to be utilized to the fullest.

[0065] The career support system can also collect employees' geographical location information, which the analysis department analyzes to make career suggestions based on the employee's work location. For example, the collection department collects data on employees' work locations and business trip destinations, and the analysis department analyzes this. Next, the calculation department calculates positions that will allow employees to shorten their commute time or positions that require fewer business trips based on the geographical location information. Based on this data, the proposal department can suggest career paths and training plans that suit the employee's lifestyle. This can improve the employee's work-life balance.

[0066] The career support system can also collect employee feedback data, which the analysis department analyzes to make career suggestions based on employee opinions. For example, the collection department collects employee feedback and survey results, which the analysis department analyzes. Next, the calculation department calculates the employee's desired position and training content based on the feedback data. Based on this data, the proposal department can propose career paths and training plans that reflect employee opinions. This can increase employee satisfaction.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The collection department collects unstructured data on employees' company history, work history, and training history. Unstructured data on employees' company history, work history, and training history includes, for example, text data, image data, and audio data. The collection department collects data such as the projects employees have worked on in the past and the content of the training they have taken. For example, data such as project objectives, results, technology used, training themes, learning content, and grades are collected. Step 2: The analysis department analyzes the data collected by the collection department and identifies patterns. Using techniques such as clustering and topic modeling, the analysis department groups the collected data and identifies groups with common characteristics. It also extracts key topics and clarifies employee capabilities and aptitudes. Step 3: The calculation unit calculates the degree of match with multiple positions within the company based on the data patterned by the analysis unit. The calculation unit uses methods such as scoring algorithms and weighting criteria to determine whether the employee's skill set and work experience are suitable for a specific position and calculates the degree of match. Step 4: The proposal department proposes personalized career path simulations and training plans to employees based on the match calculated by the calculation department. The proposal department uses methods such as scenario-based simulation and Monte Carlo simulation to propose optimal career paths based on the employee's career goals and current skill set.

[0069] (Example 2) A career support system according to an embodiment of the present invention collects unstructured data, such as an employee's company history, work history, and training history. AI analyzes and patterns the data, calculates the degree of match with various positions within the company, and proposes internal job transfers (transfers) tailored to the employee's individual abilities and aptitudes. The career support system collects unstructured data, such as an employee's company history, work history, and training history, and analyzes and patterns the data to clarify the employee's abilities and aptitudes. Next, the AI ​​calculates the degree of match with various positions within the company based on the patterned data. This identifies the position that best suits the employee's individual abilities and aptitudes. Furthermore, the AI ​​makes personalized suggestions to employees, such as career path simulations and training plans for acquiring necessary skills. For example, the career support system collects unstructured data, such as an employee's company history, work history, and training history. For example, the system collects data such as the projects the employee has worked on and the content of the training they have taken. Next, the AI ​​analyzes and patterns the collected data. The AI ​​analyzes the collected data to clarify the employee's abilities and aptitudes. For example, the career support system identifies an employee's strengths and weaknesses based on the results of projects the employee has worked on in the past and their training performance. Next, the AI ​​in the career support system calculates the degree of match with various positions within the company based on the patterned data. The AI ​​calculates the degree of match with each position within the company based on the employee's abilities and aptitudes. For example, it determines whether the employee's skill set and work experience are suitable for a particular position. Next, the career support system uses the AI ​​to make personalized suggestions to the employee, such as career path simulations and training plans for acquiring the necessary skills. The AI ​​simulates the optimal career path based on the employee's career goals and current skill set. For example, it suggests the skills and experience necessary for the employee to reach their desired position. In this way, the career support system can improve employee job satisfaction and corporate productivity. In this way, the career support system can support employees' career development and improve corporate productivity.For example, employees can have a clear vision for their own career development and actively work on their career development. In addition, by working in positions that match their abilities and aptitudes, work efficiency improves and the company's productivity increases.

[0070] A career support system according to an embodiment includes a collection unit, an analysis unit, a calculation unit, and a proposal unit. The collection unit collects unstructured data on an employee's company history, work history, and training history. The unstructured data on an employee's company history, work history, and training history includes, but is not limited to, text data, image data, and audio data. The collection unit collects data on projects the employee has previously worked on and the content of training they have taken. For example, the collection unit collects data on the objectives, results, and technologies used of projects the employee has previously worked on. The collection unit can also collect data on the themes, learning content, and grades of training the employee has taken. For example, the collection unit collects detailed data on projects the employee has participated in to clarify the project results and technologies used. The collection unit collects the content and grades of training the employee has taken to confirm the improvement of the employee's skills and knowledge. The analysis unit analyzes the data collected by the collection unit and creates patterns. The analysis unit creates patterns from the collected data using techniques such as clustering and topic modeling. For example, the analysis unit may use clustering technology to group employee data and identify groups with common characteristics. The analysis unit may also use topic modeling technology to extract major topics from the employee data and clarify the employee's abilities and aptitude. For example, the analysis unit may use clustering technology to classify the employee data into multiple clusters and analyze the characteristics of each cluster. The analysis unit may use topic modeling technology to extract major topics from the employee data and identify the employee's strengths and weaknesses. The calculation unit may calculate the degree of match with multiple positions within the company based on the data patterned by the analysis unit. The calculation unit may calculate the degree of match using methods such as a scoring algorithm or weighting criteria. For example, the calculation unit may use a scoring algorithm to determine whether an employee's skill set and work experience are suitable for a specific position. The calculation unit may also use weighting criteria to calculate the degree of match based on the employee's abilities and aptitude. For example, the calculation unit may use a scoring algorithm to evaluate an employee's skill set and work experience and calculate the degree of match for each position.The calculation unit uses weighting criteria to calculate a match degree based on the employee's abilities and aptitudes and identify an optimal position. The proposal unit performs a career path simulation and personalized training plan proposals for the employee based on the match degree calculated by the calculation unit. The proposal unit simulates a career path using methods such as scenario-based simulation and Monte Carlo simulation. For example, the proposal unit uses scenario-based simulation to propose an optimal career path based on the employee's career goals and current skill set. The proposal unit can also use Monte Carlo simulation to simulate multiple scenarios for the employee's career path and propose an optimal career path. For example, the proposal unit uses scenario-based simulation to propose an optimal career path based on the employee's career goals. The proposal unit uses Monte Carlo simulation to simulate multiple scenarios for the employee's career path and propose an optimal career path. This allows the career support system according to the embodiment to support employees' career development and improve corporate productivity. For example, employees can have a clear vision for their career development and actively engage in career development. In addition, when employees work in positions that suit their abilities and aptitudes, work efficiency improves and the company's productivity increases.

