System

Abstract

An object of the system according to the embodiment is to efficiently manage the work histories and skills of employees and quickly pick up qualified personnel.SOLUTION: A system includes a database creation part, a reference part, a pickup part, a business negotiation information reference part, and a skill confirmation part. A database making part inputs and stores the business history of the employee in a database. The reference part refers to the member of the past project according to the business type or authority of the employee. The pickup section picks up qualified personnel as members of the new project. A business negotiation information reference part refers to business negotiation information with a business partner in sales. A skill confirmation part immediately confirms the skill of the employee.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] Conventional technology has faced the challenge of making it difficult to efficiently manage employees' work history and skills and quickly select the most suitable personnel.

[0005] The system according to the embodiment aims to efficiently manage the work history and skills of employees and quickly select the most suitable personnel. [Means for solving the problem]

[0006] The system according to the embodiment comprises a database creation unit, a reference unit, a picking unit, a negotiation information reference unit, and a skill confirmation unit. The database creation unit inputs and stores employees' work history into a database. The reference unit references members of past projects according to the employee's industry or authority. The picking unit selects suitable personnel as members of a new project. The negotiation information reference unit references information on sales negotiations with clients. The skill confirmation unit instantly confirms employees' skills. [Effects of the Invention]

[0007] The system according to the embodiment efficiently manages the work history and skills of employees, and can quickly select the most suitable personnel. [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 work history management system according to an embodiment of the present invention is a system that inputs and stores the work history of all projects and all employees in a database. This system can refer to members of past projects and select suitable personnel as members of new projects based on the employee's industry and authority. It is also possible to refer to sales negotiation information with business partners. Furthermore, the ability to instantly check the skills of all employees contributes to accelerating knowledge sharing. As a result, the work history management system centrally manages the work history of all employees and can quickly select suitable personnel. Furthermore, the ability to refer to negotiation information and instantly check skills can improve the efficiency of sales activities and knowledge sharing.

[0029] A work history management system according to an embodiment includes a database creation unit, a reference unit, a selection unit, a negotiation information reference unit, and a skill confirmation unit. The database creation unit inputs and accumulates employees' work histories into a database. For example, details of projects in which employees participated, the work they were responsible for, and the skills they acquired are registered in the database. The reference unit references members of past projects based on the employee's industry and authority. For example, it searches for members of projects related to a specific industry and checks their roles and achievements in the project. The selection unit selects suitable personnel as members of a new project. For example, it searches for employees with specific skills and experience and recommends the employees as members of the new project. The negotiation information reference unit references information about sales negotiations with clients. For example, it searches for the content of past negotiations and client information to help with sales activities. The skill confirmation unit instantly checks employees' skills. For example, it searches for employees with specific skills and checks the details of the skills and how they were acquired. This allows the work history management system to centrally manage the work histories of all employees and quickly select suitable personnel. In addition, by being able to refer to sales negotiation information and instantly check skills, sales activities and knowledge sharing can be made more efficient.

[0030] The database creation unit can use generation AI to automatically analyze employees' work histories and automatically tag related projects and skills. For example, the database creation unit can use generation AI to automatically analyze the details of projects that employees have participated in in the past and tag them based on the project content and role. For example, it registers the type of project, the technology used, and the results achieved as tags. The database creation unit also analyzes employees' work histories and automatically tags the skills and experience they have acquired. For example, it registers specific programming languages ​​and management skills as tags to make them easier to search. The database creation unit also uses generation AI to build a system that analyzes employees' work histories and automatically tags related projects and skills. For example, it registers information such as the project start date, end date, and participating members as tags. This makes it possible to automatically analyze employees' work histories and efficiently tag related projects and skills.

[0031] The database creation unit can add employees' self-evaluations or evaluations from colleagues to provide more multifaceted career information. For example, the database creation unit adds employees' self-evaluations to a work history database and registers their skills and experience based on the self-evaluations. For example, an employee enters a self-evaluation sheet and the contents are reflected in the database. The database creation unit also adds evaluations from colleagues to the work history database to evaluate employees' skills and experience from multiple perspectives. For example, after a project is completed, feedback from colleagues is collected and registered in the database. The database creation unit also integrates employees' self-evaluations and evaluations from colleagues into the work history database to build a system that provides more multifaceted career information. For example, the database creation unit compares self-evaluations with evaluations from colleagues to provide a comprehensive evaluation. This allows employees' career information to be evaluated from multiple perspectives, providing more accurate information.

[0032] The database creation unit can link the work history database with databases from other companies and industries, thereby promoting personnel exchange between different industries. The database creation unit, for example, links the work history database with databases from other companies, to build a system that promotes personnel exchange between different industries. For example, the careers of employees who have participated in projects in different industries are shared. The database creation unit also integrates the work history database to promote personnel exchange between industries. For example, a project is carried out in collaboration with companies from different industries, and employees' skills and experience are shared. The database creation unit also links with databases from other companies and industries, to develop a system that promotes personnel exchange between different industries. For example, the careers of employees who have participated in projects in different industries are registered in a database and shared. This can promote personnel exchange between different industries and enable the sharing of knowledge and skills.

[0033] The database creation unit adds employees' hobbies and interests to the database, and can assign personnel in a way that takes into account compatibility in a project team. The database creation unit, for example, adds employees' hobbies and interests to a work history database, and builds a system for assigning personnel in a way that takes into account compatibility in a project team. For example, employees with similar hobbies and interests are assigned to the same project. The database creation unit also registers employees' hobbies and interests in the database, and assigns personnel in a way that takes into account compatibility in a project team. For example, employees with similar hobbies and interests are assigned to projects with priority. The database creation unit also adds employees' hobbies and interests to the database, and develops a system for assigning personnel in a way that takes into account compatibility in a project team. For example, employees with similar hobbies and interests are automatically recommended. This makes it possible to assign personnel in a way that takes into account compatibility in a project team, and improve team performance.

[0034] The reference department can use generative AI to automatically optimize access control based on employee industry and authority, thereby strengthening security. For example, the reference department uses generative AI to build a system that automatically optimizes access control based on employee industry and authority. For example, it restricts information that can be accessed only by employees in specific industries or with specific authority. The reference department also automatically optimizes access control based on employee industry and authority, thereby strengthening security. For example, it restricts access to confidential information for employees with low authority. The reference department also uses generative AI to develop a system that automatically optimizes access control based on employee industry and authority, thereby strengthening security. For example, it dynamically changes access rights depending on industry and authority. This makes it possible to automatically optimize access control based on employee industry and authority, thereby strengthening security.

