System

Abstract

To provide a system for specifying deviation between the skill of an employee and the skill required by a department, and for proposing proper training or transfer.SOLUTION: The system includes an employee information acquisition part, a department information acquisition part, a skill deviation identification part, a training proposal part, an in-house side-business proposal part, a change proposal part, and a candidate employee output part. An employee information acquisition part acquires skills and qualification information of employees, past created materials, and training information. The department information acquisition unit acquires past created materials and required skills of a department. The skill deviation identification unit identifies a deviation between the current skill of the employee and the skill required by the department to which the employee belongs. A training proposal part proposes training to the employee on the basis of the deviation. An in-house sideline proposal part proposes a suitable headquarters on the basis of the skill set of the employee in the case of inviting an in-house sideline. A change proposal part collates the skill set of the employee with a skill requested by each department, and proposes a suitable department. The candidate employee output unit outputs a list of candidate employees matching a desired person image.SELECTED DRAWING: Figure 1

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 efficiently identify discrepancies between an employee's skills and the skills required by the department, and to propose appropriate training or transfers.

[0005] The system according to the embodiment aims to identify discrepancies between an employee's skills and the skills required by the department, and to propose appropriate training or transfers. [Means for solving the problem]

[0006] The system according to the embodiment includes an employee information acquisition unit, a department information acquisition unit, a skills gap identification unit, a training proposal unit, an internal side job proposal unit, a transfer proposal unit, and a candidate employee output unit. The employee information acquisition unit acquires employee skills, qualification information, previously prepared documents, and training information. The department information acquisition unit acquires previously prepared documents and required skills for the department. The skills gap identification unit identifies the gap between the employee's current skills and the skills required by the department based on the information acquired by the employee information acquisition unit and the department information acquisition unit. The training proposal unit proposes training for the employee based on the gap identified by the skills gap identification unit. The internal side job proposal unit proposes an appropriate headquarters based on the employee's skill set when an internal side job is open for recruitment. The transfer proposal unit compares the employee's skill set with the skills required by each department when an employee requests a transfer and proposes an appropriate department. The candidate employee output unit outputs a list of candidate employees who match the desired profile when considering adding members to a department. [Effects of the Invention]

[0007] The system according to the embodiment can identify discrepancies between an employee's skills and the skills required by the department and suggest appropriate training or transfers. [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) The skill management system according to an embodiment of the present invention is a system that inputs employee skills, qualification information, past documents, and training information, and uses a generation AI to learn the characteristics of individuals and departments. This allows the skill management system to identify discrepancies between an employee's current skills and the skills required by their department, and to propose training for the skills they lack. It can also suggest suitable headquarters when recruiting for internal side jobs, which can be used as a reference when requesting a transfer. Furthermore, when considering adding members to a department, it can output a list of candidate employees who fit the desired profile.

[0029] A skill management system according to an embodiment includes an employee information acquisition unit, a department information acquisition unit, a skills gap identification unit, a training proposal unit, an in-house side job proposal unit, a transfer proposal unit, and a candidate employee output unit. The employee information acquisition unit acquires employee skills, qualification information, previously prepared documents, and training information. For example, the employee information acquisition unit acquires project reports and presentation materials prepared by employees. The employee information acquisition unit can also acquire employee qualification information and training information. The department information acquisition unit acquires previously prepared documents and required skills of departments. For example, the department information acquisition unit acquires project plans and business manuals for departments. The department information acquisition unit can also acquire skills required by departments. The skills gap identification unit identifies a gap between an employee's current skills and the skills required by their departments based on the information acquired by the employee information acquisition unit and the department information acquisition unit. For example, the skills gap identification unit identifies a gap between an employee's presentation skills and the department's data analysis skills. The training proposal unit proposes training for employees based on the gap identified by the skills gap identification unit. For example, the training proposal unit proposes training in data analysis. When an internal side job is advertised, the internal side job proposal unit proposes a suitable headquarters based on the employee's skill set. For example, the internal side job proposal unit proposes a side job in the marketing department to an employee with marketing skills. When an employee requests a transfer, the transfer proposal unit compares the employee's skill set with the skills required by each department and proposes a suitable department. For example, the transfer proposal unit proposes a transfer to the project management department to an employee with project management skills. The candidate employee output unit outputs a list of candidate employees who match the desired profile when considering adding members to a department. For example, the candidate employee output unit outputs a list of employees with data scientist skills. This enables the skill management system according to the embodiment to improve employee skills and allocate the right people to the right positions. For example, it proposes training to effectively improve employees' skills and supports employees' career paths. It also optimizes the allocation of personnel within departments and improves work efficiency.

[0030] The employee information acquisition unit can analyze work history over time to track career progress and skill development. For example, the employee information acquisition unit analyzes an employee's work history over time to track skill development for each job. For example, it analyzes changes in skills from the start to the end of a project to evaluate career progress. The employee information acquisition unit also uses time-series data to visualize an employee's career path and track skill development. For example, it draws a skill growth curve based on past project history. The employee information acquisition unit also analyzes an employee's work history to evaluate skill development over a specific period. For example, it identifies new skills acquired during a specific project period and tracks the development of those skills. This makes it possible to track an employee's career progress and skill development over time.

[0031] The employee information acquisition unit can analyze qualification information and training information, identify skill correlations, and suggest areas for strengthening skill sets. The employee information acquisition unit, for example, analyzes employee qualification information and training information and identifies correlations between each skill. For example, it analyzes the training content received by an employee qualified in data analysis and identifies related skills. The employee information acquisition unit also suggests areas for strengthening skill sets based on the qualification information and training information. For example, it suggests machine learning training for an employee qualified in data science. The employee information acquisition unit also builds a system that analyzes employee qualification information and training information and identifies skill correlations. For example, it suggests areas for strengthening skill sets based on the training content received by an employee with a specific qualification. This makes it possible to identify skill correlations and suggest areas for strengthening skill sets.

[0032] The employee information acquisition unit can include activities outside the company in the input data and perform a comprehensive skill evaluation. For example, the employee information acquisition unit adds information about volunteer activities and hobbies to the employee's input data and performs a comprehensive skill evaluation. For example, it evaluates leadership experience in volunteer activities. The employee information acquisition unit also analyzes outside activity data and reflects it in the employee's skill set. For example, it includes hobby programming experience in the skill evaluation. The employee information acquisition unit also builds a system that performs a comprehensive skill evaluation of employees based on the data on outside activities. For example, it evaluates skills acquired through hobby projects and adds them to the skill set. This makes it possible to perform a comprehensive skill evaluation that includes activities outside the company.

[0033] The employee information acquisition unit can compare input data with skill sets from different industries to evaluate aptitude in different industries. For example, the employee information acquisition unit compares an employee's input data with skill sets from different industries to evaluate aptitude in different industries. For example, an employee with skills in the IT industry is evaluated for aptitude in the manufacturing industry. The employee information acquisition unit also builds a system to evaluate employee aptitude based on skill sets from different industries. For example, an employee with marketing skills is evaluated for aptitude in the financial industry. The employee information acquisition unit also compares an employee's skill set with skill sets from different industries to evaluate aptitude in different industries. For example, an employee with design skills is evaluated for aptitude in the education industry. This makes it possible to evaluate aptitude in different industries.

[0034] The department information acquisition unit can analyze work history in chronological order to identify the factors that lead to the success or failure of a project. For example, the department information acquisition unit analyzes the work history of a department in chronological order to identify the factors that lead to the success or failure of a project. For example, it analyzes success factors based on past project history and identifies the strengths of the department. The department information acquisition unit also uses time-series data to analyze the project history of a department and builds a system that identifies the factors that lead to the success or failure of a project. For example, it analyzes the factors that lead to the success of a project over a specific period of time. The department information acquisition unit also analyzes the work history of a department to identify the factors that lead to the success or failure of a project. For example, it identifies success factors based on past project history and evaluates the strengths of the department. In this way, the strengths and weaknesses of the department can be clarified by identifying the factors that lead to the success or failure of a project.

