system

Abstract

We provide the system. [Solution] A means for collecting personal information, functional department information, and executive information, and recording it in an information storage device, Based on the aforementioned information, a means of evaluating appropriate tasks that contribute to individual growth and organizational efficiency, Based on the aforementioned evaluation results, a means of creating placement scenarios based on individual, organizational, and human resources perspectives is provided. A means for integrating the aforementioned placement scenarios to propose an optimal placement plan, A means to verify the aforementioned layout plan in real time and collect opinions, Information analysis to optimize the placement of factory machinery, and a means to automatically assign tasks based on the operating efficiency of equipment and the required skill level. A system that includes means for visualizing data using information processing means and suggesting efficient arrangement.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the placement of human resources in an organization, there is a problem that it is difficult to simultaneously satisfy the individual career growth, the organizational behavioral values, and the cultivation of next-generation cadres. Conventional systems tend to be biased towards individual perspectives and have limitations in achieving an overall optimal placement. Therefore, there is a need for an optimal human resource placement method that integrates the perspectives of individuals, organizations, and personnel.

Means for Solving the Problems

[0005] This invention provides a system that collects personal data, departmental data, and executive data, and uses this data to score appropriate roles that contribute to individual growth and organizational strengthening. Furthermore, it includes means for generating placement scenarios from individual, organizational, and HR perspectives, and integrating them to present optimal placement proposals. These placement proposals are evaluated in real time, and feedback is collected, allowing the system to continuously improve its accuracy.

[0006] "Personal data" refers to information about employees, including their name, educational background, work history, skills, and career aspirations based on their self-declaration.

[0007] "Departmental data" refers to information about the characteristics and needs of each department within an organization, including departmental goals, the status of current projects, and required skill sets.

[0008] "Position data" refers to information about managers and executive candidates within an organization, and includes job titles, past experience, achievements, and leadership abilities.

[0009] "Scoring" is a process of quantitatively evaluating the suitability of individuals and roles based on specific criteria, thereby supporting the appropriate placement of personnel.

[0010] A "placement scenario" is a proposed talent allocation plan created from the perspectives of individuals, organizations, and human resources, taking into account the advantages and risks of each allocation from that perspective.

[0011] "Integration" is the process of unifying information derived from multiple perspectives and data in order to make optimal judgments and decisions.

[0012] "Feedback" refers to information obtained from the evaluation and results of implemented deployment plans, and is used as a basis for improving the accuracy of the system and for making decisions in the future. [Brief explanation of the drawing]

[0013] [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 the data processing device and smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, when an emotion engine is combined. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] 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.

[0017] 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.

[0018] 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, etc.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This invention is implemented as a system for optimizing personnel allocation within an organization. Specifically, servers, terminals, and users each play their respective roles and cooperate to operate the system.

[0035] The server collects and integrates personal, departmental, and managerial data. This includes retrieving information from internal HR systems and external databases. The server normalizes the retrieved data and, where necessary, uses natural language processing techniques to extract keywords and skills from the text data.

[0036] The terminal uses integrated data provided by the server to operate the AI ​​agent. The terminal utilizes scoring algorithms to evaluate roles that can contribute to an individual's career growth and placements that align with the organization's strategy. The terminal generates placement scenarios corresponding to individual, organizational, and HR perspectives, and integrates these to create the optimal placement plan.

[0037] Users review deployment proposals via their terminals and evaluate the advantages and risks of the proposed scenarios. Based on feedback from the system, users refine the deployment proposals and make a final decision. This decision is recorded on the server and used as data to evaluate post-deployment performance.

[0038] As a concrete example, suppose an employee already possesses sufficient technical skills but lacks leadership experience. This employee is proposed to take on the role of sub-leader for a new project within their department. The server provides data demonstrating that this proposal contributes to the individual's leadership development and strengthens the department's capabilities. If the user approves the proposal, the employee is assigned to the sub-leader position, and the impact of the placement is monitored to support their future career growth.

[0039] Thus, the present invention enables personnel allocation that simultaneously meets the needs of individuals and organizations, and promotes the effective operation of the entire organization.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The server collects personal data, departmental data, and job title data from internal and external data sources and stores it in a database. This includes converting data provided in different formats and imputing missing values.

[0043] Step 2:

[0044] The server normalizes the collected data and uses natural language processing to extract keywords and skills from the text data. This ensures a consistent data structure for subsequent processing.

[0045] Step 3:

[0046] The terminal uses data provided by the server to execute a scoring algorithm and evaluate roles that could potentially contribute to each individual's career growth. This involves quantifying the degree of suitability based on the individual's skills and preferences.

[0047] Step 4:

[0048] The terminal generates deployment scenarios aimed at optimizing the entire organization, based on departmental needs and project requirements. These scenarios, evaluated from multiple perspectives, are also analyzed to determine whether they contribute to achieving business objectives.

[0049] Step 5:

[0050] The user reviews the deployment scenario presented through the device and evaluates the validity of the proposed content. If necessary, the user inputs feedback into the device and adjusts the scenario.

[0051] Step 6:

[0052] The server executes the user-approved assignment and records the assignment change in the system. The server then sends a notification of the assignment change to the relevant employees.

[0053] Step 7:

[0054] The server monitors deployment performance data in real time and uses the collected feedback to improve the machine learning model. This allows the system to continuously improve deployment accuracy step by step.

[0055] (Example 1)

[0056] 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."

[0057] Optimizing talent allocation within an organization requires considering multiple perspectives while simultaneously fostering individual growth and strengthening the organization as a whole. However, traditional methods suffer from inconsistent data collection, inaccurate scoring, and difficulties in properly evaluating and providing feedback on allocation proposals, making the realization of optimal allocation a challenge.

[0058] 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.

[0059] In this invention, the server includes means for collecting information and storing it in a memory device, means for evaluating appropriate roles that contribute to individual growth and group strengthening based on the information, and means for generating placement procedures based on individual perspectives, group perspectives, and group management perspectives from the evaluation results. This makes it possible to perform multifaceted evaluations based on the collected information and to present optimal personnel placement plans.

[0060] "Information" refers to all data related to the characteristics, skills, roles, performance, and positioning within an individual or group.

[0061] "Storage device" refers to a broad range of recording media, including computer hardware and software for storing digital data over long periods.

[0062] "Growth" refers to the process by which an individual improves their abilities and skills, and strives for self-realization and career development.

[0063] A "group" refers to a collection of individuals organized to achieve a specific purpose.

[0064] "Means of evaluating roles" refer to algorithms and models used to identify appropriate duties and positions based on individual characteristics and group requirements.

[0065] "Placement procedures" refer to information that outlines a series of steps for determining what role an individual should play within a group.

[0066] "Perspective" refers to a framework or angle for judging and evaluating things from a specific viewpoint.

[0067] This invention is implemented as a system that optimizes personnel allocation within an organization by utilizing information technology. An embodiment of this system is described in detail below.