[0035] The reference unit can automatically analyze the factors that contributed to the success and failure of a project when past project members are referenced, and provide the information as reference. For example, the reference unit builds a system that automatically analyzes the factors that contributed to the success and failure of a project when past project members are referenced. For example, the project's results and challenges are registered in a database and displayed when referenced. The reference unit also automatically analyzes the factors that contributed to the success and failure of a project, and provides the information as reference. For example, the factors of a successful project are analyzed and applied to similar projects. The reference unit also develops a system that automatically analyzes the factors that contributed to the success and failure of a project when past project members are referenced, and provides the information as reference. For example, the challenges of a failed project are analyzed and improvement measures are proposed. In this way, the success rate of future projects can be increased by automatically analyzing the factors that contributed to the success and failure of past projects and providing the information as reference.

[0036] The reference department can link reference functions according to industry and authority with databases of other companies and industries to share best practices across different industries. For example, the reference department links reference functions according to industry and authority with databases of other companies to build a system for sharing best practices across different industries. For example, it refers to successful cases from other industries and applies them to its own projects. The reference department also links with databases of other companies and industries to share best practices across different industries. For example, it integrates reference functions of project members from different industries. The reference department also links reference functions according to industry and authority with databases of other companies and industries to develop a system for sharing best practices across different industries. For example, it analyzes and shares the success factors of projects in different industries. This allows best practices to be shared across different industries, leading to increased efficiency and improvement of business operations.

[0037] The reference department can add employees' career paths and goals to the reference function to help select future project members. The reference department, for example, adds employees' career paths and goals to the reference function to build a system to help select future project members. For example, it recommends suitable members based on their career paths. The reference department also adds employees' career paths and goals to the reference function to help select future project members. For example, it assigns employees to projects that match their goals. The reference department also adds employees' career paths and goals to the reference function to develop a system to help select future project members. For example, it automatically selects project members based on their career paths. This makes it possible to select project members that take employees' career paths and goals into consideration, thereby increasing the success rate of projects.

[0038] The Picking Department can use generative AI to develop an algorithm that automatically matches employee skills and experience and recommends the most suitable personnel. For example, the Picking Department uses generative AI to develop an algorithm that automatically matches employee skills and experience and recommends the most suitable personnel. For example, it automatically selects the most suitable members based on the requirements of a project. The Picking Department also develops an algorithm that analyzes employee skills and experience and recommends the most suitable personnel. For example, it selects the most suitable members based on performance in past projects. The Picking Department also uses generative AI to build a system that automatically matches employee skills and experience and recommends the most suitable personnel. For example, it recommends the most suitable members based on a skill matrix. This automatically matches employee skills and experience and recommends the most suitable personnel, thereby increasing the success rate of projects.

[0039] The picking department can analyze the performance of the picked talent in past projects and predict the success rate. The picking department, for example, builds a system that analyzes the performance of the picked talent in past projects and predicts the success rate. For example, it calculates the success rate based on the project's results and evaluations. The picking department also analyzes the performance in past projects and predicts the success rate. For example, it predicts the success rate based on the project's completion rate and achievement level. The picking department also develops a system that analyzes the performance of the picked talent in past projects and predicts the success rate. For example, it calculates the success rate based on project evaluation data. In this way, by analyzing the performance in past projects and predicting the success rate, it is possible to increase the project's success rate.

[0040] The Picking Department can pick experts from different industries and fields and form cross-functional teams. For example, the Picking Department builds a system to pick experts from different industries and fields and form cross-functional teams. For example, it can bring together experts from technology, design, and marketing into one team. The Picking Department can also pick experts from different industries and fields and form cross-functional teams. For example, it can form a team combining experts from the medical and IT fields. The Picking Department can also develop a system to pick experts from different industries and fields and form cross-functional teams. For example, it can automatically recommend experts from different fields. This allows it to pick experts from different industries and fields and form cross-functional teams, thereby increasing the success rate of projects.

[0041] The Picking Department can make the skill sets of the picked personnel applicable to other projects and tasks. For example, the Picking Department builds a system that makes the skill sets of the picked personnel applicable to other projects and tasks. For example, it applies them to other projects based on a skill matrix. The Picking Department also applies the skill sets of the picked personnel to other projects and tasks. For example, it assigns employees with specific skills to multiple projects. The Picking Department also develops a system that makes the skill sets of the picked personnel applicable to other projects and tasks. For example, it applies them to other projects based on a skill database. In this way, the skill sets of the picked personnel can be applied to other projects and tasks, thereby improving work efficiency.

[0042] The sales negotiation information reference unit can use the generation AI to automatically analyze sales negotiation information and predict the needs and interests of business partners. The sales negotiation information reference unit, for example, uses the generation AI to build a system that automatically analyzes sales negotiation information and predicts the needs and interests of business partners. For example, it predicts the needs of business partners based on past sales negotiation data. The sales negotiation information reference unit also analyzes sales negotiation information and predicts the needs and interests of business partners. For example, it predicts needs based on the business partner's past actions and statements. The sales negotiation information reference unit also uses the generation AI to develop a system that automatically analyzes sales negotiation information and predicts the needs and interests of business partners. For example, it predicts the interests of business partners based on sales negotiation data. In this way, by automatically analyzing sales negotiation information and predicting the needs and interests of business partners, it is possible to improve the efficiency of sales activities.

[0043] The sales negotiation information reference unit can add past transaction history and customer evaluations to the sales negotiation information to optimize the sales strategy. The sales negotiation information reference unit, for example, adds past transaction history and customer evaluations to the sales negotiation information to build a system that optimizes the sales strategy. For example, it adjusts the sales strategy based on customer evaluation data. The sales negotiation information reference unit also adds past transaction history and customer evaluations to the sales negotiation information to optimize the sales strategy. For example, it adjusts sales activities based on customer evaluations. The sales negotiation information reference unit also develops a system that adds past transaction history and customer evaluations to the sales negotiation information to optimize the sales strategy. For example, it adjusts the sales strategy based on the transaction history. In this way, the sales strategy can be optimized based on past transaction history and customer evaluations, thereby making it possible to improve the efficiency of sales activities.

[0044] The business negotiation information reference unit can link business negotiation information with databases of other companies and industries and benchmark business partners. The business negotiation information reference unit, for example, links business negotiation information with databases of other companies and builds a system for benchmarking business partners. For example, it benchmarks business partners based on transaction data of other companies in the same industry. The business negotiation information reference unit also links with databases of other companies and industries and benchmarks business partners. For example, it benchmarks business partners based on transaction data of different industries. The business negotiation information reference unit also links business negotiation information with databases of other companies and industries and develops a system for benchmarking business partners. For example, it benchmarks based on business partner evaluation data. In this way, by linking business negotiation information with databases of other companies and industries and benchmarking business partners, it is possible to improve the efficiency of sales activities.

[0045] The business negotiation information reference unit adds client industry news and trend information to the business negotiation information, making it useful for sales activities. The business negotiation information reference unit, for example, builds a system that adds client industry news and trend information to the business negotiation information and makes it useful for sales activities. For example, it adjusts a sales strategy based on the client industry news. The business negotiation information reference unit also adds client industry news and trend information to the business negotiation information and makes it useful for sales activities. For example, it adjusts proposals to the client based on industry trends. The business negotiation information reference unit also develops a system that adds client industry news and trend information to the business negotiation information and makes it useful for sales activities. For example, it adjusts a sales strategy based on industry news. In this way, by adding client industry news and trend information to the business negotiation information and making it useful for sales activities, it is possible to improve the efficiency of sales activities.