[0035] The department information acquisition unit can analyze the required skill sets, identify the correlations between skills, and suggest areas where skills should be strengthened. For example, the department information acquisition unit analyzes the skill sets required by a department and identifies the correlations between each skill. For example, it analyzes the correlation between data analysis skills and programming skills and suggests areas where skills should be strengthened for the department. The department information acquisition unit also builds a system that suggests areas where skills should be strengthened for the entire department based on the correlations between skill sets. For example, it analyzes the correlation between data science skills and machine learning skills and suggests areas where skills should be strengthened. The department information acquisition unit also analyzes the skill sets required by a department and identifies the correlations between skills. For example, it analyzes the correlation between project management skills and communication skills and suggests areas where skills should be strengthened for the department. This makes it possible to suggest areas where skills should be strengthened for the entire department.

[0036] The department information acquisition unit can include collaboration history with other departments in the input data and evaluate interactions between departments. The department information acquisition unit, for example, adds collaboration history with other departments to the input data of a department and evaluates interactions between departments. For example, it evaluates cooperative relationships between departments based on the history of joint projects. The department information acquisition unit also analyzes collaboration history with other departments and builds a system for evaluating interactions between departments. For example, it analyzes success factors for joint projects and evaluates cooperative relationships between departments. The department information acquisition unit also includes collaboration history with other departments in the input data of a department and evaluates interactions between departments. For example, it evaluates cooperative relationships between departments based on the history of joint projects. In this way, by evaluating interactions between departments, it is possible to clarify cooperative relationships between departments.

[0037] The department information acquisition unit can compare the input data with departments in different industries to evaluate aptitude in different industries. For example, the department information acquisition unit compares input data of a department with departments in different industries to evaluate aptitude in different industries. For example, data from the IT department is compared with data from the manufacturing industry to evaluate aptitude in different industries. The department information acquisition unit also builds a system to evaluate aptitude in departments based on department data from different industries. For example, data from the marketing department is compared with data from the financial industry to evaluate aptitude in different industries. The department information acquisition unit also compares input data of a department with data from departments in different industries to evaluate aptitude in different industries. For example, data from the design department is compared with data from the education industry to evaluate aptitude in different industries. This makes it possible to evaluate aptitude in different industries.

[0038] The skill gap identification unit can analyze the learning history and evaluate past learning effectiveness. For example, when identifying skill gaps, the skill gap identification unit analyzes the employee's learning history and evaluates past learning effectiveness. For example, the learning effectiveness is evaluated based on the history of past training. The skill gap identification unit also analyzes the learning history and builds a system for identifying skill gaps. For example, the learning effectiveness is evaluated based on the past training history and the skill gap is identified. The skill gap identification unit also analyzes the employee's learning history and evaluates past learning effectiveness. For example, the skill gap identification unit analyzes the change in skills after receiving specific training and evaluates learning effectiveness. In this way, by evaluating past learning effectiveness, effective training can be proposed.

[0039] The skill gap identification unit can analyze work performance data and evaluate skill applicability in actual work. For example, when identifying skill gaps, the skill gap identification unit analyzes employee work performance data and evaluates skill applicability in actual work. For example, the skill applicability is evaluated based on project outcome data. The skill gap identification unit also analyzes work performance data and builds a system for identifying skill gaps. For example, the skill applicability is evaluated based on work evaluation data and skill gaps are identified. The skill gap identification unit also analyzes employee work performance data and evaluates skill applicability in actual work. For example, the skill applicability is evaluated based on work outcome data and skill gaps are identified. In this way, skill gaps can be identified by evaluating skill applicability in actual work.

[0040] The skill gap identification unit can compare the skill sets with those of other departments and evaluate aptitude in other departments. For example, when identifying a skill gap, the skill gap identification unit compares the skill sets with those of other departments and evaluates aptitude in other departments. For example, it compares the skill sets with those of the marketing department and evaluates aptitude in other departments. Furthermore, the skill gap identification unit builds a system for identifying skill gaps based on the skill sets of other departments. For example, it compares the skill sets with those of the IT department and evaluates aptitude in other departments. Furthermore, when identifying a skill gap, the skill gap identification unit compares the skill sets with those of other departments and evaluates aptitude in other departments. For example, it compares the skill sets with those of the design department and evaluates aptitude in other departments. In this way, by evaluating aptitude in other departments, it is possible to expand the possibility of transfer.

[0041] The skill gap identification unit can consider personal interests and hobbies to suggest training that will interest the employee. For example, when identifying a skill gap, the skill gap identification unit considers the employee's personal interests and hobbies to suggest training that will interest the employee. For example, it proposes programming training to an employee who programs as a hobby. The skill gap identification unit also builds a system that identifies skill gaps based on personal interests and hobbies. For example, it proposes design training to an employee who is interested in design. The skill gap identification unit also considers the employee's personal interests and hobbies when identifying a skill gap to suggest training that will interest the employee. For example, it proposes marketing training to an employee who is interested in marketing. This makes it possible to suggest training that takes into account the employee's personal interests and hobbies.

[0042] The in-house side job proposal department can analyze past side job histories and identify the characteristics of successful side jobs. For example, when proposing an in-house side job, the in-house side job proposal department analyzes an employee's past side job history and identifies the characteristics of a successful side job. For example, the characteristics of a successful side job are identified based on the past side job history. The in-house side job proposal department also builds a system that analyzes past side job history and identifies the characteristics of a successful side job. For example, the characteristics of a successful side job are identified based on the past side job history. The in-house side job proposal department also analyzes an employee's past side job history and identifies the characteristics of a successful side job. For example, the characteristics of a successful side job are identified based on the past side job history. In this way, by identifying the characteristics of a successful side job, it is possible to propose a side job that is suitable for the employee.

[0043] The in-house side job proposal department can analyze the correlation between skill sets and the skills required for side jobs and propose optimal side jobs. For example, when proposing in-house side jobs, the in-house side job proposal department analyzes the correlation between an employee's skill set and the skills required for the side job and proposes the optimal side job. For example, for an employee with marketing skills, it proposes a marketing-related side job. The in-house side job proposal department also builds a system that proposes optimal side jobs based on the correlation between skill sets and the skills required for side jobs. For example, for an employee with data analysis skills, it proposes a data analysis-related side job. The in-house side job proposal department also analyzes the correlation between an employee's skill set and the skills required for side jobs and proposes the optimal side job. For example, for an employee with project management skills, it proposes a project management-related side job. In this way, it is possible to propose the optimal side job by analyzing the correlation between an employee's skill set and the skills required for side jobs.

[0044] The in-house side job proposal department can evaluate suitability for different departments by referring to the side job history of other departments. For example, when proposing an in-house side job, the in-house side job proposal department evaluates suitability for different departments by referring to the side job history of other departments. For example, it evaluates suitability for different departments based on the side job history of the marketing department. The in-house side job proposal department also builds a system to evaluate suitability for different departments based on the side job history of other departments. For example, it evaluates suitability for different departments based on the side job history of the IT department. The in-house side job proposal department also builds a system to evaluate suitability for different departments by referring to the side job history of other departments when proposing an in-house side job. For example, it evaluates suitability for different departments based on the side job history of the design department. This makes it possible to evaluate suitability for different departments by referring to the side job history of other departments.

[0045] The in-house side job proposal department can propose interesting side jobs by taking into consideration personal interests and hobbies. For example, when proposing in-house side jobs, the in-house side job proposal department considers an employee's personal interests and hobbies and proposes side jobs that will interest them. For example, for an employee who programs as a hobby, the department proposes a programming-related side job. The in-house side job proposal department also builds a system that proposes optimal side jobs based on personal interests and hobbies. For example, for an employee who is interested in design, the department proposes a design-related side job. The in-house side job proposal department also considers an employee's personal interests and hobbies and proposes interesting side jobs. For example, for an employee who is interested in marketing, the department proposes a marketing-related side job. In this way, it is possible to propose side jobs that take into consideration an employee's personal interests and hobbies.

