system

Abstract

We provide the system. [Solution] A means of displaying a screen for entering personnel information, A means of integrating entered personnel information with organizational information stored in the organization's needs database, A method using a generation engine to perform aptitude analysis based on integrated information, A means of displaying the analysis results on a screen and presenting transfer candidates, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method 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] There is a problem that it is difficult to achieve optimal personnel transfers because the wishes and career plans of individual employees are intricately intertwined with the organization's policies and resource plans. In particular, it is difficult to balance and integrate the wishes of each employee with the needs of the organization and to allocate human resources efficiently and appropriately.

Means for Solving the Problems

[0005] This invention provides a system that displays a screen for inputting personnel information and integrates the input information with organizational information. Furthermore, it performs aptitude analysis using a generation engine based on this integrated information and presents the results on the user interface, making it easier for users to understand the reasons for transfers and the expected results. This allows for the proposal of optimal personnel transfers and provides a means to further optimize them through feedback.

[0006] "Personnel information" refers to information such as an individual's skills, aptitudes, and career plans, and includes detailed data about individual employees.

[0007] "Organizational information" refers to data about the organization's needs, plans, and resource situation, and summarizes the personnel requirements for the entire organization.

[0008] A "generative engine" refers to an algorithm or program that proposes optimal personnel changes based on input data, and is designed to perform efficient information processing.

[0009] A "screen" refers to a display device used to implement a user interface; it is a visual interface through which users input information or view results.

[0010] "Aptitude analysis" refers to an analytical process that matches an individual's skills and career plan with the organization's needs to identify the most suitable placement and role.

[0011] "Potential transfer candidates" refer to specific positions and duties to which an individual may be assigned, as presented based on aptitude analysis. [Brief explanation of the drawing]

[0012] [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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Embodiments for Carrying Out the Invention

[0013] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings. [[ID=。43]]

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, the numbered communication I / F (Interface) is an interface that includes a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0019] 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] As shown in Figure 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.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception 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 reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] The 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.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] This invention is a system that inputs employees' skills, aptitudes, and career plans, integrates them with information obtained from the organization's needs database, and proposes optimal personnel placement. The program processing and specific examples of this system are described below.

[0034] Terminal: The terminal provides the user with a screen for information input. The user enters their past work experience, current skill set, and future career plans. This information is transferred from the terminal to the server.

[0035] Server: Based on the received user information, the server retrieves relevant information from the organization's needs database. This database records the organization's personnel requirements, project information, and current resource status. The server integrates this information and passes it to the generation engine.

[0036] The generation engine analyzes integrated data, matching user preferences with organizational needs to create transfer candidates. In this process, the generation engine utilizes random access generation technology to efficiently analyze numerous factors.

[0037] Terminal: When a transfer candidate is created, the server sends the analysis results to the terminal. The terminal displays these results to the user in an easy-to-understand format. The screen shows details such as the reason for the transfer, the proposed position, and the expected results, which the user can review.

[0038] For example, if a mid-career engineer in their 30s wants to move to a new management position, they can input their wishes into the system. The generation engine will then suggest appropriate transfer destinations based on the engineer's skills and the organization's requirements. For instance, they might be suggested as the lead of a new IT project.

[0039] Through this process, it becomes possible to effectively connect individual career plans with organizational needs and achieve optimal personnel transfers.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The user uses the device to input their skills and career plans. The device provides an input form and retrieves detailed information from the user. This allows the user to specifically describe their current experience and future job aspirations.

[0043] Step 2:

[0044] The user information entered by the terminal is sent to the server. This data contains all the information entered by the user, and the server receives and stores this data for further processing.

[0045] Step 3:

[0046] The server queries the organization's needs database based on the received user information. The database retrieves information about the skills and roles the organization is seeking for current projects and future plans.

[0047] Step 4:

[0048] The server passes the integrated data to the generation engine. The generation engine matches the user's skills with organizational needs and performs suitability analysis using AI algorithms. Various factors are considered here to generate the most suitable transfer candidates.

[0049] Step 5:

[0050] The server sends the transfer candidates analyzed by the generation engine to the terminal. This information includes the proposed transfer destination, the reasons for the transfer, and the expected results.

[0051] Step 6:

[0052] The terminal displays data received from the server to the user. Based on this information, the user can review their transfer options and provide feedback or make decisions.

[0053] (Example 1)

[0054] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0055] Modern organizations are required to quickly and effectively allocate talent appropriately in a rapidly changing business environment. However, traditional talent allocation methods struggle to immediately connect individual career plans and skills with organizational needs, and selecting candidates for transfers, in particular, is time-consuming and problematic. Therefore, there is a need for technology that can accurately analyze the aptitudes of individual employees and quickly propose optimal talent allocation.

[0056] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0057] In this invention, the server includes a display means for inputting information, a means for integrating the input information and recorded request information, and a means using a generation engine for performing suitability analysis based on the integrated information. This makes it possible to quickly analyze the skills and preferences of individual employees and propose placements that best match the organization's needs.

[0058] "A means of displaying information for input" refers to an interface that visually presents information so that users can intuitively and accurately input information such as their work experience, skill set, and career plan.

[0059] "Means for integrating input information and recorded request information" refers to a function that can organize information entered by users and information stored in the organization's needs database and combine them into a consistent dataset.

[0060] "Methods using a generation engine" refers to a process that utilizes AI technology to efficiently analyze diverse conditions based on integrated information and optimally match the needs of users and organizations.

[0061] "Means for displaying analysis results and suggesting placement candidates" refers to a function that visually presents the analysis results derived by the generation engine to the user and clearly indicates the recommended personnel placement plan.

[0062] "A means of efficiently processing diverse combinations of conditions using a generation engine to determine the optimal placement" refers to a function that has the ability to quickly process vast and complex user information and organizational needs, and to determine the optimal placement of personnel based on their combinations.

[0063] This invention relates to a system for optimizing personnel allocation within an organization. The following describes embodiments for carrying out the invention.

[0064] Terminal: In this system, the terminal provides a user-friendly interface for inputting information. Specifically, users input their past work experience, current skill set, and future career plans into this interface. This information is stored digitally in an internal database and ready for processing.

[0065] Server: The server receives information sent from terminals and performs transaction processing with the organization's existing databases, which contain personnel needs and project requirements. It then uses its own data processing algorithms to perform initial processing to integrate user information with organizational needs.

[0066] In this process, the server utilizes a generative AI model to create a deployment plan calculated based on the integrated data obtained. The generation engine uses random access generation techniques in particular to analyze diverse elements in a short amount of time.

[0067] For example, if a mid-career engineer in their 30s wishes to acquire management skills, the user enters this wish into the terminal. The server analyzes this information to identify appropriate projects and positions within the organization. Ultimately, it can propose placement as the lead of a new IT project.

[0068] An example of a prompt message might be: "A 30-year-old engineer is seeking a management position. Their skill set includes A, B, and C, and their future career plans are X and Y. Please suggest the best placement for them within the organization."

[0069] In this way, individual career aspirations and organizational requirements can be optimized, and personnel allocation decisions can be made quickly and effectively.

[0070] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0071] Step 1:

[0072] The terminal displays an interface for the user to input information. The user enters their work experience, skills, and career plan. The entered data is checked for consistency and quality and sent to the server by the terminal. The input is organized as text data, and the output is data transfer to the server.

[0073] Step 2:

[0074] The server collects user data received from terminals and retrieves relevant information from a recorded organizational needs database. The server uses database queries to extract necessary personnel information and combines it with user data. The input is user skill data and organizational project requirements, and the output is an integrated dataset. The server prepares this information for the generation engine.

[0075] Step 3:

[0076] The server passes the integrated dataset to the generation engine, which uses a generative AI model to begin the suitability analysis. The generation engine rapidly analyzes multidimensional data using random access generation technology to find transfer candidates that best match user conditions and organizational needs. The input is the integrated dataset, and the output is the processed placement candidates.

[0077] Step 4:

[0078] The server organizes the placement candidates generated by the generation engine and sends the task results to the terminal. The terminal displays the analysis results on the monitor in a user-friendly format. Here, specific job titles, reasons for transfer candidates, and expected results are presented in detail. The input is the analyzed placement candidates, and the output is the display data for the user.

[0079] Step 5:

[0080] The user reviews the proposed deployment plan on the terminal and provides feedback as needed. The user's feedback is resent from the terminal to the server and used as feedback data for further suitability analysis. The input is user feedback, and the output is adding the feedback to the database.

[0081] (Application Example 1)

[0082] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0083] Effectively managing employee skills and aptitudes and making optimal placements aligned with career plans is challenging within organizations. Furthermore, there is a need to effectively and quickly propose appropriate placements for individuals with skills in operating machinery and equipment, but systems to achieve this are lacking. To address this challenge, a system is needed that efficiently integrates employee information with organizational needs and presents the analysis results to operators in an easily understandable manner.

[0084] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0085] In this invention, the server includes means for displaying a screen for inputting personnel information, means for integrating the input personnel information with organizational information stored in an organizational needs database, and means using a generation engine for performing suitability analysis based on the integrated information. This makes it possible to propose the optimal placement of personnel with the skills to operate machinery and equipment.

[0086] The "screen for entering personnel information" is an interface that allows individual employees to input information such as their skills, work experience, and career plans.

[0087] An "organizational needs database" is a database that records and manages the requirements of projects an organization is working on, the personnel requirements, and the current status of its resources.

[0088] A "generative engine" is a data processing system that integrates input personnel information and organizational needs data, and performs suitability analysis based on that information.

[0089] "Methods for suggesting transfer candidates" refers to a system that proposes the most suitable position or assignment to an employee based on the results of analysis by a generation engine.

[0090] "Operational skills for machinery and equipment" refers to the skills and knowledge necessary to effectively operate specific machines and devices.

[0091] "Means for proposing optimal placement" refers to a system that comprehensively assesses the skills of individual employees and the needs of the organization, and then presents them with the most suitable job or role.

[0092] The system implementing this invention provides a screen on a user terminal for inputting personnel information and, by integrating it with the organization's needs database on a server, proposes optimal personnel placement. The user inputs their past work experience, current skills, and future career plans. The terminal then transfers this user data to the server.

[0093] Based on the received data, the server retrieves project requirements and current resource status from the organization's needs database and analyzes the integrated information using a generation engine. Specifically, it uses a generation AI model to calculate the optimal correlation of multidimensional data and evaluates the user's capabilities and the organization's requirements. As a result, a suitable personnel placement proposal is generated and sent to the terminal.

[0094] The terminal displays the analysis results sent from the server to the user in an easy-to-understand visual format. This includes the reasons for the transfer candidate's job title and position, as well as the expected results. Users can also receive feedback on how their own suitability has been analyzed and which placement is deemed optimal.

[0095] For example, a technician responsible for robot management can use the system to evaluate their own technical skills and receive proposals for lead positions in smart manufacturing projects. In this way, it becomes possible to offer career suggestions that are best suited to each user.

[0096] Examples of prompts for a generative AI model:

[0097] "Please propose the most suitable robot operation position, taking into account my current skill set and future career plans."

[0098] This structure enables efficient talent management and optimal skill utilization within the organization.

[0099] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0100] Step 1:

[0101] Users log in to the terminal and use the provided input screen to enter their skill set, work experience, and career plan. The entered information is converted into a data format and securely transferred from the terminal to the server. Specific examples of input include "5 years of project management experience" and "knowledge of AI technology."

[0102] Step 2:

[0103] The server accesses the organization's needs database to analyze received user data, retrieving current requirements, project specifications, and resource availability. The server integrates this data and uses a generative AI model to extract optimal patterns. This allows for a numerical evaluation of the relationship between each user's skills and the organization's needs.

[0104] Step 3:

[0105] Based on the analysis results generated by the server, the generation engine creates a proposal for optimal personnel placement. Through data calculations, specific projects and roles that the user can lead are identified. As an example of output, positions such as "Team Leader for a New AI Project" are proposed. This information is then output back to the terminal.