[0046] The skill verification unit can use generation AI to automatically evaluate employee skills and generate a skill map. The skill verification unit, for example, uses generation AI to build a system that automatically evaluates employee skills and generates a skill map. For example, it evaluates skills based on an employee's work history and project participation history. The skill verification unit also automatically evaluates employee skills and generates a skill map. For example, it evaluates skills based on acquired qualifications and training history. The skill verification unit also uses generation AI to develop a system that automatically evaluates employee skills and generates a skill map. For example, it automatically generates a skill map based on skill evaluation data. In this way, by automatically evaluating employee skills and generating a skill map, it is possible to efficiently visualize and manage skills.

[0047] The skill confirmation unit displays the employee's skill improvement history and training history when confirming skills, making it possible to visualize growth. The skill confirmation unit, for example, builds a system that displays the employee's skill improvement history and training history when confirming skills. For example, it displays past training history and skill improvement results. The skill confirmation unit also displays the employee's skill improvement history and training history to visualize growth. For example, it displays the qualifications obtained and the history of training attended. The skill confirmation unit also develops a system that displays the employee's skill improvement history and training history when confirming skills, making growth visible. For example, it displays the progress of skill improvement in a graph. In this way, by displaying the employee's skill improvement history and training history and visualizing growth, it is possible to improve employee motivation.

[0048] The skill verification unit can link the skill verification function with databases from other companies or industries to benchmark skills. The skill verification unit, for example, links the skill verification function with databases from other companies to build a system for benchmarking skills. For example, benchmarking is performed based on skill data from other companies in the same industry. The skill verification unit also links with databases from other companies or industries to benchmark skills. For example, benchmarking is performed based on skill data from different industries. The skill verification unit also links the skill verification function with databases from other companies or industries to develop a system for benchmarking skills. For example, benchmarking is performed based on skill evaluation data. In this way, by linking the skill verification function with databases from other companies or industries and benchmarking skills, it is possible to objectively evaluate the skill levels of employees.

[0049] The skill confirmation unit can add an employee's career path and goals to the skill confirmation function and create a skill improvement plan. The skill confirmation unit, for example, builds a system that adds an employee's career path and goals to the skill confirmation function and creates a skill improvement plan. For example, it creates a skill improvement plan based on the career path. The skill confirmation unit also adds an employee's career path and goals to the skill confirmation function and creates a skill improvement plan. For example, it suggests a training program that matches the goal. The skill confirmation unit also develops a system that adds an employee's career path and goals to the skill confirmation function and creates a skill improvement plan. For example, it manages the progress of skill improvement based on the career path. In this way, by adding an employee's career path and goals to the skill confirmation function and creating a skill improvement plan, it is possible to promote employee growth.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The database creation unit inputs and stores employees' work histories into a database. For example, it registers details of projects in which employees participated, the work they were responsible for, and the skills they acquired in the database. The reference unit references members of past projects based on the employee's industry and authority. For example, it searches for members of projects related to a specific industry and checks their roles and achievements in those projects. The selection unit selects suitable personnel for new projects. For example, it searches for employees with specific skills and experience and recommends them as members of a new project. The sales negotiation information reference unit references information about sales negotiations with clients. For example, it searches for past sales negotiation details and client information to help with sales activities. The skill confirmation unit instantly checks employees' skills. For example, it searches for employees with specific skills and checks the details of those skills and how they were acquired. This allows the work history management system to centrally manage the work histories of all employees and quickly select suitable personnel. In addition, by referencing sales negotiation information and instantly checking skills, it is possible to improve the efficiency of sales activities and knowledge sharing.

[0052] The database creation department can use generation AI to automatically analyze employees' work history and automatically tag related projects and skills. For example, it can automatically analyze the details of projects that employees have participated in in the past and tag them based on the project content and role. For example, it can register the type of project, the technology used, and the results achieved as tags. The database creation department also analyzes employees' work history and automatically tags the skills and experience they have acquired. For example, it can register specific programming languages ​​or management skills as tags to make them easier to search. The database creation department also uses generation AI to build a system that analyzes employees' work history and automatically tags related projects and skills. For example, it can register information such as the project start date, end date, and participating members as tags. This makes it possible to automatically analyze employees' work history and efficiently tag related projects and skills.

[0053] The database creation unit can add employees' self-assessments or evaluations from colleagues to provide more multifaceted career information. For example, it adds employees' self-assessments to the work history database and registers their skills and experience based on the self-assessments. For example, an employee enters a self-assessment form and the contents are reflected in the database. The database creation unit also adds evaluations from colleagues to the work history database to provide a multifaceted evaluation of employees' skills and experience. For example, it collects feedback from colleagues after a project is completed and registers it in the database. The database creation unit also integrates employees' self-assessments and evaluations from colleagues into the work history database to build a system that provides more multifaceted career information. For example, it compares self-assessments with evaluations from colleagues to provide a comprehensive evaluation. This allows employees' career information to be evaluated from multiple angles, providing more accurate information.

[0054] The database creation department can link the work history database with databases from other companies and industries to promote personnel exchange between different industries. For example, the work history database can be linked with databases from other companies to build a system that promotes personnel exchange between different industries. For example, the careers of employees who have participated in projects in different industries can be shared. The database creation department also integrates work history databases to promote personnel exchange between industries. For example, a project can be carried out in collaboration with companies from different industries to share employees' skills and experience. The database creation department can also link with databases from other companies and industries to develop a system that promotes personnel exchange between different industries. For example, the careers of employees who have participated in projects in different industries can be registered in a database and shared. This can promote personnel exchange between different industries and enable the sharing of knowledge and skills.

[0055] The database creation unit adds employees' hobbies and interests to the database, and can assign personnel to projects taking into account their compatibility with the team. For example, by adding employees' hobbies and interests to a work history database, a system can be built to assign personnel to projects taking into account their compatibility with the team. For example, employees with similar hobbies and interests are assigned to the same project. The database creation unit also registers employees' hobbies and interests in the database, and assigns personnel to projects taking into account their compatibility with the team. For example, employees with similar hobbies and interests are assigned to projects with priority. The database creation unit also adds employees' hobbies and interests to the database, and develops a system to assign personnel to projects taking into account their compatibility with the team. For example, employees with similar hobbies and interests are automatically recommended. This makes it possible to assign personnel to projects taking into account their compatibility with the team, and improve team performance.