[0046] The transfer proposal unit can analyze past transfer history and identify the characteristics of successful transfers. The transfer proposal unit, for example, analyzes an employee's past transfer history as a reference when requesting a transfer and identifies the characteristics of successful transfers. For example, the characteristics of successful transfers are identified based on the past transfer history. The transfer proposal unit also builds a system that analyzes past transfer history and identifies the characteristics of successful transfers. For example, the characteristics of successful transfers are identified based on the past transfer history. The transfer proposal unit also analyzes an employee's past transfer history and identifies the characteristics of successful transfers. For example, the characteristics of successful transfers are identified based on the past transfer history. In this way, by identifying the characteristics of successful transfers, it is possible to propose a transfer destination that is suitable for the employee.

[0047] The transfer proposal unit can analyze the correlation between the skill set and the required skills of the transfer destination and propose the optimal transfer destination. For example, the transfer proposal unit analyzes the correlation between an employee's skill set and the required skills of the transfer destination as a reference when an employee requests a transfer and proposes the optimal transfer destination. For example, for an employee with project management skills, it proposes a transfer to the project management department. The transfer proposal unit also builds a system that proposes the optimal transfer destination based on the correlation between the skill set and the required skills of the transfer destination. For example, for an employee with data analysis skills, it proposes a transfer to the data analysis department. The transfer proposal unit also analyzes the correlation between an employee's skill set and the required skills of the transfer destination and proposes the optimal transfer destination. For example, for an employee with marketing skills, it proposes a transfer to the marketing department. In this way, the optimal transfer destination can be proposed by analyzing the correlation between an employee's skill set and the required skills of the transfer destination.

[0048] The transfer proposal department can evaluate suitability for a different department by referring to the transfer history of other departments. The transfer proposal department, for example, evaluates suitability for a different department by referring to the transfer history of other departments as a reference when requesting a transfer. For example, it evaluates suitability for a different department based on the transfer history of the marketing department. The transfer proposal department also builds a system for evaluating suitability for a different department based on the transfer history of other departments. For example, it evaluates suitability for a different department based on the transfer history of the IT department. The transfer proposal department also builds a system for evaluating suitability for a different department by referring to the transfer history of other departments as a reference when requesting a transfer. For example, it evaluates suitability for a different department based on the transfer history of the design department. In this way, it is possible to evaluate suitability for a different department by referring to the transfer history of other departments.

[0049] The transfer proposal department can consider personal interests and hobbies to propose interesting transfer destinations. For example, the transfer proposal department considers an employee's personal interests and hobbies to propose interesting transfer destinations as a reference when requesting a transfer. For example, for an employee who programs as a hobby, the transfer proposal department proposes a transfer to a programming-related department. The transfer proposal department also builds a system that proposes the most suitable transfer destination based on personal interests and hobbies. For example, for an employee who is interested in design, the transfer proposal department proposes a transfer to a design-related department. The transfer proposal department also considers an employee's personal interests and hobbies to propose interesting transfer destinations. For example, for an employee who is interested in marketing, the transfer proposal department proposes a transfer to a marketing-related department. In this way, it is possible to propose transfer destinations that take into account an employee's personal interests and hobbies.

[0050] The candidate employee output unit can analyze past transfer history and identify the characteristics of successful transfers. For example, when creating a list of candidate employees when adding members to a department, the candidate employee output unit analyzes an employee's past transfer history and identifies the characteristics of successful transfers. For example, the characteristics of successful transfers are identified based on the past transfer history. The candidate employee output unit also builds a system that analyzes past transfer history and identifies the characteristics of successful transfers. For example, the characteristics of successful transfers are identified based on the past transfer history. The candidate employee output unit also analyzes an employee's past transfer history and identifies the characteristics of successful transfers. For example, the characteristics of successful transfers are identified based on the past transfer history. In this way, by identifying the characteristics of successful transfers, appropriate candidate employees can be suggested.

[0051] The candidate employee output unit can analyze the correlation between skill sets and the skills required by a department and suggest the most suitable candidate employees. For example, when creating a list of candidate employees when adding members to a department, the candidate employee output unit analyzes the correlation between an employee's skill set and the skills required by the department and suggests the most suitable candidate employees. For example, an employee with data analysis skills is suggested as a candidate employee for the data analysis department. The candidate employee output unit also builds a system that suggests the most suitable candidate employees based on the correlation between skill sets and the skills required by the department. For example, an employee with project management skills is suggested as a candidate employee for the project management department. The candidate employee output unit also analyzes the correlation between an employee's skill set and the skills required by the department and suggests the most suitable candidate employees. For example, an employee with marketing skills is suggested as a candidate employee for the marketing department. In this way, the most suitable candidate employees can be suggested by analyzing the correlation between an employee's skill set and the skills required by the department.

[0052] The candidate employee output unit can evaluate suitability for different departments by referring to the transfer history of other departments. For example, when creating a list of candidate employees when a department adds members, the candidate employee output unit evaluates suitability for different departments by referring to the transfer history of other departments. For example, it evaluates suitability for different departments based on the transfer history of the marketing department. The candidate employee output unit also builds a system for evaluating suitability for different departments based on the transfer history of other departments. For example, it evaluates suitability for different departments based on the transfer history of the IT department. The candidate employee output unit also builds a system for evaluating suitability for different departments by referring to the transfer history of other departments when creating a list of candidate employees when a department adds members, by referring to the transfer history of other departments. For example, it evaluates suitability for different departments based on the transfer history of the design department. This makes it possible to evaluate suitability for different departments by referring to the transfer history of other departments.

[0053] The candidate employee output unit can consider personal interests and hobbies to suggest departments that interest employees. For example, when creating a list of candidate employees when adding members to a department, the candidate employee output unit considers the employee's personal interests and hobbies to suggest departments that interest employees. For example, it proposes a programming-related department to an employee who programs as a hobby. The candidate employee output unit also builds a system that suggests the most suitable department based on personal interests and hobbies. For example, it proposes a design-related department to an employee who is interested in design. The candidate employee output unit also considers the employee's personal interests and hobbies to suggest departments that interest employees. For example, it proposes a marketing-related department to an employee who is interested in marketing. In this way, it is possible to suggest departments that take the employee's personal interests and hobbies into consideration.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The employee information acquisition unit can also acquire employee health data and evaluate the applicability of skills based on their health condition. For example, it can acquire data from employees' fitness trackers or health apps and evaluate their work performance when their health is good. The employee information acquisition unit can also analyze the impact of health condition on work performance based on the health data. For example, it can evaluate the success rate of a project when stress levels are low. The employee information acquisition unit can also use the health data to suggest work assignments based on employees' health conditions. For example, it can suggest that employees in good health be assigned to important projects. This makes it possible to evaluate skills and assign work that takes into account employees' health conditions.

[0056] The employee information acquisition department can also analyze employees' social media activities and use them to evaluate their skills. For example, it can analyze employees' posts on LinkedIn and Twitter to evaluate their expertise and understanding of industry trends. The employee information acquisition department can also analyze networking activities on social media to evaluate their influence within the industry. For example, it can evaluate their participation in industry-related events and seminars. The employee information acquisition department can also analyze feedback and comments on social media to evaluate employees' communication skills. For example, it can evaluate reactions to posts and the quality of comments. This makes it possible to evaluate skills through social media activities.

[0057] The employee information acquisition department can also support skill development based on employees' hobbies and interests. For example, it can suggest related projects to an employee who programs as a hobby. The employee information acquisition department can also suggest training to strengthen an employee's skill set based on their hobbies and interests. For example, it can suggest design-related training to an employee who is interested in design. The employee information acquisition department can also support an employee's career path based on their hobbies and interests. For example, it can suggest a marketing-related career path to an employee who is interested in marketing. This makes it possible to provide skill development and career support that takes into account employees' hobbies and interests.

[0058] The employee information acquisition unit can also analyze an employee's learning style and suggest the most suitable learning method. For example, it can analyze the history of training that the employee has received in the past and identify an effective learning method. The employee information acquisition unit can also suggest a training format for the employee based on their learning style. For example, it can suggest online training to an employee for whom online learning is effective. The employee information acquisition unit can also provide resources to support the skill development of an employee based on their learning style. For example, it can provide visual learning materials to an employee for whom visual learning is effective. This makes it possible to develop skills taking into account the employee's learning style.

