system

Abstract

The system according to this embodiment aims to automate and streamline the process of applying for accounts for systems necessary for business operations within the company. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, an application unit, and a deletion unit. The collection unit collects information on new hires, transfers, and roles within departments. The analysis unit analyzes the information collected by the collection unit and determines the type of system and permissions required by the user. The application unit automatically submits applications to each issuing office based on the type of system and permissions determined by the analysis unit. The deletion unit submits applications to delete accounts for unnecessary systems.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the application for an account of a system necessary for work within a company is complicated and time-consuming.

[0005] The system according to the embodiment aims to automate and improve the efficiency of the application for an account of a system necessary for work within a company.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, an application unit, and a deletion unit. The collection unit collects information on new hires, transfers, and roles within departments. The analysis unit analyzes the information collected by the collection unit and determines the type of system and permissions required by the user. The application unit automatically submits applications to the respective issuing offices based on the type of system and permissions determined by the analysis unit. The deletion unit submits applications to delete accounts for unnecessary systems. [Effects of the Invention]

[0007] The system according to this embodiment can automate and streamline the process of applying for accounts for systems necessary for business operations within the company. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

[0017] [[ID=1,4]]As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An automated account application system according to an embodiment of the present invention is a system that automates the application of accounts necessary for business operations within a company. This automated account application system collects information on new hires, transfers, and roles within departments, determines the type of system and permissions required by the applicant, and automatically submits applications to the respective issuing offices. For example, when a new employee joins the company, the automated account application system collects information on new hires. When an employee is transferred, it collects information on transfers. Furthermore, when a role changes within a department, it collects information on those roles. Next, the automated account application system analyzes the collected information and determines the type of system and permissions required by the applicant. For example, if a new employee is assigned to the sales department, it determines the type of system used in the sales department and the permissions for each system. Furthermore, if a department is changed due to a transfer, it determines the type of system used in the new department and the permissions for each system. Furthermore, if a role changes, it determines the type of system and the permissions for each system according to the new role. Based on the determined system type and permissions, the automated account application system automatically submits applications to the respective issuing offices. For example, it automatically submits an account application for a system used in the sales department. It also automatically submits an account deletion application for unnecessary systems. This eliminates the need for applicants to submit applications manually, significantly reducing the time and effort required for applications. The automated account application system automatically issues, deletes, and modifies the appropriate system accounts whenever employees join or leave the company, transfer to a new department, or change roles within a department. This reduces the cost of account applications and deletions to almost zero, while also improving the efficiency and proper management of system accounts. In short, the automated account application system enables more efficient and proper account management.

[0029] The automated account application system according to this embodiment comprises a collection unit, an analysis unit, an application unit, and a deletion unit. The collection unit collects information such as hiring information, transfer information, and departmental role information. For example, the collection unit collects hiring information for new employees. The collection unit can also collect transfer information if an employee is transferred. The collection unit can also collect role information if a role within a department is changed. For example, the collection unit collects information such as the name, hiring date, and department of a new employee. As transfer information, the collection unit collects information such as the transfer date, department before and after the transfer, and job title. As role information, the collection unit collects information such as job title, assigned duties, and scope of authority. The analysis unit analyzes the information collected by the collection unit and determines the type of system and authority required by the target person. For example, if a new employee is assigned to the sales department, the analysis unit determines the type of system used in the sales department and the authority for each system. If a department is changed due to a transfer, the analysis unit can also determine the type of system used in the new department and the authority for each system. The Analysis Department can also determine the type of system and the permissions for each system according to the new role when a role changes. For example, the Analysis Department can determine that the systems used by the Sales Department include a customer management system and a sales management system. The Analysis Department determines permissions such as viewing, editing, and management for the customer management system. The Analysis Department determines permissions such as viewing, editing, and management for the sales management system. The Application Department automatically submits applications to each issuing office based on the type of system and permissions determined by the Analysis Department. For example, the Application Department automatically submits account applications for systems used by the Sales Department. The Application Department can also automatically submit account applications for the customer management system. The Application Department can also automatically submit account applications for the sales management system. For example, when the Application Department submits an account application for the customer management system, it includes information such as name, department, position, and permissions in the application details. When the Application Department submits an account application for the sales management system, it includes information such as name, department, position, and permissions in the application details. The Deletion Department submits account deletion applications for systems that are no longer needed. The deletion section, for example, submits requests to delete accounts for systems that are no longer needed due to personnel changes.The deletion unit can also submit account deletion requests for systems that are no longer needed due to a change in role. For example, the deletion unit can submit account deletion requests for customer management systems that are no longer needed due to personnel changes. The deletion unit can also submit account deletion requests for sales management systems that are no longer needed due to a change in role. As a result, the automated account application system according to this embodiment can improve the efficiency and appropriateness of account management.

[0030] The data collection department collects information on new hires, transfers, and roles within departments. Specifically, it collects information such as the name, date of hire, and department of new employees. This information is automatically obtained from the company's HR system and onboarding system. For example, when a new employee completes the onboarding process, this information is sent to the data collection department and stored in the database. For transfers, it collects information such as the date of transfer, the department before and after the transfer, and the employee's position. When a transfer occurs, the HR system sends the transfer information to the data collection department and stores it in the database. For roles, it collects information such as the employee's job title, responsibilities, and scope of authority. When a role change occurs, the department's management system sends the role information to the data collection department and stores it in the database. This allows the data collection department to collect information on HR transfers and role changes within the company in real time and centrally manage it in the database. Furthermore, the data collection department can regularly update this information to maintain its up-to-date status. For example, it retrieves the latest information from the HR system and departmental management systems at a fixed time every day and updates the database. In addition, the data collection department can perform data integrity checks and duplicate checks to ensure the accuracy of the information. This allows the data collection unit to provide accurate and up-to-date information, improving the overall reliability of the system.

[0031] The analysis department analyzes the information collected by the data collection department to determine the types of systems and permissions required by the target individual. Specifically, if a new employee is assigned to the sales department, the analysis department determines the types of systems used in the sales department and the permissions required for each system. For example, systems used in the sales department include customer management systems and sales management systems. The analysis department determines the permissions required by the new employee for these systems. For customer management systems, it determines permissions such as viewing, editing, and management. For sales management systems, it determines permissions such as viewing, editing, and management. The analysis department uses AI to make these determinations. The AI ​​uses historical data and rule-based algorithms to quickly and accurately determine the types of systems and permissions required by the target individual. For example, the AI ​​learns from past new employee assignment information and permission information to propose the optimal systems and permissions for new individuals. The AI ​​also automatically determines changes in systems and permissions due to transfers or role changes. For example, if an employee is transferred to a different department, it determines the types of systems used in the new department and the permissions required for each system. If an employee's role changes, it determines the types of systems and permissions required for each system that are appropriate for the new role. This allows the analysis unit to quickly and accurately determine the systems and permissions required by the target user, thereby improving the overall efficiency and optimization of the system.

