system

Abstract

We provide the system. [Solution] A means of collecting and storing information on the characteristics of personnel performing job functions, Means for collecting and storing information that meets the requirements of the institution, A means for applying a generation algorithm to match information on job functions with information on organizations, A means of visually displaying matching results on a smartphone or other device and providing them to the person performing the job and the manager, Means for saving matching and provision history, 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 method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Accurately matching the career plans desired by employees with the long-term plans and needs of the organization is essential for improving organizational efficiency while maintaining employee satisfaction. However, with the conventional approaches, it has been difficult to comprehensively analyze employee information and organizational information, and it has not been possible to achieve optimal personnel allocation in a short time. Furthermore, there is a problem that there is no system that can also respond to the adjustment of results by continuous feedback.

Means for Solving the Problems

[0005] This invention provides a system for collecting and storing employees' personal information and acquiring and storing information that meets the organization's needs. It then performs data matching using a generation algorithm and provides the matching results to employees and managers. Furthermore, it enables dynamic adjustment of the matching results based on acquired feedback and records past matching and provision history, allowing for continuous optimization while identifying areas for future improvement.

[0006] "Employee personal information" refers to information related to an employee's job, such as their career plan, skills, experience, and desired position.

[0007] "Information that meets the organization's needs" refers to information related to organizational management, such as the organization's medium- to long-term plans, resource allocation, budget, and the type of personnel it needs.

[0008] A "generative algorithm" is a computational method that combines employee information and organizational information to achieve optimal matching.

[0009] "Matching results" refer to the optimal personnel placement suggestions obtained as a result of applying a generation algorithm.

[0010] "Matching and Provision History" refers to records of past matching results and provision status.

[0011] "Adjusting based on feedback" is a process of re-evaluating and restructuring matching results based on opinions and suggestions from managers. [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 an 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 an 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.

[0014] First, the language 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 and various parameters, etc. 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 including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[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 for optimally matching employee personal information with organizational needs. This system consists of terminals, servers, and users. Details are provided below.

[0034] The terminal provides an interface for employees (users) to input career plans, current skills, and desired job information. It also provides an interface for administrators (users) to input organizational medium- to long-term plans, resource allocation, and budget information. The terminal verifies this input information and, if complete, sends it to the server.

[0035] The server stores the received employee personal information and organizational information in a database. This data is used to match the two using a generation algorithm. The generation algorithm analyzes each employee's career plan and skill set and proposes the most suitable personnel placement for the organization's needs.

[0036] The server then presents the generated matching results to the administrator, who is the user. The administrator reviews these results and provides feedback if necessary. The server incorporates this feedback, adjusts the matching results, and presents them again. This process allows the administrator to make the final personnel placement decision.

[0037] The server will reconfirm the final matching results and notify employees and administrators. The history of this entire process will be recorded in a database for future analysis and improvement.

[0038] For example, if employee A expresses a desire to "gain experience as a project manager" in their career plan, and organization B "needs someone with leadership skills for a new project," the generation algorithm will present employee A as a project manager candidate for organization B. The manager accepts this proposal, and the personnel placement is decided.

[0039] Thus, this invention effectively integrates the needs of employees and organizations to achieve optimal personnel transfers.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The terminal displays a screen for the employee user to input their career plan, current skill set, desired job, etc. The user can enter their information through this screen. After confirming the input, the terminal sends the data to the server.

[0043] Step 2:

[0044] The terminal displays a screen for the administrator (user) to input the organization's medium- to long-term plans, required resources, and budget information. The user enters the necessary information for the organization, and the terminal checks the data before sending it to the server.

[0045] Step 3:

[0046] The server stores the personal information of each employee and the organization's needs information received from the terminal in a database, and organizes and saves this data.

[0047] Step 4:

[0048] The server uses a generation algorithm to match and analyze employee and organizational information from the database. In this process, it determines how well employees' career plans and skill sets align with organizational requirements, and derives optimal personnel placement candidates.

[0049] Step 5:

[0050] The server displays the generated matching candidates to the administrator, who then reviews the presented results. If necessary, the administrator can provide feedback to the server.

[0051] Step 6:

[0052] Based on feedback from the administrator, the server reapplies the generation algorithm to adjust the matching results. It then presents the adjusted results again.

[0053] Step 7:

[0054] After the administrator confirms the final match, the server notifies the employee and the administrator of the result and allows them to confirm the decision.

[0055] Step 8:

[0056] The server records details of all matching processes performed in a database, which can then be used for future analysis and adjustments.

[0057] This completes the entire process in which servers, terminals, and users cooperate to achieve optimal personnel transfers.

[0058] (Example 1)

[0059] 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."

[0060] In today's business environment, a system that appropriately matches individual characteristics with group requirements is essential. However, conventional methods struggle to effectively collect and utilize user characteristic information and group requirements information, and they fail to adequately reflect administrator feedback on the resulting matching results. A new system is needed to address these challenges.

[0061] 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.

[0062] In this invention, the server includes a device for collecting and storing user characteristic information, a device for collecting and storing information related to group requests, and a device for performing generation processing to match the characteristic information with the request information. This makes it possible to effectively match the characteristic information with the request information and provide optimal results that reflect the administrator's feedback.

[0063] "User" refers to an individual or group that uses a particular system or service, and in this context, it refers to the party that inputs information.

[0064] "Characteristic information" refers to information related to the user, including data that indicates an individual's characteristics such as career plans, skills, and desired job.

[0065] A "group" refers to an organization or a part of it that shares common goals or needs, and in this context, it serves as a criterion for personnel allocation.

[0066] "Required information" refers to information related to the skills and resources needed by a group, and is data that concretizes organizational needs.

[0067] "Generation processing" refers to data processing that optimally matches feature information and request information, and is the process of deriving the result.

[0068] The term "administrator" refers to the person responsible for reviewing and providing feedback on the generated matching results, and who is involved in the final decision-making process.

[0069] "Feedback" refers to the evaluations and correction suggestions that administrators provide regarding matching results, and is information that contributes to improving the accuracy of the system.

[0070] This system is an information processing system primarily composed of servers, terminals, and users. A specific embodiment of this system will be described below.

[0071] First, the terminal provides dedicated interfaces for both employee and administrator users. Employees can use this interface to input their career plans, skills, and desired job information. Administrators are provided with an interface for inputting the organization's medium- to long-term plans, resource allocation, and budget information. The terminal verifies the entered information and, if complete, transmits it to the server using a secure communication protocol.

[0072] The server utilizes a relational database management system (RDBMS) to store various received information in a central database. The server also uses a generative AI model to analyze employee characteristic information and organizational requirements information, and performs generation processing. This generation process applies a specific algorithm to achieve the best possible match between the characteristic information and the requirements information. The server can provide the generated results to the administrator and perform optimizations based on the administrator's feedback.

[0073] For example, if an employee expresses a desire to gain more experience as a project manager, and the organization needs someone to lead a new project, this system will propose the employee as a candidate for that project manager position. Once the administrator approves the proposal, the server will formally notify the administrator of the result.

[0074] One possible prompt to input into the generating AI model is to specify, "Analyze user characteristic information and group request information to generate the best matching result." This prompt allows the server to present the optimal personnel allocation in real time.

[0075] In this way, the system effectively integrates the needs of users and groups, achieving dynamic and optimal resource allocation.

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] The terminal provides dedicated interfaces for both employee and administrator users. Employees use an interface to input career plans, skills, and desired job information, while administrators input organizational plans and resource information. The entered information is formatted in real time, and any errors are notified to the user. The input is collected in text format, and the terminal outputs it as an organized dataset, preparing it for the next processing step.

[0079] Step 2:

[0080] The terminal sends organized employee and administrator information to the server. This transmission is performed using security protocols such as SSL. The server, having received employee and organizational information as input, stores it in a database and uses an RDBMS to maintain data integrity. The output of this step is a dataset correctly loaded into the database, which is used for subsequent processing.

[0081] Step 3:

[0082] The server retrieves employee and organizational information stored in the database and analyzes the data by driving a generative AI model. The prompt "Analyze talent characteristics and organizational needs to generate optimal matching" is used to best match each employee's characteristic information with the organization's requirements. Data processing involves normalizing the characteristic data, prioritizing the requirements information, and matching them to calculate the most suitable talent placement. The output of this step is the generated matching result.

[0083] Step 4:

[0084] The server presents the generated matching results to the administrator. The administrator can review the results and provide feedback through the input interface. The server receives the administrator's feedback as data and readjusts the matching results as needed. This feedback process ensures that the readjusted matching results are even more aligned with the organization's needs.

[0085] Step 5:

[0086] The server notifies employees and administrators of the final matching results approved by the administrator. This notification is sent via a dedicated app or email notification system. The final output is the position information of the newly assigned personnel, which is shared with employees and the organization.

[0087] Step 6:

[0088] The server records the processing history of all processes in a database. This includes input information, generated prompts, feedback, and final results, which are used for future analysis and improvement. At this stage, the system outputs a historical dataset.

[0089] (Application Example 1)

[0090] 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."

[0091] In modern public institutions and large organizations, there is a need to appropriately allocate the skills and preferences of job performers to the specific requirements of the institution. However, existing systems lack a process for efficiently analyzing the diverse characteristics of job performers and presenting them intuitively in a visual format. As a result, insufficient matching occurs between job performers and institutional requirements, hindering operational efficiency and overall organizational performance. There is a strong demand for a new system to address this challenge.

[0092] 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.

[0093] In this invention, the server includes means for collecting and storing characteristic information of job performers, means for collecting and storing information corresponding to the requirements of the organization, means for applying a generation algorithm for matching job performer information with organization information, means for visually displaying the matching results on a smartphone or other device and providing them to job performers and managers, and means for storing the matching and provision history. This makes it possible to quickly and accurately present job performer placement plans through a visually intuitive interface and improve communication and management efficiency within the organization.

[0094] A "functioning professional" is an individual who is responsible for a specific job and possesses the skills and abilities related to that job.

[0095] "Characteristic information" refers to data that shows attributes such as the abilities, experience, interests, and career plans of individuals performing job functions.

[0096] "Institutional requirements" refer to information that outlines the goals a particular organization or institution aims to achieve and the conditions it must meet.

[0097] A "generative algorithm" is a computational method for calculating and proposing the optimal personnel allocation based on the characteristics of job performers and the requirements of the organization.

[0098] The term "device" refers to any electronic device used for inputting, processing, and displaying information, and includes smartphones and computers.

[0099] "Visual display" means presenting calculation results and data to the user in a graphical format.

[0100] "Saving history" means recording past data processing and results so that they can be referenced later.

[0101] This system supports the optimal allocation of personnel for various functions within public institutions and corporations. The server has the function of collecting and storing personnel characteristics information in a database. This characteristics information includes personnel abilities, experience, and career plans. It also collects and stores institutional requirements information in the database.

[0102] The server applies a generation algorithm based on the collected information to achieve optimal matching by connecting job function holders with the needs of the organization. The generation algorithm uses Python and scikit-learn to efficiently run the data analysis and matching process.

[0103] Furthermore, the system displays matching results visually to users (function-oriented personnel and administrators) via smartphones or other devices. This utilizes React Native, allowing administrators to review results and explore different options and placement suggestions through an intuitive UI.

[0104] Furthermore, the server stores a history of all operations and data processing, which can be used for future analysis and system improvements. A concrete example of its use is in a new policy project at a ward office, where it can identify suitable leadership candidates based on the career plans of employees and propose them to management.

[0105] An example of a prompt in this system would be, "We are looking for potential leaders for a new public project. Please list employees who have expressed interest in leadership roles as part of their career plans." This facilitates the appropriate placement of necessary personnel in public institutions and large corporations, thereby improving organizational efficiency.

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] The terminal receives characteristic information and organizational requirements information from job functioners and administrators. Users input their skills, experience, career plans, and organizational goals through the interface. This prepares the collected data for transmission to the server.

[0109] Step 2:

[0110] The server stores the received characteristic information and institutional requirements information in a database. The collected data is accurately classified and organized, and processed to enable quick access in subsequent processing steps.

[0111] Step 3:

[0112] The server applies a generation algorithm based on the stored information. Using Python and scikit-learn, it analyzes the skill sets of job functioning personnel to match the organization's needs. In this step, it outputs the optimal personnel placement plan based on the input information.

[0113] Step 4:

[0114] The generated matching results are sent to the device for review by the administrator, who is also the user. The results are presented through a visual user interface via React Native, allowing the administrator to visually evaluate the validity of the placement proposals.

[0115] Step 5:

[0116] The user (administrator) provides feedback on the matching results, and the server incorporates that feedback. This feedback includes approving placement plans and suggesting changes, and the generation algorithm is reapplied as needed to obtain adjusted output.