[0056] The reference department can use generative AI to automatically optimize access control based on employee industry and authority, thereby strengthening security. For example, the reference department can use generative AI to build a system that automatically optimizes access control based on employee industry and authority. For example, it can restrict information that can only be accessed by employees in specific industries or with specific authority. The reference department can also automatically optimize access control based on employee industry and authority, thereby strengthening security. For example, it can restrict access to confidential information for employees with low authority. The reference department can also use generative AI to develop a system that automatically optimizes access control based on employee industry and authority, thereby strengthening security. For example, it can dynamically change access rights depending on industry and authority. This makes it possible to automatically optimize access control based on employee industry and authority, thereby strengthening security.

[0057] The reference unit can automatically analyze the factors that contributed to a project's success or failure when referring to past project members, and provide the information as reference. For example, a system can be built that automatically analyzes the factors that contributed to a project's success or failure when referring to past project members. For example, the project's results and challenges can be registered in a database and displayed when referred to. The reference unit can also automatically analyze the factors that contributed to a project's success or failure, and provide the information as reference. For example, the factors of a successful project can be analyzed and applied to similar projects. The reference unit can also develop a system that automatically analyzes the factors that contributed to a project's success or failure when referring to past project members, and provide the information as reference. For example, the challenges of a failed project can be analyzed and improvement measures can be proposed. In this way, the success rate of future projects can be increased by automatically analyzing the factors that contributed to a project's success or failure and providing the information as reference.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The database creation department inputs and stores the employee's work history into a database. For example, details of the projects the employee participated in, the work they were responsible for, and the skills they acquired are registered in the database. Step 2: The Reference Department references members of past projects according to the employee's industry and authority. For example, it searches for members of projects related to a specific industry and checks their roles and achievements in those projects. Step 3: The Selection Department selects suitable personnel as members of the new project. For example, they search for employees with specific skills and experience and recommend them as members of the new project. Step 4: The business negotiation information reference section references business negotiation information with business partners in sales. For example, it searches for past business negotiation details and business partner information to help with sales activities. Step 5: The skills verification unit instantly verifies employee skills. For example, it searches for employees with specific skills and checks the details of those skills and how they were acquired.

[0060] (Example 2) The work history management system according to an embodiment of the present invention is a system that inputs and stores the work history of all projects and all employees in a database. This system can refer to members of past projects and select suitable personnel as members of new projects based on the employee's industry and authority. It is also possible to refer to sales negotiation information with business partners. Furthermore, the ability to instantly check the skills of all employees contributes to accelerating knowledge sharing. As a result, the work history management system centrally manages the work history of all employees and can quickly select suitable personnel. Furthermore, the ability to refer to negotiation information and instantly check skills can improve the efficiency of sales activities and knowledge sharing.

[0061] A work history management system according to an embodiment includes a database creation unit, a reference unit, a selection unit, a negotiation information reference unit, and a skill confirmation unit. The database creation unit inputs and accumulates employees' work histories into a database. For example, details of projects in which employees participated, the work they were responsible for, and the skills they acquired are registered in the database. The reference unit references members of past projects based on the employee's industry and authority. For example, it searches for members of projects related to a specific industry and checks their roles and achievements in the project. The selection unit selects suitable personnel as members of a new project. For example, it searches for employees with specific skills and experience and recommends the employees as members of the new project. The negotiation information reference unit references information about sales negotiations with clients. For example, it searches for the content of past negotiations and client information to help with sales activities. The skill confirmation unit instantly checks employees' skills. For example, it searches for employees with specific skills and checks the details of the skills and how they were acquired. This allows the work history management system to centrally manage the work histories of all employees and quickly select suitable personnel. In addition, by being able to refer to sales negotiation information and instantly check skills, sales activities and knowledge sharing can be made more efficient.

[0062] The database creation unit can use generation AI to automatically analyze employees' work histories and automatically tag related projects and skills. For example, the database creation unit can use generation AI to automatically analyze the details of projects that employees have participated in in the past and tag them based on the project content and role. For example, it registers the type of project, the technology used, and the results achieved as tags. The database creation unit also analyzes employees' work histories and automatically tags the skills and experience they have acquired. For example, it registers specific programming languages ​​and management skills as tags to make them easier to search. The database creation unit also uses generation AI to build a system that analyzes employees' work histories and automatically tags related projects and skills. For example, it registers information such as the project start date, end date, and participating members as tags. This makes it possible to automatically analyze employees' work histories and efficiently tag related projects and skills.

[0063] The database creation unit can add employees' self-evaluations or evaluations from colleagues to provide more multifaceted career information. For example, the database creation unit adds employees' self-evaluations to a work history database and registers their skills and experience based on the self-evaluations. For example, an employee enters a self-evaluation sheet and the contents are reflected in the database. The database creation unit also adds evaluations from colleagues to the work history database to evaluate employees' skills and experience from multiple perspectives. For example, after a project is completed, feedback from colleagues is collected and registered in the database. The database creation unit also integrates employees' self-evaluations and evaluations from colleagues into the work history database to build a system that provides more multifaceted career information. For example, the database creation unit compares self-evaluations with evaluations from colleagues to provide a comprehensive evaluation. This allows employees' career information to be evaluated from multiple perspectives, providing more accurate information.

[0064] The database creation department uses the emotion estimation function to record the emotions employees felt in past projects, and can analyze the project's success factors and challenges from an emotional perspective. The database creation department, for example, uses the emotion estimation function to build a system that records the emotions employees felt in past projects. For example, the emotional state is entered after the project is completed, and the data is saved in a database. The database creation department also uses the emotion estimation function to analyze the project's success factors and challenges from an emotional perspective. For example, it compares projects with a high proportion of positive emotions with projects with a high proportion of negative emotions to identify the success factors and challenges. The database creation department also develops a system that records the emotions employees felt in past projects and analyzes the project's success factors and challenges based on that data. For example, it graphs the emotion data to visualize the project's emotional trends. This makes it possible to analyze the project's success factors and challenges from an emotional perspective and identify improvement measures.

[0065] The database creation unit can link the work history database with databases from other companies and industries, thereby promoting personnel exchange between different industries. The database creation unit, for example, links the work history database with databases from other companies, to build a system that promotes personnel exchange between different industries. For example, the careers of employees who have participated in projects in different industries are shared. The database creation unit also integrates the work history database to promote personnel exchange between industries. For example, a project is carried out in collaboration with companies from different industries, and employees' skills and experience are shared. The database creation unit also links with databases from other companies and industries, to develop a system that promotes personnel exchange between different industries. For example, the careers of employees who have participated in projects in different industries are registered in a database and shared. This can promote personnel exchange between different industries and enable the sharing of knowledge and skills.