[0059] The employee information acquisition department can also evaluate employees' skills and provide career support, taking into account their life events. For example, it can evaluate the skills of employees who have taken childcare leave or family care leave after they return to work. The employee information acquisition department can also support employees' career paths based on their life events. For example, it can propose flexible working arrangements to employees returning from childcare leave. The employee information acquisition department can also provide resources for skill development that take life events into account. For example, it can provide online training to employees who are raising children. This makes it possible to evaluate employees' skills and provide career support that takes into account their life events.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The employee information acquisition unit acquires employee skills, qualifications, past documents, and training information. For example, it acquires project reports and presentation materials created by employees, as well as qualifications and training information. Step 2: The department information acquisition unit acquires the department's past documents and required skills. For example, it acquires the department's project plans, business manuals, and required skills. Step 3: The skill gap identification unit identifies the gap between the employee's current skills and the skills required by the department based on the information acquired by the employee information acquisition unit and the department information acquisition unit. For example, it identifies the gap between the employee's presentation skills and the department's data analysis skills. Step 4: The training proposal department proposes training for employees based on the gaps identified by the skills gap identification department, for example, training in data analysis. Step 5: When an employee is looking for a side job, the internal side job proposal department will suggest the appropriate department based on the employee's skill set. For example, an employee with marketing skills will be offered a side job in the marketing department. Step 6: When an employee requests a transfer, the Transfer Proposal Department compares the employee's skill set with the skills required by each department and suggests a suitable department. For example, an employee with project management skills may be suggested to transfer to the Project Management Department. Step 7: The candidate employee output section outputs a list of candidate employees who fit the profile required when considering adding members to a department. For example, it outputs a list of employees with data scientist skills.

[0062] (Example 2) The skill management system according to an embodiment of the present invention is a system that inputs employee skills, qualification information, past documents, and training information, and uses a generation AI to learn the characteristics of individuals and departments. This allows the skill management system to identify discrepancies between an employee's current skills and the skills required by their department, and to propose training for the skills they lack. It can also suggest suitable headquarters when recruiting for internal side jobs, which can be used as a reference when requesting a transfer. Furthermore, when considering adding members to a department, it can output a list of candidate employees who fit the desired profile.

[0063] A skill management system according to an embodiment includes an employee information acquisition unit, a department information acquisition unit, a skills gap identification unit, a training proposal unit, an in-house side job proposal unit, a transfer proposal unit, and a candidate employee output unit. The employee information acquisition unit acquires employee skills, qualification information, previously prepared documents, and training information. For example, the employee information acquisition unit acquires project reports and presentation materials prepared by employees. The employee information acquisition unit can also acquire employee qualification information and training information. The department information acquisition unit acquires previously prepared documents and required skills of departments. For example, the department information acquisition unit acquires project plans and business manuals for departments. The department information acquisition unit can also acquire skills required by departments. The skills gap identification unit identifies a gap between an employee's current skills and the skills required by their departments based on the information acquired by the employee information acquisition unit and the department information acquisition unit. For example, the skills gap identification unit identifies a gap between an employee's presentation skills and the department's data analysis skills. The training proposal unit proposes training for employees based on the gap identified by the skills gap identification unit. For example, the training proposal unit proposes training in data analysis. When an internal side job is advertised, the internal side job proposal unit proposes a suitable headquarters based on the employee's skill set. For example, the internal side job proposal unit proposes a side job in the marketing department to an employee with marketing skills. When an employee requests a transfer, the transfer proposal unit compares the employee's skill set with the skills required by each department and proposes a suitable department. For example, the transfer proposal unit proposes a transfer to the project management department to an employee with project management skills. The candidate employee output unit outputs a list of candidate employees who match the desired profile when considering adding members to a department. For example, the candidate employee output unit outputs a list of employees with data scientist skills. This enables the skill management system according to the embodiment to improve employee skills and allocate the right people to the right positions. For example, it proposes training to effectively improve employees' skills and supports employees' career paths. It also optimizes the allocation of personnel within departments and improves work efficiency.

[0064] The employee information acquisition unit can perform sentiment analysis on previously created documents and evaluate skills based on the intensity and type of sentiment. For example, the employee information acquisition unit can perform sentiment analysis on project reports and presentation materials created by employees to identify parts with strong positive sentiment. For example, the employee information acquisition unit can analyze the sentiment scores in reports of successful projects to evaluate the employee's areas of expertise. The employee information acquisition unit can also analyze emotional expressions contained in documents created by employees and quantify the intensity and type of sentiment. For example, the employee information acquisition unit can evaluate expressions of confidence and enthusiasm in presentation materials as sentiment scores to measure the strength of skills. The employee information acquisition unit can also use sentiment analysis to extract parts of documents created by employees that have particularly strong positive sentiment and identify skills related to those parts. For example, the employee's strengths can be evaluated based on the sentiment scores in reports of success cases. This allows for a more accurate evaluation of employee skills using sentiment analysis.

[0065] The employee information acquisition unit can analyze work history over time to track career progress and skill development. For example, the employee information acquisition unit analyzes an employee's work history over time to track skill development for each job. For example, it analyzes changes in skills from the start to the end of a project to evaluate career progress. The employee information acquisition unit also uses time-series data to visualize an employee's career path and track skill development. For example, it draws a skill growth curve based on past project history. The employee information acquisition unit also analyzes an employee's work history to evaluate skill development over a specific period. For example, it identifies new skills acquired during a specific project period and tracks the development of those skills. This makes it possible to track an employee's career progress and skill development over time.

[0066] The employee information acquisition unit can analyze qualification information and training information, identify skill correlations, and suggest areas for strengthening skill sets. The employee information acquisition unit, for example, analyzes employee qualification information and training information and identifies correlations between each skill. For example, it analyzes the training content received by an employee qualified in data analysis and identifies related skills. The employee information acquisition unit also suggests areas for strengthening skill sets based on the qualification information and training information. For example, it suggests machine learning training for an employee qualified in data science. The employee information acquisition unit also builds a system that analyzes employee qualification information and training information and identifies skill correlations. For example, it suggests areas for strengthening skill sets based on the training content received by an employee with a specific qualification. This makes it possible to identify skill correlations and suggest areas for strengthening skill sets.

[0067] The employee information acquisition unit can include activities outside the company in the input data and perform a comprehensive skill evaluation. For example, the employee information acquisition unit adds information about volunteer activities and hobbies to the employee's input data and performs a comprehensive skill evaluation. For example, it evaluates leadership experience in volunteer activities. The employee information acquisition unit also analyzes outside activity data and reflects it in the employee's skill set. For example, it includes hobby programming experience in the skill evaluation. The employee information acquisition unit also builds a system that performs a comprehensive skill evaluation of employees based on the data on outside activities. For example, it evaluates skills acquired through hobby projects and adds them to the skill set. This makes it possible to perform a comprehensive skill evaluation that includes activities outside the company.

[0068] The employee information acquisition unit can compare input data with skill sets from different industries to evaluate aptitude in different industries. For example, the employee information acquisition unit compares an employee's input data with skill sets from different industries to evaluate aptitude in different industries. For example, an employee with skills in the IT industry is evaluated for aptitude in the manufacturing industry. The employee information acquisition unit also builds a system to evaluate employee aptitude based on skill sets from different industries. For example, an employee with marketing skills is evaluated for aptitude in the financial industry. The employee information acquisition unit also compares an employee's skill set with skill sets from different industries to evaluate aptitude in different industries. For example, an employee with design skills is evaluated for aptitude in the education industry. This makes it possible to evaluate aptitude in different industries.

[0069] The employee information acquisition unit can use the emotion estimation function to analyze emotions when creating past documents and identify areas where positive emotions are strong. The employee information acquisition unit, for example, analyzes emotions when an employee creates past documents and identifies areas where positive emotions are strong. For example, the emotion score in a report on a successful project is analyzed to identify areas of expertise. The employee information acquisition unit also uses the emotion estimation function to extract parts of documents created by the employee where positive emotions are particularly strong and identify those areas. For example, the employee information acquisition unit evaluates areas of expertise based on the emotion score in presentation materials. The employee information acquisition unit also builds a system that analyzes emotional expressions included in documents created by employees and identifies areas where positive emotions are strong. For example, the employee information acquisition unit identifies areas of expertise based on the emotion score in a report on a success story. In this way, by identifying areas where positive emotions are strong, the employee's areas of expertise can be clarified.