[0068] First, the server collects information related to the characteristics and roles of individuals and groups from multiple sources and stores it in memory. This includes data acquisition from the company's internal HR information system and external databases. The server saves the acquired information to the database using tools such as Python and SQL, and performs normalization processing to ensure data consistency. The server also utilizes natural language processing technology, using libraries such as spaCy and NLTK to extract keywords and skills from employee resumes and evaluation reports.

[0069] Next, the terminal activates an AI agent based on data provided by the server, using a scoring algorithm to match individual characteristics with group requirements. This process leverages generative AI models to identify roles that contribute to individual career growth and align with the group's strategy. The terminal generates placement procedures based on individual, group, and group management perspectives, and integrates them to present the optimal placement plan.

[0070] Users review the proposed placements presented via their terminals and evaluate the merits and risks of each proposal based on feedback from the system. They then consider how the placement will contribute to individual growth and the group's benefit, ultimately deciding on the final placement. This decision is recorded on the server, and continuous performance monitoring is conducted after the placement is implemented.

[0071] As a concrete example, consider a situation where an employee possesses high technical skills but lacks leadership experience. This employee is proposed to take on the role of sub-leader for a new project within their department. The server provides data demonstrating that this proposal contributes to developing the individual's management abilities and strengthening the group's capabilities, and the user approves the proposal, resulting in the employee being assigned as sub-leader.

[0072] An example of a prompt message would be, "Please suggest an appropriate placement for a certain employee. This employee has strong technical skills but lacks leadership experience." The system generates personnel placement suggestions based on this information input.

[0073] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0074] Step 1:

[0075] The server collects information from the company's internal HR information system and external databases. The input data includes individual characteristics (skills, experience, etc.) and group requirements (position, required skills, etc.). The server retrieves this data, performs normalization processing to ensure consistency, and stores it in a unified information format as output.

[0076] Step 2:

[0077] The server uses natural language processing techniques to extract keywords and skills from resumes and performance reports. Text data is provided as input, and the server extracts the necessary information using libraries such as NLTK and spaCy. The output is stored as structured data and used for subsequent processing.

[0078] Step 3:

[0079] The terminal operates the AI ​​agent using integrated data provided by the server. The input for this step is organized information and extracted skill data. The terminal uses a generated AI model to score the appropriate role for each individual. The output is evaluation data as a result of the scoring.

[0080] Step 4:

[0081] The terminal generates placement procedures based on individual, group, and group management perspectives, using evaluation data. The input is the evaluation data obtained in the previous step, and the terminal uses a scoring algorithm to formulate the optimal placement plan. The output is multiple placement procedures and placement plans based on them.

[0082] Step 5:

[0083] The user reviews the proposed deployments presented via the terminal and evaluates the advantages and risks of each scenario. The input is the generated deployment proposals, and the user makes the final deployment decision based on the presented proposals. The output is the final deployment proposal approved by the user.

[0084] Step 6:

[0085] The server records the implementation of deployment plans after their decision and monitors ongoing results. Inputs are deployment implementation status and subsequent performance data. The server analyzes this data and records it to evaluate organizational and individual goal achievement. Outputs are feedback data for improving future deployment plans.

[0086] (Application Example 1)

[0087] 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."

[0088] Optimizing machine placement in a factory is extremely complex, as assigning tasks based on the efficiency and required skill level of each machine is essential. Traditional methods often struggle with efficient machine placement and operation, potentially leading to decreased overall productivity. This challenge involves real-time placement optimization and measuring its effectiveness.

[0089] 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.

[0090] In this invention, the server includes means for collecting personal information, functional department information, and executive information and recording it in an information storage device; means for evaluating appropriate tasks that contribute to individual growth and organizational efficiency based on the said information; and means for creating placement scenarios based on individual, organizational, and personnel perspectives from the evaluation results. This enables the optimization of factory machinery placement, efficient operation, and real-time monitoring.

[0091] "Personal information" refers to data about individual personnel, including information such as characteristics, skills, and experience.

[0092] "Functional department information" refers to information about the functions and roles of each department within an organization.

[0093] "Position information" refers to information about individuals holding specific positions within an organization, including their job duties and responsibilities.

[0094] An "information storage device" refers to a computer system or database used to collect and store data.

[0095] "Means of evaluation" refer to methods and techniques for quantifying and analyzing the efficiency and suitability of individuals and departments using data.

[0096] A "placement scenario" refers to a plan or simulation of the work assignments of individuals and departments, generated based on data.

[0097] "Factory machinery" refers to automated mechanical devices used in manufacturing processes that automatically perform specific tasks.

[0098] "Real-time monitoring" refers to continuously observing the current situation and data, and analyzing them immediately.

[0099] The embodiments for carrying out the invention are described below.

[0100] The server utilizes the necessary hardware and software to collect and integrate various data within the factory. Specifically, it stores personal information, functional department information, and executive information in a database, and extracts necessary keywords and skills using data normalization and natural language processing techniques. Analysis libraries such as Pandas and NumPy are used for data processing. Furthermore, an AI agent performs scoring based on the data and applies machine learning models to generate placement scenarios.

[0101] The terminal uses data supplied from the server to evaluate the efficiency and required skill level of each machine. The evaluation uses machine learning libraries such as Scikit-learn to perform clustering and generate optimal placement suggestions for each robot. These placement suggestions are updated in real time, allowing for immediate feedback.

[0102] Users review the generated layout proposals via their devices and evaluate them from various perspectives. They consider the advantages and risks of the proposed scenarios and make a final decision on the layout. Furthermore, monitoring how the optimized layout improves productivity can help address future challenges.

[0103] As a concrete example, if a production line in a factory is congested, the server collects and analyzes data and, as an agent, proposes a new layout plan to improve efficiency. For instance, a prompt such as, "We are considering the optimal placement of robots in the factory. Please create an optimal placement plan that shows the impact of each robot's task efficiency and skill level on overall efficiency," can be used to find the optimal placement for the AI ​​model. In this way, it is possible to provide a new method for improving the overall efficiency of the factory.

[0104] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0105] Step 1:

[0106] The server collects personal information, functional department information, and executive information from various machines within the factory and stores it in a database. This process integrates information from various data sources to create an initial dataset. The server then standardizes and normalizes the data format, preparing it for subsequent analysis.

[0107] Step 2:

[0108] The server extracts keywords and skills from the collected data using natural language processing. The input is the integrated data prepared in step 1, and the output is a list of important skills and attributes. This process involves using a natural language processing library to extract semantic information from the data.

[0109] Step 3:

[0110] The server evaluates the extracted skill information using a scoring algorithm, quantifying the efficiency and suitability of each machine. This allows the suitability of each machine to be expressed numerically. The input is a list of keywords and skills, and the output is the score for each machine. This score is used later to generate deployment scenarios.

[0111] Step 4:

[0112] The terminals use score data supplied from the server to perform clustering and create placement scenarios. The input is score data, and the output is a placement scenario. Machine learning libraries such as Scikit-learn are used for clustering, and calculations are performed to optimize the placement of each machine.