[0066] The database creation unit adds employees' hobbies and interests to the database, and can assign personnel in a way that takes into account compatibility in a project team. The database creation unit, for example, adds employees' hobbies and interests to a work history database, and builds a system for assigning personnel in a way that takes into account compatibility in a project team. For example, employees with similar hobbies and interests are assigned to the same project. The database creation unit also registers employees' hobbies and interests in the database, and assigns personnel in a way that takes into account compatibility in a project team. For example, employees with similar hobbies and interests are assigned to projects with priority. The database creation unit also adds employees' hobbies and interests to the database, and develops a system for assigning personnel in a way that takes into account compatibility in a project team. For example, employees with similar hobbies and interests are automatically recommended. This makes it possible to assign personnel in a way that takes into account compatibility in a project team, and improve team performance.

[0067] The database creation unit can use the emotion estimation function to grasp in advance the expectations and anxieties that employees have regarding new projects and provide appropriate support. For example, the database creation unit uses the emotion estimation function to build a system that grasps in advance the expectations and anxieties that employees have regarding new projects. For example, the emotional state is input before the project begins, and support is provided based on that data. The database creation unit also uses the emotion estimation function to grasp in advance the expectations and anxieties that employees have regarding new projects and provide appropriate support. For example, support is provided to employees with high expectations to maintain their motivation. The database creation unit also develops a system that grasps in advance the expectations and anxieties that employees have regarding new projects and provides appropriate support. For example, support is provided to employees with high anxiety to reduce stress. In this way, by grasping employees' expectations and anxieties in advance and providing appropriate support, the success rate of projects can be increased.

[0068] The reference department can use generative AI to automatically optimize access control based on employee industry and authority, thereby strengthening security. For example, the reference department uses generative AI to build a system that automatically optimizes access control based on employee industry and authority. For example, it restricts information that can be accessed only by employees in specific industries or with specific authority. The reference department also automatically optimizes access control based on employee industry and authority, thereby strengthening security. For example, it restricts access to confidential information for employees with low authority. The reference department also uses generative AI to develop a system that automatically optimizes access control based on employee industry and authority, thereby strengthening security. For example, it dynamically changes access rights depending on industry and authority. This makes it possible to automatically optimize access control based on employee industry and authority, thereby strengthening security.

[0069] The reference unit can automatically analyze the factors that contributed to the success and failure of a project when past project members are referenced, and provide the information as reference. For example, the reference unit builds a system that automatically analyzes the factors that contributed to the success and failure of a project when past project members are referenced. For example, the project's results and challenges are registered in a database and displayed when referenced. The reference unit also automatically analyzes the factors that contributed to the success and failure of a project, and provides the information as reference. For example, the factors of a successful project are analyzed and applied to similar projects. The reference unit also develops a system that automatically analyzes the factors that contributed to the success and failure of a project when past project members are referenced, and provides the information as reference. For example, the challenges of a failed project are analyzed and improvement measures are proposed. In this way, the success rate of future projects can be increased by automatically analyzing the factors that contributed to the success and failure of past projects and providing the information as reference.

[0070] The reference unit uses the emotion estimation function to display the emotion history of the referenced project member and understand the team dynamics. The reference unit, for example, uses the emotion estimation function to build a system that displays the emotion history of the referenced project member. For example, the emotional state during the project period is graphed to visualize the team dynamics. The reference unit also displays the emotion history of the referenced project member and understands the team dynamics. For example, support is provided to members with large emotional fluctuations. The reference unit also uses the emotion estimation function to develop a system that displays the emotion history of the referenced project member and understands the team dynamics. For example, the team's strengths and challenges are identified based on the emotion data. In this way, by displaying the emotion history of the project members and understanding the team dynamics, team performance can be improved.

[0071] The reference department can link reference functions according to industry and authority with databases of other companies and industries to share best practices across different industries. For example, the reference department links reference functions according to industry and authority with databases of other companies to build a system for sharing best practices across different industries. For example, it refers to successful cases from other industries and applies them to its own projects. The reference department also links with databases of other companies and industries to share best practices across different industries. For example, it integrates reference functions of project members from different industries. The reference department also links reference functions according to industry and authority with databases of other companies and industries to develop a system for sharing best practices across different industries. For example, it analyzes and shares the success factors of projects in different industries. This allows best practices to be shared across different industries, leading to increased efficiency and improvement of business operations.

[0072] The reference department can add employees' career paths and goals to the reference function to help select future project members. The reference department, for example, adds employees' career paths and goals to the reference function to build a system to help select future project members. For example, it recommends suitable members based on their career paths. The reference department also adds employees' career paths and goals to the reference function to help select future project members. For example, it assigns employees to projects that match their goals. The reference department also adds employees' career paths and goals to the reference function to develop a system to help select future project members. For example, it automatically selects project members based on their career paths. This makes it possible to select project members that take employees' career paths and goals into consideration, thereby increasing the success rate of projects.

[0073] The referencing department can use the emotion estimation function to collect emotional feedback from referenced project members and use it to improve the project. The referencing department, for example, uses the emotion estimation function to build a system that collects emotional feedback from referenced project members. For example, the emotional state is input after the project is completed, and improvement measures are proposed based on that data. The referencing department also collects emotional feedback from referenced project members and uses it to improve the project. For example, it identifies project issues based on the emotion data and implements improvement measures. The referencing department also uses the emotion estimation function to develop a system that collects emotional feedback from referenced project members and uses it to improve the project. For example, it analyzes the emotion data and identifies the success factors and issues of the project. In this way, it is possible to collect emotional feedback from project members and use it to improve the project.

[0074] The Picking Department can use generative AI to develop an algorithm that automatically matches employee skills and experience and recommends the most suitable personnel. For example, the Picking Department uses generative AI to develop an algorithm that automatically matches employee skills and experience and recommends the most suitable personnel. For example, it automatically selects the most suitable members based on the requirements of a project. The Picking Department also develops an algorithm that analyzes employee skills and experience and recommends the most suitable personnel. For example, it selects the most suitable members based on performance in past projects. The Picking Department also uses generative AI to build a system that automatically matches employee skills and experience and recommends the most suitable personnel. For example, it recommends the most suitable members based on a skill matrix. This automatically matches employee skills and experience and recommends the most suitable personnel, thereby increasing the success rate of projects.

[0075] The picking department can analyze the performance of the picked talent in past projects and predict the success rate. The picking department, for example, builds a system that analyzes the performance of the picked talent in past projects and predicts the success rate. For example, it calculates the success rate based on the project's results and evaluations. The picking department also analyzes the performance in past projects and predicts the success rate. For example, it predicts the success rate based on the project's completion rate and achievement level. The picking department also develops a system that analyzes the performance of the picked talent in past projects and predicts the success rate. For example, it calculates the success rate based on project evaluation data. In this way, by analyzing the performance in past projects and predicting the success rate, it is possible to increase the project's success rate.