[0106] Step 4:

[0107] The terminal receives data from the server and displays the analysis results in a user-friendly format. The screen displays details of proposed transfer candidates, the reasons for those candidates, and the expected outcomes. Based on this, users can provide feedback aligned with their own career path and enter prompts to receive even more optimal suggestions.

[0108] Step 5:

[0109] User feedback is sent back to the system, and the server updates the database to reflect this in the next data analysis. This cyclical feedback mechanism improves the system's accuracy and enables more practical suggestions for the user.

[0110] The above describes the processing flow of the system that implements this application example.

[0111] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0112] This invention relates to an optimal personnel transfer system that combines the input of personnel information, integration with organizational information, and an emotion engine. This system takes user emotions into consideration, performs aptitude analysis, and efficiently proposes transfers that meet the organization's needs.

[0113] Terminal: The terminal provides the user with an information input interface. The user inputs specific information about their skills and career plans. During this process, the emotion engine recognizes emotions from the user's input and interactions, and retrieves additional information based on those emotions. For example, if a user speaks enthusiastically about their future career, that positive emotion is recorded.

[0114] Server: The server matches user information and emotional state received from the terminal with the organization's needs database. The server passes the integrated data, including emotional state, to a generation engine to initiate the process of suggesting optimal personnel changes. This generation engine adjusts the content of the suggested changes and the reasons for the suggestions to reflect the user's emotional state.

[0115] The generation engine comprehensively analyzes information obtained from the user's skill set, career plan, and emotion recognition, and attempts to match it with the organization's requirements. The emotion engine creates emotion-based follow-ups and reasoning for suggestions, particularly to increase user satisfaction.

[0116] Terminal: Once the results from the generation engine are sent to the terminal by the server, the terminal visualizes them for the user. The screen displays transfer destination suggestions that take the user's wishes and feelings into consideration, along with the reasoning behind them. Based on these results, the user can consider their next career step and provide feedback as needed.

[0117] For example, if a 30-year-old engineer is considering a transfer to a new department and uses this system to express their preferences, the generation engine will suggest transfer destinations that reflect their positive aspirations, thereby positively influencing their career plan. This consideration, based on the user's emotions, contributes to increased user satisfaction.

[0118] This system incorporates not only technical skills and organizational needs, but also emotional factors, enabling more personalized transfers and optimizing talent utilization within the organization.

[0119] The following describes the processing flow.

[0120] Step 1:

[0121] The user uses the device to input their skills, work experience, and career plan. The device displays input forms and collects information from the user sequentially. This includes dropdowns and text boxes, prompting the user for specific input.

[0122] Step 2:

[0123] The terminal activates an emotion engine when the user enters information, instantly recognizing the user's emotions based on factors such as typing speed and mouse movements. This emotion data is then transferred to the server along with the entered user information.

[0124] Step 3:

[0125] The server queries the organizational needs database using user information and sentiment data parameters received from the terminal. This database contains information about open positions and required skills within the organization. The server retrieves the relevant organizational information and prepares it for analysis.

[0126] Step 4:

[0127] The server passes integrated information, including user emotion data, to the generation engine. The generation engine uses an AI algorithm to perform an aptitude analysis that reflects the user's emotions and generates the most suitable transfer candidates. The emotion data obtained by the emotion engine is used to refine the reasons for the proposals and for follow-up.

[0128] Step 5:

[0129] The server sends the transfer candidates generated by the generation engine, along with detailed reasons for the suggestions, to the terminal. The terminal displays the results in a user-friendly format. For example, it shows how the transfer aligns with the user's long-term career goals and what emotional considerations have been taken into account.

[0130] Step 6:

[0131] The user reviews the analysis results and suggestions provided on their device. An emotion-first feedback option is displayed, allowing the user to provide an emotional response to the transfer candidates and suggestions. This feedback may be used for further adjustments.

[0132] (Example 2)

[0133] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0134] Traditionally, personnel transfer decisions within organizations have been based primarily on skills and direct organizational needs, but have not adequately considered the emotional aspects of employees. This can lead to decreased employee satisfaction, potentially negatively impacting the overall vitality and efficiency of the organization. Therefore, a system is needed that considers the emotional state of employees and proposes more appropriate and satisfying personnel transfers.

[0135] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0136] In this invention, the server includes means for analyzing the user's emotional state, means for integrating the input user information and emotional state with the organization's request database, and means for using a generative AI model to perform suitability analysis. This makes it possible to propose appropriate and satisfying personnel changes while taking the user's emotional state into consideration.

[0137] "User information" refers to data related to an individual's skills, career plans, experience, etc.

[0138] "Emotion recognition" is the process of analyzing a user's psychological state from their input and actions to identify emotions such as positive or negative.

[0139] An "organizational requirements database" refers to a collection of information that stores data on the characteristics, skills, and job responsibilities of personnel required by an organization.

[0140] "Aptitude analysis" is a process of comprehensively evaluating user information and organizational requirements to determine which tasks or positions are best suited for the user.

[0141] A "generative AI model" refers to an algorithm or machine learning model that proposes optimal personnel changes based on user and organizational information.

[0142] "Potential transfers" refer to new positions or roles that may be proposed to the employer.

[0143] "Visualization" refers to displaying information graphically and presenting it in a way that is easy for the user to understand.

[0144] To implement this invention, the following system is specifically used.

[0145] User information input and emotion recognition

[0146] The terminal provides the user with an interface for information input. Through this interface, the user inputs information such as their skills, experience, and career plans. The terminal incorporates emotion recognition capabilities, detecting the user's emotional state from their facial expressions and text input during the input process. Specific emotion recognition software is used for this emotion analysis.

[0147] Information integration and database processing

[0148] The server processes user information and sentiment data received from the terminal and accesses the organization's requirements database. This database records the organization's overall talent needs. The server integrates the user information and organizational data and passes it to the generative AI model.

[0149] Aptitude analysis using generative AI models

[0150] The AI ​​model generated on the server proposes the most suitable personnel transfers to users based on integrated data. This model comprehensively analyzes the user's skill set, career plan, and emotional data to generate optimal transfer candidates. It also adjusts the suggestions, paying particular attention to the user's positive emotions.

[0151] Results presentation and feedback

[0152] When the generation engine sends transfer candidate suggestions from the server to the terminal, the terminal visually presents them to the user. At this time, the suggested transfer destinations and the reasons for them are described in detail, allowing the user to decide on the next career step. The user can provide feedback based on the results and input more detailed information as needed.

[0153] Specific examples and prompt statements

[0154] For example, if a 30-year-old engineer expresses a desire to transfer to a new department, the generating AI model will suggest a transfer destination that reflects the engineer's positive career aspirations. An example of a prompt in this process would be: "A 30-year-old engineer desires a new challenge and has expressed positive feelings. Please suggest the most suitable transfer destination for him." This system is expected to quickly provide optimal transfer suggestions that take the user's emotions into account.

[0155] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0156] Step 1:

[0157] The terminal presents the user with an interface for information input. The user inputs data such as skills, career plans, and experience into the terminal. The terminal analyzes the input information and prepares to send it to the server. In this step, the user's basic information is obtained as input data.

[0158] Step 2:

[0159] The device utilizes emotion recognition software to recognize the user's emotional state based on their input and behavior. The recognized emotion data, along with user information, is prepared to be sent to the server. There, the server analyzes the user's emotional patterns and generates emotional state data such as positive and negative.

[0160] Step 3:

[0161] The server receives user information and sentiment data transmitted from the terminal. The received data is integrated with the organization's requirements database. The server inputs this integrated data into a generating AI model and begins data processing. In this step, the organization's demand information is referenced from the database as output data.

[0162] Step 4:

[0163] The AI ​​model on the server generates optimal transfer candidates for the user using integrated data. The model evaluates and analyzes the input skill set, career plan, and emotional state to determine the transfer destination. The output data generated includes the candidate positions and their rationale.

[0164] Step 5:

[0165] The server sends the generated transfer candidates to the terminal. The terminal visually processes the received data and displays the proposals to the user. The user reviews the presented transfer candidates and the reasons for them, and uses this information to decide on their next career step. At this step, the final proposal information is displayed to the user as output data.

[0166] Step 6:

[0167] When a user provides feedback, the device sends that feedback information back to the server. The server analyzes the feedback and, if necessary, requests a revised suggestion from the generating AI model. This cycle can potentially lead to the output of even more optimal suggestions that incorporate the user's opinions.

[0168] (Application Example 2)

[0169] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0170] In modern organizations, proposing optimal transfers while considering the aptitudes and emotional states of individual employees is a crucial challenge. However, appropriately recognizing emotional changes and reflecting them in transfer proposals is technically difficult, and as a result, transfers that enhance individual satisfaction are not being fully realized.

[0171] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0172] In this invention, the server includes means for displaying a visualization device for inputting personnel information, means for integrating the input personnel information with organizational information stored in an organizational needs database, and means for detecting the user's emotions and optimizing suggestions based on those emotions. This makes it possible to make more accurate transfer suggestions that take emotional states into account.

[0173] "Personnel information" refers to information that includes attributes such as skills, career, and emotions related to individual people.

[0174] A "visualization device" is a display device that provides information to users in the form of images.

[0175] "Integrating" means systematically combining multiple pieces of information, establishing relationships between them, and unifying them into a single entity.

[0176] A "generative engine" is a computer program that analyzes given data and generates results that meet a specific purpose.

[0177] "Emotion detection" is the act of inferring a user's internal emotional state from their facial expressions, voice, and other factors.

[0178] "Optimizing a proposal" means adjusting it to be the most effective and efficient proposal based on the given conditions and constraints.

[0179] This invention comprises a visualization device, a server, and a user system. The visualization device functions as an interface for presenting information to the user, where the user can input information about their skills and career plans. The visualization device is preferably a wearable device such as smart glasses or a head-mounted display.

[0180] The server integrates the personnel information received from the visualization device with the information stored in the organization's needs database. Based on this integrated data, the generation engine operates and performs suitability analysis. The generation engine is equipped with a module for detecting emotions and optimizes suggestions to reflect the user's emotional state. Specifically, it recognizes the user's emotions through facial and voice analysis and adjusts the optimal movement options and suggestions based on that information.

[0181] For example, if a user is experiencing stress while working, the emotion analysis module can detect that stress and suggest relaxing music. Furthermore, the system receives feedback from the user through prompts such as "Suggest music that matches my current mood" or "Tell me how to perform this task more efficiently," and uses this feedback to improve the suggestions.

[0182] Thus, the present invention provides a system that takes user emotions into consideration and enables more personalized suggestions.

[0183] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0184] Step 1:

[0185] Users input information about their skills and career plans through a visualization device. The device receives the input data and sends it to a server. Text and selection-based data are used for input.

[0186] Step 2:

[0187] The server uses an emotion recognition module to detect the user's emotional state based on the personnel information received from the terminal. Emotion detection is performed using natural language processing and speech analysis. As a result, a tag indicating the user's emotional state is generated.

[0188] Step 3:

[0189] The server integrates personnel information and detected emotional states with the organization's needs database. Here, necessary organizational information is retrieved from the database and integrated. The output is an integrated dataset.

[0190] Step 4:

[0191] The generation engine analyzes the integrated data and performs suitability analysis. The server uses a generational AI model to generate optimal transfer candidates based on the user's skills and emotions. This process utilizes machine learning algorithms to ultimately generate transfer candidates and the reasons for their suggestion.

[0192] Step 5:

[0193] The server sends the generated transfer candidates and their rationale to the terminal. The terminal displays this on a visualization device and requests feedback from the user. This allows the user to obtain detailed information about the transfer candidates.

[0194] Step 6:

[0195] The user enters feedback on the proposed transfer candidates via a terminal. This feedback is given in the format specified as a prompt, and the information is then sent back to the server.

[0196] Step 7:

[0197] The server receives feedback, reruns the analysis process as needed, and optimizes the suggestions. After the feedback-based update process is complete, the new suggestions are presented to the user again.