[0113] Step 5:

[0114] The terminal presents the generated deployment scenarios to the user and collects feedback in real time. The user reviews the scenarios and evaluates the benefits and risks. The feedback shapes the final deployment plan. In this step, the system uses a user interface to help guide the user to the most satisfactory deployment.

[0115] Step 6:

[0116] The user reviews the evaluated layout options and determines the optimal placement. The determined placement is recorded by the server and used for later analysis and monitoring. The final output is the determined machine layout. A means is established that reflects the user's perspective and contributes to improving efficiency across the entire organization.

[0117] 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.

[0118] This invention aims to personalize the presentation and adjustment of personnel placement suggestions by incorporating an emotion engine into a system that optimizes personnel allocation within an organization, thereby recognizing the user's emotions. Specifically, the server, terminal, user, and emotion engine each play their respective roles and work together to operate the system.

[0119] The server collects personal data, departmental data, and job title data and stores it in a database. The collected data is normalized, and keywords and skills are extracted using natural language processing techniques to form a unified data structure.

[0120] The terminal operates an AI agent based on data provided by the server, scoring roles that contribute to individual growth and organizational strengthening. The terminal generates placement scenarios from individual, organizational, and HR perspectives, and integrates them to present the optimal placement plan.

[0121] The emotion engine recognizes the user's emotional response in real time to the layout suggestions presented through the device. This uses facial expression analysis and voice analysis technologies to determine the user's satisfaction level, stress level, and other factors. The device uses this emotional data to present layout suggestions in a way that is appropriate for the user, and makes adjustments as needed.

[0122] Users review the layout proposals on their devices and evaluate the suggestions based on feedback information analyzed by the sentiment engine. User feedback is recorded on the server and used to adjust the layout proposals and improve the system.

[0123] For example, suppose a scenario is proposed in which an employee is assigned to lead a new project. If the user expresses anxiety or resistance to this proposal, the emotion engine will feed that information back to the server via the device. Based on that emotion data, the server will re-propose suggestions that include training opportunities to enhance skills, helping the user comfortably adapt to the new role.

[0124] Thus, the present invention is a system that aims to improve the efficiency of organizational management and enhance employee satisfaction by optimizing personnel allocation while taking user emotions into consideration.

[0125] The following describes the processing flow.

[0126] Step 1:

[0127] The server collects personal data, departmental data, and job title data from internal systems and external sources and stores it in a database. The collected data is normalized by format conversion and imputation of missing values ​​to create a consistent dataset.

[0128] Step 2:

[0129] The server applies natural language processing to normalized data, extracting keywords and skills from resumes and self-reported data. This prepares data that clarifies an individual's abilities and career aspirations.

[0130] Step 3:

[0131] The device utilizes data provided by the server and uses an AI algorithm to score suitable roles for each individual. This scoring is quantified by considering the individual's skills, career goals, and departmental needs.

[0132] Step 4:

[0133] The device generates placement scenarios from individual, organizational, and HR perspectives based on the generated scoring results. This allows for the creation of optimal placement plans from different viewpoints.

[0134] Step 5:

[0135] The emotion engine analyzes the user's emotions in real time as they view layout plans through their device. It uses facial recognition and voice analysis technologies to determine the user's emotional state and provides immediate feedback based on that data.

[0136] Step 6:

[0137] Based on feedback from the emotion engine, the device adjusts the presentation method and content of layout suggestions according to the user's emotional state. It also suggests additional information and support options necessary to alleviate the user's anxiety.

[0138] Step 7:

[0139] The user reviews the adjusted layout proposal and makes a final approval or requests revisions. The feedback is sent to the server and recorded as data for future system improvements.

[0140] Step 8:

[0141] The server executes approved deployments, records new assignments in the database, and notifies relevant employees of the changes. This accumulates data that enables post-deployment monitoring and improvement.

[0142] (Example 2)

[0143] 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".

[0144] In modern organizational management, optimizing personnel allocation is a crucial issue, but traditional methods struggle to provide allocation proposals that adequately consider individual emotions and the dynamic changes within the organization. Furthermore, the lack of mechanisms for presenting allocation proposals based on skills and emotions, and for providing feedback based on responses, makes it difficult to make optimal allocation decisions.

[0145] 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.

[0146] This invention includes means for a server to acquire personal data, departmental information, and administrator information and store them in a storage device; means for evaluating appropriate roles that contribute to individual growth and organizational strengthening based on the said information; means for creating placement plans based on individual, organizational, and personnel perspectives from the evaluation results; and means for adjusting the proposal method based on emotional responses and re-presenting improved placement plans. This enables the presentation of placement plans that take individual emotions into consideration, and allows for dynamic and flexible personnel allocation within the organization.

[0147] "Personal data" refers to information about individual employees, including their name, age, skills, and experience.

[0148] "Departmental information" refers to information about each department within an organization, including data such as the department's role, goals, and resources.

[0149] "Administrator information" refers to information about the person in charge of managing a department or team, and includes data such as the person's name, job responsibilities, and performance evaluation.

[0150] A "storage device" is a physical or virtual device used to store data, including hard disks and cloud storage.

[0151] An "appropriate role" refers to the duties and responsibilities that are best suited to each individual employee or organization, and is a role that allows for the maximum utilization of an individual's skills and potential for growth.

[0152] "Evaluation" is the process of determining suitability based on an individual's abilities and the organization's needs through data collection and analysis.

[0153] A "staffing plan" is a blueprint for personnel placement that takes into account individual aptitudes, organizational needs, and human resources perspectives, and is a plan for efficient and effective organizational management.

[0154] "Emotional response" refers to the emotional feedback a user gives to a proposed layout, and includes feelings such as satisfaction, anxiety, and resistance.

[0155] "Adjusting the presentation method" means modifying the way placement options are presented based on user emotional responses and feedback, and optimizing the approach to encourage better acceptance.

[0156] This invention is a system for optimizing personnel allocation within an organization. By incorporating an emotion engine, it recognizes the user's emotions and personalizes the presentation and adjustment of placement proposals. The system mainly consists of a server, terminals, users, and the emotion engine.

[0157] The server first collects personal data, departmental information, and administrator information within the organization and stores it in storage. A database management system (such as MySQL® or PostgreSQL) is used for this purpose. The server normalizes the data using programming tools such as Python, and extracts keywords and skills using natural language processing technologies (such as NLTK or spaCy) to form a unified data structure.

[0158] The terminal operates an AI agent based on data supplied from the server. It utilizes a generated AI model (using TENSORFLOW® or PyTorch) to evaluate the optimal role based on the individual and organizational circumstances. The deployment plan generated based on this evaluation is integrated, taking into account individual, organizational, and HR perspectives, and presented to the user as an optimal deployment proposal.

[0159] The emotion engine uses facial expression and voice analysis technologies to recognize in real time how users react to placement suggestions. Facial expression analysis utilizes technologies such as OpenCV and DeepFace, and the results are used to analyze user satisfaction levels, stress levels, and other factors.