[0117] Step 6:

[0118] The server reconfirms the final matching results and notifies the relevant personnel and administrators. The finalized placement plan is then sent to terminals, ensuring all stakeholders share the latest information.

[0119] Step 7:

[0120] The server records the operation history and matching history of all processes in a database. This allows it to be used as evidence for future analysis and system improvements.

[0121] 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.

[0122] This invention is a system that achieves more accurate personnel matching by not only collecting and storing employee personal information and organizational needs information, but also recognizing and analyzing the emotional state of users. This system consists of a terminal, a server, an emotion engine, and a user.

[0123] The terminal provides a form for employees (users) to input their career plans and skill sets. During this process, an emotion engine analyzes the employee's attitude and behavior, and sends the results from the terminal to the server. Administrators (users) input organizational plans and required personnel information in a similar manner, but the emotion engine also analyzes the administrator's emotions during the input process and sends the results to the server.

[0124] The server stores employee information, organizational information, and their corresponding emotional states in a database. Based on this information, a generation algorithm evaluates employees' career plans and organizational needs along with their emotional states, and automatically initiates a matching process. In this process, employees' emotional states, such as motivation and stress levels, are reflected in the matching results.

[0125] The server presents the generated matching results to the administrator (user) and requests their evaluation. The administrator can review the results and provide feedback, paying attention to changes in emotion. This feedback is also analyzed by the emotion engine, and as a result, the server re-runs the matching algorithm, taking the emotional state in the feedback into consideration, to optimize it.

[0126] For example, if employee B expresses a desire for collaborative teamwork but also dissatisfaction with their current role, the Emotion Engine analyzes these negative emotions and proposes a new role that tests their team leadership skills, thereby resolving their dissatisfaction and boosting their motivation.

[0127] Thus, the present invention provides an advanced personnel transfer system that not only matches skills and career information as in the conventional system, but also takes into account the user's emotional state.

[0128] The following describes the processing flow.

[0129] Step 1:

[0130] The terminal provides employees (users) with a form to input their career plans and skill sets. Users use this form to enter information related to their job duties. The emotion engine analyzes the employee's emotional state while they are entering the information and sends that data from the terminal to the server.

[0131] Step 2:

[0132] The terminal provides an interface for the administrator (user) to input organizational plans, required personnel specifications, and budget information. As the user inputs organizational information, an emotion engine simultaneously measures the administrator's emotions and sends this data to the server.

[0133] Step 3:

[0134] The server stores employee and administrator input information and sentiment data received from terminals into a database. This stored data is used when running the generation algorithm.

[0135] Step 4:

[0136] The server uses a generation algorithm to integrate employee and organizational information retrieved from the database and analyzes it while considering each individual's emotional state. The algorithm evaluates how well employees' career plans, skill sets, and emotional states align with the organization's needs, and generates the optimal match.

[0137] Step 5:

[0138] The server notifies the administrator of the generated matching results, allowing the administrator to review them. The administrator, as a user, reviews the presented results and provides feedback based on the sentiment data analyzed by the sentiment engine.

[0139] Step 6:

[0140] The server adjusts the matching results by reapplying the algorithm, taking into account the sentiment information extracted from the administrator's feedback, and generates new results. The readjusted results are then presented to the administrator.

[0141] Step 7:

[0142] The server reviews the matching plan finalized by the administrator and notifies the employee of the final placement decision. The server records the entire process leading up to this decision and stores it in a database for future reference.

[0143] This series of steps makes it possible to implement personnel placement that takes into account the emotional state of the users.

[0144] (Example 2)

[0145] 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".

[0146] Traditional personnel matching systems are limited to evaluations based on quantitative data such as technical skills and work history, and fail to consider qualitative aspects such as the emotional state and motivational formation of employees. Therefore, it has been difficult to appropriately assess employee stress and dissatisfaction and to achieve truly optimal placement for the organization. As a result, there is a challenge in that appropriate personnel placement cannot be made, and organizational productivity and employee satisfaction cannot be improved.

[0147] 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.

[0148] In this invention, the server includes means for analyzing employee attitudes and utterances to evaluate their emotional state, means for analyzing the emotions of administrators at the time of input and similarly evaluating emotional information, and means for matching employee information with organizational information and each piece of emotional information. This makes it possible to perform advanced personnel matching that takes into account the emotional state of employees and to achieve optimal job placement.

[0149] "Employee attribute information" refers to personal data about employees, including information such as career plans, skill sets, and experience.

[0150] "Organizational requirements" refer to the necessary conditions and policies regarding the personnel that a company or organization seeks, and include specific skills, qualifications, and experience.

[0151] "Emotional state" refers to information that indicates the psychological and emotional condition of employees and managers, and includes stress, motivation, satisfaction, etc.

[0152] "Generation method" refers to algorithms and mechanisms that analyze various data, including emotional states, to derive the optimal matching between employee information and organizational information.

[0153] "Means of presentation" refers to functions and processes that make the generated matching results visible to the administrator.

[0154] "Optimization" refers to the process by which a system adjusts decisions, such as personnel assignments, based on emotional information and feedback, to make them more effective and efficient.

[0155] This invention is a system for achieving optimal personnel matching for both employees and organizations. The system utilizes terminals, servers, an emotion analysis engine, and a generative AI model.

[0156] Employees, as users, input their career plans and skill sets through a terminal. The terminal has a built-in emotion analysis engine that analyzes the employee's actions and words as they input data and evaluates their emotional state. In this process, specific software is used to analyze typing speed and word choice, and stress and motivation levels are quantified. For example, if many corrections are made in a short period of time, the analysis engine will determine that the employee is in an anxious state.

[0157] Furthermore, administrators, who are also users, input organizational plans and required personnel information via their terminals, and similar sentiment analysis is performed. All input information and sentiment data are sent to the server and stored in the database.

[0158] The server uses a generative AI model based on accumulated data to comprehensively evaluate employee information, organizational information, and emotional information. As a result, it automatically determines the optimal personnel placement. This generative AI model reflects emotional aspects such as employee motivation and stress levels, and can identify key focus points for both employees and the organization.

[0159] The generated matching results are presented to the administrator via a terminal. The administrator is required to provide feedback on the results, paying particular attention to changes in emotion during the evaluation. The feedback is also analyzed for emotion, and the server uses this emotion information to re-execute the matching algorithm and optimize placement.

[0160] For example, if employee B "desires to work collaboratively in a team" and is dissatisfied with their current position, the sentiment analysis engine will recognize this as a negative emotion, and the server will suggest a suitable new position for employee B that offers "leadership opportunities."

[0161] An example of a prompt might be, "Please suggest the most suitable job for employee B, taking into account their emotional state. They are dissatisfied with their current position but desire to work collaboratively within a team." By using such prompts, the generative AI model can provide more sophisticated matching suggestions.

[0162] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0163] Step 1:

[0164] The terminal receives career plans and skill sets entered by the employee user. The entered data is collected through an input form, and an emotion analysis engine analyzes the employee's input actions and words. The analysis detects typing speed and word choice, and quantifies emotional states such as stress and motivation. As output, emotion-analyzed data is generated and sent from the terminal to the server.

[0165] Step 2:

[0166] The terminal receives organizational needs and personnel information entered by the administrator, who is the user. At the same time, the terminal uses an emotion analysis engine to analyze the administrator's emotions at the time of input and evaluate their emotional state. The entered information and emotion data are structured and sent to the server. As a result, organizational needs data, along with the administrator's emotion information, is generated.

[0167] Step 3:

[0168] The server stores employee attribute information, organizational requirements information, and their respective emotional state data in a database. Next, a generative AI model is used to analyze the employee, organizational, and emotional information. This process evaluates the matching and prioritization of each element to determine the optimal match. The output is a proposed optimal personnel placement plan.

[0169] Step 4:

[0170] The server displays the generated matching results to the administrator's terminal. The administrator reviews the displayed results and provides detailed feedback. Since the feedback includes the administrator's emotions, the terminal's emotion analysis engine analyzes the changes in emotions again, and this data is sent to the server. As output, feedback data that takes emotion evaluation into account is generated.

[0171] Step 5:

[0172] The server uses the sentiment information based on the feedback to re-run the matching algorithm using the generative AI model. In this process, the placement plan is optimized based on the newly obtained information from the feedback. As a result, an optimized personnel placement plan is generated and presented to the manager.

[0173] (Application Example 2)

[0174] 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".

[0175] Conventional systems only match employees based on their skills and career information, failing to reflect their emotional state. This results in a lack of proper consideration of employee and manager motivation and stress levels. Furthermore, in the deployment of robots within factories, the absence of mechanisms to consider managers' emotional states prevents the achievement of optimal staffing and robot operation.

[0176] 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.

[0177] In this invention, the server includes means for collecting and storing employee personal information, means for collecting and storing information corresponding to the organization's needs, means for applying a generation algorithm for matching employee information with organizational information, means for analyzing emotional states and reflecting the results in the generation algorithm, and means for proposing robot placement optimization according to the emotional state of the manager. This makes it possible to propose optimal matching and robot placement that takes into account the emotional states of employees and managers.

[0178] "Employee personal information" refers to information about employees, including their names, contact information, skill sets, and career plans.

[0179] "Organizational needs" refers to information about the personnel and skills that an organization needs for growth and project execution.

[0180] A "generative algorithm" refers to a procedure or calculation method used to determine the optimal combination of employee and organizational needs based on collected information.

[0181] "Emotional state" refers to the psychological state and motivation analyzed from the attitudes and behaviors of employees and managers.

[0182] "Robot placement optimization" refers to the adjustment of robot placement and task allocation in a factory or work environment based on emotional states and other relevant information to achieve the most efficient results.

[0183] This invention uses terminals, servers, an emotion engine, and users to achieve optimal personnel and robot placement that takes emotional states into account.

[0184] The terminal provides an interface for employees to input their career plans and skill sets. As employees input information, an emotion engine analyzes their input behavior and identifies their emotional state. This emotional information is sent from the terminal to the server. Similarly, administrators also input organizational needs information through the terminal, and their emotional state is analyzed and sent to the server.

[0185] The server stores collected employee information, organizational information, and corresponding emotional states in a database. Based on this information, a generation algorithm evaluates employee career plans and organizational needs, incorporating emotional information, and performs initial matching. This process takes into account employee motivation and stress levels, which have been analyzed by the emotional engine.

[0186] Furthermore, robot placement is optimized to reflect the administrator's emotional state. Administrators can review the proposed matching and placement plans and provide feedback. This feedback is also analyzed through the emotion engine, and the server readjusts the matching and placement plans based on the feedback.

[0187] As a concrete example, consider a situation in a factory environment where a manager has previously experienced stress from a specific task. Based on this information, the system can rearrange the robot's placement to match that task, providing an efficient and less stressful environment.

[0188] The following example prompts apply to the generative AI model:

[0189] "Based on the following input, please propose the optimal placement of factory robots. Managers are experiencing high stress when handling Task X and require a more collaborative placement."

[0190] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0191] Step 1:

[0192] If the user is an employee, the terminal accepts input of the user's career plan and skill set. The data obtained through this input interface is then sent to an emotion engine to analyze the user's input behavior. As a result of this analysis, employee emotional state data is generated.

[0193] Step 2:

[0194] If the user is an administrator, the terminal provides an interface for entering information about the organization's needs. The administrator's input data is analyzed by an emotion engine to obtain information about the administrator's emotional state. This data is sent from the terminal to the server along with the input data.

[0195] Step 3:

[0196] The server stores employee information, organizational information, and corresponding emotional state data received from the terminals into a database. This prepares the system for running the matching generation algorithm using this data.

[0197] Step 4:

[0198] The server uses the stored data to perform an initial matching process based on a generation algorithm. This process incorporates emotional state data into the data processing to generate matching results that reflect employee motivation and stress levels.

[0199] Step 5:

[0200] The server provides the generated matching results to the administrator, who is the user. The administrator can review this and provide feedback. The feedback is then analyzed again by the emotion engine to extract the administrator's emotional state.

[0201] Step 6:

[0202] The server takes administrator feedback and emotional states into account and initiates an optimization process to readjust matching and robot placement. This ensures that a more collaborative and efficient placement is implemented where necessary.

[0203] Step 7:

[0204] As a concrete example, in a factory environment, the server optimizes robot placement and tasks based on employee and manager sentiment data. In this process, it works in conjunction with a generative AI model, using prompts to suggest improved placement options and providing the optimal solution.

[0205] 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.

[0206] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), 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.

[0207] 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.