[0198] 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.

[0199] Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0200] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0201] [Second Embodiment]

[0202] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0203] 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.

[0204] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0205] 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.

[0206] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0207] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0208] 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.

[0209] 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 using the processor 28. The storage 32 stores the specific processing program 56.

[0210] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0211] The 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.

[0212] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0213] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0214] This invention is a system that inputs employees' skills, aptitudes, and career plans, integrates them with information obtained from the organization's needs database, and proposes optimal personnel placement. The program processing and specific examples of this system are described below.

[0215] Terminal: The terminal provides the user with a screen for information input. The user enters their past work experience, current skill set, and future career plans. This information is transferred from the terminal to the server.

[0216] Server: Based on the received user information, the server retrieves relevant information from the organization's needs database. This database records the organization's personnel requirements, project information, and current resource status. The server integrates this information and passes it to the generation engine.

[0217] The generation engine analyzes integrated data, matching user preferences with organizational needs to create transfer candidates. In this process, the generation engine utilizes random access generation technology to efficiently analyze numerous factors.

[0218] Terminal: When a transfer candidate is created, the server sends the analysis results to the terminal. The terminal displays these results to the user in an easy-to-understand format. The screen shows details such as the reason for the transfer, the proposed position, and the expected results, which the user can review.

[0219] For example, if a mid-career engineer in their 30s wants to move to a new management position, they can input their wishes into the system. The generation engine will then suggest appropriate transfer destinations based on the engineer's skills and the organization's requirements. For instance, they might be suggested as the lead of a new IT project.

[0220] Through this process, it becomes possible to effectively connect individual career plans with organizational needs and achieve optimal personnel transfers.

[0221] The following describes the processing flow.

[0222] Step 1:

[0223] The user uses the device to input their skills and career plans. The device provides an input form and retrieves detailed information from the user. This allows the user to specifically describe their current experience and future job aspirations.

[0224] Step 2:

[0225] The user information entered by the terminal is sent to the server. This data contains all the information entered by the user, and the server receives and stores this data for further processing.

[0226] Step 3:

[0227] The server queries the organization's needs database based on the received user information. The database retrieves information about the skills and roles the organization is seeking for current projects and future plans.

[0228] Step 4:

[0229] The server passes the integrated data to the generation engine. The generation engine matches the user's skills with organizational needs and performs suitability analysis using AI algorithms. Various factors are considered here to generate the most suitable transfer candidates.

[0230] Step 5:

[0231] The server sends the transfer candidates analyzed by the generation engine to the terminal. This information includes the proposed transfer destination, the reasons for the transfer, and the expected results.

[0232] Step 6:

[0233] The terminal displays data received from the server to the user. Based on this information, the user can review their transfer options and provide feedback or make decisions.

[0234] (Example 1)

[0235] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0236] Modern organizations are required to quickly and effectively allocate talent appropriately in a rapidly changing business environment. However, traditional talent allocation methods struggle to immediately connect individual career plans and skills with organizational needs, and selecting candidates for transfers, in particular, is time-consuming and problematic. Therefore, there is a need for technology that can accurately analyze the aptitudes of individual employees and quickly propose optimal talent allocation.

[0237] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0238] In this invention, the server includes a display means for inputting information, a means for integrating the input information and recorded request information, and a means using a generation engine for performing suitability analysis based on the integrated information. This makes it possible to quickly analyze the skills and preferences of individual employees and propose placements that best match the organization's needs.

[0239] "A means of displaying information for input" refers to an interface that visually presents information so that users can intuitively and accurately input information such as their work experience, skill set, and career plan.

[0240] "Means for integrating input information and recorded request information" refers to a function that can organize information entered by users and information stored in the organization's needs database and combine them into a consistent dataset.

[0241] "Methods using a generation engine" refers to a process that utilizes AI technology to efficiently analyze diverse conditions based on integrated information and optimally match the needs of users and organizations.

[0242] "Means for displaying analysis results and suggesting placement candidates" refers to a function that visually presents the analysis results derived by the generation engine to the user and clearly indicates the recommended personnel placement plan.

[0243] "A means of efficiently processing diverse combinations of conditions using a generation engine to determine the optimal placement" refers to a function that has the ability to quickly process vast and complex user information and organizational needs, and to determine the optimal placement of personnel based on their combinations.

[0244] This invention relates to a system for optimizing personnel allocation within an organization. The following describes embodiments for carrying out the invention.

[0245] Terminal: In this system, the terminal provides a user-friendly interface for inputting information. Specifically, users input their past work experience, current skill set, and future career plans into this interface. This information is stored digitally in an internal database and ready for processing.

[0246] Server: The server receives information sent from terminals and performs transaction processing with the organization's existing databases, which contain personnel needs and project requirements. It then uses its own data processing algorithms to perform initial processing to integrate user information with organizational needs.

[0247] In this process, the server utilizes a generative AI model to create a deployment plan calculated based on the integrated data obtained. The generation engine uses random access generation techniques in particular to analyze diverse elements in a short amount of time.

[0248] For example, if a mid-career engineer in their 30s wishes to acquire management skills, the user enters this wish into the terminal. The server analyzes this information to identify appropriate projects and positions within the organization. Ultimately, it can propose placement as the lead of a new IT project.

[0249] An example of a prompt message might be: "A 30-year-old engineer is seeking a management position. Their skill set includes A, B, and C, and their future career plans are X and Y. Please suggest the best placement for them within the organization."

[0250] In this way, individual career aspirations and organizational requirements can be optimized, and personnel allocation decisions can be made quickly and effectively.

[0251] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0252] Step 1:

[0253] The terminal displays an interface for the user to input information. The user enters their work experience, skills, and career plan. The entered data is checked for consistency and quality and sent to the server by the terminal. The input is organized as text data, and the output is data transfer to the server.

[0254] Step 2:

[0255] The server collects user data received from terminals and retrieves relevant information from a recorded organizational needs database. The server uses database queries to extract necessary personnel information and combines it with user data. The input is user skill data and organizational project requirements, and the output is an integrated dataset. The server prepares this information for the generation engine.

[0256] Step 3:

[0257] The server passes the integrated dataset to the generation engine, which uses a generative AI model to begin the suitability analysis. The generation engine rapidly analyzes multidimensional data using random access generation technology to find transfer candidates that best match user conditions and organizational needs. The input is the integrated dataset, and the output is the processed placement candidates.

[0258] Step 4:

[0259] The server organizes the placement candidates generated by the generation engine and sends the task results to the terminal. The terminal displays the analysis results on the monitor in a user-friendly format. Here, specific job titles, reasons for transfer candidates, and expected results are presented in detail. The input is the analyzed placement candidates, and the output is the display data for the user.

[0260] Step 5:

[0261] The user reviews the proposed deployment plan on the terminal and provides feedback as needed. The user's feedback is resent from the terminal to the server and used as feedback data for further suitability analysis. The input is user feedback, and the output is adding the feedback to the database.

[0262] (Application Example 1)

[0263] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0264] Effectively managing employee skills and aptitudes and making optimal placements aligned with career plans is challenging within organizations. Furthermore, there is a need to effectively and quickly propose appropriate placements for individuals with skills in operating machinery and equipment, but systems to achieve this are lacking. To address this challenge, a system is needed that efficiently integrates employee information with organizational needs and presents the analysis results to operators in an easily understandable manner.

[0265] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0266] In this invention, the server includes means for displaying a screen for inputting personnel information, means for integrating the input personnel information with organizational information stored in an organizational needs database, and means using a generation engine for performing suitability analysis based on the integrated information. This makes it possible to propose the optimal placement of personnel with the skills to operate machinery and equipment.

[0267] The "screen for entering personnel information" is an interface that allows individual employees to input information such as their skills, work experience, and career plans.

[0268] An "organizational needs database" is a database that records and manages the requirements of projects an organization is working on, the personnel requirements, and the current status of its resources.

[0269] A "generative engine" is a data processing system that integrates input personnel information and organizational needs data, and performs suitability analysis based on that information.

[0270] "Methods for suggesting transfer candidates" refers to a system that proposes the most suitable position or assignment to an employee based on the results of analysis by a generation engine.

[0271] "Operational skills for machinery and equipment" refers to the skills and knowledge necessary to effectively operate specific machines and devices.

[0272] "Means for proposing optimal placement" refers to a system that comprehensively assesses the skills of individual employees and the needs of the organization, and then presents them with the most suitable job or role.

[0273] The system implementing this invention provides a screen on a user terminal for inputting personnel information and, by integrating it with the organization's needs database on a server, proposes optimal personnel placement. The user inputs their past work experience, current skills, and future career plans. The terminal then transfers this user data to the server.

[0274] Based on the received data, the server retrieves project requirements and current resource status from the organization's needs database and analyzes the integrated information using a generation engine. Specifically, it uses a generation AI model to calculate the optimal correlation of multidimensional data and evaluates the user's capabilities and the organization's requirements. As a result, a suitable personnel placement proposal is generated and sent to the terminal.

[0275] The terminal displays the analysis results sent from the server to the user in an easy-to-understand visual format. This includes the reasons for the transfer candidate's job title and position, as well as the expected results. Users can also receive feedback on how their own suitability has been analyzed and which placement is deemed optimal.

[0276] For example, a technician responsible for robot management can use the system to evaluate their own technical skills and receive proposals for lead positions in smart manufacturing projects. In this way, it becomes possible to offer career suggestions that are best suited to each user.

[0277] Examples of prompts for a generative AI model:

[0278] "Please propose the most suitable robot operation position, taking into account my current skill set and future career plans."

[0279] This structure enables efficient talent management and optimal skill utilization within the organization.

[0280] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0281] Step 1:

[0282] The user logs in to the terminal and uses the provided input screen to enter their skill set, work experience, and career plan. The entered information is converted into a data format and securely transferred from the terminal to the server. As a specific example of input, the user can enter "5 years of project management experience" or "knowledge of AI technology".

[0283] Step 2:

[0284] The server accesses the organization's needs database to obtain current requirements, project requirements, and resource status in order to analyze the received user data. The server integrates this data and extracts the optimal pattern using a generated AI model. This numerically evaluates the relevance between each user's skills and the organization's needs.

[0285] Step 3:

[0286] Based on the analysis results generated by the server, the generation engine creates a proposal for optimal personnel placement. Through data calculations, specific projects or positions that the user can lead are identified. As an output example, positions such as "team leader for a new AI project" are proposed. This information is output to the terminal again.

[0287] Step 4:

[0288] The terminal receives data from the server and displays the analysis results in a format that is easy for the user to understand. Details of the proposed transfer candidates, the reasons therefor, and the expected outcomes are displayed on the screen. Based on this, the user can input a prompt sentence for receiving a more optimal proposal by providing feedback along their career path.

[0289] Step 5:

[0290] User feedback is sent back to the system, and the server updates the database to reflect this in the next data analysis. This cyclical feedback mechanism improves the system's accuracy and enables more practical suggestions for the user.

[0291] The above describes the processing flow of the system that implements this application example.

[0292] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0293] This invention relates to an optimal personnel transfer system that combines the input of personnel information, integration with organizational information, and an emotion engine. This system takes user emotions into consideration, performs aptitude analysis, and efficiently proposes transfers that meet the organization's needs.

[0294] Terminal: The terminal provides the user with an information input interface. The user inputs specific information about their skills and career plans. During this process, the emotion engine recognizes emotions from the user's input and interactions, and retrieves additional information based on those emotions. For example, if a user speaks enthusiastically about their future career, that positive emotion is recorded.

[0295] Server: The server matches user information and emotional state received from the terminal with the organization's needs database. The server passes the integrated data, including emotional state, to a generation engine to initiate the process of suggesting optimal personnel changes. This generation engine adjusts the content of the suggested changes and the reasons for the suggestions to reflect the user's emotional state.

[0296] The generation engine comprehensively analyzes information obtained from the user's skill set, career plan, and emotion recognition, and attempts to match it with the organization's requirements. The emotion engine creates emotion-based follow-ups and reasoning for suggestions, particularly to increase user satisfaction.