[0032] The application department automatically submits applications to each issuing office based on the system type and permissions determined by the analysis department. Specifically, it automatically submits account applications for systems used by the sales department. For example, when applying for an account for the customer management system, the application includes information such as name, department, position, and permissions. Based on this information, the application department automatically submits applications to the issuing offices for each system. The application process follows a pre-configured workflow, and the procedures for obtaining necessary approvals are also automated. For example, when an account application for the customer management system is submitted, an approval request is sent to the system administrator, and once approval is obtained, the account is issued. The application department can monitor these processes in real time and manage their progress. In addition, the application department can perform data integrity checks and error checks to ensure the accuracy of the application content. This allows the application department to submit account applications quickly and accurately, improving the overall efficiency and optimization of the system. Furthermore, the application department saves the application history to a database for later reference. This ensures the traceability of applications, and allows for verification of application content as needed.

[0033] The deletion unit submits requests to delete accounts for systems that are no longer needed. Specifically, it submits requests to delete accounts for systems that have become unnecessary due to personnel changes. For example, it submits requests to delete accounts for customer management systems that have become unnecessary due to personnel changes. Based on this information, the deletion unit automatically submits deletion requests to the issuing contacts for each system. The deletion process follows a pre-configured workflow, and the procedures for obtaining necessary approvals are also automated. For example, when a request to delete an account for a customer management system is submitted, an approval request is sent to the system administrator, and once approval is obtained, the account is deleted. The deletion unit can monitor these processes in real time and manage the progress. In addition, the deletion unit can perform data integrity checks and error checks to ensure the accuracy of the deleted content. This allows the deletion unit to submit account deletion requests quickly and accurately, improving the efficiency and optimization of the entire system. Furthermore, the deletion unit saves the deletion history to a database for later reference. This ensures the traceability of deletions, and allows for verification of deleted content as needed.

[0034] The data collection unit can collect information on new employees' onboarding. For example, the unit collects information such as the new employee's name, onboarding date, and department. When collecting onboarding information, the unit can also refer to past onboarding information to select the most optimal collection method. For example, the unit can analyze past onboarding information to select the most efficient collection method. The unit can also refer to past onboarding information to determine the priority of the information to collect. Based on past onboarding information, the unit can also adjust the scope of the information to be collected. This provides information for determining the appropriate system type and permissions by collecting new employee onboarding information. Some or all of the above processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input new employee onboarding information into a generating AI and have the generating AI select the optimal collection method.

[0035] The data collection unit can collect employee transfer information. For example, the data collection unit collects information such as the date of the employee's transfer, the department before and after the transfer, and their job title. When collecting transfer information, the data collection unit can also analyze the frequency and patterns of transfers to improve the accuracy of the collection. For example, the data collection unit can analyze the frequency of transfers and optimize the timing of collection. The data collection unit can also analyze the patterns of transfers and adjust the scope of information to be collected. Based on the frequency and patterns of transfers, the data collection unit can also determine the priority of the information to be collected. This allows the data collection unit to respond to changes in system types and permissions associated with transfers. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input employee transfer information into a generating AI and have the generating AI perform an analysis of the frequency and patterns of transfers.

[0036] The data collection unit can collect role information within a department. For example, the data collection unit collects information such as job title, assigned duties, and scope of authority. When collecting role information, the data collection unit can also analyze the frequency and patterns of role changes to improve the accuracy of the collection. For example, the data collection unit can analyze the frequency of role changes and optimize the timing of collection. The data collection unit can also analyze the patterns of role changes and adjust the scope of information to be collected. Based on the frequency and patterns of role changes, the data collection unit can also determine the priority of the information to be collected. In this way, by collecting role information within a department, it can respond to changes in system types and permissions that accompany role changes. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the role information within the department into a generating AI and have the generating AI perform an analysis of the frequency and patterns of role changes.

[0037] The analysis unit can analyze the collected information and determine the type of system and permissions required by the target individual. For example, if a new employee is assigned to the sales department, the analysis unit will determine the type of system to be used in the sales department and the permissions for each system. If an employee is transferred to a different department, the analysis unit can also determine the type of system to be used in the new department and the permissions for each system. If an employee's role changes, the analysis unit can also determine the type of system and the permissions for each system appropriate to the new role. For example, the analysis unit might determine that the sales department will use systems such as a customer management system and a sales management system. The analysis unit will determine permissions such as viewing, editing, and management for the customer management system. The analysis unit will determine permissions such as viewing, editing, and management for the sales management system. By analyzing the collected information, the analysis unit provides the target individual with the appropriate system type and permissions. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected information into a generating AI and have the generating AI perform the determination of system types and permissions.

[0038] The application department can automatically submit applications to each issuing office based on the system type and permissions determined by the analysis department. For example, the application department can automatically submit account applications for systems used by the sales department. The application department can also automatically submit account applications for customer management systems. The application department can also automatically submit account applications for sales management systems. For example, when the application department submits an account application for a customer management system, it includes information such as name, department, position, and permissions in the application details. When the application department submits an account application for a sales management system, it includes information such as name, department, position, and permissions in the application details. This reduces the effort required for applications by automatically submitting applications based on the system type and permissions determined by the analysis department. Some or all of the above processing in the application department may be performed using AI, for example, or without AI. For example, the application department can input the system type and permissions determined by the analysis department into a generation AI and have the generation AI generate the application details.

[0039] The deletion unit can submit requests to delete accounts for systems that are no longer needed. For example, the deletion unit can submit requests to delete accounts for systems that have become unnecessary due to personnel changes. The deletion unit can also submit requests to delete accounts for systems that have become unnecessary due to changes in roles. For example, the deletion unit can submit requests to delete accounts for customer management systems that have become unnecessary due to personnel changes. The deletion unit can also submit requests to delete accounts for sales management systems that have become unnecessary due to changes in roles. This allows for the proper management of systems by submitting requests to delete accounts for unnecessary systems. Some or all of the above-described processes in the deletion unit may be performed using AI, for example, or without AI. For example, the deletion unit can input requests to delete accounts for unnecessary systems into a generating AI and have the generating AI generate the deletion details.

[0040] The data collection unit can select the optimal data collection method by referring to past hiring information when collecting new employee hiring information. For example, the data collection unit can analyze past hiring information and select the most efficient data collection method. The data collection unit can also determine the priority of the information to be collected by referring to past hiring information. The data collection unit can also adjust the scope of the information to be collected based on past hiring information. In this way, by referring to past hiring information, the optimal data collection method is selected, and efficient information collection is achieved. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input new employee hiring information into a generating AI and have the generating AI select the optimal data collection method.

[0041] The data collection unit can improve the accuracy of data collection by analyzing the frequency and patterns of transfers when collecting transfer information. For example, the data collection unit can analyze the frequency of transfers and optimize the timing of collection. The data collection unit can also analyze the patterns of transfers and adjust the scope of information to be collected. Based on the frequency and patterns of transfers, the data collection unit can also determine the priority of information to be collected. In this way, by analyzing the frequency and patterns of transfers, the accuracy of data collection is improved and appropriate information is provided. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input transfer information into a generating AI and have the generating AI perform an analysis of the frequency and patterns of transfers.

[0042] The data collection unit can analyze employees' social media activity and collect relevant information. For example, the data collection unit can analyze employees' social media activity and collect information related to work. The data collection unit can also adjust the scope of information to be collected based on employees' social media activity. The data collection unit can also determine the priority of information to be collected by referring to employees' social media activity. This allows for the efficient collection of work-related information by analyzing employees' social media activity. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input employee social media activity data into a generating AI and have the generating AI collect relevant information.