[0208] [Second Embodiment]

[0209] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0210] 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.

[0211] 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).

[0212] 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.

[0213] 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.

[0214] 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).

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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".

[0221] This invention is a system for optimally matching employee personal information with organizational needs. This system consists of terminals, servers, and users. Details are provided below.

[0222] The terminal provides an interface for employees (users) to input career plans, current skills, and desired job information. It also provides an interface for administrators (users) to input organizational medium- to long-term plans, resource allocation, and budget information. The terminal verifies this input information and, if complete, sends it to the server.

[0223] The server stores the received employee personal information and organizational information in a database. This data is used to match the two using a generation algorithm. The generation algorithm analyzes each employee's career plan and skill set and proposes the most suitable personnel placement for the organization's needs.

[0224] The server then presents the generated matching results to the administrator, who is the user. The administrator reviews these results and provides feedback if necessary. The server incorporates this feedback, adjusts the matching results, and presents them again. This process allows the administrator to make the final personnel placement decision.

[0225] The server will reconfirm the final matching results and notify employees and administrators. The history of this entire process will be recorded in a database for future analysis and improvement.

[0226] For example, if employee A expresses a desire to "gain experience as a project manager" in their career plan, and organization B "needs someone with leadership skills for a new project," the generation algorithm will present employee A as a project manager candidate for organization B. The manager accepts this proposal, and the personnel placement is decided.

[0227] Thus, the present invention effectively integrates the needs of employees and organizations to achieve optimal personnel transfers.

[0228] The following describes the processing flow.

[0229] Step 1:

[0230] The terminal displays a screen for the employee user to input their career plan, current skill set, desired job, etc. The user can enter their information through this screen. After confirming the input, the terminal sends the data to the server.

[0231] Step 2:

[0232] The terminal displays a screen for the administrator (user) to input the organization's medium- to long-term plans, required resources, and budget information. The user enters the necessary information for the organization, and the terminal checks the data before sending it to the server.

[0233] Step 3:

[0234] The server stores the personal information of each employee and the organization's needs information received from the terminal in a database, and organizes and stores this data.

[0235] Step 4:

[0236] The server uses a generation algorithm to match and analyze employee and organizational information from the database. In this process, it determines how well employees' career plans and skill sets align with organizational requirements, and derives optimal personnel placement candidates.

[0237] Step 5:

[0238] The server displays the generated matching candidates to the administrator, who then reviews the presented results. If necessary, the administrator can provide feedback to the server.

[0239] Step 6:

[0240] Based on feedback from the administrator, the server reapplies the generation algorithm to adjust the matching results. It then presents the adjusted results again.

[0241] Step 7:

[0242] After the administrator confirms the final match, the server notifies the employee and the administrator of the result and allows them to confirm the decision.

[0243] Step 8:

[0244] The server records details of all matching processes performed in a database, which can then be used for future analysis and adjustments.

[0245] This completes the entire process in which servers, terminals, and users cooperate to achieve optimal personnel transfers.

[0246] (Example 1)

[0247] 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".

[0248] In today's business environment, a system that appropriately matches individual characteristics with group requirements is essential. However, conventional methods struggle to effectively collect and utilize user characteristic information and group requirements information, and they fail to adequately reflect administrator feedback on the resulting matching results. A new system is needed to address these challenges.

[0249] 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.

[0250] In this invention, the server includes a device for collecting and storing user characteristic information, a device for collecting and storing information related to group requests, and a device for performing generation processing to match the characteristic information with the request information. This makes it possible to effectively match the characteristic information with the request information and provide optimal results that reflect the administrator's feedback.

[0251] "User" refers to an individual or group that uses a particular system or service, and in this context, it refers to the party that inputs information.

[0252] "Characteristic information" refers to information related to the user, including data that indicates an individual's characteristics such as career plans, skills, and desired job.

[0253] A "group" refers to an organization or a part of it that shares common goals or needs, and in this context, it serves as a criterion for personnel allocation.

[0254] "Required information" refers to information related to the skills and resources needed by a group, and is data that concretizes organizational needs.

[0255] "Generation processing" refers to data processing that optimally matches feature information and request information, and is the process of deriving the result.

[0256] The term "administrator" refers to the person responsible for reviewing and providing feedback on the generated matching results, and who is involved in the final decision-making process.

[0257] "Feedback" refers to the evaluations and correction suggestions that administrators provide regarding matching results, and is information that contributes to improving the accuracy of the system.

[0258] This system is an information processing system primarily composed of servers, terminals, and users. A specific embodiment of this system will be described below.

[0259] First, the terminal provides dedicated interfaces for both employee and administrator users. Employees can use this interface to input their career plans, skills, and desired job information. Administrators are provided with an interface for inputting the organization's medium- to long-term plans, resource allocation, and budget information. The terminal verifies the entered information and, if complete, transmits it to the server using a secure communication protocol.

[0260] The server utilizes a relational database management system (RDBMS) to store various received information in a central database. The server also uses a generative AI model to analyze employee characteristic information and organizational requirements information, and performs generation processing. This generation process applies a specific algorithm to achieve the best possible match between the characteristic information and the requirements information. The server can provide the generated results to the administrator and perform optimizations based on the administrator's feedback.

[0261] For example, if an employee expresses a desire to gain more experience as a project manager, and the organization needs someone to lead a new project, this system will propose the employee as a candidate for that project manager position. Once the administrator approves the proposal, the server will formally notify the administrator of the result.

[0262] One possible prompt to input into the generating AI model is to specify, "Analyze user characteristic information and group request information to generate the best matching result." This prompt allows the server to present the optimal personnel allocation in real time.

[0263] In this way, the system effectively integrates the needs of users and groups, achieving dynamic and optimal resource allocation.

[0264] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0265] Step 1:

[0266] The terminal provides dedicated interfaces for both employee and administrator users. Employees use an interface to input career plans, skills, and desired job information, while administrators input organizational plans and resource information. The entered information is formatted in real time, and any errors are notified to the user. The input is collected in text format, and the terminal outputs it as an organized dataset, preparing it for the next processing step.

[0267] Step 2:

[0268] The terminal sends organized employee and administrator information to the server. This transmission is performed using security protocols such as SSL. The server, having received employee and organizational information as input, stores it in a database and uses an RDBMS to maintain data integrity. The output of this step is a dataset correctly loaded into the database, which is used for subsequent processing.

[0269] Step 3:

[0270] The server retrieves employee and organizational information stored in the database and analyzes the data by driving a generative AI model. The prompt "Analyze talent characteristics and organizational needs to generate optimal matching" is used to best match each employee's characteristic information with the organization's requirements. Data processing involves normalizing the characteristic data, prioritizing the requirements information, and matching them to calculate the most suitable talent placement. The output of this step is the generated matching result.

[0271] Step 4:

[0272] The server presents the generated matching results to the administrator. The administrator can review the results and provide feedback through the input interface. The server receives the administrator's feedback as data and readjusts the matching results as needed. This feedback process ensures that the readjusted matching results are even more aligned with the organization's needs.

[0273] Step 5:

[0274] The server notifies employees and administrators of the final matching results approved by the administrator. This notification is sent via a dedicated app or email notification system. The final output is the position information of the newly assigned personnel, which is shared with employees and the organization.

[0275] Step 6:

[0276] The server records the processing history of all processes in a database. This includes input information, generated prompts, feedback, and final results, which are used for future analysis and improvement. At this stage, the system outputs a historical dataset.

[0277] (Application Example 1)

[0278] 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."

[0279] In modern public institutions and large organizations, there is a need to appropriately allocate the skills and preferences of job performers to the specific requirements of the institution. However, existing systems lack a process for efficiently analyzing the diverse characteristics of job performers and presenting them intuitively in a visual format. As a result, insufficient matching occurs between job performers and institutional requirements, hindering operational efficiency and overall organizational performance. There is a strong demand for a new system to address this challenge.

[0280] The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Application Example 1 is realized by the following respective means.

[0281] In this invention, the server includes means for collecting and storing the characteristic information of job functionaries, means for collecting and storing information corresponding to the requirements of an organization, means for applying a generation algorithm for matching job functionary information and organization information, means for visually displaying the matching result on a smartphone or other device and providing it to job functionaries and administrators, and means for storing the matching and providing history. Thereby, it is possible to quickly and accurately present a placement plan for job functionaries through a visually intuitive interface, and improve communication and management efficiency within the organization.

[0282] A "job functionary" is an individual who undertakes a specific job and has skills and abilities related to that business.

[0283] "Characteristic information" is data indicating attributes such as the abilities, experiences, interests, and career plans of job functionaries.

[0284] "Requirements of an organization" are information indicating the goals that a specific organization or institution wishes to achieve or the conditions to be satisfied.

[0285] A "generation algorithm" is a calculation method for calculating and proposing an optimal personnel placement based on the characteristic information of job functionaries and the requirements of an organization.

[0286] "Device" refers to all electronic devices for inputting, processing, and displaying information, including smartphones and computers.

[0287] "Visually display" means presenting calculation results and data to the user in a graphical form.

[0288] "Save the history" means recording the data processing and results performed in the past so that they can be referred to later.

[0289] This system supports the optimal allocation of personnel for various functions within public institutions and corporations. The server has the function of collecting and storing personnel characteristics information in a database. This characteristics information includes personnel abilities, experience, and career plans. It also collects and stores institutional requirements information in the database.

[0290] The server applies a generation algorithm based on the collected information to achieve optimal matching by connecting job function holders with the needs of the organization. The generation algorithm uses Python and scikit-learn to efficiently run the data analysis and matching process.

[0291] Furthermore, the system displays matching results visually to users (function-oriented personnel and administrators) via smartphones or other devices. This utilizes React Native, allowing administrators to review results and explore different options and placement suggestions through an intuitive UI.

[0292] Furthermore, the server stores a history of all operations and data processing, which can be used for future analysis and system improvements. A concrete example of its use is in a new policy project at a ward office, where it can identify suitable leadership candidates based on the career plans of employees and propose them to management.

[0293] An example of a prompt in this system would be, "We are looking for potential leaders for a new public project. Please list employees who have expressed interest in leadership roles as part of their career plans." This facilitates the appropriate placement of necessary personnel in public institutions and large corporations, thereby improving organizational efficiency.

[0294] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0295] Step 1:

[0296] The terminal receives characteristic information and organizational requirements information from job functioners and administrators. Users input their skills, experience, career plans, and organizational goals through the interface. This prepares the collected data for transmission to the server.

[0297] Step 2:

[0298] The server stores the received characteristic information and institutional requirements information in a database. The collected data is accurately classified and organized, and processed to enable quick access in subsequent processing steps.

[0299] Step 3:

[0300] The server applies a generation algorithm based on the stored information. Using Python and scikit-learn, it analyzes the skill sets of job functioning personnel to match the organization's needs. In this step, it outputs the optimal personnel placement plan based on the input information.

[0301] Step 4:

[0302] The generated matching results are sent to the device for review by the administrator, who is also the user. The results are presented through a visual user interface via React Native, allowing the administrator to visually evaluate the validity of the placement proposals.

[0303] Step 5:

[0304] The user (administrator) provides feedback on the matching results, and the server incorporates that feedback. This feedback includes approving placement plans and suggesting changes, and the generation algorithm is reapplied as needed to obtain adjusted output.

[0305] Step 6:

[0306] The server reconfirms the finally determined matching results and notifies the job functionaries and administrators. The server sends the finally determined placement plan to the terminal so that all relevant parties can share the latest information.

[0307] Step 7:

[0308] The server records the operation history and matching history in all processes in the database. This can be utilized as a basis for future analysis and system improvement.

[0309] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion identification model 59 and perform specific processing using the user's emotions.

[0310] The present invention is a system that realizes more accurate personnel matching by not only collecting and storing the personal information of employees and the needs information of the organization but also recognizing and analyzing the emotional state of the user. This system is composed of a terminal, a server, an emotion engine, and a user.

[0311] The terminal provides a form for the employee, who is the user, to input their career plan and skill set. At this time, the emotion engine analyzes the emotion from the employee's input attitude and speech, and sends the result from the terminal to the server. The administrator, who is the user, inputs the organization's plan and required personnel information in the same way, but the emotion engine also analyzes the administrator's emotion at the time of input and sends it to the server.

[0312] The server stores the employee information, organization information, and the corresponding emotional states in the database. Based on this information, the generation algorithm evaluates the employee's career plan and organizational needs together with the emotional information, and automatically starts the matching process. In this process, emotional states such as the employee's motivation and stress level are reflected in the matching results.