[0071] The collection unit can collect data on projects that employees have worked on in the past and the content of training they have taken. The collection unit collects data such as the objectives, results, and technologies used for projects that employees have worked on in the past. For example, the collection unit collects detailed data on projects in which employees have participated to clarify the project results and technologies used. The collection unit can also collect data such as the themes, learning content, and grades of training that employees have taken. For example, the collection unit collects the content and grades of training that employees have taken to confirm the improvement of employees' skills and knowledge. This enables more accurate analysis by collecting detailed information on employees' past work experience and training content. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input data on projects that employees have worked on in the past and the content of training they have taken into a generation AI and have the generation AI collect the data.

[0072] The analysis unit can analyze the collected data and clarify employee abilities and aptitudes. The analysis unit can use techniques such as clustering and topic modeling to create patterns in the collected data. For example, the analysis unit can use clustering technology to group employee data and identify groups with common characteristics. The analysis unit can also use topic modeling technology to extract major topics from the employee data and clarify employee abilities and aptitudes. For example, the analysis unit can use clustering technology to classify employee data into multiple clusters and analyze the characteristics of each cluster. The analysis unit can use topic modeling technology to extract major topics from the employee data and identify employee strengths and weaknesses. This can clarify employee abilities and aptitudes, making it possible to propose optimal positions and career paths. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI analyze the data.

[0073] The calculation unit can determine whether an employee's skill set and work experience are suitable for a specific position. The calculation unit calculates the match degree using, for example, a scoring algorithm, weighting criteria, or other methods. For example, the calculation unit can use a scoring algorithm to determine whether an employee's skill set and work experience are suitable for a specific position. The calculation unit can also calculate the match degree based on the employee's abilities and aptitude using weighting criteria. For example, the calculation unit can use a scoring algorithm to evaluate the employee's skill set and work experience and calculate the match degree for each position. The calculation unit can use weighting criteria to calculate the match degree based on the employee's abilities and aptitude and identify the optimal position. This makes it possible to identify the optimal position based on the employee's skill set and work experience. Some or all of the above-described processing by the calculation unit can be performed using, for example, AI, or without AI. For example, the calculation unit can input data on the employee's skill set and work experience into a generation AI and cause the generation AI to calculate the match degree.

[0074] The proposal unit can simulate a career path based on the employee's career goals and current skill set. The proposal unit simulates the career path using methods such as scenario-based simulation and Monte Carlo simulation. For example, the proposal unit uses the scenario-based simulation to propose an optimal career path based on the employee's career goals and current skill set. The proposal unit can also use Monte Carlo simulation to simulate multiple scenarios of the employee's career path and propose the optimal career path. For example, the proposal unit uses the scenario-based simulation to propose an optimal career path based on the employee's career goals. The proposal unit uses Monte Carlo simulation to simulate multiple scenarios of the employee's career path and propose the optimal career path. This makes it possible to propose an optimal career path based on the employee's career goals. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input data on the employee's career goals and current skill set into the generation AI and cause the generation AI to execute a career path simulation.

[0075] The suggestion unit can suggest the skills and experience required for the employee to reach their desired position. The suggestion unit suggests the required skills and experience based on, for example, the employee's career goals and current skill set. For example, the suggestion unit clarifies the skills and experience required for the employee to reach their desired position. The suggestion unit can also suggest the required skills and experience according to the employee's career goals. For example, the suggestion unit suggests specific technical skills or leadership experience based on the employee's career goals. This makes it possible to clarify the skills and experience required for the employee to reach their desired position. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input data on the employee's career goals and current skill set into the generation AI and cause the generation AI to suggest the required skills and experience.

[0076] The proposal unit can match an employee's career aspirations with the organization's needs. For example, the proposal unit makes proposals based on the employee's career goals and current skill set in order to align the employee's career aspirations with the organization's needs. For example, the proposal unit proposes a position that meets the organization's needs based on the employee's career aspirations. The proposal unit can also consider the employee's skill set and work experience in order to match the employee's career aspirations with the organization's needs. For example, the proposal unit proposes a position that meets the organization's needs based on the employee's career aspirations, supporting the employee's career development. This aligns the employee's career aspirations with the organization's needs, thereby creating benefits for both parties. Some or all of the above-described processing in the proposal unit may be performed using, or without, AI. For example, the proposal unit can input data on the employee's career aspirations and current skill set into the generation AI and cause the generation AI to match the career aspirations with the organization's needs.

[0077] The proposal unit can improve employee job satisfaction and corporate productivity. The proposal unit makes proposals for improving employee job satisfaction and corporate productivity, for example, based on the employee's career goals and current skill set. For example, the proposal unit proposes positions that will improve job satisfaction based on the employee's career goals. The proposal unit can also propose positions that will contribute to improving corporate productivity, taking into account the employee's skill set and work experience. For example, the proposal unit proposes positions that will improve job satisfaction based on the employee's career goals, thereby improving corporate productivity. This makes it possible to simultaneously improve employee job satisfaction and corporate productivity. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input data on the employee's career goals and current skill set into the generation AI, and cause the generation AI to execute proposals that will improve job satisfaction and corporate productivity.