[0043] The data collection unit can prioritize the collection of highly relevant information by considering the geographical location information of employees. For example, the data collection unit prioritizes the collection of highly relevant information based on the geographical location information of employees. The data collection unit can also adjust the scope of information to be collected by referring to the geographical location information of employees. The data collection unit can also determine the priority of information to be collected by considering the geographical location information of employees. In this way, by considering the geographical location information of employees, highly relevant information is collected preferentially. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI. For example, the data collection unit can input the geographical location information of employees into a generating AI and have the generating AI perform the collection of relevant information.

[0044] The analysis unit can optimize its analysis algorithm by referring to past analysis data when analyzing collected information. For example, the analysis unit can select the optimal analysis algorithm based on past analysis data. The analysis unit can also improve the accuracy of the analysis by referring to past analysis data. The analysis unit can also analyze past analysis data and optimize the analysis algorithm. In this way, by referring to past analysis data, the analysis algorithm is optimized and the accuracy of the analysis is improved. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past analysis data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0045] The analysis unit can apply different analysis methods depending on the category of information during analysis. For example, the analysis unit can select the optimal analysis method depending on the category of information. The analysis unit can also adjust the analysis method based on the category of information. The analysis unit can also improve the accuracy of the analysis depending on the category of information. In this way, the accuracy of the analysis is improved by applying the optimal analysis method depending on the category of information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the category of information into a generating AI and have the generating AI select the optimal analysis method.

[0046] The analysis unit can adjust the order of analysis based on the timing of information submission. For example, the analysis unit determines the order of analysis based on the timing of information submission. The analysis unit can also adjust the priority of analysis by referring to the timing of information submission. The analysis unit can also optimize the order of analysis according to the timing of information submission. This enables efficient analysis by adjusting the order of analysis based on the timing of information submission. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the timing of information submission to a generating AI and have the generating AI perform the adjustment of the order of analysis.

[0047] The analysis unit can improve the accuracy of the analysis by referring to relevant literature on the collected information. For example, the analysis unit improves the accuracy of the analysis based on relevant literature. The analysis unit can also optimize the analysis method by referring to relevant literature. The analysis unit can also improve the accuracy of the analysis by analyzing relevant literature. In this way, by referring to relevant literature, the accuracy of the analysis is improved and more accurate analysis results are provided. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input relevant literature into a generating AI and have the generating AI perform the optimization of the analysis method.

[0048] The application unit can adjust the level of detail of an application based on the importance of the system at the time of application. For example, the application unit determines the level of detail of the application content based on the importance of the system. The application unit can also adjust the priority of the application content by referring to the importance of the system. The application unit can also optimize the level of detail of the application content according to the importance of the system. This ensures that appropriate applications are made for important systems by adjusting the level of detail of the application based on the importance of the system. Some or all of the above processes in the application unit may be performed using AI, for example, or not using AI. For example, the application unit can input the importance of the system into a generating AI and have the generating AI perform the adjustment of the level of detail of the application content.

[0049] The application unit can apply different application algorithms depending on the system category at the time of application. For example, the application unit can select the optimal application algorithm depending on the system category. The application unit can also adjust the application algorithm based on the system category. The application unit can also improve the accuracy of the application depending on the system category. This improves the accuracy of the application by applying the optimal application algorithm depending on the system category. Some or all of the above processing in the application unit may be performed using AI, for example, or without AI. For example, the application unit can input the system category into a generating AI and have the generating AI select the optimal application algorithm.

[0050] The application department can adjust the order of applications based on the system's submission timing. For example, the application department determines the order of applications based on the system's submission timing. The application department can also adjust the priority of applications by referring to the system's submission timing. The application department can also optimize the order of applications according to the system's submission timing. This enables efficient applications by adjusting the order of applications based on the system's submission timing. Some or all of the above processes in the application department may be performed using AI, for example, or not using AI. For example, the application department can input the system's submission timing into a generating AI and have the generating AI perform the adjustment of the order of applications.

[0051] The application unit can improve the accuracy of the application by referring to relevant system documentation. For example, the application unit improves the accuracy of the application based on relevant documentation. The application unit can also optimize the application method by referring to relevant documentation. The application unit can also improve the accuracy of the application by analyzing relevant documentation. In this way, by referring to relevant documentation, the accuracy of the application is improved and a more accurate application is made. Some or all of the above processes in the application unit may be performed using AI, for example, or not using AI. For example, the application unit can input relevant documentation into a generating AI and have the generating AI perform the optimization of the application method.

[0052] The deletion unit can adjust the level of detail of deletions based on the importance of the system during the deletion process. For example, the deletion unit determines the level of detail of the deletion content based on the importance of the system. The deletion unit can also adjust the priority of the deletion content by referring to the importance of the system. The deletion unit can also optimize the level of detail of the deletion content according to the importance of the system. This ensures that appropriate deletions are performed for important systems by adjusting the level of detail of deletions based on the importance of the system. Some or all of the above processes in the deletion unit may be performed using AI, for example, or without AI. For example, the deletion unit can input the importance of the system into a generating AI and have the generating AI perform the adjustment of the level of detail of the deletion content.

[0053] The deletion unit can apply different deletion algorithms depending on the system category during deletion. For example, the deletion unit can select the optimal deletion algorithm depending on the system category. The deletion unit can also adjust the deletion algorithm based on the system category. The deletion unit can also improve the accuracy of deletion depending on the system category. This improves the accuracy of deletion by applying the optimal deletion algorithm depending on the system category. Some or all of the above processes in the deletion unit may be performed using AI, for example, or without AI. For example, the deletion unit can input the system category into a generating AI and have the generating AI select the optimal deletion algorithm.

[0054] The deletion unit can adjust the deletion order based on the system submission timing. For example, the deletion unit determines the deletion order based on the system submission timing. The deletion unit can also adjust the deletion priority by referring to the system submission timing. The deletion unit can also optimize the deletion order according to the system submission timing. This enables efficient deletion by adjusting the deletion order based on the system submission timing. Some or all of the above processing in the deletion unit may be performed using AI, for example, or without AI. For example, the deletion unit can input the system submission timing into a generating AI and have the generating AI perform the adjustment of the deletion order.

[0055] The deletion unit can improve the accuracy of deletions by referring to related documents within the system. For example, the deletion unit improves the accuracy of deletions based on related documents. The deletion unit can also optimize the deletion method by referring to related documents. The deletion unit can also improve the accuracy of deletions by analyzing related documents. As a result, by referring to related documents, the accuracy of deletions is improved, and more precise deletions are performed. Some or all of the above processes in the deletion unit may be performed using AI, for example, or without AI. For example, the deletion unit can input related documents into a generating AI and have the generating AI perform the optimization of the deletion method.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The data collection unit can analyze employees' social media activity and collect relevant information. For example, it can analyze employees' social media activity and collect information related to their work. It can also adjust the scope of information collected based on employees' social media activity. It can also prioritize the information to be collected by referring to employees' social media activity. This allows for the efficient collection of work-related information by analyzing employees' social media activity. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input employee social media activity data into a generating AI and have the generating AI collect relevant information.

[0058] The data collection unit can prioritize the collection of highly relevant information by considering the geographical location information of employees. For example, it can prioritize the collection of highly relevant information based on the geographical location information of employees. It can also adjust the scope of information to be collected by referring to the geographical location information of employees. It can also determine the priority of information to be collected by considering the geographical location information of employees. In this way, by considering the geographical location information of employees, it can prioritize the collection of highly relevant information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI. For example, the data collection unit can input the geographical location information of employees into a generating AI and have the generating AI perform the collection of relevant information.