[0313] The server presents the generated matching results to the administrator (user) and requests their evaluation. The administrator can review the results and provide feedback, paying attention to changes in emotion. This feedback is also analyzed by the emotion engine, and as a result, the server re-runs the matching algorithm, taking the emotional state in the feedback into consideration, to optimize it.

[0314] For example, if employee B expresses a desire for collaborative teamwork but also dissatisfaction with their current role, the Emotion Engine analyzes these negative emotions and proposes a new role that tests their team leadership skills, thereby resolving their dissatisfaction and boosting their motivation.

[0315] Thus, the present invention provides an advanced personnel transfer system that not only matches skills and career information as in the conventional system, but also takes into account the user's emotional state.

[0316] The following describes the processing flow.

[0317] Step 1:

[0318] The terminal provides employees (users) with a form to input their career plans and skill sets. Users use this form to enter information related to their job duties. The emotion engine analyzes the employee's emotional state while they are entering the information and sends that data from the terminal to the server.

[0319] Step 2:

[0320] The terminal provides an interface for the administrator (user) to input organizational plans, required personnel specifications, and budget information. As the user inputs organizational information, an emotion engine simultaneously measures the administrator's emotions and sends this data to the server.

[0321] Step 3:

[0322] The server stores employee and administrator input information and sentiment data received from terminals into a database. This stored data is used when running the generation algorithm.

[0323] Step 4:

[0324] The server uses a generation algorithm to integrate employee and organizational information retrieved from the database and analyzes it while considering each individual's emotional state. The algorithm evaluates how well employees' career plans, skill sets, and emotional states align with the organization's needs, and generates the optimal match.

[0325] Step 5:

[0326] The server notifies the administrator of the generated matching results, allowing the administrator to review them. The administrator, as a user, reviews the presented results and provides feedback based on the sentiment data analyzed by the sentiment engine.

[0327] Step 6:

[0328] The server adjusts the matching results by reapplying the algorithm, taking into account the sentiment information extracted from the administrator's feedback, and generates new results. The readjusted results are then presented to the administrator.

[0329] Step 7:

[0330] The server reviews the matching plan finalized by the administrator and notifies the employee of the final placement decision. The server records the entire process leading up to this decision and stores it in a database for future reference.

[0331] This series of steps makes it possible to implement personnel placement that takes into account the emotional state of the users.

[0332] (Example 2)

[0333] 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 glasses 214 will be referred to as the "terminal".

[0334] Traditional personnel matching systems are limited to evaluations based on quantitative data such as technical skills and work history, and fail to consider qualitative aspects such as the emotional state and motivational formation of employees. Therefore, it has been difficult to appropriately assess employee stress and dissatisfaction and to achieve truly optimal placement for the organization. As a result, there is a challenge in that appropriate personnel placement cannot be made, and organizational productivity and employee satisfaction cannot be improved.

[0335] 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.

[0336] In this invention, the server includes means for analyzing employee attitudes and utterances to evaluate their emotional state, means for analyzing the emotions of administrators at the time of input and similarly evaluating emotional information, and means for matching employee information with organizational information and each piece of emotional information. This makes it possible to perform advanced personnel matching that takes into account the emotional state of employees and to achieve optimal job placement.

[0337] "Employee attribute information" refers to personal data about employees, including information such as career plans, skill sets, and experience.

[0338] "Organizational requirements" refer to the necessary conditions and policies regarding the personnel that a company or organization seeks, and include specific skills, qualifications, and experience.

[0339] "Emotional state" refers to information that indicates the psychological and emotional condition of employees and managers, and includes stress, motivation, satisfaction, etc.

[0340] "Generation method" refers to algorithms and mechanisms that analyze various data, including emotional states, to derive the optimal matching between employee information and organizational information.

[0341] "Means of presentation" refers to functions and processes that make the generated matching results visible to the administrator.

[0342] "Optimization" refers to the process by which a system adjusts decisions, such as personnel assignments, based on emotional information and feedback, to make them more effective and efficient.

[0343] This invention is a system for achieving optimal personnel matching for both employees and organizations. The system utilizes terminals, servers, an emotion analysis engine, and a generative AI model.

[0344] Employees, as users, input their career plans and skill sets through a terminal. The terminal has a built-in emotion analysis engine that analyzes the employee's actions and words as they input data and evaluates their emotional state. In this process, specific software is used to analyze typing speed and word choice, and stress and motivation levels are quantified. For example, if many corrections are made in a short period of time, the analysis engine will determine that the employee is in an anxious state.

[0345] Furthermore, administrators, who are also users, input organizational plans and required personnel information via their terminals, and similar sentiment analysis is performed. All input information and sentiment data are sent to the server and stored in the database.

[0346] The server uses a generative AI model based on accumulated data to comprehensively evaluate employee information, organizational information, and emotional information. As a result, it automatically determines the optimal personnel placement. This generative AI model reflects emotional aspects such as employee motivation and stress levels, and can identify key focus points for both employees and the organization.

[0347] The generated matching results are presented to the administrator via a terminal. The administrator is required to provide feedback on the results, paying particular attention to changes in emotion during the evaluation. The feedback is also analyzed for emotion, and the server uses this emotion information to re-execute the matching algorithm and optimize placement.

[0348] For example, if employee B "desires to work collaboratively in a team" and is dissatisfied with their current position, the sentiment analysis engine will recognize this as a negative emotion, and the server will suggest a suitable new position for employee B that offers "leadership opportunities."

[0349] An example of a prompt might be, "Please suggest the most suitable job for employee B, taking into account their emotional state. They are dissatisfied with their current position but desire to work collaboratively within a team." By using such prompts, the generative AI model can provide more sophisticated matching suggestions.

[0350] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0351] Step 1:

[0352] The terminal receives career plans and skill sets entered by the employee user. The entered data is collected through an input form, and an emotion analysis engine analyzes the employee's input actions and words. The analysis detects typing speed and word choice, and quantifies emotional states such as stress and motivation. As output, emotion-analyzed data is generated and sent from the terminal to the server.

[0353] Step 2:

[0354] The terminal receives organizational needs and personnel information entered by the administrator, who is the user. At the same time, the terminal uses an emotion analysis engine to analyze the administrator's emotions at the time of input and evaluate their emotional state. The entered information and emotion data are structured and sent to the server. As a result, organizational needs data, along with the administrator's emotion information, is generated.

[0355] Step 3:

[0356] The server stores employee attribute information, organizational requirements information, and their respective emotional state data in a database. Next, a generative AI model is used to analyze the employee, organizational, and emotional information. This process evaluates the matching and prioritization of each element to determine the optimal match. The output is a proposed optimal personnel placement plan.

[0357] Step 4:

[0358] The server displays the generated matching results to the administrator's terminal. The administrator reviews the displayed results and provides detailed feedback. Since the feedback includes the administrator's emotions, the terminal's emotion analysis engine analyzes the changes in emotions again, and this data is sent to the server. As output, feedback data that takes emotion evaluation into account is generated.

[0359] Step 5:

[0360] The server uses the sentiment information based on the feedback to re-run the matching algorithm using the generative AI model. In this process, the placement plan is optimized based on the newly obtained information from the feedback. As a result, an optimized personnel placement plan is generated and presented to the manager.

[0361] (Application Example 2)

[0362] 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 as the "terminal".

[0363] Conventional systems only match employees based on their skills and career information, failing to reflect their emotional state. This results in a lack of proper consideration of employee and manager motivation and stress levels. Furthermore, in the deployment of robots within factories, the absence of mechanisms to consider managers' emotional states prevents the achievement of optimal staffing and robot operation.

[0364] 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.

[0365] In this invention, the server includes means for collecting and storing employee personal information, means for collecting and storing information corresponding to the organization's needs, means for applying a generation algorithm for matching employee information with organizational information, means for analyzing emotional states and reflecting the results in the generation algorithm, and means for proposing robot placement optimization according to the emotional state of the manager. This makes it possible to propose optimal matching and robot placement that takes into account the emotional states of employees and managers.

[0366] "Employee personal information" refers to information about employees, including their names, contact information, skill sets, and career plans.

[0367] "Organizational needs" refers to information about the personnel and skills that an organization needs for growth and project execution.

[0368] A "generative algorithm" refers to a procedure or calculation method used to determine the optimal combination of employee and organizational needs based on collected information.

[0369] "Emotional state" refers to the psychological state and motivation analyzed from the attitudes and behaviors of employees and managers.

[0370] "Robot placement optimization" refers to the adjustment of robot placement and task allocation in a factory or work environment based on emotional states and other relevant information to achieve the most efficient results.

[0371] This invention uses terminals, servers, an emotion engine, and users to achieve optimal personnel and robot placement that takes emotional states into account.

[0372] The terminal provides an interface for employees to input their career plans and skill sets. As employees input information, an emotion engine analyzes their input behavior and identifies their emotional state. This emotional information is sent from the terminal to the server. Similarly, administrators also input organizational needs information through the terminal, and their emotional state is analyzed and sent to the server.

[0373] The server stores collected employee information, organizational information, and corresponding emotional states in a database. Based on this information, a generation algorithm evaluates employee career plans and organizational needs, incorporating emotional information, and performs initial matching. This process takes into account employee motivation and stress levels, which have been analyzed by the emotional engine.

[0374] Furthermore, robot placement is optimized to reflect the administrator's emotional state. Administrators can review the proposed matching and placement plans and provide feedback. This feedback is also analyzed through the emotion engine, and the server readjusts the matching and placement plans based on the feedback.

[0375] As a concrete example, consider a situation in a factory environment where a manager has previously experienced stress from a specific task. Based on this information, the system can rearrange the robot's placement to match that task, providing an efficient and less stressful environment.

[0376] The following example prompts apply to the generative AI model:

[0377] "Based on the following input, please propose the optimal placement of factory robots. Managers are experiencing high stress when handling Task X and require a more collaborative placement."

[0378] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0379] Step 1:

[0380] If the user is an employee, the terminal accepts input of the user's career plan and skill set. The data obtained through this input interface is then sent to an emotion engine to analyze the user's input behavior. As a result of this analysis, employee emotional state data is generated.

[0381] Step 2:

[0382] If the user is an administrator, the terminal provides an interface for entering information about the organization's needs. The administrator's input data is analyzed by an emotion engine to obtain information about the administrator's emotional state. This data is sent from the terminal to the server along with the input data.

[0383] Step 3:

[0384] The server stores employee information, organizational information, and corresponding emotional state data received from the terminals into a database. This prepares the system for running the matching generation algorithm using this data.

[0385] Step 4:

[0386] The server uses the stored data to perform an initial matching process based on a generation algorithm. This process incorporates emotional state data into the data processing to generate matching results that reflect employee motivation and stress levels.

[0387] Step 5:

[0388] The server provides the generated matching results to the administrator, who is the user. The administrator can review this and provide feedback. The feedback is then analyzed again by the sentiment engine to extract the administrator's emotional state.

[0389] Step 6:

[0390] The server takes administrator feedback and emotional states into account and initiates an optimization process to readjust matching and robot placement. This ensures that a more collaborative and efficient placement is implemented where necessary.

[0391] Step 7:

[0392] As a concrete example, in a factory environment, the server optimizes robot placement and tasks based on employee and manager sentiment data. In this process, it works in conjunction with a generative AI model, using prompts to suggest improved placement options and providing the optimal solution.

[0393] 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.

[0394] 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.

[0395] 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.

[0396] [Third Embodiment]

[0397] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0398] 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.

[0399] 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).

[0400] 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.

[0401] 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.

[0402] 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).

[0403] 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.

[0404] 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.

[0405] 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.

[0406] 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.

[0407] 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.

[0408] 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".

[0409] This invention is a system for optimally matching employee personal information with organizational needs. This system consists of terminals, servers, and users. Details are provided below.

[0410] The terminal provides an interface for employees (users) to input career plans, current skills, and desired job information. It also provides an interface for administrators (users) to input organizational medium- to long-term plans, resource allocation, and budget information. The terminal verifies this input information and, if complete, sends it to the server.

[0411] The server stores the received employee personal information and organizational information in a database. This data is used to match the two using a generation algorithm. The generation algorithm analyzes each employee's career plan and skill set and proposes the most suitable personnel placement for the organization's needs.

[0412] The server then presents the generated matching results to the administrator, who is the user. The administrator reviews these results and provides feedback if necessary. The server incorporates this feedback, adjusts the matching results, and presents them again. This process allows the administrator to make the final personnel placement decision.

[0413] The server will reconfirm the final matching results and notify employees and administrators. The history of this entire process will be recorded in a database for future analysis and improvement.

[0414] For example, if employee A expresses a desire to "gain experience as a project manager" in their career plan, and organization B "needs someone with leadership skills for a new project," the generation algorithm will present employee A as a project manager candidate for organization B. The manager accepts this proposal, and the personnel placement is decided.