[0070] The department information acquisition unit can perform sentiment analysis on previously created documents to evaluate the culture and atmosphere. The department information acquisition unit, for example, performs sentiment analysis on previously created documents for a department to evaluate the culture and atmosphere of the department. For example, the department information acquisition unit analyzes sentiment scores in project reports and business manuals to evaluate the atmosphere of the department. The department information acquisition unit also uses sentiment analysis to analyze emotional expressions contained in documents created by the department to evaluate the culture of the department. For example, the culture of the department can be identified based on documents with strong positive emotions. The department information acquisition unit also analyzes emotional expressions contained in previously created documents for a department to build a system to evaluate the culture and atmosphere of the department. For example, the atmosphere of the department can be evaluated based on the sentiment scores. In this way, the characteristics of the department can be clarified by evaluating the culture and atmosphere of the department.

[0071] The department information acquisition unit can analyze work history in chronological order to identify the factors that lead to the success or failure of a project. For example, the department information acquisition unit analyzes the work history of a department in chronological order to identify the factors that lead to the success or failure of a project. For example, it analyzes success factors based on past project history and identifies the strengths of the department. The department information acquisition unit also uses time-series data to analyze the project history of a department and builds a system that identifies the factors that lead to the success or failure of a project. For example, it analyzes the factors that lead to the success of a project over a specific period of time. The department information acquisition unit also analyzes the work history of a department to identify the factors that lead to the success or failure of a project. For example, it identifies success factors based on past project history and evaluates the strengths of the department. In this way, the strengths and weaknesses of the department can be clarified by identifying the factors that lead to the success or failure of a project.

[0072] The department information acquisition unit can analyze the required skill sets, identify the correlations between skills, and suggest areas where skills should be strengthened. For example, the department information acquisition unit analyzes the skill sets required by a department and identifies the correlations between each skill. For example, it analyzes the correlation between data analysis skills and programming skills and suggests areas where skills should be strengthened for the department. The department information acquisition unit also builds a system that suggests areas where skills should be strengthened for the entire department based on the correlations between skill sets. For example, it analyzes the correlation between data science skills and machine learning skills and suggests areas where skills should be strengthened. The department information acquisition unit also analyzes the skill sets required by a department and identifies the correlations between skills. For example, it analyzes the correlation between project management skills and communication skills and suggests areas where skills should be strengthened for the department. This makes it possible to suggest areas where skills should be strengthened for the entire department.

[0073] The department information acquisition unit can include collaboration history with other departments in the input data and evaluate interactions between departments. The department information acquisition unit, for example, adds collaboration history with other departments to the input data of a department and evaluates interactions between departments. For example, it evaluates cooperative relationships between departments based on the history of joint projects. The department information acquisition unit also analyzes collaboration history with other departments and builds a system for evaluating interactions between departments. For example, it analyzes success factors for joint projects and evaluates cooperative relationships between departments. The department information acquisition unit also includes collaboration history with other departments in the input data of a department and evaluates interactions between departments. For example, it evaluates cooperative relationships between departments based on the history of joint projects. In this way, by evaluating interactions between departments, it is possible to clarify cooperative relationships between departments.

[0074] The department information acquisition unit can compare the input data with departments in different industries to evaluate aptitude in different industries. For example, the department information acquisition unit compares input data of a department with departments in different industries to evaluate aptitude in different industries. For example, data from the IT department is compared with data from the manufacturing industry to evaluate aptitude in different industries. The department information acquisition unit also builds a system to evaluate aptitude in departments based on department data from different industries. For example, data from the marketing department is compared with data from the financial industry to evaluate aptitude in different industries. The department information acquisition unit also compares input data of a department with data from departments in different industries to evaluate aptitude in different industries. For example, data from the design department is compared with data from the education industry to evaluate aptitude in different industries. This makes it possible to evaluate aptitude in different industries.

[0075] The department information acquisition unit can use the emotion estimation function to analyze emotions when creating past documents and identify projects with strong positive emotions. The department information acquisition unit, for example, analyzes emotions when creating past documents for a department and identifies projects with strong positive emotions. For example, it analyzes emotion scores in reports of successful projects and identifies positive projects. The department information acquisition unit also uses the emotion estimation function to analyze emotional expressions included in documents created by a department and identifies projects with strong positive emotions. For example, it identifies positive projects based on emotion scores in project reports. The department information acquisition unit also builds a system that analyzes emotional expressions included in past documents created by a department and identifies projects with strong positive emotions. For example, it identifies positive projects based on emotion scores. In this way, by identifying projects with strong positive emotions, it is possible to clarify the areas of expertise of a department.

[0076] The skill gap identification unit can use the emotion estimation function to identify skills that an employee feels weak in. For example, when identifying a gap between an employee's skills and the skills required by the department, the skill gap identification unit uses the emotion estimation function to identify skills that the employee feels weak in. For example, the skill gap identification unit analyzes negative emotion scores for presentation skills to identify areas of weakness. The skill gap identification unit also uses the emotion estimation function to build a system that identifies skills that an employee feels weak in. For example, the skill gap identification unit identifies areas of weakness based on negative emotion scores for data analysis skills. The skill gap identification unit also analyzes emotion estimation data to identify skills that the employee feels weak in when identifying a gap between an employee's skills and the skills required by the department. For example, the skill gap identification unit identifies areas of weakness based on negative emotion scores for communication skills. This makes it possible to suggest appropriate training by identifying skills that an employee feels weak in.

[0077] The skill gap identification unit can analyze the learning history and evaluate past learning effectiveness. For example, when identifying skill gaps, the skill gap identification unit analyzes the employee's learning history and evaluates past learning effectiveness. For example, the learning effectiveness is evaluated based on the history of past training. The skill gap identification unit also analyzes the learning history and builds a system for identifying skill gaps. For example, the learning effectiveness is evaluated based on the past training history and the skill gap is identified. The skill gap identification unit also analyzes the employee's learning history and evaluates past learning effectiveness. For example, the skill gap identification unit analyzes the change in skills after receiving specific training and evaluates learning effectiveness. In this way, by evaluating past learning effectiveness, effective training can be proposed.

[0078] The skill gap identification unit can analyze work performance data and evaluate skill applicability in actual work. For example, when identifying skill gaps, the skill gap identification unit analyzes employee work performance data and evaluates skill applicability in actual work. For example, the skill applicability is evaluated based on project outcome data. The skill gap identification unit also analyzes work performance data and builds a system for identifying skill gaps. For example, the skill applicability is evaluated based on work evaluation data and skill gaps are identified. The skill gap identification unit also analyzes employee work performance data and evaluates skill applicability in actual work. For example, the skill applicability is evaluated based on work outcome data and skill gaps are identified. In this way, skill gaps can be identified by evaluating skill applicability in actual work.

[0079] The skill gap identification unit can compare the skill sets with those of other departments and evaluate aptitude in other departments. For example, when identifying a skill gap, the skill gap identification unit compares the skill sets with those of other departments and evaluates aptitude in other departments. For example, it compares the skill sets with those of the marketing department and evaluates aptitude in other departments. Furthermore, the skill gap identification unit builds a system for identifying skill gaps based on the skill sets of other departments. For example, it compares the skill sets with those of the IT department and evaluates aptitude in other departments. Furthermore, when identifying a skill gap, the skill gap identification unit compares the skill sets with those of other departments and evaluates aptitude in other departments. For example, it compares the skill sets with those of the design department and evaluates aptitude in other departments. In this way, by evaluating aptitude in other departments, it is possible to expand the possibility of transfer.