[0076] The picking department can use the emotion estimation function to evaluate the emotional state of the selected talent and determine their suitability for the project. The picking department, for example, uses the emotion estimation function to build a system for evaluating the emotional state of the selected talent. For example, the emotional state is input before the start of the project and suitability is determined based on that data. The picking department also evaluates the emotional state of the selected talent and determines their suitability for the project. For example, stress levels and motivation are evaluated based on the emotion data. The picking department also uses the emotion estimation function to develop a system for evaluating the emotional state of the selected talent and determining their suitability for the project. For example, adaptability is scored based on the emotion data. In this way, by evaluating the emotional state of talent and determining their suitability for the project, the success rate of the project can be increased.

[0077] The Picking Department can pick experts from different industries and fields and form cross-functional teams. For example, the Picking Department builds a system to pick experts from different industries and fields and form cross-functional teams. For example, it can bring together experts from technology, design, and marketing into one team. The Picking Department can also pick experts from different industries and fields and form cross-functional teams. For example, it can form a team combining experts from the medical and IT fields. The Picking Department can also develop a system to pick experts from different industries and fields and form cross-functional teams. For example, it can automatically recommend experts from different fields. This allows it to pick experts from different industries and fields and form cross-functional teams, thereby increasing the success rate of projects.

[0078] The Picking Department can make the skill sets of the picked personnel applicable to other projects and tasks. For example, the Picking Department builds a system that makes the skill sets of the picked personnel applicable to other projects and tasks. For example, it applies them to other projects based on a skill matrix. The Picking Department also applies the skill sets of the picked personnel to other projects and tasks. For example, it assigns employees with specific skills to multiple projects. The Picking Department also develops a system that makes the skill sets of the picked personnel applicable to other projects and tasks. For example, it applies them to other projects based on a skill database. In this way, the skill sets of the picked personnel can be applied to other projects and tasks, thereby improving work efficiency.

[0079] The picking department can use the emotion estimation function to collect emotional feedback from the picked talent and provide support for the success of the project. The picking department, for example, uses the emotion estimation function to build a system that collects emotional feedback from the picked talent. For example, the emotional state is input during the project period, and support is provided based on that data. The picking department also collects emotional feedback from the picked talent and provides support for the success of the project. For example, support is provided to reduce stress based on the emotion data. The picking department also uses the emotion estimation function to develop a system that collects emotional feedback from the picked talent and provides support for the success of the project. For example, support is provided to improve motivation based on the emotion data. In this way, by collecting emotional feedback from talent and providing support for the success of the project, the success rate of the project can be increased.

[0080] The sales negotiation information reference unit can use the generation AI to automatically analyze sales negotiation information and predict the needs and interests of business partners. The sales negotiation information reference unit, for example, uses the generation AI to build a system that automatically analyzes sales negotiation information and predicts the needs and interests of business partners. For example, it predicts the needs of business partners based on past sales negotiation data. The sales negotiation information reference unit also analyzes sales negotiation information and predicts the needs and interests of business partners. For example, it predicts needs based on the business partner's past actions and statements. The sales negotiation information reference unit also uses the generation AI to develop a system that automatically analyzes sales negotiation information and predicts the needs and interests of business partners. For example, it predicts the interests of business partners based on sales negotiation data. In this way, by automatically analyzing sales negotiation information and predicting the needs and interests of business partners, it is possible to improve the efficiency of sales activities.

[0081] The sales negotiation information reference unit can add past transaction history and customer evaluations to the sales negotiation information to optimize the sales strategy. The sales negotiation information reference unit, for example, adds past transaction history and customer evaluations to the sales negotiation information to build a system that optimizes the sales strategy. For example, it adjusts the sales strategy based on customer evaluation data. The sales negotiation information reference unit also adds past transaction history and customer evaluations to the sales negotiation information to optimize the sales strategy. For example, it adjusts sales activities based on customer evaluations. The sales negotiation information reference unit also develops a system that adds past transaction history and customer evaluations to the sales negotiation information to optimize the sales strategy. For example, it adjusts the sales strategy based on the transaction history. In this way, the sales strategy can be optimized based on past transaction history and customer evaluations, thereby making it possible to improve the efficiency of sales activities.

[0082] The sales negotiation information reference unit can use the emotion estimation function to analyze the emotions of a business partner during a sales negotiation in real time and adjust the sales talk. The sales negotiation information reference unit, for example, uses the emotion estimation function to build a system that analyzes the emotions of a business partner during a sales negotiation in real time. For example, it analyzes the business partner's facial expressions and voice to understand their emotional state. The sales negotiation information reference unit also analyzes the emotions of a business partner during a sales negotiation in real time and adjusts the sales talk. For example, if the business partner shows positive emotions, it strengthens the proposal. The sales negotiation information reference unit also uses the emotion estimation function to develop a system that analyzes the emotions of a business partner during a sales negotiation in real time and adjusts the sales talk. For example, it adjusts the sales talk based on the business partner's emotional data. In this way, the success rate of sales negotiations can be increased by analyzing the emotions of a business partner during a sales negotiation in real time and adjusting the sales talk.

[0083] The business negotiation information reference unit can link business negotiation information with databases of other companies and industries and benchmark business partners. The business negotiation information reference unit, for example, links business negotiation information with databases of other companies and builds a system for benchmarking business partners. For example, it benchmarks business partners based on transaction data of other companies in the same industry. The business negotiation information reference unit also links with databases of other companies and industries and benchmarks business partners. For example, it benchmarks business partners based on transaction data of different industries. The business negotiation information reference unit also links business negotiation information with databases of other companies and industries and develops a system for benchmarking business partners. For example, it benchmarks based on business partner evaluation data. In this way, by linking business negotiation information with databases of other companies and industries and benchmarking business partners, it is possible to improve the efficiency of sales activities.

[0084] The business negotiation information reference unit adds client industry news and trend information to the business negotiation information, making it useful for sales activities. The business negotiation information reference unit, for example, builds a system that adds client industry news and trend information to the business negotiation information and makes it useful for sales activities. For example, it adjusts a sales strategy based on the client industry news. The business negotiation information reference unit also adds client industry news and trend information to the business negotiation information and makes it useful for sales activities. For example, it adjusts proposals to the client based on industry trends. The business negotiation information reference unit also develops a system that adds client industry news and trend information to the business negotiation information and makes it useful for sales activities. For example, it adjusts a sales strategy based on industry news. In this way, by adding client industry news and trend information to the business negotiation information and making it useful for sales activities, it is possible to improve the efficiency of sales activities.

[0085] The sales negotiation information reference unit uses the emotion estimation function to collect the emotional reactions of business partners after a sales negotiation and can formulate a strategy for the next sales negotiation. The sales negotiation information reference unit, for example, uses the emotion estimation function to build a system that collects the emotional reactions of business partners after a sales negotiation. For example, the emotional state of the business partner after the sales negotiation is input and a strategy for the next sales negotiation is formulated based on that data. The sales negotiation information reference unit also collects the emotional reactions of business partners after a sales negotiation and formulates a strategy for the next sales negotiation. For example, proactive proposals are made to business partners who show positive emotions. The sales negotiation information reference unit also uses the emotion estimation function to develop a system that collects the emotional reactions of business partners after a sales negotiation and formulates a strategy for the next sales negotiation. For example, follow-up is strengthened for business partners who show negative emotions. In this way, the emotional reactions of business partners after a sales negotiation can be collected and a strategy for the next sales negotiation can be formulated, thereby increasing the success rate of sales negotiations.