[0297] Terminal: Once the results from the generation engine are sent to the terminal by the server, the terminal visualizes them for the user. The screen displays transfer destination suggestions that take the user's wishes and feelings into consideration, along with the reasoning behind them. Based on these results, the user can consider their next career step and provide feedback as needed.

[0298] For example, if a 30-year-old engineer is considering a transfer to a new department and uses this system to express their preferences, the generation engine will suggest transfer destinations that reflect their positive aspirations, thereby positively influencing their career plan. This consideration, based on the user's emotions, contributes to increased user satisfaction.

[0299] This system incorporates not only technical skills and organizational needs, but also emotional factors, enabling more personalized transfers and optimizing talent utilization within the organization.

[0300] The following describes the processing flow.

[0301] Step 1:

[0302] The user uses the device to input their skills, work experience, and career plan. The device displays input forms and collects information from the user sequentially. This includes dropdowns and text boxes, prompting the user for specific input.

[0303] Step 2:

[0304] The terminal activates an emotion engine when the user enters information, instantly recognizing the user's emotions based on factors such as typing speed and mouse movements. This emotion data is then transferred to the server along with the entered user information.

[0305] Step 3:

[0306] The server queries the organizational needs database with the user information and emotion data parameters received from the terminal. This database describes the open positions and required skills of the organization. The server obtains the relevant organizational information and prepares it for analysis.

[0307] Step 4:

[0308] The server passes the integrated information including the user's emotion data to the generation engine. The generation engine performs an appropriateness analysis reflecting the user's emotion using AI algorithms and generates optimal transfer candidates. The emotion data obtained by the emotion engine is used for adjusting the reasons for the proposal and follow-up.

[0309] Step 5:

[0310] The server sends the transfer candidates generated by the generation engine and the detailed reasons for the proposal to the terminal. The terminal displays the results in a user-friendly format. For example, how the transfer matches the user's long-term career goals and what the considerations for emotion are are presented.

[0311] Step 6:

[0312] The user examines the analysis results and proposal contents provided on the terminal. An emotion-priority feedback option is displayed, allowing the user to give an emotional reaction to the transfer candidates and proposals. This feedback may be used for further adjustment.

[0313] (Example 2)

[0314] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0315] Traditionally, personnel transfer decisions within organizations have been based primarily on skills and direct organizational needs, but have not adequately considered the emotional aspects of employees. This can lead to decreased employee satisfaction, potentially negatively impacting the overall vitality and efficiency of the organization. Therefore, a system is needed that considers the emotional state of employees and proposes more appropriate and satisfying personnel transfers.

[0316] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0317] In this invention, the server includes means for analyzing the user's emotional state, means for integrating the input user information and emotional state with the organization's request database, and means for using a generative AI model to perform suitability analysis. This makes it possible to propose appropriate and satisfying personnel changes while taking the user's emotional state into consideration.

[0318] "User information" refers to data related to an individual's skills, career plans, experience, etc.

[0319] "Emotion recognition" is the process of analyzing a user's psychological state from their input and actions to identify emotions such as positive or negative.

[0320] An "organizational requirements database" refers to a collection of information that stores data on the characteristics, skills, and job responsibilities of personnel required by an organization.

[0321] "Aptitude analysis" is a process of comprehensively evaluating user information and organizational requirements to determine which tasks or positions are best suited for the user.

[0322] A "generative AI model" refers to an algorithm or machine learning model that proposes optimal personnel changes based on user and organizational information.

[0323] "Potential transfers" refer to new positions or roles that may be proposed to the employer.

[0324] "Visualization" refers to displaying information graphically and presenting it in a way that is easy for the user to understand.

[0325] To implement this invention, the following system is specifically used.

[0326] User information input and emotion recognition

[0327] The terminal provides the user with an interface for information input. Through this interface, the user inputs information such as their skills, experience, and career plans. The terminal incorporates emotion recognition capabilities, detecting the user's emotional state from their facial expressions and text input during the input process. Specific emotion recognition software is used for this emotion analysis.

[0328] Information integration and database processing

[0329] The server processes user information and sentiment data received from the terminal and accesses the organization's requirements database. This database records the organization's overall talent needs. The server integrates the user information and organizational data and passes it to the generative AI model.

[0330] Aptitude analysis using generative AI models

[0331] The AI ​​model generated on the server proposes the most suitable personnel transfers to users based on integrated data. This model comprehensively analyzes the user's skill set, career plan, and emotional data to generate optimal transfer candidates. It also adjusts the suggestions, paying particular attention to the user's positive emotions.

[0332] Results presentation and feedback

[0333] When the generation engine sends transfer candidate suggestions from the server to the terminal, the terminal visually presents them to the user. At this time, the suggested transfer destinations and the reasons for them are described in detail, allowing the user to decide on the next career step. The user can provide feedback based on the results and input more detailed information as needed.

[0334] Specific examples and prompt statements

[0335] For example, if a 30-year-old engineer expresses a desire to transfer to a new department, the generating AI model will suggest a transfer destination that reflects the engineer's positive career aspirations. An example of a prompt in this process would be: "A 30-year-old engineer desires a new challenge and has expressed positive feelings. Please suggest the most suitable transfer destination for him." This system is expected to quickly provide optimal transfer suggestions that take the user's emotions into account.

[0336] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0337] Step 1:

[0338] The terminal presents the user with an interface for information input. The user inputs data such as skills, career plans, and experience into the terminal. The terminal analyzes the input information and prepares to send it to the server. In this step, the user's basic information is obtained as input data.

[0339] Step 2:

[0340] The device utilizes emotion recognition software to recognize the user's emotional state based on their input and behavior. The recognized emotion data, along with user information, is prepared to be sent to the server. There, the server analyzes the user's emotional patterns and generates emotional state data such as positive and negative.

[0341] Step 3:

[0342] The server receives user information and sentiment data transmitted from the terminal. The received data is integrated with the organization's requirements database. The server inputs this integrated data into a generating AI model and begins data processing. In this step, the organization's demand information is referenced from the database as output data.

[0343] Step 4:

[0344] The AI ​​model on the server generates optimal transfer candidates for the user using integrated data. The model evaluates and analyzes the input skill set, career plan, and emotional state to determine the transfer destination. The output data generated includes the candidate positions and their rationale.

[0345] Step 5:

[0346] The server sends the generated transfer candidates to the terminal. The terminal visually processes the received data and displays the proposals to the user. The user reviews the presented transfer candidates and the reasons for them, and uses this information to decide on their next career step. At this step, the final proposal information is displayed to the user as output data.

[0347] Step 6:

[0348] When a user provides feedback, the device sends that feedback information back to the server. The server analyzes the feedback and, if necessary, requests a revised suggestion from the generating AI model. This cycle can potentially lead to the output of even more optimal suggestions that incorporate the user's opinions.

[0349] (Application Example 2)

[0350] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0351] In modern organizations, proposing optimal transfers while considering the aptitudes and emotional states of individual employees is a crucial challenge. However, appropriately recognizing emotional changes and reflecting them in transfer proposals is technically difficult, and as a result, transfers that enhance individual satisfaction are not being fully realized.

[0352] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0353] In this invention, the server includes means for displaying a visualization device for inputting personnel information, means for integrating the input personnel information with organizational information stored in an organizational needs database, and means for detecting the user's emotions and optimizing suggestions based on those emotions. This makes it possible to make more accurate transfer suggestions that take emotional states into account.

[0354] "Personnel information" refers to information that includes attributes such as skills, career, and emotions related to individual people.

[0355] A "visualization device" is a display device that provides information to users in the form of images.

[0356] "Integrating" means systematically combining multiple pieces of information, establishing relationships between them, and unifying them into a single entity.

[0357] A "generative engine" is a computer program that analyzes given data and generates results that meet a specific purpose.

[0358] "Emotion detection" is the act of inferring a user's internal emotional state from their facial expressions, voice, and other factors.

[0359] "Optimizing a proposal" means adjusting it to be the most effective and efficient proposal based on the given conditions and constraints.

[0360] This invention comprises a visualization device, a server, and a user system. The visualization device functions as an interface for presenting information to the user, where the user can input information about their skills and career plans. The visualization device is preferably a wearable device such as smart glasses or a head-mounted display.

[0361] The server integrates the personnel information received from the visualization device with the information stored in the organization's needs database. Based on this integrated data, the generation engine operates and performs suitability analysis. The generation engine is equipped with a module for detecting emotions and optimizes suggestions to reflect the user's emotional state. Specifically, it recognizes the user's emotions through facial and voice analysis and adjusts the optimal movement options and suggestions based on that information.

[0362] For example, if a user is experiencing stress while working, the emotion analysis module can detect that stress and suggest relaxing music. Furthermore, the system receives feedback from the user through prompts such as "Suggest music that matches my current mood" or "Tell me how to perform this task more efficiently," and uses this feedback to improve the suggestions.

[0363] Thus, the present invention provides a system that takes user emotions into consideration and enables more personalized suggestions.

[0364] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0365] Step 1:

[0366] Users input information about their skills and career plans through a visualization device. The device receives the input data and sends it to a server. Text and selection-based data are used for input.

[0367] Step 2:

[0368] The server uses an emotion recognition module to detect the user's emotional state based on the personnel information received from the terminal. Emotion detection is performed using natural language processing and speech analysis. As a result, a tag indicating the user's emotional state is generated.

[0369] Step 3:

[0370] The server integrates personnel information and detected emotional states with the organization's needs database. Here, necessary organizational information is retrieved from the database and integrated. The output is an integrated dataset.

[0371] Step 4:

[0372] The generation engine analyzes the integrated data and performs suitability analysis. The server uses a generational AI model to generate optimal transfer candidates based on the user's skills and emotions. This process utilizes machine learning algorithms to ultimately generate transfer candidates and the reasons for their suggestion.

[0373] Step 5:

[0374] The server sends the generated transfer candidates and their rationale to the terminal. The terminal displays this on a visualization device and requests feedback from the user. This allows the user to obtain detailed information about the transfer candidates.

[0375] Step 6:

[0376] The user enters feedback on the proposed transfer candidates via a terminal. This feedback is given in the format specified as a prompt, and the information is then sent back to the server.

[0377] Step 7:

[0378] The server receives feedback, reruns the analysis process as needed, and optimizes the suggestions. After the feedback-based update process is complete, the new suggestions are presented to the user again.

[0379] 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.

[0380] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0381] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0382] [Third Embodiment]

[0383] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0384] 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.

[0385] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0386] 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.

[0387] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0388] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0389] 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.

[0390] 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.

[0391] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0392] The 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.

[0393] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0394] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0395] This invention is a system that inputs employees' skills, aptitudes, and career plans, integrates them with information obtained from the organization's needs database, and proposes optimal personnel placement. The program processing and specific examples of this system are described below.

[0396] Terminal: The terminal provides the user with a screen for information input. The user enters their past work experience, current skill set, and future career plans. This information is transferred from the terminal to the server.

[0397] Server: Based on the received user information, the server retrieves relevant information from the organization's needs database. This database records the organization's personnel requirements, project information, and current resource status. The server integrates this information and passes it to the generation engine.

[0398] The generation engine analyzes integrated data, matching user preferences with organizational needs to create transfer candidates. In this process, the generation engine utilizes random access generation technology to efficiently analyze numerous factors.

[0399] Terminal: When a transfer candidate is created, the server sends the analysis results to the terminal. The terminal displays these results to the user in an easy-to-understand format. The screen shows details such as the reason for the transfer, the proposed position, and the expected results, which the user can review.

[0400] For example, if a mid-career engineer in their 30s wants to move to a new management position, they can input their wishes into the system. The generation engine will then suggest appropriate transfer destinations based on the engineer's skills and the organization's requirements. For instance, they might be suggested as the lead of a new IT project.

[0401] Through this process, it becomes possible to effectively connect individual career plans with organizational needs and achieve optimal personnel transfers.