[0080] The skill gap identification unit can consider personal interests and hobbies to suggest training that will interest the employee. For example, when identifying a skill gap, the skill gap identification unit considers the employee's personal interests and hobbies to suggest training that will interest the employee. For example, it proposes programming training to an employee who programs as a hobby. The skill gap identification unit also builds a system that identifies skill gaps based on personal interests and hobbies. For example, it proposes design training to an employee who is interested in design. The skill gap identification unit also considers the employee's personal interests and hobbies when identifying a skill gap to suggest training that will interest the employee. For example, it proposes marketing training to an employee who is interested in marketing. This makes it possible to suggest training that takes into account the employee's personal interests and hobbies.

[0081] The skill gap identification unit can use the emotion estimation function to analyze emotions when receiving training and suggest training that elicits positive emotions. The skill gap identification unit, for example, uses the emotion estimation function to analyze emotions when employees receive training and suggest training that elicits positive emotions. For example, it analyzes emotion scores in past training history and suggests training that elicits positive emotions. The skill gap identification unit also uses the emotion estimation function to build a system that analyzes emotions when employees receive training and suggests training that elicits positive emotions. For example, it suggests training that elicits positive emotions based on emotion scores during training. The skill gap identification unit also analyzes emotional expressions included in the employee's training history and suggests training that elicits positive emotions. For example, it suggests training that elicits positive emotions based on emotion scores in past training history. In this way, by suggesting training that elicits positive emotions, it is possible to increase employee motivation.

[0082] The in-house side job proposal unit can use the emotion estimation function to identify side jobs that employees are interested in. The in-house side job proposal unit, for example, uses the emotion estimation function to identify side jobs that employees are interested in. For example, it analyzes emotion scores in past work history to identify side jobs that employees are interested in. The in-house side job proposal unit also uses the emotion estimation function to build a system that identifies side jobs that employees are interested in. For example, it identifies side jobs that employees are interested in based on emotion scores in past work history. The in-house side job proposal unit also analyzes emotional expressions included in the employee's work history to identify side jobs that employees are interested in. For example, it identifies side jobs that employees are interested in based on emotion scores in past work history. In this way, by identifying side jobs that employees are interested in, it is possible to suggest side jobs that can utilize the employee's skills.

[0083] The in-house side job proposal department can analyze past side job histories and identify the characteristics of successful side jobs. For example, when proposing an in-house side job, the in-house side job proposal department analyzes an employee's past side job history and identifies the characteristics of a successful side job. For example, the characteristics of a successful side job are identified based on the past side job history. The in-house side job proposal department also builds a system that analyzes past side job history and identifies the characteristics of a successful side job. For example, the characteristics of a successful side job are identified based on the past side job history. The in-house side job proposal department also analyzes an employee's past side job history and identifies the characteristics of a successful side job. For example, the characteristics of a successful side job are identified based on the past side job history. In this way, by identifying the characteristics of a successful side job, it is possible to propose a side job that is suitable for the employee.

[0084] The in-house side job proposal department can analyze the correlation between skill sets and the skills required for side jobs and propose optimal side jobs. For example, when proposing in-house side jobs, the in-house side job proposal department analyzes the correlation between an employee's skill set and the skills required for the side job and proposes the optimal side job. For example, for an employee with marketing skills, it proposes a marketing-related side job. The in-house side job proposal department also builds a system that proposes optimal side jobs based on the correlation between skill sets and the skills required for side jobs. For example, for an employee with data analysis skills, it proposes a data analysis-related side job. The in-house side job proposal department also analyzes the correlation between an employee's skill set and the skills required for side jobs and proposes the optimal side job. For example, for an employee with project management skills, it proposes a project management-related side job. In this way, it is possible to propose the optimal side job by analyzing the correlation between an employee's skill set and the skills required for side jobs.

[0085] The in-house side job proposal department can evaluate suitability for different departments by referring to the side job history of other departments. For example, when proposing an in-house side job, the in-house side job proposal department evaluates suitability for different departments by referring to the side job history of other departments. For example, it evaluates suitability for different departments based on the side job history of the marketing department. The in-house side job proposal department also builds a system to evaluate suitability for different departments based on the side job history of other departments. For example, it evaluates suitability for different departments based on the side job history of the IT department. The in-house side job proposal department also builds a system to evaluate suitability for different departments by referring to the side job history of other departments when proposing an in-house side job. For example, it evaluates suitability for different departments based on the side job history of the design department. This makes it possible to evaluate suitability for different departments by referring to the side job history of other departments.

[0086] The in-house side job proposal department can propose interesting side jobs by taking into consideration personal interests and hobbies. For example, when proposing in-house side jobs, the in-house side job proposal department considers an employee's personal interests and hobbies and proposes side jobs that will interest them. For example, for an employee who programs as a hobby, the department proposes a programming-related side job. The in-house side job proposal department also builds a system that proposes optimal side jobs based on personal interests and hobbies. For example, for an employee who is interested in design, the department proposes a design-related side job. The in-house side job proposal department also considers an employee's personal interests and hobbies and proposes interesting side jobs. For example, for an employee who is interested in marketing, the department proposes a marketing-related side job. In this way, it is possible to propose side jobs that take into consideration an employee's personal interests and hobbies.

[0087] The in-house side job suggestion unit can use the emotion estimation function to analyze emotions when performing a side job and suggest side jobs that elicit positive emotions. The in-house side job suggestion unit, for example, uses the emotion estimation function to analyze emotions when an employee performs a side job and suggest side jobs that elicit positive emotions. For example, it analyzes emotion scores in past side job history and suggests side jobs that elicit positive emotions. The in-house side job suggestion unit also uses the emotion estimation function to build a system that analyzes emotions when an employee performs a side job and suggests side jobs that elicit positive emotions. For example, it suggests side jobs that elicit positive emotions based on emotion scores during the side job. The in-house side job suggestion unit also analyzes emotional expressions included in the employee's side job history and suggests side jobs that elicit positive emotions. For example, it suggests side jobs that elicit positive emotions based on emotion scores in past side job history. In this way, by suggesting side jobs that elicit positive emotions, it is possible to increase employee motivation.

[0088] The transfer proposal unit can use the emotion estimation function to identify departments in which an employee is interested. The transfer proposal unit, for example, uses the emotion estimation function to identify departments in which an employee is interested. For example, it analyzes emotion scores in past work history to identify departments in which an employee is interested. The transfer proposal unit also uses the emotion estimation function to build a system for identifying departments in which an employee is interested. For example, it identifies departments in which an employee is interested based on emotion scores in past work history. The transfer proposal unit also analyzes emotional expressions included in the employee's work history to identify departments in which an employee is interested. For example, it identifies departments in which an employee is interested based on emotion scores in past work history. In this way, by identifying departments in which an employee is interested, it is possible to propose an appropriate transfer destination.

[0089] The transfer proposal unit can analyze past transfer history and identify the characteristics of successful transfers. The transfer proposal unit, for example, analyzes an employee's past transfer history as a reference when requesting a transfer and identifies the characteristics of successful transfers. For example, the characteristics of successful transfers are identified based on the past transfer history. The transfer proposal unit also builds a system that analyzes past transfer history and identifies the characteristics of successful transfers. For example, the characteristics of successful transfers are identified based on the past transfer history. The transfer proposal unit also analyzes an employee's past transfer history and identifies the characteristics of successful transfers. For example, the characteristics of successful transfers are identified based on the past transfer history. In this way, by identifying the characteristics of successful transfers, it is possible to propose a transfer destination that is suitable for the employee.

[0090] The transfer proposal unit can analyze the correlation between the skill set and the required skills of the transfer destination and propose the optimal transfer destination. For example, the transfer proposal unit analyzes the correlation between an employee's skill set and the required skills of the transfer destination as a reference when an employee requests a transfer and proposes the optimal transfer destination. For example, for an employee with project management skills, it proposes a transfer to the project management department. The transfer proposal unit also builds a system that proposes the optimal transfer destination based on the correlation between the skill set and the required skills of the transfer destination. For example, for an employee with data analysis skills, it proposes a transfer to the data analysis department. The transfer proposal unit also analyzes the correlation between an employee's skill set and the required skills of the transfer destination and proposes the optimal transfer destination. For example, for an employee with marketing skills, it proposes a transfer to the marketing department. In this way, the optimal transfer destination can be proposed by analyzing the correlation between an employee's skill set and the required skills of the transfer destination.