[0078] The collection unit can estimate the employee's emotions and adjust the timing of data collection based on the estimated employee emotions. For example, if an employee is feeling stressed, the collection unit collects data during times when the employee is relaxing to avoid data collection. For example, the collection unit collects data during times when the employee is relaxing, thereby minimizing disruption to work. Furthermore, if an employee is concentrating, the collection unit can also collect data at that time to avoid disrupting work. For example, the collection unit collects data during times when the employee is concentrating, thereby improving work efficiency. Furthermore, if an employee is on a break, the collection unit can collect data during that time to reduce the burden on the employee during work hours. For example, the collection unit collects data during the employee's break to reduce the burden on the employee during work hours. In this way, by adjusting the timing of data collection according to the employee's emotions, disruption to work can be minimized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input employee emotion data into the generation AI and cause the generation AI to adjust the timing of data collection based on the emotion.

[0079] The collection unit can evaluate the results of employees' past projects and training and select a data collection method. For example, the collection unit can prioritize collecting data on employees' past successful projects and analyze the factors behind their success. For example, the collection unit can prioritize collecting data on employees' past successful projects and analyze the factors behind their success. The collection unit can also collect related data based on the employees' performance in training and confirm skill improvement. For example, the collection unit can collect related data based on the employees' performance in training and confirm skill improvement. The collection unit can also evaluate employees' past work performance and collect data related to specific tasks. For example, the collection unit can evaluate employees' past work performance and collect data related to specific tasks. This enables efficient data collection by selecting the optimal data collection method based on the employees' past performance. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input data on the results of employees' past projects and training into the generation AI and have the generation AI select the optimal data collection method.

[0080] When collecting data, the collection unit can filter the data based on the employee's current work situation and areas of interest. For example, the collection unit prioritizes collecting data related to projects the employee is currently working on. For example, the collection unit prioritizes collecting data related to projects the employee is currently working on. The collection unit can also filter and collect relevant data based on the employee's areas of interest. For example, the collection unit filters and collects relevant data based on the employee's areas of interest. The collection unit can also collect work-related data by taking the employee's work situation into consideration. For example, the collection unit collects work-related data by taking the employee's work situation into consideration. In this way, highly relevant data can be collected by filtering the data based on the employee's work situation and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input data on the employee's current work situation and areas of interest into the generation AI and have the generation AI filter the data.

[0081] When collecting data, the collection unit can select the optimal collection means depending on the employee's input method. For example, if the employee prefers voice input, the collection unit prioritizes collecting voice data. For example, if the employee prefers voice input, the collection unit prioritizes collecting voice data. Also, if the employee prefers text input, the collection unit can prioritize collecting text data. For example, if the employee prefers text input, the collection unit prioritizes collecting text data. Also, if the employee prefers image input, the collection unit can prioritize collecting image data. For example, if the employee prefers image input, the collection unit prioritizes collecting image data. This enables efficient data collection by selecting the optimal collection means depending on the employee's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the employee's input method into the generation AI and have the generation AI select the optimal collection means.

[0082] The collection unit can estimate the employee's emotions and determine the priority of data to be collected based on the estimated employee emotions. For example, if the employee is feeling stressed, the collection unit prioritizes collecting data that is useful for stress reduction. For example, if the employee is feeling stressed, the collection unit prioritizes collecting data that is useful for stress reduction. Furthermore, if the employee is relaxed, the collection unit can prioritize collecting data that is useful for improving work efficiency. For example, if the employee is relaxed, the collection unit prioritizes collecting data that is useful for improving work efficiency. Furthermore, if the employee is concentrating, the collection unit can prioritize collecting data that is directly related to work. For example, if the employee is concentrating, the collection unit prioritizes collecting data that is directly related to work. This enables more effective data collection by determining the priority of data based on the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection department can input employee emotional data into the generation AI and have the generation AI determine the priority of the data based on the emotions.

[0083] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the employee's geographical location information. For example, if an employee is on a business trip, the collection unit prioritizes collecting data related to the business trip destination. For example, if an employee is on a business trip, the collection unit prioritizes collecting data related to the business trip destination. Furthermore, if an employee works in a specific region, the collection unit can prioritize collecting data related to that region. For example, if an employee works in a specific region, the collection unit prioritizes collecting data related to that region. Furthermore, if an employee works remotely, the collection unit can prioritize collecting data related to remote work. For example, if an employee works remotely, the collection unit prioritizes collecting data related to remote work. This allows for efficient collection of highly relevant data by taking into account the employee's geographical location information. Some or all of the above-described processing by the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input data on the employee's geographical location information into the generation AI and cause the generation AI to prioritize collection of highly relevant data.

[0084] The collection unit can analyze employees' social media activities and collect related data when collecting data. The collection unit, for example, collects work-related information shared by employees on social media. For example, the collection unit collects work-related information shared by employees on social media. The collection unit can also analyze employees' social media activities and collect work-related data. For example, the collection unit analyzes employees' social media activities and collects work-related data. The collection unit can also collect related data based on the content of employees' social media posts. For example, the collection unit collects related data based on the content of employees' social media posts. In this way, work-related data can be efficiently collected by analyzing employees' social media activities. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input data on employees' social media activities to the generation AI and cause the generation AI to collect related data.