[0160] Users review placement suggestions provided via their devices and evaluate the suggestions based on feedback information analyzed by the sentiment engine. This evaluation is recorded on the server and used to improve placement and model accuracy in the future.

[0161] As a concrete example, suppose an employee is selected as a leader for a new project, and the emotion engine detects the user's anxiety. Based on this information, the server can consider providing alternative solutions, such as training opportunities to improve skills, and present these to the user on their device.

[0162] An example of a prompt message is, "Generate the optimal feedback proposal for employee A's new project leader scenario, taking into account the employee's emotional response." This system enables organizations to achieve dynamic and flexible personnel allocation and efficient operations that reflect individual emotions.

[0163] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0164] Step 1:

[0165] The server retrieves personal data, departmental information, and administrator information from external systems and internal databases. This process involves collecting information via APIs and database connections and storing it in storage. Input is raw data, and output is structured database entries.

[0166] Step 2:

[0167] The server normalizes the acquired data using Python's pandas library and SQL. Normalization ensures data consistency by unifying data formats and imputing missing values. The input data is in various formats, and the normalized output data forms the basis for subsequent processing.

[0168] Step 3:

[0169] The server extracts keywords and skills from the data using natural language processing techniques. Text data is analyzed using libraries such as NLTK and spaCy, and the required information is extracted. In this process, natural language text is provided as input, and skill sets and attribute information are generated as output.

[0170] Step 4:

[0171] The terminal runs a generated AI model based on normalized data supplied from the server to evaluate roles from both an individual and organizational perspective. The machine learning model operates using TensorFlow and PyTorch. As part of the data processing, scored evaluation results are output from the input data, and the appropriate role for each employee is calculated.

[0172] Step 5:

[0173] The terminal creates a placement plan based on the evaluation results. The plan integrates individual, organizational, and HR perspectives. The generated placement proposal is presented as output, visualizing appropriate placement within the organization. Specifically, role evaluations are used as input, and the integrated placement proposal is the output.

[0174] Step 6:

[0175] The emotion engine analyzes the user's emotional response to placement suggestions in real time. It uses facial expression analysis technologies such as OpenCV and DeepFace to analyze the user's facial expressions and voice. The input is real-time data obtained from the user, and emotional evaluations such as satisfaction level and stress level are generated as output.

[0176] Step 7:

[0177] The user reviews the placement suggestions provided via the terminal and provides feedback. Based on the sentiment evaluation and feedback from the sentiment engine, the server records information to adjust the next suggestion. The input is the user's reactions and opinions, and the adjustment information based on them is used as the output.

[0178] (Application Example 2)

[0179] 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".

[0180] In caregiving settings, optimizing the work environment is crucial to reducing the decline in work efficiency and staff stress caused by inappropriate staffing, and to providing high-quality care services. However, conventional methods make it difficult to flexibly adjust staffing based on staff emotions and individual growth, and have not been able to sufficiently improve staff satisfaction.

[0181] 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.

[0182] This invention includes a server that collects personal information, departmental information, and managerial information and stores it in a storage device; a server that quantifies and evaluates appropriate roles that contribute to individual growth and organizational strengthening based on the said information; and a server that creates a staffing plan based on individual, organizational, and human resource management perspectives from the results of the said quantification evaluation. This makes it possible to optimize the work environment within actual care facilities based on the emotions and skills of the staff, enabling more efficient and satisfying service provision.

[0183] "Personal information" refers to information used to identify a specific individual, and includes names, ages, skills, and past achievements.

[0184] "Departmental information" refers to information about groups within an organization that handle different tasks or functions, and includes the roles and members of each department.

[0185] "Position information" refers to information about individuals within an organization who hold specific roles or positions, including job titles, responsibilities, and past performance.

[0186] A "storage device" is a device for permanently or temporarily storing information, and includes hard disks, SSDs, and cloud storage.

[0187] "Quantitative evaluation" is a method of quantitatively evaluating a target element and expressing it as a numerical value.

[0188] A "staffing plan" is a plan for assigning staff and resources to the most optimal locations and roles based on specific conditions.

[0189] "Emotional response" refers to the changes and expressions of emotions that an individual shows in response to presented information or situations, and can be detected from facial expressions, voice, etc.

[0190] A "generative AI model" is an artificial intelligence algorithm trained to generate new information or suggestions based on given data.

[0191] "Work environment" refers to the physical or interpersonal conditions and circumstances necessary for an employee to perform their duties.

[0192] The system for realizing this invention is designed for the collaborative operation of a server, terminal, and user. Details are described below.

[0193] The server efficiently collects personal information, departmental information, and job title information, and stores this information in storage. This allows for quick retrieval of necessary information for analysis. Specifically, the server requires a high-performance storage device, and it is desirable to use SSDs or cloud storage, for example. Furthermore, natural language processing technology is used for data analysis to extract keywords and skills. This utilizes Google's Cloud Natural Language API.

[0194] The terminal quantifies and evaluates appropriate roles that contribute to individual growth and organizational strengthening, based on information received from the server. The terminal uses an AI model to create placement plans from individual, organizational, and talent management perspectives. This AI model performs machine learning using TensorFlow and Scikit-learn. Furthermore, the terminal uses the Google Cloud Vision API to capture and evaluate users' emotional responses in real time. Based on the emotional data obtained, the terminal uses a generative AI model to revise placement suggestions, helping to optimize the work environment.

[0195] Users review placement suggestions via their devices and send feedback to the server. If a user feels anxious about a new role, for example, that emotional response is immediately fed back to the system. This allows the system to quickly optimize placement suggestions using a generative AI model, making adjustments to ensure the user can comfortably embrace their new role.

[0196] As a concrete example, suppose a caregiver is proposed to be assigned to the night shift. If the caregiver shows signs of stress, the system will detect this emotion and provide a new work shift proposal that takes this into consideration.

[0197] Example of a prompt:

[0198] "Please create a staffing plan that takes into account employee emotional data and skills. Employee A has expressed anxiety regarding night shifts. Please propose an alternative plan."

[0199] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0200] Step 1:

[0201] The server collects personal information, departmental information, and job title information and stores it in storage. It receives individual datasets from each information source as input. This data undergoes data normalization and formatting, and keywords and skills are extracted using natural language processing techniques. The formatted information is then stored in storage as output. Operationally, it analyzes data using a Python script and refines the information using the Google Cloud Natural Language API.

[0202] Step 2:

[0203] The terminal receives information processed by the server and performs a numerical evaluation. It receives formatted personal information and skill data as input. Based on this data, it uses TensorFlow to numerically evaluate an individual's growth potential and contribution. The output is a numerical evaluation of each individual's role. A dedicated AI model runs to calculate the evaluation score.

[0204] Step 3:

[0205] The terminal creates a deployment plan based on the evaluation results. The role evaluation scores generated in step 2 are used as input. An AI model is used to analyze the data and derive optimal deployment proposals from individual, organizational, and human resource management perspectives. The output is a deployment proposal that integrates these perspectives. The system uses Scikit-learn's clustering algorithm to create deployment scenarios.