[0059] The analysis unit can optimize its analysis algorithm by referring to past analysis data when analyzing collected information. For example, it can select the optimal analysis algorithm based on past analysis data. It can also improve the accuracy of the analysis by referring to past analysis data. It can also optimize the analysis algorithm by analyzing past analysis data. In this way, by referring to past analysis data, the analysis algorithm is optimized and the accuracy of the analysis is improved. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past analysis data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0060] The application department can adjust the level of detail of an application based on the importance of the system at the time of application. For example, it can determine the level of detail of the application content based on the importance of the system. It can also adjust the priority of the application content by referring to the importance of the system. It can also optimize the level of detail of the application content according to the importance of the system. In this way, by adjusting the level of detail of the application based on the importance of the system, appropriate applications can be made for important systems. Some or all of the above processes in the application department may be performed using AI, for example, or not using AI. For example, the application department can input the importance of the system into a generating AI and have the generating AI perform the adjustment of the level of detail of the application content.

[0061] The deletion unit can adjust the deletion order based on the system's submission timing. For example, it can determine the deletion order based on the system's submission timing. It can also adjust the deletion priority by referring to the system's submission timing. It can also optimize the deletion order according to the system's submission timing. This enables efficient deletion by adjusting the deletion order based on the system's submission timing. Some or all of the above-described processes in the deletion unit may be performed using AI, for example, or without AI. For example, the deletion unit can input the system's submission timing into a generating AI and have the generating AI perform the adjustment of the deletion order.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The collection department collects information on new hires, transfers, and roles within departments. For example, it collects information such as the name, date of hire, and department of new employees. For transfers, it collects information such as the date of transfer, the department before and after the transfer, and the position. For roles, it collects information such as the job title, responsibilities, and scope of authority. Step 2: The analysis unit analyzes the information collected by the collection unit to determine the type of systems and permissions required by the subject. For example, if a new employee is assigned to the sales department, the analysis unit determines the types of systems used in the sales department and the permissions for each system. If the department changes due to a transfer, the analysis unit determines the types of systems used in the new department and the permissions for each system. If the role changes, the analysis unit determines the types of systems and the permissions for each system appropriate to the new role. Step 3: The application department automatically submits applications to each issuing office based on the system type and permissions determined by the analysis department. For example, it automatically submits account applications for systems used by the sales department. When automatically submitting account applications for customer management systems and sales management systems, the application includes information such as name, department, job title, and permissions. Step 4: The deletion unit submits a request to delete accounts for systems that are no longer needed. For example, they submit a request to delete accounts for systems that are no longer needed due to a personnel change, or accounts that are no longer needed due to a change in role.

[0064] (Example of form 2) An automated account application system according to an embodiment of the present invention is a system that automates the application of accounts necessary for business operations within a company. This automated account application system collects information on new hires, transfers, and roles within departments, determines the type of system and permissions required by the applicant, and automatically submits applications to the respective issuing offices. For example, when a new employee joins the company, the automated account application system collects information on new hires. When an employee is transferred, it collects information on transfers. Furthermore, when a role changes within a department, it collects information on those roles. Next, the automated account application system analyzes the collected information and determines the type of system and permissions required by the applicant. For example, if a new employee is assigned to the sales department, it determines the type of system used in the sales department and the permissions for each system. Furthermore, if a department is changed due to a transfer, it determines the type of system used in the new department and the permissions for each system. Furthermore, if a role changes, it determines the type of system and the permissions for each system according to the new role. Based on the determined system type and permissions, the automated account application system automatically submits applications to the respective issuing offices. For example, it automatically submits an account application for a system used in the sales department. It also automatically submits an account deletion application for unnecessary systems. This eliminates the need for applicants to submit applications manually, significantly reducing the time and effort required for applications. The automated account application system automatically issues, deletes, and modifies the appropriate system accounts whenever employees join or leave the company, transfer to a new department, or change roles within a department. This reduces the cost of account applications and deletions to almost zero, while also improving the efficiency and proper management of system accounts. In short, the automated account application system enables more efficient and proper account management.

[0065] The automated account application system according to this embodiment comprises a collection unit, an analysis unit, an application unit, and a deletion unit. The collection unit collects information such as hiring information, transfer information, and departmental role information. For example, the collection unit collects hiring information for new employees. The collection unit can also collect transfer information if an employee is transferred. The collection unit can also collect role information if a role within a department is changed. For example, the collection unit collects information such as the name, hiring date, and department of a new employee. As transfer information, the collection unit collects information such as the transfer date, department before and after the transfer, and job title. As role information, the collection unit collects information such as job title, assigned duties, and scope of authority. The analysis unit analyzes the information collected by the collection unit and determines the type of system and authority required by the target person. For example, if a new employee is assigned to the sales department, the analysis unit determines the type of system used in the sales department and the authority for each system. If a department is changed due to a transfer, the analysis unit can also determine the type of system used in the new department and the authority for each system. The Analysis Department can also determine the type of system and the permissions for each system according to the new role when a role changes. For example, the Analysis Department can determine that the systems used by the Sales Department include a customer management system and a sales management system. The Analysis Department determines permissions such as viewing, editing, and management for the customer management system. The Analysis Department determines permissions such as viewing, editing, and management for the sales management system. The Application Department automatically submits applications to each issuing office based on the type of system and permissions determined by the Analysis Department. For example, the Application Department automatically submits account applications for systems used by the Sales Department. The Application Department can also automatically submit account applications for the customer management system. The Application Department can also automatically submit account applications for the sales management system. For example, when the Application Department submits an account application for the customer management system, it includes information such as name, department, position, and permissions in the application details. When the Application Department submits an account application for the sales management system, it includes information such as name, department, position, and permissions in the application details. The Deletion Department submits account deletion applications for systems that are no longer needed. The deletion section, for example, submits requests to delete accounts for systems that are no longer needed due to personnel changes.The deletion unit can also submit account deletion requests for systems that are no longer needed due to a change in role. For example, the deletion unit can submit account deletion requests for customer management systems that are no longer needed due to personnel changes. The deletion unit can also submit account deletion requests for sales management systems that are no longer needed due to a change in role. As a result, the automated account application system according to this embodiment can improve the efficiency and appropriateness of account management.

[0066] The data collection department collects information on new hires, transfers, and roles within departments. Specifically, it collects information such as the name, date of hire, and department of new employees. This information is automatically obtained from the company's HR system and onboarding system. For example, when a new employee completes the onboarding process, this information is sent to the data collection department and stored in the database. For transfers, it collects information such as the date of transfer, the department before and after the transfer, and the employee's position. When a transfer occurs, the HR system sends the transfer information to the data collection department and stores it in the database. For roles, it collects information such as the employee's job title, responsibilities, and scope of authority. When a role change occurs, the department's management system sends the role information to the data collection department and stores it in the database. This allows the data collection department to collect information on HR transfers and role changes within the company in real time and centrally manage it in the database. Furthermore, the data collection department can regularly update this information to maintain its up-to-date status. For example, it retrieves the latest information from the HR system and departmental management systems at a fixed time every day and updates the database. In addition, the data collection department can perform data integrity checks and duplicate checks to ensure the accuracy of the information. This allows the data collection unit to provide accurate and up-to-date information, improving the overall reliability of the system.