[0415] Thus, the present invention effectively integrates the needs of employees and organizations to achieve optimal personnel transfers.

[0416] The following describes the processing flow.

[0417] Step 1:

[0418] The terminal displays a screen for the employee user to input their career plan, current skill set, desired job, etc. The user can enter their information through this screen. After confirming the input, the terminal sends the data to the server.

[0419] Step 2:

[0420] The terminal displays a screen for the administrator (user) to input the organization's medium- to long-term plans, required resources, and budget information. The user enters the necessary information for the organization, and the terminal checks the data before sending it to the server.

[0421] Step 3:

[0422] The server stores the personal information of each employee and the organization's needs information received from the terminal in a database, and organizes and stores this data.

[0423] Step 4:

[0424] The server uses a generation algorithm to match and analyze employee and organizational information from the database. In this process, it determines how well employees' career plans and skill sets align with organizational requirements, and derives optimal personnel placement candidates.

[0425] Step 5:

[0426] The server displays the generated matching candidates to the administrator, who then reviews the presented results. If necessary, the administrator can provide feedback to the server.

[0427] Step 6:

[0428] Based on feedback from the administrator, the server reapplies the generation algorithm to adjust the matching results. It then presents the adjusted results again.

[0429] Step 7:

[0430] After the administrator confirms the final match, the server notifies the employee and the administrator of the result and allows them to confirm the decision.

[0431] Step 8:

[0432] The server records details of all matching processes performed in a database, which can then be used for future analysis and adjustments.

[0433] This completes the entire process in which servers, terminals, and users cooperate to achieve optimal personnel transfers.

[0434] (Example 1)

[0435] 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."

[0436] In today's business environment, a system that appropriately matches individual characteristics with group requirements is essential. However, conventional methods struggle to effectively collect and utilize user characteristic information and group requirements information, and they fail to adequately reflect administrator feedback on the resulting matching results. A new system is needed to address these challenges.

[0437] 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.

[0438] In this invention, the server includes a device for collecting and storing user characteristic information, a device for collecting and storing information related to group requests, and a device for performing generation processing to match the characteristic information with the request information. This makes it possible to effectively match the characteristic information with the request information and provide optimal results that reflect the administrator's feedback.

[0439] "User" refers to an individual or group that uses a particular system or service, and in this context, it refers to the party that inputs information.

[0440] "Characteristic information" refers to information related to the user, including data that indicates an individual's characteristics such as career plans, skills, and desired job.

[0441] A "group" refers to an organization or a part of it that shares common goals or needs, and in this context, it serves as a criterion for personnel allocation.

[0442] "Required information" refers to information related to the skills and resources needed by a group, and is data that concretizes organizational needs.

[0443] "Generation processing" refers to data processing that optimally matches feature information and request information, and is the process of deriving the result.

[0444] The term "administrator" refers to the person responsible for reviewing and providing feedback on the generated matching results, and who is involved in the final decision-making process.

[0445] "Feedback" refers to the evaluations and correction suggestions that administrators provide regarding matching results, and is information that contributes to improving the accuracy of the system.

[0446] This system is an information processing system primarily composed of servers, terminals, and users. A specific embodiment of this system will be described below.

[0447] First, the terminal provides dedicated interfaces for both employee and administrator users. Employees can use this interface to input their career plans, skills, and desired job information. Administrators are provided with an interface for inputting the organization's medium- to long-term plans, resource allocation, and budget information. The terminal verifies the entered information and, if complete, transmits it to the server using a secure communication protocol.

[0448] The server utilizes a relational database management system (RDBMS) to store various received information in a central database. The server also uses a generative AI model to analyze employee characteristic information and organizational requirements information, and performs generation processing. This generation process applies a specific algorithm to achieve the best possible match between the characteristic information and the requirements information. The server can provide the generated results to the administrator and perform optimizations based on the administrator's feedback.

[0449] For example, if an employee expresses a desire to gain more experience as a project manager, and the organization needs someone to lead a new project, this system will propose the employee as a candidate for that project manager position. Once the administrator approves the proposal, the server will formally notify the administrator of the result.

[0450] One possible prompt to input into the generating AI model is to specify, "Analyze user characteristic information and group request information to generate the best matching result." This prompt allows the server to present the optimal personnel allocation in real time.

[0451] In this way, the system effectively integrates the needs of users and groups, achieving dynamic and optimal resource allocation.

[0452] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0453] Step 1:

[0454] The terminal provides dedicated interfaces for both employee and administrator users. Employees use an interface to input career plans, skills, and desired job information, while administrators input organizational plans and resource information. The entered information is formatted in real time, and any errors are notified to the user. The input is collected in text format, and the terminal outputs it as an organized dataset, preparing it for the next processing step.

[0455] Step 2:

[0456] The terminal sends organized employee and administrator information to the server. This transmission is performed using security protocols such as SSL. The server, having received employee and organizational information as input, stores it in a database and uses an RDBMS to maintain data integrity. The output of this step is a dataset correctly loaded into the database, which is used for subsequent processing.

[0457] Step 3:

[0458] The server retrieves employee and organizational information stored in the database and analyzes the data by driving a generative AI model. The prompt "Analyze talent characteristics and organizational needs to generate optimal matching" is used to best match each employee's characteristic information with the organization's requirements. Data processing involves normalizing the characteristic data, prioritizing the requirements information, and matching them to calculate the most suitable talent placement. The output of this step is the generated matching result.

[0459] Step 4:

[0460] The server presents the generated matching results to the administrator. The administrator can review the results and provide feedback through the input interface. The server receives the administrator's feedback as data and readjusts the matching results as needed. This feedback process ensures that the readjusted matching results are even more aligned with the organization's needs.

[0461] Step 5:

[0462] The server notifies employees and administrators of the final matching results approved by the administrator. This notification is sent via a dedicated app or email notification system. The final output is the position information of the newly assigned personnel, which is shared with employees and the organization.

[0463] Step 6:

[0464] The server records the processing history of all processes in a database. This includes input information, generated prompts, feedback, and final results, which are used for future analysis and improvement. At this stage, the system outputs a historical dataset.

[0465] (Application Example 1)

[0466] 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."

[0467] In modern public institutions and large organizations, there is a need to appropriately allocate the skills and preferences of job performers to the specific requirements of the institution. However, existing systems lack a process for efficiently analyzing the diverse characteristics of job performers and presenting them intuitively in a visual format. As a result, insufficient matching occurs between job performers and institutional requirements, hindering operational efficiency and overall organizational performance. There is a strong demand for a new system to address this challenge.

[0468] 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.

[0469] In this invention, the server includes means for collecting and storing characteristic information of job performers, means for collecting and storing information corresponding to the requirements of the organization, means for applying a generation algorithm for matching job performer information with organization information, means for visually displaying the matching results on a smartphone or other device and providing them to job performers and managers, and means for storing the matching and provision history. This makes it possible to quickly and accurately present job performer placement plans through a visually intuitive interface and improve communication and management efficiency within the organization.

[0470] A "functioning professional" is an individual who is responsible for a specific job and possesses the skills and abilities related to that job.

[0471] "Characteristic information" refers to data that shows attributes such as the abilities, experience, interests, and career plans of individuals performing job functions.

[0472] "Institutional requirements" refer to information that outlines the goals a particular organization or institution aims to achieve and the conditions it must meet.

[0473] A "generative algorithm" is a computational method for calculating and proposing the optimal personnel allocation based on the characteristics of job performers and the requirements of the organization.

[0474] The term "device" refers to any electronic device used for inputting, processing, and displaying information, and includes smartphones and computers.

[0475] "Visual display" means presenting calculation results and data to the user in a graphical format.

[0476] "Saving history" means recording past data processing and results so that they can be referenced later.

[0477] This system supports the optimal allocation of personnel for various functions within public institutions and corporations. The server has the function of collecting and storing personnel characteristics information in a database. This characteristics information includes personnel abilities, experience, and career plans. It also collects and stores institutional requirements information in the database.

[0478] The server applies a generation algorithm based on the collected information to achieve optimal matching by connecting job function holders with the needs of the organization. The generation algorithm uses Python and scikit-learn to efficiently run the data analysis and matching process.

[0479] Furthermore, the system displays matching results visually to users (function-oriented personnel and administrators) via smartphones or other devices. This utilizes React Native, allowing administrators to review results and explore different options and placement suggestions through an intuitive UI.

[0480] Furthermore, the server stores a history of all operations and data processing, which can be used for future analysis and system improvements. A concrete example of its use is in a new policy project at a ward office, where it can identify suitable leadership candidates based on the career plans of employees and propose them to management.

[0481] An example of a prompt in this system would be, "We are looking for potential leaders for a new public project. Please list employees who have expressed interest in leadership roles as part of their career plans." This facilitates the appropriate placement of necessary personnel in public institutions and large corporations, thereby improving organizational efficiency.

[0482] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0483] Step 1:

[0484] The terminal receives characteristic information and organizational requirements information from job functioners and administrators. Users input their skills, experience, career plans, and organizational goals through the interface. This prepares the collected data for transmission to the server.

[0485] Step 2:

[0486] The server stores the received characteristic information and institutional requirements information in a database. The collected data is accurately classified and organized, and processed to enable quick access in subsequent processing steps.

[0487] Step 3:

[0488] The server applies a generation algorithm based on the stored information. Using Python and scikit-learn, it analyzes the skill sets of job functioning personnel to match the organization's needs. In this step, it outputs the optimal personnel placement plan based on the input information.

[0489] Step 4:

[0490] The generated matching results are sent to the device for review by the administrator, who is also the user. The results are presented through a visual user interface via React Native, allowing the administrator to visually evaluate the validity of the placement proposals.

[0491] Step 5:

[0492] The user (administrator) provides feedback on the matching results, and the server incorporates that feedback. This feedback includes approving placement plans and suggesting changes, and the generation algorithm is reapplied as needed to obtain adjusted output.

[0493] Step 6:

[0494] The server reconfirms the final matching results and notifies the relevant personnel and administrators. The finalized placement plan is then sent to terminals, ensuring all stakeholders share the latest information.

[0495] Step 7:

[0496] The server records the operation history and matching history of all processes in a database. This allows it to be used as evidence for future analysis and system improvements.

[0497] 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.

[0498] This invention is a system that achieves more accurate personnel matching by not only collecting and storing employee personal information and organizational needs information, but also recognizing and analyzing the emotional state of users. This system consists of a terminal, a server, an emotion engine, and a user.

[0499] The terminal provides a form for employees (users) to input their career plans and skill sets. During this process, an emotion engine analyzes the employee's attitude and behavior, and sends the results from the terminal to the server. Administrators (users) input organizational plans and required personnel information in a similar manner, but the emotion engine also analyzes the administrator's emotions during the input process and sends the results to the server.

[0500] The server stores employee information, organizational information, and their corresponding emotional states in a database. Based on this information, a generation algorithm evaluates employees' career plans and organizational needs along with their emotional states, and automatically initiates a matching process. In this process, employees' emotional states, such as motivation and stress levels, are reflected in the matching results.

[0501] The server presents the generated matching results to the administrator (user) and requests their evaluation. The administrator can review the results and provide feedback, paying attention to changes in emotion. This feedback is also analyzed by the emotion engine, and as a result, the server re-runs the matching algorithm, taking the emotional state in the feedback into consideration, to optimize it.

[0502] For example, if employee B expresses a desire for collaborative teamwork but also dissatisfaction with their current role, the Emotion Engine analyzes these negative emotions and proposes a new role that tests their team leadership skills, thereby resolving their dissatisfaction and boosting their motivation.

[0503] Thus, the present invention provides an advanced personnel transfer system that not only matches skills and career information as in the conventional system, but also takes into account the user's emotional state.

[0504] The following describes the processing flow.

[0505] Step 1:

[0506] The terminal provides employees (users) with a form to input their career plans and skill sets. Users use this form to enter information related to their job duties. The emotion engine analyzes the employee's emotional state while they are entering the information and sends that data from the terminal to the server.

[0507] Step 2:

[0508] The terminal provides an interface for the administrator (user) to input organizational plans, required personnel specifications, and budget information. As the user inputs organizational information, an emotion engine simultaneously measures the administrator's emotions and sends this data to the server.

[0509] Step 3:

[0510] The server stores employee and administrator input information and sentiment data received from terminals into a database. This stored data is used when running the generation algorithm.

[0511] Step 4:

[0512] The server uses a generation algorithm to integrate employee and organizational information retrieved from the database and analyzes it while considering each individual's emotional state. The algorithm evaluates how well employees' career plans, skill sets, and emotional states align with the organization's needs, and generates the optimal match.