[0206] Step 4:

[0207] The device presents the generated layout plan to the user and collects the user's emotional response. The input is the presented layout plan itself. The user reviews the layout plan on the device and generates emotional response data via the webcam and microphone. The output is emotional evaluation data. In terms of operation, it uses the Google Cloud Vision API for facial expression analysis and the Natural Language API for voice input analysis.

[0208] Step 5:

[0209] The server modifies the layout plan based on user emotional responses and feedback data. It takes emotional evaluation data and user feedback as input. Using a generative AI model, it processes this data and reconstructs the layout plan as needed. The output is the newly modified layout plan. Its operation involves recalculating based on the collected data to generate the optimal layout suggestion to communicate to the user.

[0210] Step 6:

[0211] The user reviews the new layout plan via their terminal and provides final feedback. The input is the revised layout plan. The user evaluates the final layout plan and sends as detailed feedback as possible to the server. The output is the final record including the feedback. The user is prompted to log in again to review the layout plan and submit feedback.

[0212] 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.

[0213] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0214] 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.

[0215] [Second Embodiment]

[0216] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0217] 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.

[0218] 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).

[0219] 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.

[0220] 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.

[0221] 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).

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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".

[0228] This invention is implemented as a system for optimizing personnel allocation within an organization. Specifically, servers, terminals, and users each play their respective roles and cooperate to operate the system.

[0229] The server collects and integrates personal, departmental, and managerial data. This includes retrieving information from internal HR systems and external databases. The server normalizes the retrieved data and, where necessary, uses natural language processing techniques to extract keywords and skills from the text data.

[0230] The terminal uses integrated data provided by the server to operate the AI ​​agent. The terminal utilizes scoring algorithms to evaluate roles that can contribute to an individual's career growth and placements that align with the organization's strategy. The terminal generates placement scenarios corresponding to individual, organizational, and HR perspectives, and integrates these to create the optimal placement plan.

[0231] Users review deployment proposals via their terminals and evaluate the advantages and risks of the proposed scenarios. Based on feedback from the system, users refine the deployment proposals and make a final decision. This decision is recorded on the server and used as data to evaluate post-deployment performance.

[0232] As a concrete example, suppose an employee already possesses sufficient technical skills but lacks leadership experience. This employee is proposed to take on the role of sub-leader for a new project within their department. The server provides data demonstrating that this proposal contributes to the individual's leadership development and strengthens the department's capabilities. If the user approves the proposal, the employee is assigned to the sub-leader position, and the impact of the placement is monitored to support their future career growth.

[0233] Thus, the present invention enables personnel allocation that simultaneously meets the needs of individuals and organizations, and promotes the effective operation of the entire organization.

[0234] The following describes the processing flow.

[0235] Step 1:

[0236] The server collects personal data, departmental data, and job title data from internal and external data sources and stores it in a database. This includes converting data provided in different formats and imputing missing values.

[0237] Step 2:

[0238] The server normalizes the collected data and uses natural language processing to extract keywords and skills from the text data. This ensures a consistent data structure for subsequent processing.

[0239] Step 3:

[0240] The terminal uses data provided by the server to execute a scoring algorithm and evaluate roles that could potentially contribute to each individual's career growth. This involves quantifying the degree of suitability based on the individual's skills and preferences.

[0241] Step 4:

[0242] The terminal generates deployment scenarios aimed at optimizing the entire organization, based on departmental needs and project requirements. These scenarios, evaluated from multiple perspectives, are also analyzed to determine whether they contribute to achieving business objectives.

[0243] Step 5:

[0244] The user reviews the deployment scenario presented through the device and evaluates the validity of the proposed content. If necessary, the user inputs feedback into the device and adjusts the scenario.

[0245] Step 6:

[0246] The server executes the user-approved assignment and records the assignment change in the system. The server then sends a notification of the assignment change to the relevant employees.

[0247] Step 7:

[0248] The server monitors deployment performance data in real time and uses the collected feedback to improve the machine learning model. This allows the system to continuously improve deployment accuracy step by step.

[0249] (Example 1)

[0250] 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."

[0251] Optimizing talent allocation within an organization requires considering multiple perspectives while simultaneously fostering individual growth and strengthening the organization as a whole. However, traditional methods suffer from inconsistent data collection, inaccurate scoring, and difficulties in properly evaluating and providing feedback on placement proposals, making the realization of optimal allocation a challenge.

[0252] 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.

[0253] In this invention, the server includes means for collecting information and storing it in a memory device, means for evaluating appropriate roles that contribute to individual growth and group strengthening based on the information, and means for generating placement procedures based on individual perspectives, group perspectives, and group management perspectives from the evaluation results. This makes it possible to perform multifaceted evaluations based on the collected information and to present optimal personnel placement plans.

[0254] "Information" refers to all data related to the characteristics, skills, roles, performance, and positioning within an individual or group.

[0255] "Storage device" refers to a broad range of recording media, including computer hardware and software for storing digital data over long periods of time.

[0256] "Growth" refers to the process by which an individual improves their abilities and skills, and strives for self-realization and career development.

[0257] A "group" refers to a collection of individuals organized to achieve a specific purpose.

[0258] "Means of evaluating roles" refer to algorithms and models used to identify appropriate duties and positions based on individual characteristics and group requirements.

[0259] "Placement procedures" refer to information that outlines a series of steps for determining what role an individual should play within a group.

[0260] "Perspective" refers to a framework or angle for judging and evaluating things from a specific viewpoint.

[0261] This invention is implemented as a system that optimizes personnel allocation within an organization by utilizing information technology. An embodiment of this system is described in detail below.

[0262] First, the server collects information related to the characteristics and roles of individuals and groups from multiple sources and stores it in memory. This includes data acquisition from the company's internal HR information system and external databases. The server saves the acquired information to the database using tools such as Python and SQL, and performs normalization processing to ensure data consistency. The server also utilizes natural language processing technology, using libraries such as spaCy and NLTK to extract keywords and skills from employee resumes and evaluation reports.

[0263] Next, the terminal activates an AI agent based on data provided by the server, using a scoring algorithm to match individual characteristics with group requirements. This process leverages generative AI models to identify roles that contribute to individual career growth and align with the group's strategy. The terminal generates placement procedures based on individual, group, and group management perspectives, and integrates them to present the optimal placement plan.

[0264] Users review the proposed placements presented via their terminals and evaluate the merits and risks of each proposal based on feedback from the system. They then consider how the placement will contribute to individual growth and the group's benefit, ultimately deciding on the final placement. This decision is recorded on the server, and continuous performance monitoring is conducted after the placement is implemented.

[0265] As a concrete example, consider a situation where an employee possesses high technical skills but lacks leadership experience. This employee is proposed to take on the role of sub-leader for a new project within their department. The server provides data demonstrating that this proposal contributes to developing the individual's management abilities and strengthening the group's capabilities, and the user approves the proposal, resulting in the employee being assigned as sub-leader.