[0402] The following describes the processing flow.

[0403] Step 1:

[0404] The user uses the device to input their skills and career plans. The device provides an input form and retrieves detailed information from the user. This allows the user to specifically describe their current experience and future job aspirations.

[0405] Step 2:

[0406] The user information entered by the terminal is sent to the server. This data contains all the information entered by the user, and the server receives and stores this data for further processing.

[0407] Step 3:

[0408] The server queries the organization's needs database based on the received user information. The database retrieves information about the skills and roles the organization is seeking for current projects and future plans.

[0409] Step 4:

[0410] The server passes the integrated data to the generation engine. The generation engine matches the user's skills with organizational needs and performs suitability analysis using AI algorithms. Various factors are considered here to generate the most suitable transfer candidates.

[0411] Step 5:

[0412] The server sends the transfer candidates analyzed by the generation engine to the terminal. This information includes the proposed transfer destination, the reasons for the transfer, and the expected results.

[0413] Step 6:

[0414] The terminal displays data received from the server to the user. Based on this information, the user can review their transfer options and provide feedback or make decisions.

[0415] (Example 1)

[0416] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0417] Modern organizations are required to quickly and effectively allocate talent appropriately in a rapidly changing business environment. However, traditional talent allocation methods struggle to immediately connect individual career plans and skills with organizational needs, and selecting candidates for transfers, in particular, is time-consuming and problematic. Therefore, there is a need for technology that can accurately analyze the aptitudes of individual employees and quickly propose optimal talent allocation.

[0418] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0419] In this invention, the server includes a display means for inputting information, a means for integrating the input information and recorded request information, and a means using a generation engine for performing suitability analysis based on the integrated information. This makes it possible to quickly analyze the skills and preferences of individual employees and propose placements that best match the organization's needs.

[0420] "A means of displaying information for input" refers to an interface that visually presents information so that users can intuitively and accurately input information such as their work experience, skill set, and career plan.

[0421] "Means for integrating input information and recorded request information" refers to a function that can organize information entered by users and information stored in the organization's needs database and combine them into a consistent dataset.

[0422] "Methods using a generation engine" refers to a process that utilizes AI technology to efficiently analyze diverse conditions based on integrated information and optimally match the needs of users and organizations.

[0423] "Means for displaying analysis results and suggesting placement candidates" refers to a function that visually presents the analysis results derived by the generation engine to the user and clearly indicates the recommended personnel placement plan.

[0424] "A means of efficiently processing diverse combinations of conditions using a generation engine to determine the optimal placement" refers to a function that has the ability to quickly process vast and complex user information and organizational needs, and to determine the optimal placement of personnel based on their combinations.

[0425] This invention relates to a system for optimizing personnel allocation within an organization. The following describes embodiments for carrying out the invention.

[0426] Terminal: In this system, the terminal provides a user-friendly interface for inputting information. Specifically, users input their past work experience, current skill set, and future career plans into this interface. This information is stored digitally in an internal database and ready for processing.

[0427] Server: The server receives information sent from terminals and performs transaction processing with the organization's existing databases, which contain personnel needs and project requirements. It then uses its own data processing algorithms to perform initial processing to integrate user information with organizational needs.

[0428] In this process, the server utilizes a generative AI model to create a deployment plan calculated based on the integrated data obtained. The generation engine uses random access generation techniques in particular to analyze diverse elements in a short amount of time.

[0429] For example, if a mid-career engineer in their 30s wishes to acquire management skills, the user enters this wish into the terminal. The server analyzes this information to identify appropriate projects and positions within the organization. Ultimately, it can propose placement as the lead of a new IT project.

[0430] An example of a prompt message might be: "A 30-year-old engineer is seeking a management position. Their skill set includes A, B, and C, and their future career plans are X and Y. Please suggest the best placement for them within the organization."

[0431] In this way, individual career aspirations and organizational requirements can be optimized, and personnel allocation decisions can be made quickly and effectively.

[0432] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0433] Step 1:

[0434] The terminal displays an interface for the user to input information. The user enters their work experience, skills, and career plan. The entered data is checked for consistency and quality and sent to the server by the terminal. The input is organized as text data, and the output is data transfer to the server.

[0435] Step 2:

[0436] The server collects user data received from terminals and retrieves relevant information from a recorded organizational needs database. The server uses database queries to extract necessary personnel information and combines it with user data. The input is user skill data and organizational project requirements, and the output is an integrated dataset. The server prepares this information for the generation engine.

[0437] Step 3:

[0438] The server passes the integrated dataset to the generation engine, which uses a generative AI model to begin the suitability analysis. The generation engine rapidly analyzes multidimensional data using random access generation technology to find transfer candidates that best match user conditions and organizational needs. The input is the integrated dataset, and the output is the processed placement candidates.

[0439] Step 4:

[0440] The server organizes the placement candidates generated by the generation engine and sends the task results to the terminal. The terminal displays the analysis results on the monitor in a user-friendly format. Here, specific job titles, reasons for transfer candidates, and expected results are presented in detail. The input is the analyzed placement candidates, and the output is the display data for the user.

[0441] Step 5:

[0442] The user reviews the proposed deployment plan on the terminal and provides feedback as needed. The user's feedback is resent from the terminal to the server and used as feedback data for further suitability analysis. The input is user feedback, and the output is adding the feedback to the database.

[0443] (Application Example 1)

[0444] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0445] Effectively managing employee skills and aptitudes and making optimal placements aligned with career plans is challenging within organizations. Furthermore, there is a need to effectively and quickly propose appropriate placements for individuals with skills in operating machinery and equipment, but systems to achieve this are lacking. To address this challenge, a system is needed that efficiently integrates employee information with organizational needs and presents the analysis results to operators in an easily understandable manner.

[0446] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0447] In this invention, the server includes means for displaying a screen for inputting personnel information, means for integrating the input personnel information with organizational information stored in an organizational needs database, and means using a generation engine for performing suitability analysis based on the integrated information. This makes it possible to propose the optimal placement of personnel with the skills to operate machinery and equipment.

[0448] The "screen for entering personnel information" is an interface that allows individual employees to input information such as their skills, work experience, and career plans.

[0449] An "organizational needs database" is a database that records and manages the requirements of projects an organization is working on, the personnel requirements, and the current status of its resources.

[0450] A "generative engine" is a data processing system that integrates input personnel information and organizational needs data, and performs suitability analysis based on that information.

[0451] "Methods for suggesting transfer candidates" refers to a system that proposes the most suitable position or assignment to an employee based on the results of analysis by a generation engine.

[0452] "Operational skills for machinery and equipment" refers to the skills and knowledge necessary to effectively operate specific machines and devices.

[0453] "Means for proposing optimal placement" refers to a system that comprehensively assesses the skills of individual employees and the needs of the organization, and then presents them with the most suitable job or role.

[0454] The system implementing this invention provides a screen on a user terminal for inputting personnel information and, by integrating it with the organization's needs database on a server, proposes optimal personnel placement. The user inputs their past work experience, current skills, and future career plans. The terminal then transfers this user data to the server.

[0455] Based on the received data, the server retrieves project requirements and current resource status from the organization's needs database and analyzes the integrated information using a generation engine. Specifically, it uses a generation AI model to calculate the optimal correlation of multidimensional data and evaluates the user's capabilities and the organization's requirements. As a result, a suitable personnel placement proposal is generated and sent to the terminal.

[0456] The terminal displays the analysis results sent from the server to the user in an easy-to-understand visual format. This includes the reasons for the transfer candidate's job title and position, as well as the expected results. Users can also receive feedback on how their own suitability has been analyzed and which placement is deemed optimal.

[0457] For example, a technician responsible for robot management can use the system to evaluate their own technical skills and receive proposals for lead positions in smart manufacturing projects. In this way, it becomes possible to offer career suggestions that are best suited to each user.

[0458] Examples of prompts for a generative AI model:

[0459] "Please propose the most suitable robot operation position, taking into account my current skill set and future career plans."

[0460] This structure enables efficient talent management and optimal skill utilization within the organization.

[0461] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0462] Step 1:

[0463] Users log in to the terminal and use the provided input screen to enter their skill set, work experience, and career plan. The entered information is converted into a data format and securely transferred from the terminal to the server. Specific examples of input include "5 years of project management experience" and "knowledge of AI technology."

[0464] Step 2:

[0465] The server accesses the organization's needs database to analyze received user data, retrieving current requirements, project specifications, and resource availability. The server integrates this data and uses a generative AI model to extract optimal patterns. This allows for a numerical evaluation of the relationship between each user's skills and the organization's needs.

[0466] Step 3:

[0467] Based on the analysis results generated by the server, the generation engine creates a proposal for optimal personnel placement. Through data calculations, specific projects and roles that the user can lead are identified. As an example of output, positions such as "Team Leader for a New AI Project" are proposed. This information is then output back to the terminal.

[0468] Step 4:

[0469] The terminal receives data from the server and displays the analysis results in a user-friendly format. The screen displays details of proposed transfer candidates, the reasons for those candidates, and the expected outcomes. Based on this, users can provide feedback aligned with their own career path and enter prompts to receive even more optimal suggestions.

[0470] Step 5:

[0471] User feedback is sent back to the system, and the server updates the database to reflect this in the next data analysis. This cyclical feedback mechanism improves the system's accuracy and enables more practical suggestions for the user.

[0472] The above describes the processing flow of the system that implements this application example.

[0473] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0474] This invention relates to an optimal personnel transfer system that combines the input of personnel information, integration with organizational information, and an emotion engine. This system takes user emotions into consideration, performs aptitude analysis, and efficiently proposes transfers that meet the organization's needs.

[0475] Terminal: The terminal provides the user with an information input interface. The user inputs specific information about their skills and career plans. During this process, the emotion engine recognizes emotions from the user's input and interactions, and retrieves additional information based on those emotions. For example, if a user speaks enthusiastically about their future career, that positive emotion is recorded.

[0476] Server: The server matches user information and emotional state received from the terminal with the organization's needs database. The server passes the integrated data, including emotional state, to a generation engine to initiate the process of suggesting optimal personnel changes. This generation engine adjusts the content of the suggested changes and the reasons for the suggestions to reflect the user's emotional state.

[0477] The generation engine comprehensively analyzes information obtained from the user's skill set, career plan, and emotion recognition, and attempts to match it with the organization's requirements. The emotion engine creates emotion-based follow-ups and reasoning for suggestions, particularly to increase user satisfaction.

[0478] Terminal: Once the results from the generation engine are sent to the terminal by the server, the terminal visualizes them for the user. The screen displays transfer destination suggestions that take the user's wishes and feelings into consideration, along with the reasoning behind them. Based on these results, the user can consider their next career step and provide feedback as needed.

[0479] For example, if a 30-year-old engineer is considering a transfer to a new department and uses this system to express their preferences, the generation engine will suggest transfer destinations that reflect their positive aspirations, thereby positively influencing their career plan. This consideration, based on the user's emotions, contributes to increased user satisfaction.

[0480] This system incorporates not only technical skills and organizational needs, but also emotional factors, enabling more personalized transfers and optimizing talent utilization within the organization.

[0481] The following describes the processing flow.

[0482] Step 1:

[0483] The user uses the device to input their skills, work experience, and career plan. The device displays input forms and collects information from the user sequentially. This includes dropdowns and text boxes, prompting the user for specific input.

[0484] Step 2:

[0485] The terminal activates an emotion engine when the user enters information, instantly recognizing the user's emotions based on factors such as typing speed and mouse movements. This emotion data is then transferred to the server along with the entered user information.

[0486] Step 3:

[0487] The server queries the organizational needs database using user information and sentiment data parameters received from the terminal. This database contains information about open positions and required skills within the organization. The server retrieves the relevant organizational information and prepares it for analysis.