[0086] The skill verification unit can use generation AI to automatically evaluate employee skills and generate a skill map. The skill verification unit, for example, uses generation AI to build a system that automatically evaluates employee skills and generates a skill map. For example, it evaluates skills based on an employee's work history and project participation history. The skill verification unit also automatically evaluates employee skills and generates a skill map. For example, it evaluates skills based on acquired qualifications and training history. The skill verification unit also uses generation AI to develop a system that automatically evaluates employee skills and generates a skill map. For example, it automatically generates a skill map based on skill evaluation data. In this way, by automatically evaluating employee skills and generating a skill map, it is possible to efficiently visualize and manage skills.

[0087] The skill confirmation unit displays the employee's skill improvement history and training history when confirming skills, making it possible to visualize growth. The skill confirmation unit, for example, builds a system that displays the employee's skill improvement history and training history when confirming skills. For example, it displays past training history and skill improvement results. The skill confirmation unit also displays the employee's skill improvement history and training history to visualize growth. For example, it displays the qualifications obtained and the history of training attended. The skill confirmation unit also develops a system that displays the employee's skill improvement history and training history when confirming skills, making growth visible. For example, it displays the progress of skill improvement in a graph. In this way, by displaying the employee's skill improvement history and training history and visualizing growth, it is possible to improve employee motivation.

[0088] The skill confirmation unit uses the emotion estimation function to record the emotions of employees when they acquire skills, thereby improving learning efficiency. The skill confirmation unit, for example, uses the emotion estimation function to build a system that records the emotions of employees when they acquire skills. For example, the emotional state during training is input and learning efficiency is improved based on that data. The skill confirmation unit also records the emotions of employees when they acquire skills and improves learning efficiency. For example, a training method that involves a high proportion of positive emotions is preferentially adopted. The skill confirmation unit also uses the emotion estimation function to develop a system that records the emotions of employees when they acquire skills and improves learning efficiency. For example, a training program is adjusted based on the emotion data. In this way, it is possible to record the emotions of employees when they acquire skills and improve learning efficiency, thereby promoting the improvement of employees' skills.

[0089] The skill verification unit can link the skill verification function with databases from other companies or industries to benchmark skills. The skill verification unit, for example, links the skill verification function with databases from other companies to build a system for benchmarking skills. For example, benchmarking is performed based on skill data from other companies in the same industry. The skill verification unit also links with databases from other companies or industries to benchmark skills. For example, benchmarking is performed based on skill data from different industries. The skill verification unit also links the skill verification function with databases from other companies or industries to develop a system for benchmarking skills. For example, benchmarking is performed based on skill evaluation data. In this way, by linking the skill verification function with databases from other companies or industries and benchmarking skills, it is possible to objectively evaluate the skill levels of employees.

[0090] The skill confirmation unit can add an employee's career path and goals to the skill confirmation function and create a skill improvement plan. The skill confirmation unit, for example, builds a system that adds an employee's career path and goals to the skill confirmation function and creates a skill improvement plan. For example, it creates a skill improvement plan based on the career path. The skill confirmation unit also adds an employee's career path and goals to the skill confirmation function and creates a skill improvement plan. For example, it suggests a training program that matches the goal. The skill confirmation unit also develops a system that adds an employee's career path and goals to the skill confirmation function and creates a skill improvement plan. For example, it manages the progress of skill improvement based on the career path. In this way, by adding an employee's career path and goals to the skill confirmation function and creating a skill improvement plan, it is possible to promote employee growth.

[0091] The skill verification unit can use the emotion estimation function to collect emotional feedback when employees acquire skills and improve the training program. The skill verification unit, for example, uses the emotion estimation function to build a system that collects emotional feedback when employees acquire skills. For example, the emotional state during training is input and the training program is improved based on the data. The skill verification unit also collects emotional feedback when employees acquire skills and improves the training program. For example, training methods that involve a high proportion of positive emotions are preferentially adopted. The skill verification unit also uses the emotion estimation function to collect emotional feedback when employees acquire skills and develops a system that improves the training program. For example, the training program is adjusted based on the emotion data. In this way, learning efficiency can be improved by collecting emotional feedback when employees acquire skills and improving the training program.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The database creation unit inputs and stores employees' work histories into a database. For example, it registers details of projects in which employees participated, the work they were responsible for, and the skills they acquired in the database. The reference unit references members of past projects based on the employee's industry and authority. For example, it searches for members of projects related to a specific industry and checks their roles and achievements in those projects. The selection unit selects suitable personnel for new projects. For example, it searches for employees with specific skills and experience and recommends them as members of a new project. The sales negotiation information reference unit references information about sales negotiations with clients. For example, it searches for past sales negotiation details and client information to help with sales activities. The skill confirmation unit instantly checks employees' skills. For example, it searches for employees with specific skills and checks the details of those skills and how they were acquired. This allows the work history management system to centrally manage the work histories of all employees and quickly select suitable personnel. In addition, by referencing sales negotiation information and instantly checking skills, it is possible to improve the efficiency of sales activities and knowledge sharing.

[0094] The database creation department can use generation AI to automatically analyze employees' work history and automatically tag related projects and skills. For example, it can automatically analyze the details of projects that employees have participated in in the past and tag them based on the project content and role. For example, it can register the type of project, the technology used, and the results achieved as tags. The database creation department also analyzes employees' work history and automatically tags the skills and experience they have acquired. For example, it can register specific programming languages ​​or management skills as tags to make them easier to search. The database creation department also uses generation AI to build a system that analyzes employees' work history and automatically tags related projects and skills. For example, it can register information such as the project start date, end date, and participating members as tags. This makes it possible to automatically analyze employees' work history and efficiently tag related projects and skills.

[0095] The database creation unit can add employees' self-assessments or evaluations from colleagues to provide more multifaceted career information. For example, it adds employees' self-assessments to the work history database and registers their skills and experience based on the self-assessments. For example, an employee enters a self-assessment form and the contents are reflected in the database. The database creation unit also adds evaluations from colleagues to the work history database to provide a multifaceted evaluation of employees' skills and experience. For example, it collects feedback from colleagues after a project is completed and registers it in the database. The database creation unit also integrates employees' self-assessments and evaluations from colleagues into the work history database to build a system that provides more multifaceted career information. For example, it compares self-assessments with evaluations from colleagues to provide a comprehensive evaluation. This allows employees' career information to be evaluated from multiple angles, providing more accurate information.