[0091] The transfer proposal department can evaluate suitability for a different department by referring to the transfer history of other departments. The transfer proposal department, for example, evaluates suitability for a different department by referring to the transfer history of other departments as a reference when requesting a transfer. For example, it evaluates suitability for a different department based on the transfer history of the marketing department. The transfer proposal department also builds a system for evaluating suitability for a different department based on the transfer history of other departments. For example, it evaluates suitability for a different department based on the transfer history of the IT department. The transfer proposal department also builds a system for evaluating suitability for a different department by referring to the transfer history of other departments as a reference when requesting a transfer. For example, it evaluates suitability for a different department based on the transfer history of the design department. In this way, it is possible to evaluate suitability for a different department by referring to the transfer history of other departments.

[0092] The transfer proposal department can consider personal interests and hobbies to propose interesting transfer destinations. For example, the transfer proposal department considers an employee's personal interests and hobbies to propose interesting transfer destinations as a reference when requesting a transfer. For example, for an employee who programs as a hobby, the transfer proposal department proposes a transfer to a programming-related department. The transfer proposal department also builds a system that proposes the most suitable transfer destination based on personal interests and hobbies. For example, for an employee who is interested in design, the transfer proposal department proposes a transfer to a design-related department. The transfer proposal department also considers an employee's personal interests and hobbies to propose interesting transfer destinations. For example, for an employee who is interested in marketing, the transfer proposal department proposes a transfer to a marketing-related department. In this way, it is possible to propose transfer destinations that take into account an employee's personal interests and hobbies.

[0093] The transfer suggestion unit can use the emotion estimation function to analyze emotions when an employee is transferred and suggest a transfer destination that will elicit positive emotions. The transfer suggestion unit, for example, uses the emotion estimation function to analyze emotions when an employee is transferred and suggest a transfer destination that will elicit positive emotions. For example, it analyzes emotion scores in past transfer history and suggests a transfer destination that will elicit positive emotions. The transfer suggestion unit also uses the emotion estimation function to build a system that analyzes emotions when an employee is transferred and suggests a transfer destination that will elicit positive emotions. For example, it suggests a transfer destination that will elicit positive emotions based on the emotion score during the transfer. The transfer suggestion unit also analyzes emotional expressions included in the employee's transfer history and suggests a transfer destination that will elicit positive emotions. For example, it suggests a transfer destination that will elicit positive emotions based on the emotion scores in past transfer history. In this way, by suggesting a transfer destination that will elicit positive emotions, it is possible to increase employee motivation.

[0094] The candidate employee output unit can use the emotion estimation function to identify departments in which an employee is interested. The candidate employee output unit, for example, uses the emotion estimation function to identify departments in which an employee is interested. For example, it analyzes emotion scores in past work history to identify departments in which an employee is interested. The candidate employee output unit also uses the emotion estimation function to build a system for identifying departments in which an employee is interested. For example, it identifies departments in which an employee is interested based on emotion scores in past work history. The candidate employee output unit also analyzes emotional expressions included in the employee's work history to identify departments in which an employee is interested. For example, it identifies departments in which an employee is interested based on emotion scores in past work history. In this way, by identifying departments in which an employee is interested, it is possible to propose appropriate candidate employees.

[0095] The candidate employee output unit can analyze past transfer history and identify the characteristics of successful transfers. For example, when creating a list of candidate employees when adding members to a department, the candidate employee output unit analyzes an employee's past transfer history and identifies the characteristics of successful transfers. For example, the characteristics of successful transfers are identified based on the past transfer history. The candidate employee output unit also builds a system that analyzes past transfer history and identifies the characteristics of successful transfers. For example, the characteristics of successful transfers are identified based on the past transfer history. The candidate employee output unit also analyzes an employee's past transfer history and identifies the characteristics of successful transfers. For example, the characteristics of successful transfers are identified based on the past transfer history. In this way, by identifying the characteristics of successful transfers, appropriate candidate employees can be suggested.

[0096] The candidate employee output unit can analyze the correlation between skill sets and the skills required by a department and suggest the most suitable candidate employees. For example, when creating a list of candidate employees when adding members to a department, the candidate employee output unit analyzes the correlation between an employee's skill set and the skills required by the department and suggests the most suitable candidate employees. For example, an employee with data analysis skills is suggested as a candidate employee for the data analysis department. The candidate employee output unit also builds a system that suggests the most suitable candidate employees based on the correlation between skill sets and the skills required by the department. For example, an employee with project management skills is suggested as a candidate employee for the project management department. The candidate employee output unit also analyzes the correlation between an employee's skill set and the skills required by the department and suggests the most suitable candidate employees. For example, an employee with marketing skills is suggested as a candidate employee for the marketing department. In this way, the most suitable candidate employees can be suggested by analyzing the correlation between an employee's skill set and the skills required by the department.

[0097] The candidate employee output unit can evaluate suitability for different departments by referring to the transfer history of other departments. For example, when creating a list of candidate employees when a department adds members, the candidate employee output unit evaluates suitability for different departments by referring to the transfer history of other departments. For example, it evaluates suitability for different departments based on the transfer history of the marketing department. The candidate employee output unit also builds a system for evaluating suitability for different departments based on the transfer history of other departments. For example, it evaluates suitability for different departments based on the transfer history of the IT department. The candidate employee output unit also builds a system for evaluating suitability for different departments by referring to the transfer history of other departments when creating a list of candidate employees when a department adds members, by referring to the transfer history of other departments. For example, it evaluates suitability for different departments based on the transfer history of the design department. This makes it possible to evaluate suitability for different departments by referring to the transfer history of other departments.

[0098] The candidate employee output unit can consider personal interests and hobbies to suggest departments that interest employees. For example, when creating a list of candidate employees when adding members to a department, the candidate employee output unit considers the employee's personal interests and hobbies to suggest departments that interest employees. For example, it proposes a programming-related department to an employee who programs as a hobby. The candidate employee output unit also builds a system that suggests the most suitable department based on personal interests and hobbies. For example, it proposes a design-related department to an employee who is interested in design. The candidate employee output unit also considers the employee's personal interests and hobbies to suggest departments that interest employees. For example, it proposes a marketing-related department to an employee who is interested in marketing. In this way, it is possible to suggest departments that take the employee's personal interests and hobbies into consideration.

[0099] The candidate employee output unit can use the emotion estimation function to analyze emotions when an employee is transferred and suggest a department that will elicit positive emotions. The candidate employee output unit, for example, uses the emotion estimation function to analyze emotions when an employee is transferred and suggest a department that will elicit positive emotions. For example, it analyzes emotion scores in past transfer history and suggests a department that will elicit positive emotions. The candidate employee output unit also uses the emotion estimation function to build a system that analyzes emotions when an employee is transferred and suggests a department that will elicit positive emotions. For example, it suggests a department that will elicit positive emotions based on emotion scores during the transfer. The candidate employee output unit also analyzes emotional expressions included in the employee's transfer history and suggests a department that will elicit positive emotions. For example, it suggests a department that will elicit positive emotions based on emotion scores in past transfer history. In this way, by suggesting a department that will elicit positive emotions, it is possible to increase employee motivation.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The employee information acquisition unit can also acquire employee health data and evaluate the applicability of skills based on their health condition. For example, it can acquire data from employees' fitness trackers or health apps and evaluate their work performance when their health is good. The employee information acquisition unit can also analyze the impact of health condition on work performance based on the health data. For example, it can evaluate the success rate of a project when stress levels are low. The employee information acquisition unit can also use the health data to suggest work assignments based on employees' health conditions. For example, it can suggest that employees in good health be assigned to important projects. This makes it possible to evaluate skills and assign work that takes into account employees' health conditions.