[0085] The collection unit can customize the collection method by reflecting employees' past feedback when collecting data. For example, the collection unit adjusts the data collection method based on feedback provided by employees in the past. For example, the collection unit adjusts the data collection method based on feedback provided by employees in the past. The collection unit can also customize the type of data to be collected by reflecting employees' past feedback. For example, the collection unit customizes the type of data to be collected by reflecting employees' past feedback. The collection unit can also adjust the frequency of data collection based on employees' feedback. For example, the collection unit adjusts the frequency of data collection based on employees' feedback. This enables more effective data collection by reflecting employees' past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input employees' past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0086] The analysis unit can estimate the employee's emotions and adjust the data analysis method based on the estimated employee emotions. For example, if the employee is feeling stressed, the analysis unit applies an analysis method that helps reduce stress. For example, if the employee is feeling stressed, the analysis unit applies an analysis method that helps reduce stress. Furthermore, if the employee is relaxed, the analysis unit can perform a detailed analysis and provide information that helps improve work efficiency. For example, if the employee is relaxed, the analysis unit can perform a detailed analysis and provide information that helps improve work efficiency. Furthermore, if the employee is concentrating, the analysis unit can prioritize analyzing data directly related to work. For example, if the employee is concentrating, the analysis unit prioritizes analyzing data directly related to work. This allows for more appropriate analysis by adjusting the data analysis method based on the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis department can input employee emotional data into the generation AI and have the generation AI adjust the method of analyzing the data based on the emotions.

[0087] The analysis unit can apply a new algorithm for detailed evaluation of an employee's abilities and aptitudes during data analysis. For example, the analysis unit applies a new algorithm for evaluating abilities and aptitudes based on an employee's work results. For example, the analysis unit applies a new algorithm for evaluating abilities and aptitudes based on an employee's work results. The analysis unit can also apply a new algorithm for evaluating skill improvement based on an employee's training results. For example, the analysis unit applies a new algorithm for evaluating skill improvement based on an employee's training results. The analysis unit can also apply a new algorithm for evaluating aptitudes based on an employee's project participation history. For example, the analysis unit applies a new algorithm for evaluating aptitudes based on an employee's project participation history. In this way, by applying the new algorithm, an employee's abilities and aptitudes can be evaluated more accurately. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input data on an employee's work results and training results into a generation AI and have the generation AI apply the new algorithm.

[0088] During data analysis, the analysis unit can identify strengths and weaknesses based on an employee's past work performance and training results. For example, the analysis unit can analyze an employee's past work performance and identify strengths and weaknesses. For example, the analysis unit can analyze an employee's past work performance and identify strengths and weaknesses. The analysis unit can also identify skill strengths and weaknesses based on an employee's training performance. For example, the analysis unit can identify skill strengths and weaknesses based on an employee's training performance. The analysis unit can also analyze an employee's project participation history and identify strengths and weaknesses. For example, the analysis unit can analyze an employee's project participation history and identify strengths and weaknesses. This makes it possible to identify strengths and weaknesses based on an employee's past work performance and training results, thereby proposing a more appropriate career path. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input data on an employee's past work performance and training results into a generation AI and have the generation AI identify strengths and weaknesses.

[0089] When analyzing data, the analysis unit can apply different analysis methods depending on the employee's career goals. For example, the analysis unit applies an analysis method for identifying skill gaps depending on the employee's career goals. For example, the analysis unit applies an analysis method for identifying skill gaps depending on the employee's career goals. The analysis unit can also apply an analysis method for acquiring necessary skills based on the employee's career goals. For example, the analysis unit applies an analysis method for acquiring necessary skills based on the employee's career goals. The analysis unit can also apply an analysis method for proposing an optimal career path depending on the employee's career goals. For example, the analysis unit applies an analysis method for proposing an optimal career path depending on the employee's career goals. In this way, by applying an analysis method depending on the employee's career goals, it is possible to propose a more appropriate career path. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the employee's career goals into the generation AI and cause the generation AI to apply different analysis methods.

[0090] The analysis unit can estimate the employee's emotions and adjust the display method of the analysis results based on the estimated employee emotions. For example, if the employee is feeling stressed, the analysis unit provides a simple, highly visible display method. For example, if the employee is feeling stressed, the analysis unit provides a simple, highly visible display method. Furthermore, if the employee is relaxed, the analysis unit can provide a display method including detailed information. For example, if the employee is relaxed, the analysis unit provides a display method including detailed information. Furthermore, if the employee is concentrating, the analysis unit can prioritize displaying information directly related to work. For example, if the employee is concentrating, the analysis unit prioritizes displaying information directly related to work. This allows for adjusting the display method of the analysis results based on the employee's emotions, resulting in a more visible display. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis department can input employee emotional data into the generation AI and have the generation AI adjust the way the analysis results are displayed based on the emotions.

[0091] During data analysis, the analysis unit can prioritize data based on the time of employee submission. For example, the analysis unit prioritizes analysis of data recently submitted by employees. The analysis unit can also prioritize analysis of data of high importance based on the time of employee submission. For example, the analysis unit prioritizes analysis of data of high importance based on the time of employee submission. The analysis unit can also prioritize analysis of timely data by taking into account the time of employee submission. For example, the analysis unit prioritizes analysis of timely data by taking into account the time of employee submission. This enables timely data analysis by determining the priority of data based on the time of employee submission. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data submitted by employees to the generation AI and have the generation AI determine the priority of the data.

[0092] When analyzing data, the analysis unit can improve the accuracy of the analysis by referring to literature related to the employee. For example, the analysis unit improves the accuracy of the analysis based on related literature that the employee has referenced in the past. For example, the analysis unit improves the accuracy of the analysis based on related literature that the employee has referenced in the past. The analysis unit can also improve the accuracy of the analysis by referring to literature related to the employee's work. For example, the analysis unit improves the accuracy of the analysis by referring to literature related to the employee's work. The analysis unit can also improve the accuracy of the analysis by referring to literature related to the employee's training content. For example, the analysis unit improves the accuracy of the analysis by referring to literature related to the employee's training content. In this way, the accuracy of the analysis is improved by referring to the employee's related literature. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input data on the employee's related literature into the generation AI and have the generation AI improve the accuracy of the analysis.