[0266] An example of a prompt message would be, "Please suggest an appropriate placement for a certain employee. This employee has strong technical skills but lacks leadership experience." The system generates personnel placement suggestions based on this information input.

[0267] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0268] Step 1:

[0269] The server collects information from the company's internal HR information system and external databases. The input data includes individual characteristics (skills, experience, etc.) and group requirements (position, required skills, etc.). The server retrieves this data, performs normalization processing to ensure consistency, and stores it in a unified information format as output.

[0270] Step 2:

[0271] The server uses natural language processing techniques to extract keywords and skills from resumes and performance reports. Text data is provided as input, and the server extracts the necessary information using libraries such as NLTK and spaCy. The output is stored as structured data and used for subsequent processing.

[0272] Step 3:

[0273] The terminal operates the AI ​​agent using integrated data provided by the server. The input for this step is organized information and extracted skill data. The terminal uses a generated AI model to score the appropriate role for each individual. The output is evaluation data as a result of the scoring.

[0274] Step 4:

[0275] The terminal generates placement procedures based on individual, group, and group management perspectives, using evaluation data. The input is the evaluation data obtained in the previous step, and the terminal uses a scoring algorithm to formulate the optimal placement plan. The output is multiple placement procedures and placement plans based on them.

[0276] Step 5:

[0277] The user reviews the proposed deployments presented via the terminal and evaluates the advantages and risks of each scenario. The input is the generated deployment proposals, and the user makes the final deployment decision based on the presented proposals. The output is the final deployment proposal approved by the user.

[0278] Step 6:

[0279] The server records the implementation of deployment plans after their decision and monitors ongoing results. Inputs are deployment implementation status and subsequent performance data. The server analyzes this data and records it to evaluate organizational and individual goal achievement. Outputs are feedback data for improving future deployment plans.

[0280] (Application Example 1)

[0281] 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."

[0282] In the optimization of machine placement in a factory, it is very complex to assign work based on the efficiency of each machine and the required technical level. With conventional methods, it is difficult to achieve efficient machine placement and operation, and the overall productivity may decrease. This issue includes real-time placement optimization and measurement of its effects.

[0283] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0284] In this invention, the server includes means for collecting personal information, functional department information, and employee information and recording them in an information storage device, means for evaluating appropriate tasks that contribute to personal growth and organizational efficiency based on the information, and means for creating placement scenarios based on the evaluation results from the personal perspective, organizational perspective, and personnel perspective. As a result, the placement of factory machines can be optimized, enabling efficient operation and real-time monitoring.

[0285] "Personal information" is data related to individual personnel and includes information such as characteristics, skills, and experience.

[0286] "Functional department information" is information related to the functions and roles of each department within an organization.

[0287] "Employee information" is information related to those in specific positions within an organization and includes their job content and scope of responsibility.

[0288] "Information storage device" refers to a device such as a computer system or database for collecting and storing data.

[0289] "Means for evaluating" is a method or technique for quantifying and analyzing the efficiency and appropriateness of each individual or department using data.

[0290] "Placement scenario" refers to a plan or simulation related to the work placement of individuals or departments generated based on data.

[0291] "Factory machinery" refers to automated mechanical devices used in manufacturing processes that automatically perform specific tasks.

[0292] "Real-time monitoring" refers to continuously observing the current situation and data, and analyzing them immediately.

[0293] The embodiments for carrying out the invention are described below.

[0294] The server utilizes the necessary hardware and software to collect and integrate various data within the factory. Specifically, it stores personal information, functional department information, and executive information in a database, and extracts necessary keywords and skills using data normalization and natural language processing techniques. Analysis libraries such as Pandas and NumPy are used for data processing. Furthermore, an AI agent performs scoring based on the data and applies machine learning models to generate placement scenarios.

[0295] The terminal uses data supplied from the server to evaluate the efficiency and required skill level of each machine. The evaluation uses machine learning libraries such as Scikit-learn to perform clustering and generate optimal placement suggestions for each robot. These placement suggestions are updated in real time, allowing for immediate feedback.

[0296] Users review the generated layout proposals via their devices and evaluate them from various perspectives. They consider the advantages and risks of the proposed scenarios and make a final decision on the layout. Furthermore, monitoring how the optimized layout improves productivity can help address future challenges.

[0297] As a concrete example, if a production line in a factory is congested, the server collects and analyzes data and, as an agent, proposes a new layout plan to improve efficiency. For instance, a prompt such as, "We are considering the optimal placement of robots in the factory. Please create an optimal placement plan that shows the impact of each robot's task efficiency and skill level on overall efficiency," can be used to find the optimal placement for the AI ​​model. In this way, it is possible to provide a new method for improving the overall efficiency of the factory.

[0298] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0299] Step 1:

[0300] The server collects personal information, functional department information, and executive information from various machines within the factory and stores it in a database. This process integrates information from various data sources to create an initial dataset. The server then standardizes and normalizes the data format, preparing it for subsequent analysis.

[0301] Step 2:

[0302] The server extracts keywords and skills from the collected data using natural language processing. The input is the integrated data prepared in step 1, and the output is a list of important skills and attributes. This process involves using a natural language processing library to extract semantic information from the data.

[0303] Step 3:

[0304] The server evaluates the extracted skill information using a scoring algorithm, quantifying the efficiency and suitability of each machine. This allows the suitability of each machine to be expressed numerically. The input is a list of keywords and skills, and the output is the score for each machine. This score is used later to generate deployment scenarios.

[0305] Step 4:

[0306] The terminal performs clustering using the score data supplied from the server to create an arrangement scenario. The input is the score data, and an arrangement scenario is generated as the output. A machine learning library such as Scikit-learn is used for clustering, and calculations are performed so that the arrangement of each machine is optimized.

[0307] Step 5:

[0308] The terminal presents the generated arrangement scenario to the user and collects real-time feedback. The user checks the scenario and evaluates the advantages and risks. Based on the feedback, the final plan for the arrangement is formed. In this step, the system uses the user interface to assist in leading to the most convincing arrangement.

[0309] Step 6:

[0310] The user checks the evaluated arrangement plan and determines the optimal arrangement. The determined arrangement is recorded by the server and used for later analysis and monitoring. The final output is the determined machine arrangement. A means is established that reflects the user's perspective and contributes to improving the efficiency of the entire organization.

[0311] 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.

[0312] The present invention aims to personalize the presentation and adjustment of the arrangement plan by incorporating an emotion engine into a system for optimizing the placement of human resources within an organization and recognizing the user's emotions. Specifically, the server, the terminal, the user, and the emotion engine each play their respective roles and cooperate to operate the system.

[0313] The server collects personal data, departmental data, and job title data and stores it in a database. The collected data is normalized, and keywords and skills are extracted using natural language processing techniques to form a unified data structure.