[0067] The analysis department analyzes the information collected by the data collection department to determine the types of systems and permissions required by the target individual. Specifically, if a new employee is assigned to the sales department, the analysis department determines the types of systems used in the sales department and the permissions required for each system. For example, systems used in the sales department include customer management systems and sales management systems. The analysis department determines the permissions required by the new employee for these systems. For customer management systems, it determines permissions such as viewing, editing, and management. For sales management systems, it determines permissions such as viewing, editing, and management. The analysis department uses AI to make these determinations. The AI ​​uses historical data and rule-based algorithms to quickly and accurately determine the types of systems and permissions required by the target individual. For example, the AI ​​learns from past new employee assignment information and permission information to propose the optimal systems and permissions for new individuals. The AI ​​also automatically determines changes in systems and permissions due to transfers or role changes. For example, if an employee is transferred to a different department, it determines the types of systems used in the new department and the permissions required for each system. If an employee's role changes, it determines the types of systems and permissions required for each system that are appropriate for the new role. This allows the analysis unit to quickly and accurately determine the systems and permissions required by the target user, thereby improving the overall efficiency and optimization of the system.

[0068] The application department automatically submits applications to each issuing office based on the system type and permissions determined by the analysis department. Specifically, it automatically submits account applications for systems used by the sales department. For example, when applying for an account for the customer management system, the application includes information such as name, department, position, and permissions. Based on this information, the application department automatically submits applications to the issuing offices for each system. The application process follows a pre-configured workflow, and the procedures for obtaining necessary approvals are also automated. For example, when an account application for the customer management system is submitted, an approval request is sent to the system administrator, and once approval is obtained, the account is issued. The application department can monitor these processes in real time and manage their progress. In addition, the application department can perform data integrity checks and error checks to ensure the accuracy of the application content. This allows the application department to submit account applications quickly and accurately, improving the overall efficiency and optimization of the system. Furthermore, the application department saves the application history to a database for later reference. This ensures the traceability of applications, and allows for verification of application content as needed.

[0069] The deletion unit submits requests to delete accounts for systems that are no longer needed. Specifically, it submits requests to delete accounts for systems that have become unnecessary due to personnel changes. For example, it submits requests to delete accounts for customer management systems that have become unnecessary due to personnel changes. Based on this information, the deletion unit automatically submits deletion requests to the issuing contacts for each system. The deletion process follows a pre-configured workflow, and the procedures for obtaining necessary approvals are also automated. For example, when a request to delete an account for a customer management system is submitted, an approval request is sent to the system administrator, and once approval is obtained, the account is deleted. The deletion unit can monitor these processes in real time and manage the progress. In addition, the deletion unit can perform data integrity checks and error checks to ensure the accuracy of the deleted content. This allows the deletion unit to submit account deletion requests quickly and accurately, improving the efficiency and optimization of the entire system. Furthermore, the deletion unit saves the deletion history to a database for later reference. This ensures the traceability of deletions, and allows for verification of deleted content as needed.

[0070] The data collection unit can collect information on new employees' onboarding. For example, the unit collects information such as the new employee's name, onboarding date, and department. When collecting onboarding information, the unit can also refer to past onboarding information to select the most optimal collection method. For example, the unit can analyze past onboarding information to select the most efficient collection method. The unit can also refer to past onboarding information to determine the priority of the information to collect. Based on past onboarding information, the unit can also adjust the scope of the information to be collected. This provides information for determining the appropriate system type and permissions by collecting new employee onboarding information. Some or all of the above processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input new employee onboarding information into a generating AI and have the generating AI select the optimal collection method.

[0071] The data collection unit can collect employee transfer information. For example, the data collection unit collects information such as the date of the employee's transfer, the department before and after the transfer, and their job title. When collecting transfer information, the data collection unit can also analyze the frequency and patterns of transfers to improve the accuracy of the collection. For example, the data collection unit can analyze the frequency of transfers and optimize the timing of collection. The data collection unit can also analyze the patterns of transfers and adjust the scope of information to be collected. Based on the frequency and patterns of transfers, the data collection unit can also determine the priority of the information to be collected. This allows the data collection unit to respond to changes in system types and permissions associated with transfers. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input employee transfer information into a generating AI and have the generating AI perform an analysis of the frequency and patterns of transfers.

[0072] The data collection unit can collect role information within a department. For example, the data collection unit collects information such as job title, assigned duties, and scope of authority. When collecting role information, the data collection unit can also analyze the frequency and patterns of role changes to improve the accuracy of the collection. For example, the data collection unit can analyze the frequency of role changes and optimize the timing of collection. The data collection unit can also analyze the patterns of role changes and adjust the scope of information to be collected. Based on the frequency and patterns of role changes, the data collection unit can also determine the priority of the information to be collected. In this way, by collecting role information within a department, it can respond to changes in system types and permissions that accompany role changes. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the role information within the department into a generating AI and have the generating AI perform an analysis of the frequency and patterns of role changes.

[0073] The analysis unit can analyze the collected information and determine the type of system and permissions required by the target individual. For example, if a new employee is assigned to the sales department, the analysis unit will determine the type of system to be used in the sales department and the permissions for each system. If an employee is transferred to a different department, the analysis unit can also determine the type of system to be used in the new department and the permissions for each system. If an employee's role changes, the analysis unit can also determine the type of system and the permissions for each system appropriate to the new role. For example, the analysis unit might determine that the sales department will use systems such as a customer management system and a sales management system. The analysis unit will determine permissions such as viewing, editing, and management for the customer management system. The analysis unit will determine permissions such as viewing, editing, and management for the sales management system. By analyzing the collected information, the analysis unit provides the target individual with the appropriate system type and permissions. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected information into a generating AI and have the generating AI perform the determination of system types and permissions.

[0074] The application department can automatically submit applications to each issuing office based on the system type and permissions determined by the analysis department. For example, the application department can automatically submit account applications for systems used by the sales department. The application department can also automatically submit account applications for customer management systems. The application department can also automatically submit account applications for sales management systems. For example, when the application department submits an account application for a customer management system, it includes information such as name, department, position, and permissions in the application details. When the application department submits an account application for a sales management system, it includes information such as name, department, position, and permissions in the application details. This reduces the effort required for applications by automatically submitting applications based on the system type and permissions determined by the analysis department. Some or all of the above processing in the application department may be performed using AI, for example, or without AI. For example, the application department can input the system type and permissions determined by the analysis department into a generation AI and have the generation AI generate the application details.

[0075] The deletion unit can submit requests to delete accounts for systems that are no longer needed. For example, the deletion unit can submit requests to delete accounts for systems that have become unnecessary due to personnel changes. The deletion unit can also submit requests to delete accounts for systems that have become unnecessary due to changes in roles. For example, the deletion unit can submit requests to delete accounts for customer management systems that have become unnecessary due to personnel changes. The deletion unit can also submit requests to delete accounts for sales management systems that have become unnecessary due to changes in roles. This allows for the proper management of systems by submitting requests to delete accounts for unnecessary systems. Some or all of the above-described processes in the deletion unit may be performed using AI, for example, or without AI. For example, the deletion unit can input requests to delete accounts for unnecessary systems into a generating AI and have the generating AI generate the deletion details.