[0093] When analyzing data, the analysis unit can adjust the level of detail of the analysis according to the employee's level of expertise. For example, if the employee's level of expertise is high, the analysis unit provides detailed analysis results. For example, if the employee's level of expertise is high, the analysis unit provides detailed analysis results. The analysis unit can also provide simple, easy-to-understand analysis results if the employee's level of expertise is low. For example, if the employee's level of expertise is low, the analysis unit provides simple, easy-to-understand analysis results. The analysis unit can also adjust the level of detail of the analysis results according to the employee's level of expertise. For example, the analysis unit adjusts the level of detail of the analysis results according to the employee's level of expertise. In this way, by adjusting the level of detail of the analysis according to the employee's level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the employee's level of expertise to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0094] The calculation unit can estimate the employee's emotions and adjust the calculation criteria for the degree of match based on the estimated employee emotions. For example, if the employee is feeling stressed, the calculation unit prioritizes calculating positions that will help reduce stress. For example, if the employee is feeling stressed, the calculation unit prioritizes calculating positions that will help reduce stress. Furthermore, if the employee is relaxed, the calculation unit can prioritize calculating positions that will help improve work efficiency. For example, if the employee is relaxed, the calculation unit prioritizes calculating positions that will help improve work efficiency. Furthermore, if the employee is concentrating, the calculation unit can prioritize calculating positions that are directly related to work. For example, if the employee is concentrating, the calculation unit prioritizes calculating positions that are directly related to work. In this way, by adjusting the calculation criteria for the degree of match based on the employee's emotions, more appropriate positions can be proposed. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing by the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit may input employee emotion data to the generation AI and cause the generation AI to adjust the calculation criteria for the degree of match based on the emotion.

[0095] The calculation unit can apply a new algorithm for evaluating an employee's skill set and work experience in detail when calculating the match degree. The calculation unit, for example, applies a new algorithm for evaluating the match degree in detail based on the employee's skill set. For example, the calculation unit applies a new algorithm for evaluating the match degree in detail based on the employee's skill set. The calculation unit can also apply a new algorithm for evaluating the match degree in detail based on the employee's work experience. For example, the calculation unit applies a new algorithm for evaluating the match degree in detail based on the employee's work experience. The calculation unit can also apply a new algorithm for evaluating the match degree in detail based on the employee's project participation history. For example, the calculation unit applies a new algorithm for evaluating the match degree in detail based on the employee's project participation history. In this way, by applying the new algorithm, the employee's skill set and work experience can be evaluated more accurately. Some or all of the above-described processing in the calculation unit may be performed using AI, for example, or may be performed without using AI. For example, the calculation unit can input data on the employee's skill set and work experience into the generation AI and cause the generation AI to apply the new algorithm.

[0096] When calculating the match degree, the calculation unit can identify the optimal position based on the employee's past work performance and training results. The calculation unit, for example, identifies the optimal position based on the employee's past work performance. For example, the calculation unit identifies the optimal position based on the employee's past work performance. The calculation unit can also identify the optimal position based on the employee's training performance. For example, the calculation unit identifies the optimal position based on the employee's training performance. The calculation unit can also identify the optimal position based on the employee's project participation history. For example, the calculation unit identifies the optimal position based on the employee's project participation history. In this way, by identifying the optimal position based on the employee's past work performance and training performance, it is possible to propose a more appropriate position. Some or all of the above-mentioned processing by the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input data on the employee's past work performance and training performance into the generation AI and cause the generation AI to identify the optimal position.

[0097] When calculating the match degree, the calculation unit can apply different calculation methods depending on the employee's career goals. For example, the calculation unit applies a calculation method for identifying a skill gap depending on the employee's career goals. For example, the calculation unit applies a calculation method for identifying a skill gap depending on the employee's career goals. The calculation unit can also apply a calculation method for acquiring necessary skills based on the employee's career goals. For example, the calculation unit applies a calculation method for acquiring necessary skills based on the employee's career goals. The calculation unit can also apply a calculation method for proposing an optimal career path depending on the employee's career goals. For example, the calculation unit applies a calculation method for proposing an optimal career path depending on the employee's career goals. As a result, by applying a calculation method depending on the employee's career goals, it is possible to propose a more appropriate career path. Some or all of the above-mentioned processing by the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input data on the employee's career goals into the generation AI and cause the generation AI to apply different calculation methods.

[0098] The calculation unit can estimate the employee's emotions and adjust the display method of the calculation results based on the estimated employee's emotions. For example, when the employee is feeling stressed, the calculation unit provides a simple, highly visible display method. For example, when the employee is feeling stressed, the calculation unit provides a simple, highly visible display method. Furthermore, when the employee is relaxed, the calculation unit can also provide a display method including detailed information. For example, when the employee is relaxed, the calculation unit provides a display method including detailed information. Furthermore, when the employee is concentrating, the calculation unit can prioritize displaying information directly related to work. For example, when the employee is concentrating, the calculation unit prioritizes displaying information directly related to work. In this way, by adjusting the display method of the calculation results based on the employee's emotions, a more highly visible display is possible. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the calculation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the calculation unit can input employee emotion data into the generation AI and have the generation AI adjust the way the calculation results are displayed based on the emotion.

[0099] When calculating the degree of match, the calculation unit can determine the calculation priority based on the employee's submission time. For example, the calculation unit prioritizes data recently submitted by the employee in the calculation. For example, the calculation unit prioritizes data recently submitted by the employee in the calculation. The calculation unit can also prioritize data of high importance based on the employee's submission time. For example, the calculation unit prioritizes data of high importance based on the employee's submission time. The calculation unit can also prioritize data that is timely, taking into account the employee's submission time. For example, the calculation unit prioritizes data that is timely, taking into account the employee's submission time. In this way, determining the calculation priority based on the employee's submission time enables timely data calculation. Some or all of the above-described processing by the calculation unit may be performed using AI, or may be performed without AI. For example, the calculation unit can input data of the employee's submission time into the generation AI and have the generation AI determine the calculation priority.