[0488] Step 4:

[0489] The server passes integrated information, including user emotion data, to the generation engine. The generation engine uses an AI algorithm to perform an aptitude analysis that reflects the user's emotions and generates the most suitable transfer candidates. The emotion data obtained by the emotion engine is used to refine the reasons for the proposals and for follow-up.

[0490] Step 5:

[0491] The server sends the transfer candidates generated by the generation engine, along with detailed reasons for the suggestions, to the terminal. The terminal displays the results in a user-friendly format. For example, it shows how the transfer aligns with the user's long-term career goals and what emotional considerations have been taken into account.

[0492] Step 6:

[0493] The user reviews the analysis results and suggestions provided on their device. An emotion-first feedback option is displayed, allowing the user to provide an emotional response to the transfer candidates and suggestions. This feedback may be used for further adjustments.

[0494] (Example 2)

[0495] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0496] Traditionally, personnel transfer decisions within organizations have been based primarily on skills and direct organizational needs, but have not adequately considered the emotional aspects of employees. This can lead to decreased employee satisfaction, potentially negatively impacting the overall vitality and efficiency of the organization. Therefore, a system is needed that considers the emotional state of employees and proposes more appropriate and satisfying personnel transfers.

[0497] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0498] In this invention, the server includes means for analyzing the user's emotional state, means for integrating the input user information and emotional state with the organization's request database, and means for using a generative AI model to perform suitability analysis. This makes it possible to propose appropriate and satisfying personnel changes while taking the user's emotional state into consideration.

[0499] "User information" refers to data related to an individual's skills, career plans, experience, etc.

[0500] "Emotion recognition" is the process of analyzing a user's psychological state from their input and actions to identify emotions such as positive or negative.

[0501] An "organizational requirements database" refers to a collection of information that stores data on the characteristics, skills, and job responsibilities of personnel required by an organization.

[0502] "Aptitude analysis" is a process of comprehensively evaluating user information and organizational requirements to determine which tasks or positions are best suited for the user.

[0503] A "generative AI model" refers to an algorithm or machine learning model that proposes optimal personnel changes based on user and organizational information.

[0504] "Potential transfers" refer to new positions or roles that may be proposed to the employer.

[0505] "Visualization" refers to displaying information graphically and presenting it in a way that is easy for the user to understand.

[0506] To implement this invention, the following system is specifically used.

[0507] User information input and emotion recognition

[0508] The terminal provides the user with an interface for information input. Through this interface, the user inputs information such as their skills, experience, and career plans. The terminal incorporates emotion recognition capabilities, detecting the user's emotional state from their facial expressions and text input during the input process. Specific emotion recognition software is used for this emotion analysis.

[0509] Information integration and database processing

[0510] The server processes user information and sentiment data received from the terminal and accesses the organization's requirements database. This database records the organization's overall talent needs. The server integrates the user information and organizational data and passes it to the generative AI model.

[0511] Aptitude analysis using generative AI models

[0512] The AI ​​model generated on the server proposes the most suitable personnel transfers to users based on integrated data. This model comprehensively analyzes the user's skill set, career plan, and emotional data to generate optimal transfer candidates. It also adjusts the suggestions, paying particular attention to the user's positive emotions.

[0513] Results presentation and feedback

[0514] When the generation engine sends transfer candidate suggestions from the server to the terminal, the terminal visually presents them to the user. At this time, the suggested transfer destinations and the reasons for them are described in detail, allowing the user to decide on the next career step. The user can provide feedback based on the results and input more detailed information as needed.

[0515] Specific examples and prompt statements

[0516] For example, if a 30-year-old engineer expresses a desire to transfer to a new department, the generating AI model will suggest a transfer destination that reflects the engineer's positive career aspirations. An example of a prompt in this process would be: "A 30-year-old engineer desires a new challenge and has expressed positive feelings. Please suggest the most suitable transfer destination for him." This system is expected to quickly provide optimal transfer suggestions that take the user's emotions into account.

[0517] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0518] Step 1:

[0519] The terminal presents the user with an interface for information input. The user inputs data such as skills, career plans, and experience into the terminal. The terminal analyzes the input information and prepares to send it to the server. In this step, the user's basic information is obtained as input data.

[0520] Step 2:

[0521] The device utilizes emotion recognition software to recognize the user's emotional state based on their input and behavior. The recognized emotion data, along with user information, is prepared to be sent to the server. There, the server analyzes the user's emotional patterns and generates emotional state data such as positive and negative.

[0522] Step 3:

[0523] The server receives user information and sentiment data transmitted from the terminal. The received data is integrated with the organization's requirements database. The server inputs this integrated data into a generating AI model and begins data processing. In this step, the organization's demand information is referenced from the database as output data.

[0524] Step 4:

[0525] The AI ​​model on the server generates optimal transfer candidates for the user using integrated data. The model evaluates and analyzes the input skill set, career plan, and emotional state to determine the transfer destination. The output data generated includes the candidate positions and their rationale.

[0526] Step 5:

[0527] The server sends the generated transfer candidates to the terminal. The terminal visually processes the received data and displays the proposals to the user. The user reviews the presented transfer candidates and the reasons for them, and uses this information to decide on their next career step. At this step, the final proposal information is displayed to the user as output data.

[0528] Step 6:

[0529] When a user provides feedback, the device sends that feedback information back to the server. The server analyzes the feedback and, if necessary, requests a revised suggestion from the generating AI model. This cycle can potentially lead to the output of even more optimal suggestions that incorporate the user's opinions.

[0530] (Application Example 2)

[0531] Next, we will explain Application Example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0532] In modern organizations, proposing optimal transfers while considering the aptitudes and emotional states of individual employees is a crucial challenge. However, appropriately recognizing emotional changes and reflecting them in transfer proposals is technically difficult, and as a result, transfers that enhance individual satisfaction are not being fully realized.

[0533] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0534] In this invention, the server includes means for displaying a visualization device for inputting personnel information, means for integrating the input personnel information with organizational information stored in an organizational needs database, and means for detecting the user's emotions and optimizing suggestions based on those emotions. This makes it possible to make more accurate transfer suggestions that take emotional states into account.

[0535] "Personnel information" refers to information that includes attributes such as skills, career, and emotions related to individual people.

[0536] A "visualization device" is a display device that provides information to users in the form of images.

[0537] "Integrating" means systematically combining multiple pieces of information, establishing relationships between them, and unifying them into a single entity.

[0538] A "generative engine" is a computer program that analyzes given data and generates results that meet a specific purpose.

[0539] "Emotion detection" is the act of inferring a user's internal emotional state from their facial expressions, voice, and other factors.

[0540] "Optimizing a proposal" means adjusting it to be the most effective and efficient proposal based on the given conditions and constraints.

[0541] This invention comprises a visualization device, a server, and a user system. The visualization device functions as an interface for presenting information to the user, where the user can input information about their skills and career plans. The visualization device is preferably a wearable device such as smart glasses or a head-mounted display.

[0542] The server integrates the personnel information received from the visualization device with the information stored in the organization's needs database. Based on this integrated data, the generation engine operates and performs suitability analysis. The generation engine is equipped with a module for detecting emotions and optimizes suggestions to reflect the user's emotional state. Specifically, it recognizes the user's emotions through facial and voice analysis and adjusts the optimal movement options and suggestions based on that information.

[0543] For example, if a user is experiencing stress while working, the emotion analysis module can detect that stress and suggest relaxing music. Furthermore, the system receives feedback from the user through prompts such as "Suggest music that matches my current mood" or "Tell me how to perform this task more efficiently," and uses this feedback to improve the suggestions.

[0544] Thus, the present invention provides a system that takes user emotions into consideration and enables more personalized suggestions.

[0545] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0546] Step 1:

[0547] Users input information about their skills and career plans through a visualization device. The device receives the input data and sends it to a server. Text and selection-based data are used for input.

[0548] Step 2:

[0549] The server uses an emotion recognition module to detect the user's emotional state based on the personnel information received from the terminal. Emotion detection is performed using natural language processing and speech analysis. As a result, a tag indicating the user's emotional state is generated.

[0550] Step 3:

[0551] The server integrates personnel information and detected emotional states with the organization's needs database. Here, necessary organizational information is retrieved from the database and integrated. The output is an integrated dataset.

[0552] Step 4:

[0553] The generation engine analyzes the integrated data and performs suitability analysis. The server uses a generational AI model to generate optimal transfer candidates based on the user's skills and emotions. This process utilizes machine learning algorithms to ultimately generate transfer candidates and the reasons for their suggestion.

[0554] Step 5:

[0555] The server sends the generated transfer candidates and their rationale to the terminal. The terminal displays this on a visualization device and requests feedback from the user. This allows the user to obtain detailed information about the transfer candidates.

[0556] Step 6:

[0557] The user enters feedback on the proposed transfer candidates via a terminal. This feedback is given in the format specified as a prompt, and the information is then sent back to the server.

[0558] Step 7:

[0559] The server receives feedback, reruns the analysis process as needed, and optimizes the suggestions. After the feedback-based update process is complete, the new suggestions are presented to the user again.

[0560] 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.

[0561] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0562] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0563] [Fourth Embodiment]

[0564] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0565] 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.

[0566] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0567] 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.

[0568] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0569] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0570] 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.

[0571] 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0572] 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.

[0573] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0574] The 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.

[0575] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0576] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0577] This invention is a system that inputs employees' skills, aptitudes, and career plans, integrates them with information obtained from the organization's needs database, and proposes optimal personnel placement. The program processing and specific examples of this system are described below.

[0578] Terminal: The terminal provides the user with a screen for information input. The user enters their past work experience, current skill set, and future career plans. This information is transferred from the terminal to the server.

[0579] Server: Based on the received user information, the server retrieves relevant information from the organization's needs database. This database records the organization's personnel requirements, project information, and current resource status. The server integrates this information and passes it to the generation engine.

[0580] The generation engine analyzes integrated data, matching user preferences with organizational needs to create transfer candidates. In this process, the generation engine utilizes random access generation technology to efficiently analyze numerous factors.

[0581] Terminal: When a transfer candidate is created, the server sends the analysis results to the terminal. The terminal displays these results to the user in an easy-to-understand format. The screen shows details such as the reason for the transfer, the proposed position, and the expected results, which the user can review.

[0582] For example, if a mid-career engineer in their 30s wants to move to a new management position, they can input their wishes into the system. The generation engine will then suggest appropriate transfer destinations based on the engineer's skills and the organization's requirements. For instance, they might be suggested as the lead of a new IT project.

[0583] Through this process, it becomes possible to effectively connect individual career plans with organizational needs and achieve optimal personnel transfers.

[0584] The following describes the processing flow.

[0585] Step 1:

[0586] The user uses the device to input their skills and career plans. The device provides an input form and retrieves detailed information from the user. This allows the user to specifically describe their current experience and future job aspirations.

[0587] Step 2:

[0588] The user information entered by the terminal is sent to the server. This data contains all the information entered by the user, and the server receives and stores this data for further processing.

[0589] Step 3:

[0590] The server queries the organization's needs database based on the received user information. The database retrieves information about the skills and roles the organization is seeking for current projects and future plans.

[0591] Step 4:

[0592] The server passes the integrated data to the generation engine. The generation engine matches the user's skills with organizational needs and performs suitability analysis using AI algorithms. Various factors are considered here to generate the most suitable transfer candidates.

[0593] Step 5:

[0594] The server sends the transfer candidates analyzed by the generation engine to the terminal. This information includes the proposed transfer destination, the reasons for the transfer, and the expected results.

[0595] Step 6:

[0596] The terminal displays data received from the server to the user. Based on this information, the user can review their transfer options and provide feedback or make decisions.

[0597] (Example 1)

[0598] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0599] Modern organizations are required to quickly and effectively allocate talent appropriately in a rapidly changing business environment. However, traditional talent allocation methods struggle to immediately connect individual career plans and skills with organizational needs, and selecting candidates for transfers, in particular, is time-consuming and problematic. Therefore, there is a need for technology that can accurately analyze the aptitudes of individual employees and quickly propose optimal talent allocation.