[0513] Step 5:

[0514] The server notifies the administrator of the generated matching results, allowing the administrator to review them. The administrator, as a user, reviews the presented results and provides feedback based on the sentiment data analyzed by the sentiment engine.

[0515] Step 6:

[0516] The server adjusts the matching results by reapplying the algorithm, taking into account the sentiment information extracted from the administrator's feedback, and generates new results. The readjusted results are then presented to the administrator.

[0517] Step 7:

[0518] The server reviews the matching plan finalized by the administrator and notifies the employee of the final placement decision. The server records the entire process leading up to this decision and stores it in a database for future reference.

[0519] This series of steps makes it possible to implement personnel placement that takes into account the emotional state of the users.

[0520] (Example 2)

[0521] 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."

[0522] Traditional personnel matching systems are limited to evaluations based on quantitative data such as technical skills and work history, and fail to consider qualitative aspects such as the emotional state and motivational formation of employees. Therefore, it has been difficult to appropriately assess employee stress and dissatisfaction and to achieve truly optimal placement for the organization. As a result, there is a challenge in that appropriate personnel placement cannot be made, and organizational productivity and employee satisfaction cannot be improved.

[0523] 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.

[0524] In this invention, the server includes means for analyzing employee attitudes and utterances to evaluate their emotional state, means for analyzing the emotions of administrators at the time of input and similarly evaluating emotional information, and means for matching employee information with organizational information and each piece of emotional information. This makes it possible to perform advanced personnel matching that takes into account the emotional state of employees and to achieve optimal job placement.

[0525] "Employee attribute information" refers to personal data about employees, including information such as career plans, skill sets, and experience.

[0526] "Organizational requirements" refer to the necessary conditions and policies regarding the personnel that a company or organization seeks, and include specific skills, qualifications, and experience.

[0527] "Emotional state" refers to information that indicates the psychological and emotional condition of employees and managers, and includes stress, motivation, satisfaction, etc.

[0528] "Generation method" refers to algorithms and mechanisms that analyze various data, including emotional states, to derive the optimal matching between employee information and organizational information.

[0529] "Means of presentation" refers to functions and processes that make the generated matching results visible to the administrator.

[0530] "Optimization" refers to the process by which a system adjusts decisions, such as personnel assignments, based on emotional information and feedback, to make them more effective and efficient.

[0531] This invention is a system for achieving optimal personnel matching for both employees and organizations. The system utilizes terminals, servers, an emotion analysis engine, and a generative AI model.

[0532] Employees, as users, input their career plans and skill sets through a terminal. The terminal has a built-in emotion analysis engine that analyzes the employee's actions and words as they input data and evaluates their emotional state. In this process, specific software is used to analyze typing speed and word choice, and stress and motivation levels are quantified. For example, if many corrections are made in a short period of time, the analysis engine will determine that the employee is in an anxious state.

[0533] Furthermore, administrators, who are also users, input organizational plans and required personnel information via their terminals, and similar sentiment analysis is performed. All input information and sentiment data are sent to the server and stored in the database.

[0534] The server uses a generative AI model based on accumulated data to comprehensively evaluate employee information, organizational information, and emotional information. As a result, it automatically determines the optimal personnel placement. This generative AI model reflects emotional aspects such as employee motivation and stress levels, and can identify key focus points for both employees and the organization.

[0535] The generated matching results are presented to the administrator via a terminal. The administrator is required to provide feedback on the results, paying particular attention to changes in emotion during the evaluation. The feedback is also analyzed for emotion, and the server uses this emotion information to re-execute the matching algorithm and optimize placement.

[0536] For example, if employee B "desires to work collaboratively in a team" and is dissatisfied with their current position, the sentiment analysis engine will recognize this as a negative emotion, and the server will suggest a suitable new position for employee B that offers "leadership opportunities."

[0537] An example of a prompt might be, "Please suggest the most suitable job for employee B, taking into account their emotional state. They are dissatisfied with their current position but desire to work collaboratively within a team." By using such prompts, the generative AI model can provide more sophisticated matching suggestions.

[0538] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0539] Step 1:

[0540] The terminal receives career plans and skill sets entered by the employee user. The entered data is collected through an input form, and an emotion analysis engine analyzes the employee's input actions and words. The analysis detects typing speed and word choice, and quantifies emotional states such as stress and motivation. As output, emotion-analyzed data is generated and sent from the terminal to the server.

[0541] Step 2:

[0542] The terminal receives organizational needs and personnel information entered by the administrator, who is the user. At the same time, the terminal uses an emotion analysis engine to analyze the administrator's emotions at the time of input and evaluate their emotional state. The entered information and emotion data are structured and sent to the server. As a result, organizational needs data, along with the administrator's emotion information, is generated.

[0543] Step 3:

[0544] The server stores employee attribute information, organizational requirements information, and their respective emotional state data in a database. Next, a generative AI model is used to analyze the employee, organizational, and emotional information. This process evaluates the matching and prioritization of each element to determine the optimal match. The output is a proposed optimal personnel placement plan.

[0545] Step 4:

[0546] The server displays the generated matching results to the administrator's terminal. The administrator reviews the displayed results and provides detailed feedback. Since the feedback includes the administrator's emotions, the terminal's emotion analysis engine analyzes the changes in emotions again, and this data is sent to the server. As output, feedback data that takes emotion evaluation into account is generated.

[0547] Step 5:

[0548] The server uses the sentiment information based on the feedback to re-run the matching algorithm using the generative AI model. In this process, the placement plan is optimized based on the newly obtained information from the feedback. As a result, an optimized personnel placement plan is generated and presented to the manager.

[0549] (Application Example 2)

[0550] 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."

[0551] Conventional systems only match employees based on their skills and career information, failing to reflect their emotional state. This results in a lack of proper consideration of employee and manager motivation and stress levels. Furthermore, in the deployment of robots within factories, the absence of mechanisms to consider managers' emotional states prevents the achievement of optimal staffing and robot operation.

[0552] 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.

[0553] In this invention, the server includes means for collecting and storing employee personal information, means for collecting and storing information corresponding to the organization's needs, means for applying a generation algorithm for matching employee information with organizational information, means for analyzing emotional states and reflecting the results in the generation algorithm, and means for proposing robot placement optimization according to the emotional state of the manager. This makes it possible to propose optimal matching and robot placement that takes into account the emotional states of employees and managers.

[0554] "Employee personal information" refers to information about employees, including their names, contact information, skill sets, and career plans.

[0555] "Organizational needs" refers to information about the personnel and skills that an organization needs for growth and project execution.

[0556] A "generative algorithm" refers to a procedure or calculation method used to determine the optimal combination of employee and organizational needs based on collected information.

[0557] "Emotional state" refers to the psychological state and motivation analyzed from the attitudes and behaviors of employees and managers.

[0558] "Robot placement optimization" refers to the adjustment of robot placement and task allocation in a factory or work environment based on emotional states and other relevant information to achieve the most efficient results.

[0559] This invention uses terminals, servers, an emotion engine, and users to achieve optimal personnel and robot placement that takes emotional states into account.

[0560] The terminal provides an interface for employees to input their career plans and skill sets. As employees input information, an emotion engine analyzes their input behavior and identifies their emotional state. This emotional information is sent from the terminal to the server. Similarly, administrators also input organizational needs information through the terminal, and their emotional state is analyzed and sent to the server.

[0561] The server stores collected employee information, organizational information, and corresponding emotional states in a database. Based on this information, a generation algorithm evaluates employee career plans and organizational needs, incorporating emotional information, and performs initial matching. This process takes into account employee motivation and stress levels, which have been analyzed by the emotional engine.

[0562] Furthermore, robot placement is optimized to reflect the administrator's emotional state. Administrators can review the proposed matching and placement plans and provide feedback. This feedback is also analyzed through the emotion engine, and the server readjusts the matching and placement plans based on the feedback.

[0563] As a concrete example, consider a situation in a factory environment where a manager has previously experienced stress from a specific task. Based on this information, the system can rearrange the robot's placement to match that task, providing an efficient and less stressful environment.

[0564] The following example prompts apply to the generative AI model:

[0565] "Based on the following input, please propose the optimal placement of factory robots. Managers are experiencing high stress when handling Task X and require a more collaborative placement."

[0566] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0567] Step 1:

[0568] If the user is an employee, the terminal accepts input of the user's career plan and skill set. The data obtained through this input interface is then sent to an emotion engine to analyze the user's input behavior. As a result of this analysis, employee emotional state data is generated.

[0569] Step 2:

[0570] If the user is an administrator, the terminal provides an interface for entering information about the organization's needs. The administrator's input data is analyzed by an emotion engine to obtain information about the administrator's emotional state. This data is sent from the terminal to the server along with the input data.

[0571] Step 3:

[0572] The server stores employee information, organizational information, and corresponding emotional state data received from the terminals into a database. This prepares the system for running the matching generation algorithm using this data.

[0573] Step 4:

[0574] The server uses the stored data to perform an initial matching process based on a generation algorithm. This process incorporates emotional state data into the data processing to generate matching results that reflect employee motivation and stress levels.

[0575] Step 5:

[0576] The server provides the generated matching results to the administrator, who is the user. The administrator can review this and provide feedback. The feedback is then analyzed again by the sentiment engine to extract the administrator's emotional state.

[0577] Step 6:

[0578] The server takes administrator feedback and emotional states into account and initiates an optimization process to readjust matching and robot placement. This ensures that a more collaborative and efficient placement is implemented where necessary.

[0579] Step 7:

[0580] As a concrete example, in a factory environment, the server optimizes robot placement and tasks based on employee and manager sentiment data. In this process, it works in conjunction with a generative AI model, using prompts to suggest improved placement options and providing the optimal solution.

[0581] 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.

[0582] 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.

[0583] 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.

[0584] [Fourth Embodiment]

[0585] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0586] 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.

[0587] 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).

[0588] 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.

[0589] 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.

[0590] 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).

[0591] 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.

[0592] 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.

[0593] 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.

[0594] 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.

[0595] 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.

[0596] 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.

[0597] 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".

[0598] This invention is a system for optimally matching employee personal information with organizational needs. This system consists of terminals, servers, and users. Details are provided below.

[0599] The terminal provides an interface for employees (users) to input career plans, current skills, and desired job information. It also provides an interface for administrators (users) to input organizational medium- to long-term plans, resource allocation, and budget information. The terminal verifies this input information and, if complete, sends it to the server.

[0600] The server stores the received employee personal information and organizational information in a database. This data is used to match the two using a generation algorithm. The generation algorithm analyzes each employee's career plan and skill set and proposes the most suitable personnel placement for the organization's needs.

[0601] The server then presents the generated matching results to the administrator, who is the user. The administrator reviews these results and provides feedback if necessary. The server incorporates this feedback, adjusts the matching results, and presents them again. This process allows the administrator to make the final personnel placement decision.

[0602] The server will reconfirm the final matching results and notify employees and administrators. The history of this entire process will be recorded in a database for future analysis and improvement.

[0603] For example, if employee A expresses a desire to "gain experience as a project manager" in their career plan, and organization B "needs someone with leadership skills for a new project," the generation algorithm will present employee A as a project manager candidate for organization B. The manager accepts this proposal, and the personnel placement is decided.

[0604] Thus, the present invention effectively integrates the needs of employees and organizations to achieve optimal personnel transfers.

[0605] The following describes the processing flow.

[0606] Step 1:

[0607] The terminal displays a screen for the employee user to input their career plan, current skill set, desired job, etc. The user can enter their information through this screen. After confirming the input, the terminal sends the data to the server.

[0608] Step 2:

[0609] The terminal displays a screen for the administrator (user) to input the organization's medium- to long-term plans, required resources, and budget information. The user enters the necessary information for the organization, and the terminal checks the data before sending it to the server.

[0610] Step 3:

[0611] The server stores the personal information of each employee and the organization's needs information received from the terminal in a database, and organizes and stores this data.

[0612] Step 4:

[0613] The server uses a generation algorithm to match and analyze employee and organizational information from the database. In this process, it determines how well employees' career plans and skill sets align with organizational requirements, and derives optimal personnel placement candidates.

[0614] Step 5:

[0615] The server displays the generated matching candidates to the administrator, who then reviews the presented results. If necessary, the administrator can provide feedback to the server.

[0616] Step 6:

[0617] Based on feedback from the administrator, the server reapplies the generation algorithm to adjust the matching results. It then presents the adjusted results again.

[0618] Step 7:

[0619] After the administrator confirms the final match, the server notifies the employee and the administrator of the result and allows them to confirm the decision.