[0100] When calculating the degree of match, the calculation unit can improve the accuracy of the calculation by referring to related literature of the employee. For example, the calculation unit improves the accuracy of the calculation based on related literature that the employee has referenced in the past. For example, the calculation unit improves the accuracy of the calculation based on related literature that the employee has referenced in the past. The calculation unit can also improve the accuracy of the calculation by referring to literature related to the employee's work. For example, the calculation unit improves the accuracy of the calculation by referring to literature related to the employee's work. The calculation unit can also improve the accuracy of the calculation by referring to literature related to the employee's training content. For example, the calculation unit improves the accuracy of the calculation by referring to literature related to the employee's training content. In this way, the accuracy of the calculation is improved by referring to the related literature of the employee. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input data on related literature of the employee into the generation AI and cause the generation AI to improve the accuracy of the calculation.

[0101] When calculating the match degree, the calculation unit can adjust the level of detail of the calculation according to the employee's level of expertise. For example, if the employee's level of expertise is high, the calculation unit provides a detailed calculation result. For example, if the employee's level of expertise is high, the calculation unit provides a detailed calculation result. Furthermore, if the employee's level of expertise is low, the calculation unit can provide a simple, easy-to-understand calculation result. For example, if the employee's level of expertise is low, the calculation unit can provide a simple, easy-to-understand calculation result. Furthermore, the calculation unit can adjust the level of detail of the calculation result according to the employee's level of expertise. For example, the calculation unit adjusts the level of detail of the calculation result according to the employee's level of expertise. In this way, by adjusting the level of detail of the calculation according to the employee's level of expertise, it is possible to provide a calculation result that is easier to understand. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input data on the employee's level of expertise to the generation AI and cause the generation AI to adjust the level of detail of the calculation.

[0102] The suggestion unit can estimate the employee's emotions and adjust the way the suggestions are expressed based on the estimated employee emotions. For example, if the employee is feeling stressed, the suggestion unit makes a simple, highly visible suggestion. For example, if the employee is feeling stressed, the suggestion unit makes a simple, highly visible suggestion. Furthermore, if the employee is relaxed, the suggestion unit can make a suggestion including detailed information. For example, if the employee is relaxed, the suggestion unit can make a suggestion including detailed information. Furthermore, if the employee is concentrating, the suggestion unit can prioritize suggesting information directly related to work. For example, if the employee is concentrating, the suggestion unit prioritizes suggesting information directly related to work. This allows for adjusting the way the suggestions are expressed based on the employee's emotions, enabling more visible suggestions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the proposal department can input employee emotional data into the generation AI and have the generation AI adjust the way the proposal is expressed based on the employee's emotions.

[0103] When making a proposal, the proposal unit can simulate an optimal career path based on the employee's career goals and current skill set. The proposal unit, for example, proposes necessary skills and experience based on the employee's career goals. For example, the proposal unit proposes necessary skills and experience based on the employee's career goals. The proposal unit can also simulate an optimal career path based on the employee's current skill set. For example, the proposal unit simulates an optimal career path based on the employee's current skill set. The proposal unit can also simulate and compare different career paths according to the employee's career goals. For example, the proposal unit simulates and compares different career paths according to the employee's career goals. This makes it possible to propose an optimal career path based on the employee's career goals and skill set. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, AI. For example, the proposal unit can input data on the employee's career goals and current skill set into a generation AI and cause the generation AI to execute a career path simulation.

[0104] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the employee's past proposal results. The suggestion unit, for example, analyzes the employee's past proposal results and makes a new proposal based on successful proposals. For example, the suggestion unit analyzes the employee's past proposal results and makes a new proposal based on successful proposals. The suggestion unit can also make a proposal that reflects improvements by referring to the employee's past proposal results. For example, the suggestion unit makes a proposal that reflects improvements by referring to the employee's past proposal results. The suggestion unit can also make an optimal proposal based on the employee's past proposal results. For example, the suggestion unit makes an optimal proposal based on the employee's past proposal results. As a result, the accuracy of the proposal is improved by referring to the employee's past proposal results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the employee's past proposal results into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0105] When making a proposal, the proposal unit can apply different proposal algorithms depending on the employee's career goals. For example, the proposal unit applies a proposal algorithm that identifies skill gaps depending on the employee's career goals. For example, the proposal unit applies a proposal algorithm that identifies skill gaps depending on the employee's career goals. The proposal unit can also apply a proposal algorithm for acquiring necessary skills based on the employee's career goals. For example, the proposal unit applies a proposal algorithm for acquiring necessary skills based on the employee's career goals. The proposal unit can also apply a proposal algorithm for proposing an optimal career path depending on the employee's career goals. For example, the proposal unit applies a proposal algorithm for proposing an optimal career path depending on the employee's career goals. This makes it possible to propose a more appropriate career path by applying a proposal algorithm depending on the employee's career goals. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input data on the employee's career goals into the generation AI and cause the generation AI to apply different proposal algorithms.

[0106] The suggestion unit can estimate the employee's emotions and adjust the length of the suggestion based on the estimated employee's emotions. For example, if the employee is feeling stressed, the suggestion unit can make a short, to-the-point suggestion. For example, if the employee is feeling stressed, the suggestion unit can make a short, to-the-point suggestion. Furthermore, if the employee is relaxed, the suggestion unit can make a longer suggestion with detailed explanations. For example, if the employee is relaxed, the suggestion unit can make a longer suggestion with detailed explanations. Furthermore, if the employee is concentrating, the suggestion unit can prioritize suggesting information directly related to work. For example, if the employee is concentrating, the suggestion unit prioritizes suggesting information directly related to work. This enables more appropriate suggestions by adjusting the length of the suggestion based on the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI, or without an AI. For example, the suggestion department can input employee emotional data into the generation AI and have the generation AI adjust the length of suggestions based on the emotions.