[0600] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0601] In this invention, the server includes a display means for inputting information, a means for integrating the input information and recorded request information, and a means using a generation engine for performing suitability analysis based on the integrated information. This makes it possible to quickly analyze the skills and preferences of individual employees and propose placements that best match the organization's needs.

[0602] "A means of displaying information for input" refers to an interface that visually presents information so that users can intuitively and accurately input information such as their work experience, skill set, and career plan.

[0603] "Means for integrating input information and recorded request information" refers to a function that can organize information entered by users and information stored in the organization's needs database and combine them into a consistent dataset.

[0604] "Methods using a generation engine" refers to a process that utilizes AI technology to efficiently analyze diverse conditions based on integrated information and optimally match the needs of users and organizations.

[0605] "Means for displaying analysis results and suggesting placement candidates" refers to a function that visually presents the analysis results derived by the generation engine to the user and clearly indicates the recommended personnel placement plan.

[0606] "A means of efficiently processing diverse combinations of conditions using a generation engine to determine the optimal placement" refers to a function that has the ability to quickly process vast and complex user information and organizational needs, and to determine the optimal placement of personnel based on their combinations.

[0607] This invention relates to a system for optimizing personnel allocation within an organization. The following describes embodiments for carrying out the invention.

[0608] Terminal: In this system, the terminal provides a user-friendly interface for inputting information. Specifically, users input their past work experience, current skill set, and future career plans into this interface. This information is stored digitally in an internal database and ready for processing.

[0609] Server: The server receives information sent from terminals and performs transaction processing with the organization's existing databases, which contain personnel needs and project requirements. It then uses its own data processing algorithms to perform initial processing to integrate user information with organizational needs.

[0610] In this process, the server utilizes a generative AI model to create a deployment plan calculated based on the integrated data obtained. The generation engine uses random access generation techniques in particular to analyze diverse elements in a short amount of time.

[0611] For example, if a mid-career engineer in their 30s wishes to acquire management skills, the user enters this wish into the terminal. The server analyzes this information to identify appropriate projects and positions within the organization. Ultimately, it can propose placement as the lead of a new IT project.

[0612] An example of a prompt message might be: "A 30-year-old engineer is seeking a management position. Their skill set includes A, B, and C, and their future career plans are X and Y. Please suggest the best placement for them within the organization."

[0613] In this way, individual career aspirations and organizational requirements can be optimized, and personnel allocation decisions can be made quickly and effectively.

[0614] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0615] Step 1:

[0616] The terminal displays an interface for the user to input information. The user enters their work experience, skills, and career plan. The entered data is checked for consistency and quality and sent to the server by the terminal. The input is organized as text data, and the output is data transfer to the server.

[0617] Step 2:

[0618] The server collects user data received from terminals and retrieves relevant information from a recorded organizational needs database. The server uses database queries to extract necessary personnel information and combines it with user data. The input is user skill data and organizational project requirements, and the output is an integrated dataset. The server prepares this information for the generation engine.

[0619] Step 3:

[0620] The server passes the integrated dataset to the generation engine, which uses a generative AI model to begin the suitability analysis. The generation engine rapidly analyzes multidimensional data using random access generation technology to find transfer candidates that best match user conditions and organizational needs. The input is the integrated dataset, and the output is the processed placement candidates.

[0621] Step 4:

[0622] The server organizes the placement candidates generated by the generation engine and sends the task results to the terminal. The terminal displays the analysis results on the monitor in a user-friendly format. Here, specific job titles, reasons for transfer candidates, and expected results are presented in detail. The input is the analyzed placement candidates, and the output is the display data for the user.

[0623] Step 5:

[0624] The user reviews the proposed deployment plan on the terminal and provides feedback as needed. The user's feedback is resent from the terminal to the server and used as feedback data for further suitability analysis. The input is user feedback, and the output is adding the feedback to the database.

[0625] (Application Example 1)

[0626] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0627] Effectively managing employee skills and aptitudes and making optimal placements aligned with career plans is challenging within organizations. Furthermore, there is a need to effectively and quickly propose appropriate placements for individuals with skills in operating machinery and equipment, but systems to achieve this are lacking. To address this challenge, a system is needed that efficiently integrates employee information with organizational needs and presents the analysis results to operators in an easily understandable manner.

[0628] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0629] In this invention, the server includes means for displaying a screen for inputting personnel information, means for integrating the input personnel information with organizational information stored in an organizational needs database, and means using a generation engine for performing suitability analysis based on the integrated information. This makes it possible to propose the optimal placement of personnel with the skills to operate machinery and equipment.

[0630] The "screen for entering personnel information" is an interface that allows individual employees to input information such as their skills, work experience, and career plans.

[0631] An "organizational needs database" is a database that records and manages the requirements of projects an organization is working on, the personnel requirements, and the current status of its resources.

[0632] A "generative engine" is a data processing system that integrates input personnel information and organizational needs data, and performs suitability analysis based on that information.

[0633] "Methods for suggesting transfer candidates" refers to a system that proposes the most suitable position or assignment to an employee based on the results of analysis by a generation engine.

[0634] "Operational skills for machinery and equipment" refers to the skills and knowledge necessary to effectively operate specific machines and devices.

[0635] "Means for proposing optimal placement" refers to a system that comprehensively assesses the skills of individual employees and the needs of the organization, and then presents them with the most suitable job or role.

[0636] The system implementing this invention provides a screen on a user terminal for inputting personnel information and, by integrating it with the organization's needs database on a server, proposes optimal personnel placement. The user inputs their past work experience, current skills, and future career plans. The terminal then transfers this user data to the server.

[0637] Based on the received data, the server retrieves project requirements and current resource status from the organization's needs database and analyzes the integrated information using a generation engine. Specifically, it uses a generation AI model to calculate the optimal correlation of multidimensional data and evaluates the user's capabilities and the organization's requirements. As a result, a suitable personnel placement proposal is generated and sent to the terminal.

[0638] The terminal displays the analysis results sent from the server to the user in an easy-to-understand visual format. This includes the reasons for the transfer candidate's job title and position, as well as the expected results. Users can also receive feedback on how their own suitability has been analyzed and which placement is deemed optimal.

[0639] For example, a technician responsible for robot management can use the system to evaluate their own technical skills and receive proposals for lead positions in smart manufacturing projects. In this way, it becomes possible to offer career suggestions that are best suited to each user.

[0640] Examples of prompts for a generative AI model:

[0641] "Please propose the most suitable robot operation position, taking into account my current skill set and future career plans."

[0642] This structure enables efficient talent management and optimal skill utilization within the organization.

[0643] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0644] Step 1:

[0645] Users log in to the terminal and use the provided input screen to enter their skill set, work experience, and career plan. The entered information is converted into a data format and securely transferred from the terminal to the server. Specific examples of input include "5 years of project management experience" and "knowledge of AI technology."

[0646] Step 2:

[0647] The server accesses the organization's needs database to analyze received user data, retrieving current requirements, project specifications, and resource availability. The server integrates this data and uses a generative AI model to extract optimal patterns. This allows for a numerical evaluation of the relationship between each user's skills and the organization's needs.

[0648] Step 3:

[0649] Based on the analysis results generated by the server, the generation engine creates a proposal for optimal personnel placement. Through data calculations, specific projects and roles that the user can lead are identified. As an example of output, positions such as "Team Leader for a New AI Project" are proposed. This information is then output back to the terminal.

[0650] Step 4:

[0651] The terminal receives data from the server and displays the analysis results in a user-friendly format. The screen displays details of proposed transfer candidates, the reasons for those candidates, and the expected outcomes. Based on this, users can provide feedback aligned with their own career path and enter prompts to receive even more optimal suggestions.

[0652] Step 5:

[0653] User feedback is sent back to the system, and the server updates the database to reflect this in the next data analysis. This cyclical feedback mechanism improves the system's accuracy and enables more practical suggestions for the user.

[0654] The above describes the processing flow of the system that implements this application example.

[0655] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0656] This invention relates to an optimal personnel transfer system that combines the input of personnel information, integration with organizational information, and an emotion engine. This system takes user emotions into consideration, performs aptitude analysis, and efficiently proposes transfers that meet the organization's needs.

[0657] Terminal: The terminal provides the user with an information input interface. The user inputs specific information about their skills and career plans. During this process, the emotion engine recognizes emotions from the user's input and interactions, and retrieves additional information based on those emotions. For example, if a user speaks enthusiastically about their future career, that positive emotion is recorded.

[0658] Server: The server matches user information and emotional state received from the terminal with the organization's needs database. The server passes the integrated data, including emotional state, to a generation engine to initiate the process of suggesting optimal personnel changes. This generation engine adjusts the content of the suggested changes and the reasons for the suggestions to reflect the user's emotional state.

[0659] The generation engine comprehensively analyzes information obtained from the user's skill set, career plan, and emotion recognition, and attempts to match it with the organization's requirements. The emotion engine creates emotion-based follow-ups and reasoning for suggestions, particularly to increase user satisfaction.

[0660] Terminal: Once the results from the generation engine are sent to the terminal by the server, the terminal visualizes them for the user. The screen displays transfer destination suggestions that take the user's wishes and feelings into consideration, along with the reasoning behind them. Based on these results, the user can consider their next career step and provide feedback as needed.

[0661] For example, if a 30-year-old engineer is considering a transfer to a new department and uses this system to express their preferences, the generation engine will suggest transfer destinations that reflect their positive aspirations, thereby positively influencing their career plan. This consideration, based on the user's emotions, contributes to increased user satisfaction.

[0662] This system incorporates not only technical skills and organizational needs, but also emotional factors, enabling more personalized transfers and optimizing talent utilization within the organization.

[0663] The following describes the processing flow.

[0664] Step 1:

[0665] The user uses the device to input their skills, work experience, and career plan. The device displays input forms and collects information from the user sequentially. This includes dropdowns and text boxes, prompting the user for specific input.

[0666] Step 2:

[0667] The terminal activates an emotion engine when the user enters information, instantly recognizing the user's emotions based on factors such as typing speed and mouse movements. This emotion data is then transferred to the server along with the entered user information.

[0668] Step 3:

[0669] The server queries the organizational needs database using user information and sentiment data parameters received from the terminal. This database contains information about open positions and required skills within the organization. The server retrieves the relevant organizational information and prepares it for analysis.

[0670] Step 4:

[0671] The server passes integrated information, including user emotion data, to the generation engine. The generation engine uses an AI algorithm to perform an aptitude analysis that reflects the user's emotions and generates the most suitable transfer candidates. The emotion data obtained by the emotion engine is used to refine the reasons for the proposals and for follow-up.

[0672] Step 5:

[0673] The server sends the transfer candidates generated by the generation engine, along with detailed reasons for the suggestions, to the terminal. The terminal displays the results in a user-friendly format. For example, it shows how the transfer aligns with the user's long-term career goals and what emotional considerations have been taken into account.

[0674] Step 6:

[0675] The user reviews the analysis results and suggestions provided on their device. An emotion-first feedback option is displayed, allowing the user to provide an emotional response to the transfer candidates and suggestions. This feedback may be used for further adjustments.

[0676] (Example 2)

[0677] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0678] Traditionally, personnel transfer decisions within organizations have been based primarily on skills and direct organizational needs, but have not adequately considered the emotional aspects of employees. This can lead to decreased employee satisfaction, potentially negatively impacting the overall vitality and efficiency of the organization. Therefore, a system is needed that considers the emotional state of employees and proposes more appropriate and satisfying personnel transfers.

[0679] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0680] In this invention, the server includes means for analyzing the user's emotional state, means for integrating the input user information and emotional state with the organization's request database, and means for using a generative AI model to perform suitability analysis. This makes it possible to propose appropriate and satisfying personnel changes while taking the user's emotional state into consideration.

[0681] "User information" refers to data related to an individual's skills, career plans, experience, etc.

[0682] "Emotion recognition" is the process of analyzing a user's psychological state from their input and actions to identify emotions such as positive or negative.