[0076] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit may delay the timing of information collection. If the user is relaxed, the data collection unit may also speed up the timing of information collection. If the user is in a hurry, the data collection unit may also collect information immediately. This reduces the burden on the user by adjusting the timing of information collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into the generative AI and have the generative AI adjust the timing of information collection.

[0077] The data collection unit can select the optimal data collection method by referring to past hiring information when collecting new employee hiring information. For example, the data collection unit can analyze past hiring information and select the most efficient data collection method. The data collection unit can also determine the priority of the information to be collected by referring to past hiring information. The data collection unit can also adjust the scope of the information to be collected based on past hiring information. In this way, by referring to past hiring information, the optimal data collection method is selected, and efficient information collection is achieved. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input new employee hiring information into a generating AI and have the generating AI select the optimal data collection method.

[0078] The data collection unit can improve the accuracy of data collection by analyzing the frequency and patterns of transfers when collecting transfer information. For example, the data collection unit can analyze the frequency of transfers and optimize the timing of collection. The data collection unit can also analyze the patterns of transfers and adjust the scope of information to be collected. Based on the frequency and patterns of transfers, the data collection unit can also determine the priority of information to be collected. In this way, by analyzing the frequency and patterns of transfers, the accuracy of data collection is improved and appropriate information is provided. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input transfer information into a generating AI and have the generating AI perform an analysis of the frequency and patterns of transfers.

[0079] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting important information. If the user is relaxed, the data collection unit can also collect detailed information. If the user is in a hurry, the data collection unit can also collect only the minimum necessary information. This prioritizes the collection of important information by determining the priority of information to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of information collection.

[0080] The data collection unit can analyze employees' social media activity and collect relevant information. For example, the data collection unit can analyze employees' social media activity and collect information related to work. The data collection unit can also adjust the scope of information to be collected based on employees' social media activity. The data collection unit can also determine the priority of information to be collected by referring to employees' social media activity. This allows for the efficient collection of work-related information by analyzing employees' social media activity. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input employee social media activity data into a generating AI and have the generating AI collect relevant information.

[0081] The data collection unit can prioritize the collection of highly relevant information by considering the geographical location information of employees. For example, the data collection unit prioritizes the collection of highly relevant information based on the geographical location information of employees. The data collection unit can also adjust the scope of information to be collected by referring to the geographical location information of employees. The data collection unit can also determine the priority of information to be collected by considering the geographical location information of employees. In this way, by considering the geographical location information of employees, highly relevant information is collected preferentially. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI. For example, the data collection unit can input the geographical location information of employees into a generating AI and have the generating AI perform the collection of relevant information.

[0082] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results. If the user is excited, the analysis unit can also provide visually stimulating analysis results. In this way, by adjusting the presentation of the analysis according to the user's emotions, the analysis results are made easier for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0083] The analysis unit can optimize its analysis algorithm by referring to past analysis data when analyzing collected information. For example, the analysis unit can select the optimal analysis algorithm based on past analysis data. The analysis unit can also improve the accuracy of the analysis by referring to past analysis data. The analysis unit can also analyze past analysis data and optimize the analysis algorithm. In this way, by referring to past analysis data, the analysis algorithm is optimized and the accuracy of the analysis is improved. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past analysis data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0084] The analysis unit can apply different analysis methods depending on the category of information during analysis. For example, the analysis unit can select the optimal analysis method depending on the category of information. The analysis unit can also adjust the analysis method based on the category of information. The analysis unit can also improve the accuracy of the analysis depending on the category of information. In this way, the accuracy of the analysis is improved by applying the optimal analysis method depending on the category of information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the category of information into a generating AI and have the generating AI select the optimal analysis method.

[0085] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit will prioritize analyzing important information. If the user is relaxed, the analysis unit can also analyze detailed information. If the user is in a hurry, the analysis unit can also prioritize analyzing only the essential information. In this way, by determining the priority of analysis according to the user's emotions, important information is prioritized. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI determine the priority of analysis.

[0086] The analysis unit can adjust the order of analysis based on the timing of information submission. For example, the analysis unit determines the order of analysis based on the timing of information submission. The analysis unit can also adjust the priority of analysis by referring to the timing of information submission. The analysis unit can also optimize the order of analysis according to the timing of information submission. This enables efficient analysis by adjusting the order of analysis based on the timing of information submission. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the timing of information submission to a generating AI and have the generating AI perform the adjustment of the order of analysis.

[0087] The analysis unit can improve the accuracy of the analysis by referring to relevant literature on the collected information. For example, the analysis unit improves the accuracy of the analysis based on relevant literature. The analysis unit can also optimize the analysis method by referring to relevant literature. The analysis unit can also improve the accuracy of the analysis by analyzing relevant literature. In this way, by referring to relevant literature, the accuracy of the analysis is improved and more accurate analysis results are provided. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input relevant literature into a generating AI and have the generating AI perform the optimization of the analysis method.

[0088] The application unit can estimate the user's emotions and adjust the way the application is presented based on the estimated emotions. For example, if the user is relaxed, the application unit can provide a detailed application. If the user is in a hurry, the application unit can also provide a concise application. If the user is excited, the application unit can also provide a visually stimulating application. By adjusting the way the application is presented according to the user's emotions, the application is made easier for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the application unit may be performed using AI or not using AI. For example, the application unit can input user emotion data into a generative AI and have the generative AI adjust the way the application is presented.

[0089] The application unit can adjust the level of detail of an application based on the importance of the system at the time of application. For example, the application unit determines the level of detail of the application content based on the importance of the system. The application unit can also adjust the priority of the application content by referring to the importance of the system. The application unit can also optimize the level of detail of the application content according to the importance of the system. This ensures that appropriate applications are made for important systems by adjusting the level of detail of the application based on the importance of the system. Some or all of the above processes in the application unit may be performed using AI, for example, or not using AI. For example, the application unit can input the importance of the system into a generating AI and have the generating AI perform the adjustment of the level of detail of the application content.

[0090] The application unit can apply different application algorithms depending on the system category at the time of application. For example, the application unit can select the optimal application algorithm depending on the system category. The application unit can also adjust the application algorithm based on the system category. The application unit can also improve the accuracy of the application depending on the system category. This improves the accuracy of the application by applying the optimal application algorithm depending on the system category. Some or all of the above processing in the application unit may be performed using AI, for example, or without AI. For example, the application unit can input the system category into a generating AI and have the generating AI select the optimal application algorithm.

[0091] The application unit can estimate the user's emotions and determine the priority of applications based on the estimated emotions. For example, if the user is stressed, the application unit will prioritize important applications. If the user is relaxed, the application unit can also prioritize detailed applications. If the user is in a hurry, the application unit can also prioritize only the essential applications. This ensures that important applications are prioritized by determining the priority of applications according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the application unit may be performed using AI or not. For example, the application unit can input user emotion data into a generative AI and have the generative AI determine the priority of applications.

[0092] The application department can adjust the order of applications based on the system's submission timing. For example, the application department determines the order of applications based on the system's submission timing. The application department can also adjust the priority of applications by referring to the system's submission timing. The application department can also optimize the order of applications according to the system's submission timing. This enables efficient applications by adjusting the order of applications based on the system's submission timing. Some or all of the above processes in the application department may be performed using AI, for example, or not using AI. For example, the application department can input the system's submission timing into a generating AI and have the generating AI perform the adjustment of the order of applications.