[0102] The employee information acquisition department can also analyze employees' social media activities and use them to evaluate their skills. For example, it can analyze employees' posts on LinkedIn and Twitter to evaluate their expertise and understanding of industry trends. The employee information acquisition department can also analyze networking activities on social media to evaluate their influence within the industry. For example, it can evaluate their participation in industry-related events and seminars. The employee information acquisition department can also analyze feedback and comments on social media to evaluate employees' communication skills. For example, it can evaluate reactions to posts and the quality of comments. This makes it possible to evaluate skills through social media activities.

[0103] The employee information acquisition department can also support skill development based on employees' hobbies and interests. For example, it can suggest related projects to an employee who programs as a hobby. The employee information acquisition department can also suggest training to strengthen an employee's skill set based on their hobbies and interests. For example, it can suggest design-related training to an employee who is interested in design. The employee information acquisition department can also support an employee's career path based on their hobbies and interests. For example, it can suggest a marketing-related career path to an employee who is interested in marketing. This makes it possible to provide skill development and career support that takes into account employees' hobbies and interests.

[0104] The employee information acquisition unit can also analyze an employee's learning style and suggest the most suitable learning method. For example, it can analyze the history of training that the employee has received in the past and identify an effective learning method. The employee information acquisition unit can also suggest a training format for the employee based on their learning style. For example, it can suggest online training to an employee for whom online learning is effective. The employee information acquisition unit can also provide resources to support the skill development of an employee based on their learning style. For example, it can provide visual learning materials to an employee for whom visual learning is effective. This makes it possible to develop skills taking into account the employee's learning style.

[0105] The employee information acquisition department can also evaluate employees' skills and provide career support, taking into account their life events. For example, it can evaluate the skills of employees who have taken childcare leave or family care leave after they return to work. The employee information acquisition department can also support employees' career paths based on their life events. For example, it can propose flexible working arrangements to employees returning from childcare leave. The employee information acquisition department can also provide resources for skill development that take life events into account. For example, it can provide online training to employees who are raising children. This makes it possible to evaluate employees' skills and provide career support that takes into account their life events.

[0106] The employee information acquisition unit can use the emotion estimation function to analyze an employee's motivation and evaluate the applicability of skills when motivation was high. For example, it can analyze the emotion score in an employee's work history and evaluate their work performance when motivation was high. The employee information acquisition unit can also use the emotion estimation function to propose measures to increase employee motivation. For example, it can propose similar work based on the work content performed when motivation was high. The employee information acquisition unit can also use the emotion estimation function to provide resources to maintain employee motivation. For example, it can propose ongoing training based on training received when motivation was high. This makes it possible to provide skill evaluation and career support that takes employee motivation into account.

[0107] The employee information acquisition unit can use the emotion estimation function to analyze an employee's stress level and evaluate the applicability of skills during periods of low stress. For example, it can analyze the emotion score in an employee's work history and evaluate their work performance during periods of low stress. The employee information acquisition unit can also use the emotion estimation function to suggest measures to reduce employee stress. For example, it can suggest similar work based on the work content performed during periods of low stress. The employee information acquisition unit can also use the emotion estimation function to provide resources to reduce employee stress. For example, it can suggest training with a relaxing effect based on training received during periods of low stress. This makes it possible to provide skill evaluation and career support that takes employee stress into account.

[0108] The employee information acquisition unit can use the emotion estimation function to analyze an employee's self-evaluation and evaluate the applicability of skills when self-evaluation was high. For example, it can analyze the emotion scores in the employee's work history and evaluate work performance when self-evaluation was high. The employee information acquisition unit can also use the emotion estimation function to suggest measures to improve an employee's self-evaluation. For example, it can suggest similar work based on the work content performed when self-evaluation was high. The employee information acquisition unit can also use the emotion estimation function to provide resources to maintain an employee's self-evaluation. For example, it can suggest ongoing training based on training received when self-evaluation was high. This makes it possible to provide skill evaluation and career support that takes into account an employee's self-evaluation.

[0109] The employee information acquisition unit can use the emotion estimation function to analyze an employee's teamwork skills and evaluate the applicability of those skills during periods of good teamwork. For example, it can analyze the emotion scores in an employee's work history and evaluate their work performance during periods of good teamwork. The employee information acquisition unit can also use the emotion estimation function to propose measures to improve an employee's teamwork skills. For example, it can propose similar tasks based on the work content performed during periods of good teamwork. The employee information acquisition unit can also use the emotion estimation function to provide resources to maintain an employee's teamwork skills. For example, it can propose team building training based on training received during periods of good teamwork. This makes it possible to provide skill evaluation and career support that take into account an employee's teamwork skills.

[0110] The employee information acquisition unit can use the emotion estimation function to analyze an employee's leadership skills and evaluate the applicability of those skills at the time when leadership was demonstrated. For example, it can analyze the emotion score in the employee's work history and evaluate their work performance at the time when leadership was demonstrated. The employee information acquisition unit can also use the emotion estimation function to propose measures to improve an employee's leadership skills. For example, it can propose similar work based on the work content performed at the time when leadership was demonstrated. The employee information acquisition unit can also use the emotion estimation function to provide resources to maintain an employee's leadership skills. For example, it can propose leadership training based on the training received at the time when leadership was demonstrated. This makes it possible to provide skill evaluation and career support that takes into account an employee's leadership skills.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The employee information acquisition unit acquires employee skills, qualifications, past documents, and training information. For example, it acquires project reports and presentation materials created by employees, as well as qualifications and training information. Step 2: The department information acquisition unit acquires the department's past documents and required skills. For example, it acquires the department's project plans, business manuals, and required skills. Step 3: The skill gap identification unit identifies the gap between the employee's current skills and the skills required by the department based on the information acquired by the employee information acquisition unit and the department information acquisition unit. For example, it identifies the gap between the employee's presentation skills and the department's data analysis skills. Step 4: The training proposal department proposes training for employees based on the gaps identified by the skills gap identification department, for example, training in data analysis. Step 5: When an employee is looking for a side job, the internal side job proposal department will suggest the appropriate department based on the employee's skill set. For example, an employee with marketing skills will be offered a side job in the marketing department. Step 6: When an employee requests a transfer, the Transfer Proposal Department compares the employee's skill set with the skills required by each department and suggests a suitable department. For example, an employee with project management skills may be suggested to transfer to the Project Management Department. Step 7: The candidate employee output section outputs a list of candidate employees who fit the profile required when considering adding members to a department. For example, it outputs a list of employees with data scientist skills.

[0113] 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.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0115] 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.

[0116] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0117] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] In the robot 414, 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 robot 414 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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. [Explanation of symbols]

[0180] 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. The employee information acquisition department collects employee skills, qualifications, past documents, and training information. Department information acquisition department that acquires departmental past documents and required skills; a skill discrepancy identification unit that identifies a discrepancy between the employee's current skills and the skills required by the department to which the employee belongs, based on the information acquired by the employee information acquisition unit and the department information acquisition unit; a training proposal unit that proposes training for employees based on the skill gap identified by the skill gap identification unit; When an internal side job is advertised, the internal side job proposal department proposes the most suitable headquarters based on the employee's skill set. When an employee requests a transfer, the transfer proposal department compares the employee's skill set with the skills required by each department and suggests the appropriate department. A candidate employee output unit that outputs a list of candidate employees who match the profile of the person desired when considering adding members to a department. A system characterized by:

2. The employee information acquisition unit Conducting sentiment analysis on the previously created materials and evaluating the skills based on the intensity and type of emotions 2. The system of claim 1.

3. The employee information acquisition unit Analyze work history over time to track career progression and the development of said skills 2. The system of claim 1.

4. The employee information acquisition unit Analyzing the qualification information and training information, identifying correlations between the skills, and suggesting areas for strengthening the skill set.

2. The system of claim 1.

5. The employee information acquisition unit Input data includes outside activities to conduct a comprehensive skill assessment 2. The system of claim 1.

6. The employee information acquisition unit Compare the input data with the skill sets in the different industries to assess suitability in different industries.

2. The system of claim 1.

7. The employee information acquisition unit Analyzing emotions when creating the past created materials and identifying areas where positive emotions are strong 2. The system of claim 1.

8. The department information acquisition unit Conduct sentiment analysis on the previously created materials to evaluate the culture and atmosphere 2. The system of claim 1.

Images