[0314] The terminal operates an AI agent based on data provided by the server, scoring roles that contribute to individual growth and organizational strengthening. The terminal generates placement scenarios from individual, organizational, and HR perspectives, and integrates them to present the optimal placement plan.

[0315] The emotion engine recognizes the user's emotional response in real time to the layout suggestions presented through the device. This uses facial expression analysis and voice analysis technologies to determine the user's satisfaction level, stress level, and other factors. The device uses this emotional data to present layout suggestions in a way that is appropriate for the user, and makes adjustments as needed.

[0316] Users review the layout proposals on their devices and evaluate the suggestions based on feedback information analyzed by the sentiment engine. User feedback is recorded on the server and used to adjust the layout proposals and improve the system.

[0317] For example, suppose a scenario is proposed in which an employee is assigned to lead a new project. If the user expresses anxiety or resistance to this proposal, the emotion engine will feed that information back to the server via the device. Based on that emotion data, the server will re-propose suggestions that include training opportunities to enhance skills, helping the user comfortably adapt to the new role.

[0318] Thus, the present invention is a system that aims to improve the efficiency of organizational management and enhance employee satisfaction by optimizing personnel allocation while taking user emotions into consideration.

[0319] The following describes the processing flow.

[0320] Step 1:

[0321] The server collects personal data, departmental data, and job title data from internal systems and external sources and stores it in a database. The collected data is normalized by format conversion and imputation of missing values ​​to create a consistent dataset.

[0322] Step 2:

[0323] The server applies natural language processing to normalized data, extracting keywords and skills from resumes and self-reported data. This prepares data that clarifies an individual's abilities and career aspirations.

[0324] Step 3:

[0325] The device utilizes data provided by the server and uses an AI algorithm to score suitable roles for each individual. This scoring is quantified by considering the individual's skills, career goals, and departmental needs.

[0326] Step 4:

[0327] The device generates placement scenarios from individual, organizational, and HR perspectives based on the generated scoring results. This allows for the creation of optimal placement plans from different viewpoints.

[0328] Step 5:

[0329] The emotion engine analyzes the user's emotions in real time as they view layout plans through their device. It uses facial recognition and voice analysis technologies to determine the user's emotional state and provides immediate feedback based on that data.

[0330] Step 6:

[0331] Based on feedback from the emotion engine, the device adjusts the presentation method and content of layout suggestions according to the user's emotional state. It also suggests additional information and support options necessary to alleviate the user's anxiety.

[0332] Step 7:

[0333] The user reviews the adjusted layout proposal and makes a final approval or requests revisions. The feedback is sent to the server and recorded as data for future system improvements.

[0334] Step 8:

[0335] The server executes the approved deployment, records the new assignment in the database, and notifies the relevant employees of the change. This accumulates data that enables post-deployment monitoring and improvement.

[0336] (Example 2)

[0337] 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".

[0338] In modern organizational management, optimizing personnel allocation is a crucial issue, but traditional methods struggle to provide allocation proposals that adequately consider individual emotions and the dynamic changes within the organization. Furthermore, the lack of mechanisms for presenting allocation proposals based on skills and emotions, and for providing feedback based on responses, makes it difficult to make optimal allocation decisions.

[0339] 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.

[0340] This invention includes means for a server to acquire personal data, departmental information, and administrator information and store them in a storage device; means for evaluating appropriate roles that contribute to individual growth and organizational strengthening based on the said information; means for creating placement plans based on individual, organizational, and personnel perspectives from the evaluation results; and means for adjusting the proposal method based on emotional responses and re-presenting improved placement plans. This enables the presentation of placement plans that take individual emotions into consideration, and allows for dynamic and flexible personnel allocation within the organization.

[0341] "Personal data" refers to information about individual employees, including their name, age, skills, and experience.

[0342] "Departmental information" refers to information about each department within an organization, including data such as the department's role, goals, and resources.

[0343] "Administrator information" refers to information about the person in charge of managing a department or team, and includes data such as the person's name, job responsibilities, and performance evaluation.

[0344] A "storage device" is a physical or virtual device used to store data, including hard disks and cloud storage.

[0345] An "appropriate role" refers to the duties and responsibilities that are best suited to each individual employee or organization, and is a role that allows for the maximum utilization of an individual's skills and potential for growth.

[0346] "Evaluation" is the process of determining suitability based on an individual's abilities and the organization's needs through data collection and analysis.

[0347] A "staffing plan" is a blueprint for personnel placement that takes into account individual aptitudes, organizational needs, and human resources perspectives, and is a plan for efficient and effective organizational management.

[0348] "Emotional response" refers to the emotional feedback a user gives to a proposed layout, and includes feelings such as satisfaction, anxiety, and resistance.

[0349] "Adjusting the presentation method" means modifying the way placement options are presented based on user emotional responses and feedback, and optimizing the approach to encourage better acceptance.

[0350] This invention is a system for optimizing personnel allocation within an organization. By incorporating an emotion engine, it recognizes the user's emotions and personalizes the presentation and adjustment of placement proposals. The system mainly consists of a server, terminals, users, and the emotion engine.

[0351] The server first collects personal data, departmental information, and administrator information within the organization and stores it in storage. A database management system (such as MySQL or PostgreSQL) is used for this purpose. The server normalizes the data using programming tools such as Python, and extracts keywords and skills using natural language processing techniques (such as NLTK or spaCy) to form a unified data structure.

[0352] The terminal operates an AI agent based on data supplied from the server. It utilizes a generated AI model (using TensorFlow or PyTorch) to evaluate the optimal role based on the individual and organizational circumstances. The deployment plan generated based on this evaluation is integrated, taking into account individual, organizational, and HR perspectives, and presented to the user as an optimal deployment proposal.

[0353] The emotion engine uses facial expression and voice analysis technologies to recognize in real time how users react to placement suggestions. Facial expression analysis utilizes technologies such as OpenCV and DeepFace, and the results are used to analyze user satisfaction levels, stress levels, and other factors.

[0354] Users review placement suggestions provided via their devices and evaluate the suggestions based on feedback information analyzed by the sentiment engine. This evaluation is recorded on the server and used to improve placement and model accuracy in the future.

[0355] As a concrete example, suppose an employee is selected as a leader for a new project, and the emotion engine detects the user's anxiety. Based on this information, the server can consider providing alternative solutions, such as training opportunities to improve skills, and present these to the user on their device.

[0356] An example of a prompt message is, "Generate the optimal feedback proposal for employee A's new project leader scenario, taking into account the employee's emotional response." This system enables organizations to achieve dynamic and flexible personnel allocation and efficient operations that reflect individual emotions.

[0357] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0358] Step 1:

[0359] The server retrieves personal data, departmental information, and administrator information from external systems and internal databases. In this process, information is collected via APIs and database connections and stored in storage. Input is raw data, and output is structured database entries.

[0360] Step 2:

[0361] The server normalizes the acquired data using Python's pandas library and SQL. Normalization ensures data consistency by unifying data formats and imputing missing values. The input data is in various formats, and the normalized output data forms the basis for subsequent processing.