[0093] The application unit can improve the accuracy of the application by referring to relevant system documentation. For example, the application unit improves the accuracy of the application based on relevant documentation. The application unit can also optimize the application method by referring to relevant documentation. The application unit can also improve the accuracy of the application by analyzing relevant documentation. In this way, by referring to relevant documentation, the accuracy of the application is improved and a more accurate application is made. Some or all of the above processes in the application unit may be performed using AI, for example, or not using AI. For example, the application unit can input relevant documentation into a generating AI and have the generating AI perform the optimization of the application method.

[0094] The deletion unit can estimate the user's emotions and adjust the way the deletion is presented based on the estimated emotions. For example, if the user is relaxed, the deletion unit can provide detailed deletion content. If the user is in a hurry, the deletion unit can also provide concise deletion content. If the user is excited, the deletion unit can also provide visually stimulating deletion content. By adjusting the way the deletion is presented according to the user's emotions, the deletion unit provides content that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the deletion unit may be performed using AI, for example, or without AI. For example, the deletion unit can input user emotion data into the generative AI and have the generative AI adjust the way the deletion is presented.

[0095] The deletion unit can adjust the level of detail of deletions based on the importance of the system during the deletion process. For example, the deletion unit determines the level of detail of the deletion content based on the importance of the system. The deletion unit can also adjust the priority of the deletion content by referring to the importance of the system. The deletion unit can also optimize the level of detail of the deletion content according to the importance of the system. This ensures that appropriate deletions are performed for important systems by adjusting the level of detail of deletions based on the importance of the system. Some or all of the above processes in the deletion unit may be performed using AI, for example, or without AI. For example, the deletion unit can input the importance of the system into a generating AI and have the generating AI perform the adjustment of the level of detail of the deletion content.

[0096] The deletion unit can apply different deletion algorithms depending on the system category during deletion. For example, the deletion unit can select the optimal deletion algorithm depending on the system category. The deletion unit can also adjust the deletion algorithm based on the system category. The deletion unit can also improve the accuracy of deletion depending on the system category. This improves the accuracy of deletion by applying the optimal deletion algorithm depending on the system category. Some or all of the above processes in the deletion unit may be performed using AI, for example, or without AI. For example, the deletion unit can input the system category into a generating AI and have the generating AI select the optimal deletion algorithm.

[0097] The deletion unit can estimate the user's emotions and determine the priority of deletions based on the estimated emotions. For example, if the user is stressed, the deletion unit will prioritize important deletions. If the user is relaxed, the deletion unit can also perform detailed deletions. If the user is in a hurry, the deletion unit can also prioritize the minimum necessary deletions. In this way, by determining the priority of deletions according to the user's emotions, important deletions are prioritized. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the deletion unit may be performed using AI, or not using AI. For example, the deletion unit can input user emotion data into a generative AI and have the generative AI determine the priority of deletions.

[0098] The deletion unit can adjust the deletion order based on the system submission timing. For example, the deletion unit determines the deletion order based on the system submission timing. The deletion unit can also adjust the deletion priority by referring to the system submission timing. The deletion unit can also optimize the deletion order according to the system submission timing. This enables efficient deletion by adjusting the deletion order based on the system submission timing. Some or all of the above processing in the deletion unit may be performed using AI, for example, or without AI. For example, the deletion unit can input the system submission timing into a generating AI and have the generating AI perform the adjustment of the deletion order.

[0099] The deletion unit can improve the accuracy of deletions by referring to related documents within the system. For example, the deletion unit improves the accuracy of deletions based on related documents. The deletion unit can also optimize the deletion method by referring to related documents. The deletion unit can also improve the accuracy of deletions by analyzing related documents. As a result, by referring to related documents, the accuracy of deletions is improved, and more precise deletions are performed. Some or all of the above processes in the deletion unit may be performed using AI, for example, or without AI. For example, the deletion unit can input related documents into a generating AI and have the generating AI perform the optimization of the deletion method.

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

[0101] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is stressed, important information will be prioritized for analysis. If the user is relaxed, detailed information can also be analyzed. If the user is in a hurry, only the essential information can be prioritized for analysis. In this way, by determining the priority of analysis according to the user's emotions, important information is prioritized for analysis. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI determine the priority of analysis.

[0102] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is stressed, the timing of information collection can be delayed. If the user is relaxed, the timing of information collection can be accelerated. If the user is in a hurry, the timing of information collection can be immediate. This reduces the burden on the user by adjusting the timing of information collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of information collection.

[0103] The application unit can estimate the user's emotions and adjust the way the application is presented based on the estimated emotions. For example, if the user is relaxed, it can provide detailed application information. If the user is in a hurry, it can provide concise application information. If the user is excited, it can provide visually stimulating application information. By adjusting the way the application is presented according to the user's emotions, the application is made easier for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the application unit may be performed using AI or not. For example, the application unit can input user emotion data into a generative AI and have the generative AI adjust the way the application is presented.

[0104] The deletion unit can estimate the user's emotions and adjust the way the deletion is presented based on the estimated emotions. For example, if the user is relaxed, it can provide detailed deletion information. If the user is in a hurry, it can provide concise deletion information. If the user is excited, it can provide visually stimulating deletion information. By adjusting the way the deletion is presented according to the user's emotions, the deletion information is made easier for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the deletion unit may be performed using AI, or not using AI. For example, the deletion unit can input user emotion data into the generative AI and have the generative AI adjust the way the deletion is presented.

[0105] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, it can provide detailed analysis results. If the user is in a hurry, it can provide concise analysis results. If the user is excited, it can provide visually stimulating analysis results. In this way, by adjusting the presentation of the analysis according to the user's emotions, the analysis results are made easier for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of the analysis.

[0106] The data collection unit can analyze employees' social media activity and collect relevant information. For example, it can analyze employees' social media activity and collect information related to their work. It can also adjust the scope of information collected based on employees' social media activity. It can also prioritize the information to be collected by referring to employees' social media activity. This allows for the efficient collection of work-related information by analyzing employees' social media activity. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input employee social media activity data into a generating AI and have the generating AI collect relevant information.

[0107] The data collection unit can prioritize the collection of highly relevant information by considering the geographical location information of employees. For example, it can prioritize the collection of highly relevant information based on the geographical location information of employees. It can also adjust the scope of information to be collected by referring to the geographical location information of employees. It can also determine the priority of information to be collected by considering the geographical location information of employees. In this way, by considering the geographical location information of employees, it can prioritize the collection of highly relevant information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI. For example, the data collection unit can input the geographical location information of employees into a generating AI and have the generating AI perform the collection of relevant information.

[0108] The analysis unit can optimize its analysis algorithm by referring to past analysis data when analyzing collected information. For example, it can select the optimal analysis algorithm based on past analysis data. It can also improve the accuracy of the analysis by referring to past analysis data. It can also optimize the analysis algorithm by analyzing past analysis data. In this way, by referring to past analysis data, the analysis algorithm is optimized and the accuracy of the analysis is improved. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past analysis data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0109] The application department can adjust the level of detail of an application based on the importance of the system at the time of application. For example, it can determine the level of detail of the application content based on the importance of the system. It can also adjust the priority of the application content by referring to the importance of the system. It can also optimize the level of detail of the application content according to the importance of the system. In this way, by adjusting the level of detail of the application based on the importance of the system, appropriate applications can be made for important systems. Some or all of the above processes in the application department may be performed using AI, for example, or not using AI. For example, the application department can input the importance of the system into a generating AI and have the generating AI perform the adjustment of the level of detail of the application content.