[0683] An "organizational requirements database" refers to a collection of information that stores data on the characteristics, skills, and job responsibilities of personnel required by an organization.

[0684] "Aptitude analysis" is a process of comprehensively evaluating user information and organizational requirements to determine which tasks or positions are best suited for the user.

[0685] A "generative AI model" refers to an algorithm or machine learning model that proposes optimal personnel changes based on user and organizational information.

[0686] "Potential transfers" refer to new positions or roles that may be proposed to the employer.

[0687] "Visualization" refers to displaying information graphically and presenting it in a way that is easy for the user to understand.

[0688] To implement this invention, the following system is specifically used.

[0689] User information input and emotion recognition

[0690] The terminal provides the user with an interface for information input. Through this interface, the user inputs information such as their skills, experience, and career plans. The terminal incorporates emotion recognition capabilities, detecting the user's emotional state from their facial expressions and text input during the input process. Specific emotion recognition software is used for this emotion analysis.

[0691] Information integration and database processing

[0692] The server processes user information and sentiment data received from the terminal and accesses the organization's requirements database. This database records the organization's overall talent needs. The server integrates the user information and organizational data and passes it to the generative AI model.

[0693] Aptitude analysis using generative AI models

[0694] The AI ​​model generated on the server proposes the most suitable personnel transfers to users based on integrated data. This model comprehensively analyzes the user's skill set, career plan, and emotional data to generate optimal transfer candidates. It also adjusts the suggestions, paying particular attention to the user's positive emotions.

[0695] Results presentation and feedback

[0696] When the generation engine sends transfer candidate suggestions from the server to the terminal, the terminal visually presents them to the user. At this time, the suggested transfer destinations and the reasons for them are described in detail, allowing the user to decide on the next career step. The user can provide feedback based on the results and input more detailed information as needed.

[0697] Specific examples and prompt statements

[0698] For example, if a 30-year-old engineer expresses a desire to transfer to a new department, the generating AI model will suggest a transfer destination that reflects the engineer's positive career aspirations. An example of a prompt in this process would be: "A 30-year-old engineer desires a new challenge and has expressed positive feelings. Please suggest the most suitable transfer destination for him." This system is expected to quickly provide optimal transfer suggestions that take the user's emotions into account.

[0699] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0700] Step 1:

[0701] The terminal presents the user with an interface for information input. The user inputs data such as skills, career plans, and experience into the terminal. The terminal analyzes the input information and prepares to send it to the server. In this step, the user's basic information is obtained as input data.

[0702] Step 2:

[0703] The device utilizes emotion recognition software to recognize the user's emotional state based on their input and behavior. The recognized emotion data, along with user information, is prepared to be sent to the server. There, the server analyzes the user's emotional patterns and generates emotional state data such as positive and negative.

[0704] Step 3:

[0705] The server receives user information and sentiment data transmitted from the terminal. The received data is integrated with the organization's requirements database. The server inputs this integrated data into a generating AI model and begins data processing. In this step, the organization's demand information is referenced from the database as output data.

[0706] Step 4:

[0707] The AI ​​model on the server generates optimal transfer candidates for the user using integrated data. The model evaluates and analyzes the input skill set, career plan, and emotional state to determine the transfer destination. The output data generated includes the candidate positions and their rationale.

[0708] Step 5:

[0709] The server sends the generated transfer candidates to the terminal. The terminal visually processes the received data and displays the proposals to the user. The user reviews the presented transfer candidates and the reasons for them, and uses this information to decide on their next career step. At this step, the final proposal information is displayed to the user as output data.

[0710] Step 6:

[0711] When a user provides feedback, the device sends that feedback information back to the server. The server analyzes the feedback and, if necessary, requests a revised suggestion from the generating AI model. This cycle can potentially lead to the output of even more optimal suggestions that incorporate the user's opinions.

[0712] (Application Example 2)

[0713] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0714] In modern organizations, proposing optimal transfers while considering the aptitudes and emotional states of individual employees is a crucial challenge. However, appropriately recognizing emotional changes and reflecting them in transfer proposals is technically difficult, and as a result, transfers that enhance individual satisfaction are not being fully realized.

[0715] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0716] In this invention, the server includes means for displaying a visualization device for inputting personnel information, means for integrating the input personnel information with organizational information stored in an organizational needs database, and means for detecting the user's emotions and optimizing suggestions based on those emotions. This makes it possible to make more accurate transfer suggestions that take emotional states into account.

[0717] "Personnel information" refers to information that includes attributes such as skills, career, and emotions related to individual people.

[0718] A "visualization device" is a display device that provides information to users in the form of images.

[0719] "Integrating" means systematically combining multiple pieces of information, establishing relationships between them, and unifying them into a single entity.

[0720] A "generative engine" is a computer program that analyzes given data and generates results that meet a specific purpose.

[0721] "Emotion detection" is the act of inferring a user's internal emotional state from their facial expressions, voice, and other factors.

[0722] "Optimizing a proposal" means adjusting it to be the most effective and efficient proposal based on the given conditions and constraints.

[0723] This invention comprises a visualization device, a server, and a user system. The visualization device functions as an interface for presenting information to the user, where the user can input information about their skills and career plans. The visualization device is preferably a wearable device such as smart glasses or a head-mounted display.

[0724] The server integrates the personnel information received from the visualization device with the information stored in the organization's needs database. Based on this integrated data, the generation engine operates and performs suitability analysis. The generation engine is equipped with a module for detecting emotions and optimizes suggestions to reflect the user's emotional state. Specifically, it recognizes the user's emotions through facial and voice analysis and adjusts the optimal movement options and suggestions based on that information.

[0725] For example, if a user is experiencing stress while working, the emotion analysis module can detect that stress and suggest relaxing music. Furthermore, the system receives feedback from the user through prompts such as "Suggest music that matches my current mood" or "Tell me how to perform this task more efficiently," and uses this feedback to improve the suggestions.

[0726] Thus, the present invention provides a system that takes user emotions into consideration and enables more personalized suggestions.

[0727] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0728] Step 1:

[0729] Users input information about their skills and career plans through a visualization device. The device receives the input data and sends it to a server. Text and selection-based data are used for input.

[0730] Step 2:

[0731] The server uses an emotion recognition module to detect the user's emotional state based on the personnel information received from the terminal. Emotion detection is performed using natural language processing and speech analysis. As a result, a tag indicating the user's emotional state is generated.

[0732] Step 3:

[0733] The server integrates personnel information and detected emotional states with the organization's needs database. Here, necessary organizational information is retrieved from the database and integrated. The output is an integrated dataset.

[0734] Step 4:

[0735] The generation engine analyzes the integrated data and performs suitability analysis. The server uses a generational AI model to generate optimal transfer candidates based on the user's skills and emotions. This process utilizes machine learning algorithms to ultimately generate transfer candidates and the reasons for their suggestion.

[0736] Step 5:

[0737] The server sends the generated transfer candidates and their rationale to the terminal. The terminal displays this on a visualization device and requests feedback from the user. This allows the user to obtain detailed information about the transfer candidates.

[0738] Step 6:

[0739] The user enters feedback on the proposed transfer candidates via a terminal. This feedback is given in the format specified as a prompt, and the information is then sent back to the server.

[0740] Step 7:

[0741] The server receives feedback, reruns the analysis process as needed, and optimizes the suggestions. After the feedback-based update process is complete, the new suggestions are presented to the user again.

[0742] 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.

[0743] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0744] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0745] 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.

[0746] Figure 9 shows an 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.

[0747] 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.

[0748] 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.

[0749] 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, motorcycles, etc., 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, for example, based 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.

[0750] 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."

[0751] 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.

[0752] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0753] 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0754] 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.

[0755] 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.

[0756] 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.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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 the like 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.

[0762] 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.

[0763] The following is further disclosed regarding the embodiments described above.

[0764] (Claim 1)

[0765] A means of displaying a screen for entering personnel information,

[0766] A means of integrating entered personnel information with organizational information stored in the organization's needs database,

[0767] A method using a generation engine to perform aptitude analysis based on integrated information,

[0768] A means of displaying the analysis results on a screen and presenting transfer candidates,

[0769] A system that includes this.

[0770] (Claim 2)

[0771] The system according to claim 1, comprising means for reintegrating user feedback based on the analysis results and conducting further suitability analysis.

[0772] (Claim 3)

[0773] The system according to claim 1, comprising means for displaying the reasons and expected results for transfer candidates identified as a result of the analysis.

[0774] "Example 1"

[0775] (Claim 1)

[0776] A display means for inputting information,

[0777] A means for integrating input information and recorded request information,

[0778] A method using a generation engine to perform aptitude analysis based on integrated information,

[0779] A means of displaying the analysis results and suggesting placement candidates,

[0780] A means for efficiently processing various combinations of conditions using a generation engine and determining the optimal arrangement,

[0781] A system that includes this.

[0782] (Claim 2)

[0783] The system according to claim 1, further comprising means for reintegrating user responses based on the analysis results and conducting a further suitability analysis.

[0784] (Claim 3)

[0785] The system according to claim 1, comprising means for displaying the reasons and expected results for the proposed placements as a result of the analysis.

[0786] "Application Example 1"

[0787] (Claim 1)

[0788] A means of displaying a screen for entering personnel information,

[0789] A means of integrating entered personnel information with organizational information stored in the organization's needs database,

[0790] A method using a generation engine to perform aptitude analysis based on integrated information,

[0791] A means of displaying the analysis results on a screen and presenting transfer candidates,

[0792] A means of inputting information on the operating skills and aptitudes of machinery and equipment, and proposing the optimal placement,

[0793] A system that includes this.

[0794] (Claim 2)

[0795] The system according to claim 1, comprising means for reintegrating user feedback based on the analysis results and conducting further suitability analysis.

[0796] (Claim 3)

[0797] The system according to claim 1, comprising means for displaying the reasons and expected results for transfer candidates identified as a result of the analysis.

[0798] "Example 2 of combining an emotion engine"

[0799] (Claim 1)

[0800] A device for inputting user information,

[0801] A means of analyzing the user's emotional state using an emotion recognition device,

[0802] A means for storing the entered user information and their emotional state, and integrating it with the information stored in the organization's request database,

[0803] A method using a generative AI model to perform aptitude analysis based on integrated information,

[0804] A means of recommending the analysis results to the device and suggesting transfer candidates,

[0805] A system that includes this.

[0806] (Claim 2)

[0807] The system according to claim 1, which includes means for creating proposal reasons and follow-ups based on the analysis results and taking into account the user's emotional state.

[0808] (Claim 3)

[0809] The system according to claim 1, which includes means for visualizing and displaying the rationale and merits of transfer candidates identified as a result of the analysis.

[0810] "Application example 2 of combining emotional engines"

[0811] (Claim 1)

[0812] A means for displaying a visualization device for inputting personnel information,

[0813] A means of integrating entered personnel information with organizational information stored in the organization's needs database,

[0814] A method using a generation engine to perform aptitude analysis based on integrated information,

[0815] A means of displaying the analysis results on a visualization device and presenting transfer candidates,

[0816] A means to detect user emotions and optimize suggestions based on those emotions,

[0817] A system that includes this.

[0818] (Claim 2)

[0819] The system according to claim 1, comprising means for reintegrating user feedback based on the analysis results and conducting further suitability analysis.

[0820] (Claim 3)

[0821] The system according to claim 1, comprising means for displaying the reasons and expected outcomes of activity candidates based on the results of emotion analysis. [Explanation of Symbols]

[0822] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of displaying a screen for entering personnel information, A means of integrating entered personnel information with organizational information stored in the organization's needs database, A method using a generation engine to perform aptitude analysis based on integrated information, A means of displaying the analysis results on a screen and presenting transfer candidates, A system that includes this.

2. The system according to claim 1, further comprising means for reintegrating user feedback based on the analysis results and conducting a further suitability analysis.

3. The system according to claim 1, comprising means for displaying the reasons and expected results for transfer candidates identified as a result of the analysis.

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