[0620] Step 8:

[0621] The server records details of all matching processes performed in a database, which can then be used for future analysis and adjustments.

[0622] This completes the entire process in which servers, terminals, and users cooperate to achieve optimal personnel transfers.

[0623] (Example 1)

[0624] 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".

[0625] In today's business environment, a system that appropriately matches individual characteristics with group requirements is essential. However, conventional methods struggle to effectively collect and utilize user characteristic information and group requirements information, and they fail to adequately reflect administrator feedback on the resulting matching results. A new system is needed to address these challenges.

[0626] 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.

[0627] In this invention, the server includes a device for collecting and storing user characteristic information, a device for collecting and storing information related to group requests, and a device for performing generation processing to match the characteristic information with the request information. This makes it possible to effectively match the characteristic information with the request information and provide optimal results that reflect the administrator's feedback.

[0628] "User" refers to an individual or group that uses a particular system or service, and in this context, it refers to the party that inputs information.

[0629] "Characteristic information" refers to information related to the user, including data that indicates an individual's characteristics such as career plans, skills, and desired job.

[0630] A "group" refers to an organization or a part of it that shares common goals or needs, and in this context, it serves as a criterion for personnel allocation.

[0631] "Required information" refers to information related to the skills and resources needed by a group, and is data that concretizes organizational needs.

[0632] "Generation processing" refers to data processing that optimally matches feature information and request information, and is the process of deriving the result.

[0633] The term "administrator" refers to the person responsible for reviewing and providing feedback on the generated matching results, and who is involved in the final decision-making process.

[0634] "Feedback" refers to the evaluations and correction suggestions that administrators provide regarding matching results, and is information that contributes to improving the accuracy of the system.

[0635] This system is an information processing system primarily composed of servers, terminals, and users. A specific embodiment of this system will be described below.

[0636] First, the terminal provides dedicated interfaces for both employee and administrator users. Employees can use this interface to input their career plans, skills, and desired job information. Administrators are provided with an interface for inputting the organization's medium- to long-term plans, resource allocation, and budget information. The terminal verifies the entered information and, if complete, transmits it to the server using a secure communication protocol.

[0637] The server utilizes a relational database management system (RDBMS) to store various received information in a central database. The server also uses a generative AI model to analyze employee characteristic information and organizational requirements information, and performs generation processing. This generation process applies a specific algorithm to achieve the best possible match between the characteristic information and the requirements information. The server can provide the generated results to the administrator and perform optimizations based on the administrator's feedback.

[0638] For example, if an employee expresses a desire to gain more experience as a project manager, and the organization needs someone to lead a new project, this system will propose the employee as a candidate for that project manager position. Once the administrator approves the proposal, the server will formally notify the administrator of the result.

[0639] One possible prompt to input into the generating AI model is to specify, "Analyze user characteristic information and group request information to generate the best matching result." This prompt allows the server to present the optimal personnel allocation in real time.

[0640] In this way, the system effectively integrates the needs of users and groups, achieving dynamic and optimal resource allocation.

[0641] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0642] Step 1:

[0643] The terminal provides dedicated interfaces for both employee and administrator users. Employees use an interface to input career plans, skills, and desired job information, while administrators input organizational plans and resource information. The entered information is formatted in real time, and any errors are notified to the user. The input is collected in text format, and the terminal outputs it as an organized dataset, preparing it for the next processing step.

[0644] Step 2:

[0645] The terminal sends organized employee and administrator information to the server. This transmission is performed using security protocols such as SSL. The server, having received employee and organizational information as input, stores it in a database and uses an RDBMS to maintain data integrity. The output of this step is a dataset correctly loaded into the database, which is used for subsequent processing.

[0646] Step 3:

[0647] The server retrieves employee and organizational information stored in the database and analyzes the data by driving a generative AI model. The prompt "Analyze talent characteristics and organizational needs to generate optimal matching" is used to best match each employee's characteristic information with the organization's requirements. Data processing involves normalizing the characteristic data, prioritizing the requirements information, and matching them to calculate the most suitable talent placement. The output of this step is the generated matching result.

[0648] Step 4:

[0649] The server presents the generated matching results to the administrator. The administrator can review the results and provide feedback through the input interface. The server receives the administrator's feedback as data and readjusts the matching results as needed. This feedback process ensures that the readjusted matching results are even more aligned with the organization's needs.

[0650] Step 5:

[0651] The server notifies employees and administrators of the final matching results approved by the administrator. This notification is sent via a dedicated app or email notification system. The final output is the position information of the newly assigned personnel, which is shared with employees and the organization.

[0652] Step 6:

[0653] The server records the processing history of all processes in a database. This includes input information, generated prompts, feedback, and final results, which are used for future analysis and improvement. At this stage, the system outputs a historical dataset.

[0654] (Application Example 1)

[0655] 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".

[0656] In modern public institutions and large organizations, there is a need to appropriately allocate the skills and preferences of job performers to the specific requirements of the institution. However, existing systems lack a process for efficiently analyzing the diverse characteristics of job performers and presenting them intuitively in a visual format. As a result, insufficient matching occurs between job performers and institutional requirements, hindering operational efficiency and overall organizational performance. There is a strong demand for a new system to address this challenge.

[0657] 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.

[0658] In this invention, the server includes means for collecting and storing characteristic information of job performers, means for collecting and storing information corresponding to the requirements of the organization, means for applying a generation algorithm for matching job performer information with organization information, means for visually displaying the matching results on a smartphone or other device and providing them to job performers and managers, and means for storing the matching and provision history. This makes it possible to quickly and accurately present job performer placement plans through a visually intuitive interface and improve communication and management efficiency within the organization.

[0659] A "functioning professional" is an individual who is responsible for a specific job and possesses the skills and abilities related to that job.

[0660] "Characteristic information" refers to data that shows attributes such as the abilities, experience, interests, and career plans of individuals performing job functions.

[0661] "Institutional requirements" refer to information that outlines the goals a particular organization or institution aims to achieve and the conditions it must meet.

[0662] A "generative algorithm" is a computational method for calculating and proposing the optimal personnel allocation based on the characteristics of job performers and the requirements of the organization.

[0663] The term "device" refers to any electronic device used for inputting, processing, and displaying information, and includes smartphones and computers.

[0664] "Visual display" means presenting calculation results and data to the user in a graphical format.

[0665] "Saving history" means recording past data processing and results so that they can be referenced later.

[0666] This system supports the optimal allocation of personnel for various functions within public institutions and corporations. The server has the function of collecting and storing personnel characteristics information in a database. This characteristics information includes personnel abilities, experience, and career plans. It also collects and stores institutional requirements information in the database.

[0667] The server applies a generation algorithm based on the collected information to achieve optimal matching by connecting job function holders with the needs of the organization. The generation algorithm uses Python and scikit-learn to efficiently run the data analysis and matching process.

[0668] Furthermore, the system displays matching results visually to users (function-oriented personnel and administrators) via smartphones or other devices. This utilizes React Native, allowing administrators to review results and explore different options and placement suggestions through an intuitive UI.

[0669] Furthermore, the server stores a history of all operations and data processing, which can be used for future analysis and system improvements. A concrete example of its use is in a new policy project at a ward office, where it can identify suitable leadership candidates based on the career plans of employees and propose them to management.

[0670] An example of a prompt in this system would be, "We are looking for potential leaders for a new public project. Please list employees who have expressed interest in leadership roles as part of their career plans." This facilitates the appropriate placement of necessary personnel in public institutions and large corporations, thereby improving organizational efficiency.

[0671] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0672] Step 1:

[0673] The terminal receives characteristic information and organizational requirements information from job functioners and administrators. Users input their skills, experience, career plans, and organizational goals through the interface. This prepares the collected data for transmission to the server.

[0674] Step 2:

[0675] The server stores the received characteristic information and institutional requirements information in a database. The collected data is accurately classified and organized, and processed to enable quick access in subsequent processing steps.

[0676] Step 3:

[0677] The server applies a generation algorithm based on the stored information. Using Python and scikit-learn, it analyzes the skill sets of job functioning personnel to match the organization's needs. In this step, it outputs the optimal personnel placement plan based on the input information.

[0678] Step 4:

[0679] The generated matching results are sent to the device for review by the administrator, who is also the user. The results are presented through a visual user interface via React Native, allowing the administrator to visually evaluate the validity of the placement proposals.

[0680] Step 5:

[0681] The user (administrator) provides feedback on the matching results, and the server incorporates that feedback. This feedback includes approving placement plans and suggesting changes, and the generation algorithm is reapplied as needed to obtain adjusted output.

[0682] Step 6:

[0683] The server reconfirms the final matching results and notifies the relevant personnel and administrators. The finalized placement plan is then sent to terminals, ensuring all stakeholders share the latest information.

[0684] Step 7:

[0685] The server records the operation history and matching history of all processes in a database. This allows it to be used as evidence for future analysis and system improvements.

[0686] 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.

[0687] This invention is a system that achieves more accurate personnel matching by not only collecting and storing employee personal information and organizational needs information, but also recognizing and analyzing the emotional state of users. This system consists of a terminal, a server, an emotion engine, and a user.

[0688] The terminal provides a form for employees (users) to input their career plans and skill sets. During this process, an emotion engine analyzes the employee's attitude and behavior, and sends the results from the terminal to the server. Administrators (users) input organizational plans and required personnel information in a similar manner, but the emotion engine also analyzes the administrator's emotions during the input process and sends the results to the server.

[0689] The server stores employee information, organizational information, and their corresponding emotional states in a database. Based on this information, a generation algorithm evaluates employees' career plans and organizational needs along with their emotional states, and automatically initiates a matching process. In this process, employees' emotional states, such as motivation and stress levels, are reflected in the matching results.

[0690] The server presents the generated matching results to the administrator (user) and requests their evaluation. The administrator can review the results and provide feedback, paying attention to changes in emotion. This feedback is also analyzed by the emotion engine, and as a result, the server re-runs the matching algorithm, taking the emotional state in the feedback into consideration, to optimize it.

[0691] For example, if employee B expresses a desire for collaborative teamwork but also dissatisfaction with their current role, the Emotion Engine analyzes these negative emotions and proposes a new role that tests their team leadership skills, thereby resolving their dissatisfaction and boosting their motivation.

[0692] Thus, the present invention provides an advanced personnel transfer system that not only matches skills and career information as in the conventional system, but also takes into account the user's emotional state.

[0693] The following describes the processing flow.

[0694] Step 1:

[0695] The terminal provides employees (users) with a form to input their career plans and skill sets. Users use this form to enter information related to their job duties. The emotion engine analyzes the employee's emotional state while they are entering the information and sends that data from the terminal to the server.

[0696] Step 2:

[0697] The terminal provides an interface for the administrator (user) to input organizational plans, required personnel specifications, and budget information. As the user inputs organizational information, an emotion engine simultaneously measures the administrator's emotions and sends this data to the server.

[0698] Step 3:

[0699] The server stores employee and administrator input information and sentiment data received from terminals into a database. This stored data is used when running the generation algorithm.

[0700] Step 4:

[0701] The server uses a generation algorithm to integrate employee and organizational information retrieved from the database and analyzes it while considering each individual's emotional state. The algorithm evaluates how well employees' career plans, skill sets, and emotional states align with the organization's needs, and generates the optimal match.

[0702] Step 5:

[0703] The server notifies the administrator of the generated matching results, allowing the administrator to review them. The administrator, as a user, reviews the presented results and provides feedback based on the sentiment data analyzed by the sentiment engine.

[0704] Step 6:

[0705] The server adjusts the matching results by reapplying the algorithm, taking into account the sentiment information extracted from the administrator's feedback, and generates new results. The readjusted results are then presented to the administrator.

[0706] Step 7:

[0707] The server reviews the matching plan finalized by the administrator and notifies the employee of the final placement decision. The server records the entire process leading up to this decision and stores it in a database for future reference.

[0708] This series of steps makes it possible to implement personnel placement that takes into account the emotional state of the users.

[0709] (Example 2)

[0710] 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".

[0711] Traditional personnel matching systems are limited to evaluations based on quantitative data such as technical skills and work history, and fail to consider qualitative aspects such as the emotional state and motivational formation of employees. Therefore, it has been difficult to appropriately assess employee stress and dissatisfaction and to achieve truly optimal placement for the organization. As a result, there is a challenge in that appropriate personnel placement cannot be made, and organizational productivity and employee satisfaction cannot be improved.

[0712] 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.