[0096] The database creation department uses the emotion estimation function to record the emotions employees felt in past projects, allowing them to analyze the project's success factors and challenges from an emotional perspective. For example, the emotion estimation function is used to build a system that records the emotions employees felt in past projects. For example, the emotional state is entered after the project is completed, and the data is saved in a database. The database creation department also uses the emotion estimation function to analyze the project's success factors and challenges from an emotional perspective. For example, it compares projects with a high proportion of positive emotions with projects with a high proportion of negative emotions to identify the success factors and challenges. The database creation department also records the emotions employees felt in past projects, and develops a system that analyzes the project's success factors and challenges based on that data. For example, it graphs the emotion data to visualize the project's emotional trends. This makes it possible to analyze the project's success factors and challenges from an emotional perspective and identify improvement measures.

[0097] The database creation department can link the work history database with databases from other companies and industries to promote personnel exchange between different industries. For example, the work history database can be linked with databases from other companies to build a system that promotes personnel exchange between different industries. For example, the careers of employees who have participated in projects in different industries can be shared. The database creation department also integrates work history databases to promote personnel exchange between industries. For example, a project can be carried out in collaboration with companies from different industries to share employees' skills and experience. The database creation department can also link with databases from other companies and industries to develop a system that promotes personnel exchange between different industries. For example, the careers of employees who have participated in projects in different industries can be registered in a database and shared. This can promote personnel exchange between different industries and enable the sharing of knowledge and skills.

[0098] The database creation unit adds employees' hobbies and interests to the database, and can assign personnel to projects taking into account their compatibility with the team. For example, by adding employees' hobbies and interests to a work history database, a system can be built to assign personnel to projects taking into account their compatibility with the team. For example, employees with similar hobbies and interests are assigned to the same project. The database creation unit also registers employees' hobbies and interests in the database, and assigns personnel to projects taking into account their compatibility with the team. For example, employees with similar hobbies and interests are assigned to projects with priority. The database creation unit also adds employees' hobbies and interests to the database, and develops a system to assign personnel to projects taking into account their compatibility with the team. For example, employees with similar hobbies and interests are automatically recommended. This makes it possible to assign personnel to projects taking into account their compatibility with the team, and improve team performance.

[0099] The database creation unit can use the emotion estimation function to understand in advance the expectations and anxieties that employees have regarding new projects and provide appropriate support. For example, the emotion estimation function is used to build a system that understands in advance the expectations and anxieties that employees have regarding new projects. For example, the emotional state is entered before the project begins, and support is provided based on that data. The database creation unit also uses the emotion estimation function to understand in advance the expectations and anxieties that employees have regarding new projects and provide appropriate support. For example, support is provided to employees with high expectations to maintain their motivation. The database creation unit also develops a system that understands in advance the expectations and anxieties that employees have regarding new projects and provides appropriate support. For example, support is provided to employees with high anxiety to reduce stress. In this way, by understanding employees' expectations and anxieties in advance and providing appropriate support, the success rate of projects can be increased.

[0100] The reference department can use generative AI to automatically optimize access control based on employee industry and authority, thereby strengthening security. For example, the reference department can use generative AI to build a system that automatically optimizes access control based on employee industry and authority. For example, it can restrict information that can only be accessed by employees in specific industries or with specific authority. The reference department can also automatically optimize access control based on employee industry and authority, thereby strengthening security. For example, it can restrict access to confidential information for employees with low authority. The reference department can also use generative AI to develop a system that automatically optimizes access control based on employee industry and authority, thereby strengthening security. For example, it can dynamically change access rights depending on industry and authority. This makes it possible to automatically optimize access control based on employee industry and authority, thereby strengthening security.

[0101] The reference unit can automatically analyze the factors that contributed to a project's success or failure when referring to past project members, and provide the information as reference. For example, a system can be built that automatically analyzes the factors that contributed to a project's success or failure when referring to past project members. For example, the project's results and challenges can be registered in a database and displayed when referred to. The reference unit can also automatically analyze the factors that contributed to a project's success or failure, and provide the information as reference. For example, the factors of a successful project can be analyzed and applied to similar projects. The reference unit can also develop a system that automatically analyzes the factors that contributed to a project's success or failure when referring to past project members, and provide the information as reference. For example, the challenges of a failed project can be analyzed and improvement measures can be proposed. In this way, the success rate of future projects can be increased by automatically analyzing the factors that contributed to a project's success or failure and providing the information as reference.

[0102] The reference department can use the emotion estimation function to display the emotion history of the referenced project member and understand the team dynamics. For example, a system is constructed that uses the emotion estimation function to display the emotion history of the referenced project member. For example, the emotional states during the project period are graphed to visualize the team dynamics. The reference department also displays the emotion history of the referenced project member and understands the team dynamics. For example, support is provided to members with large emotional fluctuations. The reference department also uses the emotion estimation function to develop a system that displays the emotion history of the referenced project member and understands the team dynamics. For example, the team's strengths and challenges are identified based on the emotion data. In this way, by displaying the emotion history of the project members and understanding the team dynamics, team performance can be improved.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The database creation department inputs and stores the employee's work history into a database. For example, details of the projects the employee participated in, the work they were responsible for, and the skills they acquired are registered in the database. Step 2: The Reference Department references members of past projects according to the employee's industry and authority. For example, it searches for members of projects related to a specific industry and checks their roles and achievements in those projects. Step 3: The Selection Department selects suitable personnel as members of the new project. For example, they search for employees with specific skills and experience and recommend them as members of the new project. Step 4: The business negotiation information reference section references business negotiation information with business partners in sales. For example, it searches for past business negotiation details and business partner information to help with sales activities. Step 5: The skills verification unit instantly verifies employee skills. For example, it searches for employees with specific skills and checks the details of those skills and how they were acquired.

[0105] 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.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] 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.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A database department that inputs and accumulates employee work history into a database; A reference section for referencing members of past projects according to the type of business or authority of the employee; A selection department that selects suitable personnel for new project members, A sales negotiation information reference section for referencing sales negotiation information with business partners; A skill confirmation unit that immediately confirms the skills of the employee. A system characterized by:

2. The database creation unit Using generative AI, the employee's work history is automatically analyzed and relevant projects and skills are automatically tagged.

2. The system of claim 1.

3. The database creation unit Add the employee's self-assessment or peer reviews to provide more comprehensive background information 2. The system of claim 1.

4. The database creation unit Record the emotions that the employee felt in the past project and analyze the success factors or challenges of the project from an emotional perspective.

2. The system of claim 1.

5. The database creation unit Linking work history databases with databases from other companies or industries to promote human resource exchanges across different industries 2. The system of claim 1.

6. The database creation unit Add the employee's hobbies or interests to the database and assign personnel to project teams based on their compatibility.

2. The system of claim 1.

7. The database creation unit Understand in advance the expectations or anxieties that the employee has about the new project and provide appropriate support 2. The system of claim 1.

8. The reference portion is Using generative AI, access control based on the employee's business type or authority is automatically optimized to enhance security.

2. The system of claim 1.

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