[0107] When making a proposal, the suggestion unit can determine the priority of the proposal based on the employee's submission time. For example, the suggestion unit prioritizes proposals based on data recently submitted by the employee. For example, the suggestion unit prioritizes proposals based on data recently submitted by the employee. The suggestion unit can also prioritize proposals with higher importance based on the employee's submission time. For example, the suggestion unit prioritizes proposals with higher importance based on the employee's submission time. The suggestion unit can also prioritize timely proposals by taking into account the employee's submission time. For example, the suggestion unit prioritizes timely proposals by taking into account the employee's submission time. This enables timely proposals by determining the priority of proposals based on the employee's submission time. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input data on the employee's submission time into the generation AI and have the generation AI determine the priority of the proposals.

[0108] When making a proposal, the suggestion unit can adjust the order of the proposals based on the employee's relevance. For example, the suggestion unit prioritizes proposals related to the employee's current work. For example, the suggestion unit prioritizes proposals related to the employee's current work. The suggestion unit can also prioritize proposals related to the employee's career goals. For example, the suggestion unit prioritizes proposals related to the employee's career goals. The suggestion unit can also prioritize proposals related to the employee's skill set. For example, the suggestion unit prioritizes proposals related to the employee's skill set. This enables more effective proposals by adjusting the order of proposals based on the employee's relevance. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input data on the employee's relevance to the generation AI and cause the generation AI to adjust the order of the proposals.

[0109] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal depending on the employee's level of expertise. For example, if the employee's level of expertise is high, the suggestion unit makes a proposal that uses a lot of technical terminology. For example, if the employee's level of expertise is high, the suggestion unit makes a proposal that uses a lot of technical terminology. Furthermore, if the employee's level of expertise is low, the suggestion unit can make a proposal that is simple and easy to understand. For example, if the employee's level of expertise is low, the suggestion unit can make a proposal that is simple and easy to understand. Furthermore, the suggestion unit can adjust the use of technical terminology in the proposal depending on the employee's level of expertise. In this way, by adjusting the use of technical terminology depending on the employee's level of expertise, a more understandable proposal can be made. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the employee's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terminology. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, calculation unit, and proposal unit, is implemented, for example, in at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects unstructured data on employees' company history, work history, and training history using the camera 42 and microphone 38B of the smart device 14, and the collected data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, patterns the collected data using clustering or topic modeling techniques. The calculation unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, calculates the degree of match with multiple positions within the company based on the patterned data. The proposal unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, provides employees with career path simulations and personalized proposals for training plans. The collection unit, for example, can estimate employees' emotions and adjust the timing of data collection based on the estimated emotions. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, calculation unit, and suggestion unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects unstructured data on employees' company history, work history, and training history using the camera 42 and microphone 238 of the smart glasses 214, and the collected data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and patterns the collected data using clustering or topic modeling techniques. The calculation unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and calculates the degree of match with multiple positions within the company based on the patterned data. The suggestion unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and proposes career path simulations and personalized training plans to employees. The collection unit, for example, can estimate employees' emotions and adjust the timing of data collection based on the estimated emotions. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, calculation unit, and proposal unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects unstructured data on employees' company history, work history, and training history using the camera 42 and microphone 238 of the headset terminal 314, and the collected data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, patterns the collected data using clustering or topic modeling techniques. The calculation unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, calculates the degree of match with multiple positions within the company based on the patterned data. The proposal unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, provides employees with career path simulations and personalized proposals for training plans. The collection unit, for example, can estimate employees' emotions and adjust the timing of data collection based on the estimated emotions. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, calculation unit, and proposal unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects unstructured data on employees' company history, work history, and training history using the camera 42 and microphone 238 of the robot 414, and the collected data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, patterns the collected data using clustering or topic modeling techniques. The calculation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, calculates the degree of match with multiple positions within the company based on the patterned data. The proposal unit, realized, for example, by the specific processing unit 290 of the data processing device 12, provides employees with career path simulations and personalized proposals for training plans. The collection unit, for example, can estimate employees' emotions and adjust the timing of data collection based on the estimated emotions.

[0110] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0111] The career support system can also collect employee health data, which the analysis department analyzes to make career suggestions based on the employee's health status. For example, the collection department collects employees' health checkup results and fitness tracker data, which the analysis department analyzes. Next, the calculation department calculates positions that are less stressful for the employee and positions that are suitable for maintaining health based on their health status. Based on this data, the proposal department can propose career paths and training plans that take employee health into consideration. This makes it possible to balance employee health with career development.

[0112] The career support system can also collect data on employees' hobbies and interests, which the analysis department analyzes to make career suggestions based on the employee's interests. For example, the collection department collects survey data on employees' hobbies and interests, which the analysis department analyzes. Next, the calculation department calculates positions where employees can enjoy working based on their hobbies and interests. Based on this data, the proposal department can propose career paths and training plans that reflect the employee's interests. This can increase employee motivation.

[0113] The career support system can also collect employees' social media activities, which the analysis department analyzes to make career suggestions based on the employee's online presence. For example, the collection department collects employees' social media activity data, which the analysis department analyzes. Next, the calculation department calculates positions suitable for the employee in digital marketing or social media management based on their online presence. Based on this data, the proposal department can suggest career paths and training plans that make use of the employee's online activities. This allows the digital skills of employees to be utilized to the fullest.