[0713] In this invention, the server includes means for analyzing employee attitudes and utterances to evaluate their emotional state, means for analyzing the emotions of administrators at the time of input and similarly evaluating emotional information, and means for matching employee information with organizational information and each piece of emotional information. This makes it possible to perform advanced personnel matching that takes into account the emotional state of employees and to achieve optimal job placement.

[0714] "Employee attribute information" refers to personal data about employees, including information such as career plans, skill sets, and experience.

[0715] "Organizational requirements" refer to the necessary conditions and policies regarding the personnel that a company or organization seeks, and include specific skills, qualifications, and experience.

[0716] "Emotional state" refers to information that indicates the psychological and emotional condition of employees and managers, and includes stress, motivation, satisfaction, etc.

[0717] "Generation method" refers to algorithms and mechanisms that analyze various data, including emotional states, to derive the optimal matching between employee information and organizational information.

[0718] "Means of presentation" refers to functions and processes that make the generated matching results visible to the administrator.

[0719] "Optimization" refers to the process by which a system adjusts decisions, such as personnel assignments, based on emotional information and feedback, to make them more effective and efficient.

[0720] This invention is a system for achieving optimal personnel matching for both employees and organizations. The system utilizes terminals, servers, an emotion analysis engine, and a generative AI model.

[0721] Employees, as users, input their career plans and skill sets through a terminal. The terminal has a built-in emotion analysis engine that analyzes the employee's actions and words as they input data and evaluates their emotional state. In this process, specific software is used to analyze typing speed and word choice, and stress and motivation levels are quantified. For example, if many corrections are made in a short period of time, the analysis engine will determine that the employee is in an anxious state.

[0722] Furthermore, administrators, who are also users, input organizational plans and required personnel information via their terminals, and similar sentiment analysis is performed. All input information and sentiment data are sent to the server and stored in the database.

[0723] The server uses a generative AI model based on accumulated data to comprehensively evaluate employee information, organizational information, and emotional information. As a result, it automatically determines the optimal personnel placement. This generative AI model reflects emotional aspects such as employee motivation and stress levels, and can identify key focus points for both employees and the organization.

[0724] The generated matching results are presented to the administrator via a terminal. The administrator is required to provide feedback on the results, paying particular attention to changes in emotion during the evaluation. The feedback is also analyzed for emotion, and the server uses this emotion information to re-execute the matching algorithm and optimize placement.

[0725] For example, if employee B "desires to work collaboratively in a team" and is dissatisfied with their current position, the sentiment analysis engine will recognize this as a negative emotion, and the server will suggest a suitable new position for employee B that offers "leadership opportunities."

[0726] An example of a prompt might be, "Please suggest the most suitable job for employee B, taking into account their emotional state. They are dissatisfied with their current position but desire to work collaboratively within a team." By using such prompts, the generative AI model can provide more sophisticated matching suggestions.

[0727] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0728] Step 1:

[0729] The terminal receives career plans and skill sets entered by the employee user. The entered data is collected through an input form, and an emotion analysis engine analyzes the employee's input actions and words. The analysis detects typing speed and word choice, and quantifies emotional states such as stress and motivation. As output, emotion-analyzed data is generated and sent from the terminal to the server.

[0730] Step 2:

[0731] The terminal receives organizational needs and personnel information entered by the administrator, who is the user. At the same time, the terminal uses an emotion analysis engine to analyze the administrator's emotions at the time of input and evaluate their emotional state. The entered information and emotion data are structured and sent to the server. As a result, organizational needs data, along with the administrator's emotion information, is generated.

[0732] Step 3:

[0733] The server stores employee attribute information, organizational requirements information, and their respective emotional state data in a database. Next, a generative AI model is used to analyze the employee, organizational, and emotional information. This process evaluates the matching and prioritization of each element to determine the optimal match. The output is a proposed optimal personnel placement plan.

[0734] Step 4:

[0735] The server displays the generated matching results to the administrator's terminal. The administrator reviews the displayed results and provides detailed feedback. Since the feedback includes the administrator's emotions, the terminal's emotion analysis engine analyzes the changes in emotions again, and this data is sent to the server. As output, feedback data that takes emotion evaluation into account is generated.

[0736] Step 5:

[0737] The server uses the sentiment information based on the feedback to re-run the matching algorithm using the generative AI model. In this process, the placement plan is optimized based on the newly obtained information from the feedback. As a result, an optimized personnel placement plan is generated and presented to the manager.

[0738] (Application Example 2)

[0739] 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".

[0740] Conventional systems only match employees based on their skills and career information, failing to reflect their emotional state. This results in a lack of proper consideration of employee and manager motivation and stress levels. Furthermore, in the deployment of robots within factories, the absence of mechanisms to consider managers' emotional states prevents the achievement of optimal staffing and robot operation.

[0741] 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.

[0742] In this invention, the server includes means for collecting and storing employee personal information, means for collecting and storing information corresponding to the organization's needs, means for applying a generation algorithm for matching employee information with organizational information, means for analyzing emotional states and reflecting the results in the generation algorithm, and means for proposing robot placement optimization according to the emotional state of the manager. This makes it possible to propose optimal matching and robot placement that takes into account the emotional states of employees and managers.

[0743] "Employee personal information" refers to information about employees, including their names, contact information, skill sets, and career plans.

[0744] "Organizational needs" refers to information about the personnel and skills that an organization needs for growth and project execution.

[0745] A "generative algorithm" refers to a procedure or calculation method used to determine the optimal combination of employee and organizational needs based on collected information.

[0746] "Emotional state" refers to the psychological state and motivation analyzed from the attitudes and behaviors of employees and managers.

[0747] "Robot placement optimization" refers to the adjustment of robot placement and task allocation in a factory or work environment based on emotional states and other relevant information to achieve the most efficient results.

[0748] This invention uses terminals, servers, an emotion engine, and users to achieve optimal personnel and robot placement that takes emotional states into account.

[0749] The terminal provides an interface for employees to input their career plans and skill sets. As employees input information, an emotion engine analyzes their input behavior and identifies their emotional state. This emotional information is sent from the terminal to the server. Similarly, administrators also input organizational needs information through the terminal, and their emotional state is analyzed and sent to the server.

[0750] The server stores collected employee information, organizational information, and corresponding emotional states in a database. Based on this information, a generation algorithm evaluates employee career plans and organizational needs, incorporating emotional information, and performs initial matching. This process takes into account employee motivation and stress levels, which have been analyzed by the emotional engine.

[0751] Furthermore, robot placement is optimized to reflect the administrator's emotional state. Administrators can review the proposed matching and placement plans and provide feedback. This feedback is also analyzed through the emotion engine, and the server readjusts the matching and placement plans based on the feedback.

[0752] As a concrete example, consider a situation in a factory environment where a manager has previously experienced stress from a specific task. Based on this information, the system can rearrange the robot's placement to match that task, providing an efficient and less stressful environment.

[0753] The following example prompts apply to the generative AI model:

[0754] "Based on the following input, please propose the optimal placement of factory robots. Managers are experiencing high stress when handling Task X and require a more collaborative placement."

[0755] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0756] Step 1:

[0757] If the user is an employee, the terminal accepts input of the user's career plan and skill set. The data obtained through this input interface is then sent to an emotion engine to analyze the user's input behavior. As a result of this analysis, employee emotional state data is generated.

[0758] Step 2:

[0759] If the user is an administrator, the terminal provides an interface for entering information about the organization's needs. The administrator's input data is analyzed by an emotion engine to obtain information about the administrator's emotional state. This data is sent from the terminal to the server along with the input data.

[0760] Step 3:

[0761] The server stores employee information, organizational information, and corresponding emotional state data received from the terminals into a database. This prepares the system for running the matching generation algorithm using this data.

[0762] Step 4:

[0763] The server uses the stored data to perform an initial matching process based on a generation algorithm. This process incorporates emotional state data into the data processing to generate matching results that reflect employee motivation and stress levels.

[0764] Step 5:

[0765] The server provides the generated matching results to the administrator, who is the user. The administrator can review this and provide feedback. The feedback is then analyzed again by the sentiment engine to extract the administrator's emotional state.

[0766] Step 6:

[0767] The server takes administrator feedback and emotional states into account and initiates an optimization process to readjust matching and robot placement. This ensures that a more collaborative and efficient placement is implemented where necessary.

[0768] Step 7:

[0769] As a concrete example, in a factory environment, the server optimizes robot placement and tasks based on employee and manager sentiment data. In this process, it works in conjunction with a generative AI model, using prompts to suggest improved placement options and providing the optimal solution.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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."

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] The following is further disclosed regarding the embodiments described above.

[0792] (Claim 1)

[0793] Means of collecting and storing employees' personal information,

[0794] Means for collecting and storing information that meets the needs of the organization,

[0795] A means for applying a generation algorithm to match employee information and organizational information,

[0796] Means for providing matching results to employees and managers,

[0797] Means for saving matching and provision history,

[0798] A system that includes this.

[0799] (Claim 2)

[0800] The system according to claim 1, wherein the generation algorithm is optimized taking into account the career plan and skill set.

[0801] (Claim 3)

[0802] The system according to claim 1, which adjusts the matching results to reflect administrator feedback.

[0803] "Example 1"

[0804] (Claim 1)

[0805] A device that collects and stores user characteristic information,

[0806] A device for collecting and storing information related to the demands of a group,

[0807] A device that performs generation processing to match feature information and request information,

[0808] A device that presents matching results to users and administrators,

[0809] A device for accumulating matching and presentation history,

[0810] An information processing system that includes this.

[0811] (Claim 2)

[0812] The information processing system according to claim 1, which optimizes the generation process by taking into account future plans and skill sets.

[0813] (Claim 3)

[0814] The information processing system according to claim 1, which adjusts the matching results to reflect the administrator's response.

[0815] "Application Example 1"

[0816] (Claim 1)

[0817] A means of collecting and storing information on the characteristics of personnel performing job functions,

[0818] Means for collecting and storing information that meets the requirements of the institution,

[0819] A means for applying a generation algorithm to match information on job functions with information on organizations,

[0820] A means of visually displaying matching results on a smartphone or other device and providing them to the person performing the job and the manager,

[0821] Means for saving matching and provision history,

[0822] A system that includes this.

[0823] (Claim 2)

[0824] The system according to claim 1, wherein the generation algorithm is optimized taking into account a capability improvement plan and a set of technologies.

[0825] (Claim 3)

[0826] The system according to claim 1, which adjusts the matching results to reflect feedback from administrators.

[0827] "Example 2 of combining an emotion engine"

[0828] (Claim 1)

[0829] Means for collecting and storing employee attribute information,

[0830] Means for collecting and storing information in accordance with the organization's requirements,

[0831] A method for evaluating emotional states by analyzing employee input attitudes and speech,

[0832] A means to analyze the emotions of administrators when they input data, and similarly evaluate emotional information,

[0833] A generation means for matching employee information, organizational information, and individual sentiment information,

[0834] A means of presenting the generated matching results to the administrator,

[0835] A means to analyze the sentiment of feedback on the presented results and optimize the results,

[0836] Means for saving matching and provision history,

[0837] A system that includes this.

[0838] (Claim 2)

[0839] The system according to claim 1, which optimizes by taking into account emotional state in addition to career plan and ability set.

[0840] (Claim 3)

[0841] The system according to claim 1, which adjusts the matching results by reflecting sentiment information based on administrator feedback.

[0842] "Application example 2 when combining with an emotional engine"

[0843] (Claim 1)

[0844] Means of collecting and storing employees' personal information,

[0845] Means for collecting and storing information that meets the needs of the organization,

[0846] A means for applying a generation algorithm to match employee information and organizational information,

[0847] A means of analyzing emotional states and reflecting the results in a generation algorithm,

[0848] Means for providing matching results to employees and managers,

[0849] A method for proposing robot placement optimization according to the emotional state of the administrator,

[0850] Means for saving matching and provision history,

[0851] A system that includes this.

[0852] (Claim 2)

[0853] The system according to claim 1, which optimizes the generation algorithm, including emotional state, while taking into account career plan and skill set.

[0854] (Claim 3)

[0855] The system according to claim 1, which adjusts matching results and robot placement in accordance with the emotional feedback of the administrator. [Explanation of Symbols]

[0856] 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 collecting and storing information on the characteristics of personnel performing job functions, Means for collecting and storing information that meets the requirements of the institution, A means for applying a generation algorithm to match information on job functions with information on organizations, A means of visually displaying matching results on a smartphone or other device and providing them to the person performing the job and the manager, Means for saving matching and provision history, A system that includes this.

2. The system according to claim 1, wherein the generation algorithm is optimized taking into account a capability improvement plan and a set of technologies.

3. The system according to claim 1, which adjusts the matching results to reflect feedback from the administrator.

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