[0110] The deletion unit can adjust the deletion order based on the system's submission timing. For example, it can determine the deletion order based on the system's submission timing. It can also adjust the deletion priority by referring to the system's submission timing. It can also optimize the deletion order according to the system's submission timing. This enables efficient deletion by adjusting the deletion order based on the system's submission timing. Some or all of the above-described processes in the deletion unit may be performed using AI, for example, or without AI. For example, the deletion unit can input the system's submission timing into a generating AI and have the generating AI perform the adjustment of the deletion order.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The collection department collects information on new hires, transfers, and roles within departments. For example, it collects information such as the name, date of hire, and department of new employees. For transfers, it collects information such as the date of transfer, the department before and after the transfer, and the position. For roles, it collects information such as the job title, responsibilities, and scope of authority. Step 2: The analysis unit analyzes the information collected by the collection unit to determine the type of systems and permissions required by the subject. For example, if a new employee is assigned to the sales department, the analysis unit determines the types of systems used in the sales department and the permissions for each system. If the department changes due to a transfer, the analysis unit determines the types of systems used in the new department and the permissions for each system. If the role changes, the analysis unit determines the types of systems and the permissions for each system appropriate to the new role. Step 3: The application department automatically submits applications to each issuing office based on the system type and permissions determined by the analysis department. For example, it automatically submits account applications for systems used by the sales department. When automatically submitting account applications for customer management systems and sales management systems, the application includes information such as name, department, job title, and permissions. Step 4: The deletion unit submits a request to delete accounts for systems that are no longer needed. For example, they submit a request to delete accounts for systems that are no longer needed due to a personnel change, or accounts that are no longer needed due to a change in role.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0115] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements described above, including the collection unit, analysis unit, application unit, and deletion unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects information on hiring, transfers, and roles within departments. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected information to determine the type of system and authority required. The application unit is implemented by the control unit 46A of the smart device 14 and automatically submits applications to each issuing office based on the determined system type and authority. The deletion unit is implemented by the identification processing unit 290 of the data processing unit 12 and submits an application to delete accounts for unnecessary systems. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0124] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the collection unit, analysis unit, application unit, and deletion unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects information on hiring, transfers, and roles within departments. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the collected information to determine the type of system and authority required. The application unit is implemented, for example, by the control unit 46A of the smart glasses 214 and automatically submits applications to each issuing office based on the determined system type and authority. The deletion unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and submits an application to delete accounts for unnecessary systems. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the collection unit, analysis unit, application unit, and deletion unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects information on hiring, transfers, and roles within departments. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected information to determine the type of system and authority required. The application unit is implemented by the control unit 46A of the headset terminal 314 and automatically submits applications to each issuing office based on the determined system type and authority. The deletion unit is implemented by the identification processing unit 290 of the data processing unit 12 and submits an application to delete accounts for unnecessary systems. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0157] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0158] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0161] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0162] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0163] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0164] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0165] Each of the multiple elements described above, including the collection unit, analysis unit, application unit, and deletion unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects information on hiring, transfers, and roles within departments. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the collected information to determine the type of system and authority required. The application unit is implemented by, for example, the control unit 46A of the robot 414 and automatically submits applications to each issuing office based on the determined system type and authority. The deletion unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and submits an application to delete accounts for unnecessary systems. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0166] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0167] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0168] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0169] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0170] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0171] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0174] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0175] 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.

[0176] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0177] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0178] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0179] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0180] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0181] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0182] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0183] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0184] (Note 1) The collection department collects information on new hires, transfers, and roles within departments. An analysis unit analyzes the information collected by the aforementioned collection unit and determines the type of system and permissions required by the target person, An application unit that automatically submits applications to each issuing office based on the type of system and authority determined by the aforementioned analysis unit, It includes a deletion unit that submits requests to delete accounts of unnecessary systems. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect information on new employees' onboarding. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Collect information on employee transfers. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is Collect information on roles within the department. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, The collected information is analyzed to determine the type of system and permissions required by the target individual. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned application department, Based on the system type and authority determined by the analysis department, applications are automatically submitted to each issuing office. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned deletion section is, Submit a request to delete an account for an unnecessary system. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting information on new employees joining the company, we will refer to past hiring data to select the most suitable collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting personnel change information, analyze the frequency and patterns of transfers to improve the accuracy of the collection. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is Analyze employees' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is Prioritize the collection of highly relevant information, taking into account employees' geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, When analyzing the collected information, the analysis algorithm is optimized by referring to past analysis data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, different analytical methods are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, The order of analysis will be adjusted based on when the collected information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, Improve the accuracy of the analysis by referring to relevant literature on the collected information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned application department, The system estimates the user's emotions and adjusts the way the application is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned application department, When submitting an application, adjust the level of detail based on the importance of the system. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned application department, When submitting an application, a different application algorithm is applied depending on the system category. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned application department, The system estimates the user's emotions and determines the priority of applications based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned application department, The order of applications will be adjusted based on the submission date in the system. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned application department, Refer to relevant system documentation to improve the accuracy of your application. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned deletion section is, The system estimates the user's emotions and adjusts the way deletion is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned deletion section is, When deleting, adjust the level of detail of the deletion based on the importance of the system. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned deletion section is, When deleting, different deletion algorithms are applied depending on the system category. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned deletion section is, It estimates user sentiment and determines deletion priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned deletion section is, The order of deletions will be adjusted based on the system submission date. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned deletion section is, Improve the accuracy of deletions by referring to related system documentation. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection department collects information on new hires, transfers, and roles within departments. An analysis unit analyzes the information collected by the aforementioned collection unit and determines the type of system and permissions required by the target person, An application unit that automatically submits applications to each issuing office based on the type of system and authority determined by the aforementioned analysis unit, It includes a deletion unit that submits requests to delete accounts of unnecessary systems. A system characterized by the following features.

2. The aforementioned collection unit is Collect information on new employees' onboarding. The system according to feature 1.

3. The aforementioned collection unit is Collect information on employee transfers. The system according to feature 1.

4. The aforementioned collection unit is Collect information on roles within the department. The system according to feature 1.

5. The aforementioned analysis unit, The collected information is analyzed to determine the type of system and permissions required by the target individual. The system according to feature 1.

6. The aforementioned application department, Based on the system type and authority determined by the aforementioned analysis unit, applications are automatically submitted to each issuing office. The system according to feature 1.

7. The aforementioned deletion section is, Submit a request to delete an account for an unnecessary system. The system according to feature 1.

8. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system according to feature 1.

9. The aforementioned collection unit is When collecting information on new employees joining the company, we will refer to past hiring data to select the most suitable collection method. The system according to feature 1.

10. The aforementioned collection unit is When collecting personnel change information, analyze the frequency and patterns of transfers to improve the accuracy of the collection. The system according to feature 1.

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