[0114] The career support system can also collect employees' geographical location information, which the analysis department analyzes to make career suggestions based on the employee's work location. For example, the collection department collects data on employees' work locations and business trip destinations, and the analysis department analyzes this. Next, the calculation department calculates positions that will allow employees to shorten their commute time or positions that require fewer business trips based on the geographical location information. Based on this data, the proposal department can suggest career paths and training plans that suit the employee's lifestyle. This can improve the employee's work-life balance.

[0115] The career support system can also collect employee feedback data, which the analysis department analyzes to make career suggestions based on employee opinions. For example, the collection department collects employee feedback and survey results, which the analysis department analyzes. Next, the calculation department calculates the employee's desired position and training content based on the feedback data. Based on this data, the proposal department can propose career paths and training plans that reflect employee opinions. This can increase employee satisfaction.

[0116] The career support system can also estimate employees' emotions and adjust the timing of career suggestions based on the estimated emotions. For example, the collection unit collects employee emotional data, which the analysis unit analyzes. Next, the calculation unit makes career suggestions based on the emotional data at a time when the employee is relaxed. Based on this data, the suggestion unit can propose career paths and training plans that take employees' emotions into consideration. This reduces employee stress and makes suggestions more likely to be accepted.

[0117] The career support system can further estimate employees' emotions and adjust the content of career suggestions based on the estimated emotions. For example, the collection unit collects employee emotional data, and the analysis unit analyzes it. Next, based on the emotional data, the calculation unit suggests a position that helps reduce stress if the employee is feeling stressed, or a challenging position if the employee is relaxed. Based on this data, the suggestion unit can suggest career paths and training plans that correspond to the employee's emotions. This makes it possible to make suggestions that take into account the employee's emotions.

[0118] The career support system can further estimate employees' emotions and adjust the way career suggestions are presented based on the estimated emotions. For example, the collection unit collects employee emotional data, which the analysis unit analyzes. Next, based on the emotional data, the calculation unit makes simple, highly visible suggestions if the employee is feeling stressed, or suggestions with more detailed information if the employee is relaxed. Based on this data, the suggestion unit can suggest career paths and training plans that correspond to the employee's emotions. This makes it possible to make suggestions that take employees' emotions into consideration.

[0119] The career support system can further estimate employees' emotions and prioritize career suggestions based on the estimated emotions. For example, the collection unit collects employees' emotional data, and the analysis unit analyzes it. Next, based on the emotional data, the calculation unit prioritizes suggestions that will help reduce stress if the employee is feeling stressed, and suggestions that will help advance their career if the employee is relaxed. Based on this data, the suggestion unit can suggest career paths and training plans that correspond to the employee's emotions. This makes it possible to make suggestions that take employees' emotions into consideration.

[0120] The career support system can further estimate employees' emotions and adjust the frequency of career suggestions based on the estimated emotions. For example, the collection unit collects employee emotional data, which the analysis unit analyzes. Next, based on the emotional data, the calculation unit reduces the frequency of suggestions if the employee is feeling stressed, and increases the frequency of suggestions if the employee is relaxed. Based on this data, the suggestion unit can suggest career paths and training plans that correspond to the employee's emotions. This makes it possible to make suggestions that take into account the employee's emotions.

[0121] The processing flow of the second embodiment will be briefly explained below.

[0122] Step 1: The collection department collects unstructured data on employees' company history, work history, and training history. Unstructured data on employees' company history, work history, and training history includes, for example, text data, image data, and audio data. The collection department collects data such as the projects employees have worked on in the past and the content of the training they have taken. For example, data such as project objectives, results, technology used, training themes, learning content, and grades are collected. Step 2: The analysis department analyzes the data collected by the collection department and identifies patterns. Using techniques such as clustering and topic modeling, the analysis department groups the collected data and identifies groups with common characteristics. It also extracts key topics and clarifies employee capabilities and aptitudes. Step 3: The calculation unit calculates the degree of match with multiple positions within the company based on the data patterned by the analysis unit. The calculation unit uses methods such as scoring algorithms and weighting criteria to determine whether the employee's skill set and work experience are suitable for a specific position and calculates the degree of match. Step 4: The proposal department proposes personalized career path simulations and training plans to employees based on the match calculated by the calculation department. The proposal department uses methods such as scenario-based simulation and Monte Carlo simulation to propose optimal career paths based on the employee's career goals and current skill set.

[0123] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0124] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

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

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0134] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0135] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0136] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0137] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0144] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0146] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0149] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0150] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0151] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0152] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0154] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0160] 7, a 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.

[0161] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0163] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0165] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0166] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0167] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0168] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0169] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0170] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0171] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0172] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0173] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0174] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0175] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0176] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0178] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0179] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0180] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0184] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0185] 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.

[0186] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0187] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0188] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0189] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0191] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0192] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0193] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0194] [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection department that collects unstructured data on employees' company history, work history, and training history; an analysis unit that analyzes and patterns the data collected by the collection unit; a calculation unit that calculates a degree of match with a plurality of positions within the company based on the data patterned by the analysis unit; a proposal unit that makes personalized proposals for career path simulations and training plans to employees based on the degree of match calculated by the calculation unit; Equipped with A system characterized by:

2. The collecting unit Collect data on projects employees have worked on in the past and the training they have taken 2. The system of claim 1.

3. The analysis unit Analyze the collected data to clarify employees' abilities and aptitudes 2. The system of claim 1.

4. The calculation unit Determine whether an employee's skill set and work experience are suitable for a particular position 2. The system of claim 1.

5. The proposal unit Simulate career paths based on employees' career goals and current skill sets 2. The system of claim 1.

6. The proposal unit Suggest the skills and experience employees need to reach their desired position 2. The system of claim 1.

7. The proposal unit Matching employees' career aspirations with the needs of the organization 2. The system of claim 1.

8. The proposal unit Improving employee job satisfaction and corporate productivity 2. The system of claim 1.

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