[0362] Step 3:

[0363] The server extracts keywords and skills from the data using natural language processing techniques. Text data is analyzed using libraries such as NLTK and spaCy, and the required information is extracted. In this process, natural language text is provided as input, and skill sets and attribute information are generated as output.

[0364] Step 4:

[0365] The terminal runs a generated AI model based on normalized data supplied from the server to evaluate roles from both an individual and organizational perspective. The machine learning model operates using TensorFlow and PyTorch. As part of the data processing, scored evaluation results are output from the input data, and the appropriate role for each employee is calculated.

[0366] Step 5:

[0367] The terminal creates a placement plan based on the evaluation results. The plan integrates individual, organizational, and HR perspectives. The generated placement proposal is presented as output, visualizing appropriate placement within the organization. Specifically, role evaluations are used as input, and the integrated placement proposal is the output.

[0368] Step 6:

[0369] The emotion engine analyzes the user's emotional response to placement suggestions in real time. It uses facial expression analysis technologies such as OpenCV and DeepFace to analyze the user's facial expressions and voice. The input is real-time data obtained from the user, and emotional evaluations such as satisfaction level and stress level are generated as output.

[0370] Step 7:

[0371] The user reviews the placement suggestions provided via the terminal and provides feedback. Based on the sentiment evaluation and feedback from the sentiment engine, the server records information to adjust the next suggestion. The input is the user's reactions and opinions, and the adjustment information based on them is used as the output.

[0372] (Application Example 2)

[0373] 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".

[0374] In caregiving settings, optimizing the work environment is crucial to reducing the decline in work efficiency and staff stress caused by inappropriate staffing, and to providing high-quality care services. However, conventional methods make it difficult to flexibly adjust staffing based on staff emotions and individual growth, and have not been able to sufficiently improve staff satisfaction.

[0375] 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.

[0376] This invention includes a server that collects personal information, departmental information, and managerial information and stores it in a storage device; a server that quantifies and evaluates appropriate roles that contribute to individual growth and organizational strengthening based on the said information; and a server that creates a staffing plan based on individual, organizational, and human resource management perspectives from the results of the said quantification evaluation. This makes it possible to optimize the work environment within actual care facilities based on the emotions and skills of the staff, enabling more efficient and satisfying service provision.

[0377] "Personal information" refers to information used to identify a specific individual, and includes names, ages, skills, and past achievements.

[0378] "Departmental information" refers to information about groups within an organization that handle different tasks or functions, and includes the roles and members of each department.

[0379] "Position information" refers to information about individuals within an organization who hold specific roles or positions, including job titles, responsibilities, and past performance.

[0380] A "storage device" is a device for permanently or temporarily storing information, and includes hard disks, SSDs, and cloud storage.

[0381] "Quantitative evaluation" is a method of quantitatively evaluating a target element and expressing it as a numerical value.

[0382] A "staffing plan" is a plan for assigning staff and resources to the most optimal locations and roles based on specific conditions.

[0383] "Emotional response" refers to the changes and expressions of emotions that an individual shows in response to presented information or situations, and can be detected from facial expressions, voice, etc.

[0384] A "generative AI model" is an artificial intelligence algorithm trained to generate new information or suggestions based on given data.

[0385] "Work environment" refers to the physical or interpersonal conditions and circumstances necessary for an employee to perform their duties.

[0386] The system for realizing this invention is designed for the collaborative operation of a server, terminal, and user. Details are described below.

[0387] The server efficiently collects personal information, departmental information, and job title information, and stores this information in storage. This allows for quick retrieval of necessary information for analysis. Specifically, the server requires a high-performance storage device, and it is desirable to use SSDs or cloud storage, for example. Furthermore, natural language processing technology is used for data analysis to extract keywords and skills. Google's Cloud Natural Language API is utilized for this purpose.

[0388] The terminal quantifies and evaluates appropriate roles that contribute to individual growth and organizational strengthening, based on information received from the server. The terminal uses an AI model to create placement plans from individual, organizational, and talent management perspectives. This AI model performs machine learning using TensorFlow and Scikit-learn. Furthermore, the terminal uses the Google Cloud Vision API to capture and evaluate users' emotional responses in real time. Based on the emotional data obtained, the terminal uses a generative AI model to revise placement suggestions, helping to optimize the work environment.

[0389] Users review placement suggestions via their devices and send feedback to the server. If a user feels anxious about a new role, for example, that emotional response is immediately fed back to the system. This allows the system to quickly optimize placement suggestions using a generative AI model, making adjustments to ensure the user can comfortably embrace their new role.

[0390] As a concrete example, suppose a caregiver is proposed to be assigned to the night shift. If the caregiver shows signs of stress, the system will detect this emotion and provide a new work shift proposal that takes this into consideration.

[0391] Example of a prompt:

[0392] "Please create a staffing plan that takes into account employee emotional data and skills. Employee A has expressed anxiety regarding night shifts. Please propose an alternative plan."

[0393] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0394] Step 1:

[0395] The server collects personal information, departmental information, and job title information and stores it in storage. It receives individual datasets from each information source as input. This data undergoes data normalization and formatting, and keywords and skills are extracted using natural language processing techniques. The formatted information is then stored in storage as output. Operationally, it analyzes data using a Python script and refines the information using the Google Cloud Natural Language API.

[0396] Step 2:

[0397] The terminal receives information processed by the server and performs a numerical evaluation. It receives formatted personal information and skill data as input. Based on this data, it uses TensorFlow to numerically evaluate an individual's growth potential and contribution. The output is a numerical evaluation of each individual's role. A dedicated AI model runs to calculate the evaluation score.

[0398] Step 3:

[0399] The terminal creates a deployment plan based on the evaluation results. The role evaluation scores generated in step 2 are used as input. An AI model is used to analyze the data and derive optimal deployment proposals from individual, organizational, and human resource management perspectives. The output is a deployment proposal that integrates these perspectives. The system uses Scikit-learn's clustering algorithm to create deployment scenarios.

[0400] Step 4:

[0401] The device presents the generated layout plan to the user and collects the user's emotional response. The input is the presented layout plan itself. The user reviews the layout plan on the device and generates emotional response data via the webcam and microphone. The output is emotional evaluation data. In terms of operation, it uses the Google Cloud Vision API for facial expression analysis and the Natural Language API for voice input analysis.

[0402] Step 5:

[0403] The server modifies the layout plan based on user emotional responses and feedback data. It takes emotional evaluation data and user feedback as input. Using a generative AI model, it processes this data and reconstructs the layout plan as needed. The output is the newly modified layout plan. Its operation involves recalculating based on the collected data to generate the optimal layout suggestion to communicate to the user.

[0404] Step 6:

[0405] The user reviews the new layout plan via their terminal and provides final feedback. The input is the revised layout plan. The user evaluates the final layout plan and sends as detailed feedback as possible to the server. The output is the final record including the feedback. The user is prompted to log in again to review the layout plan and submit feedback.

[0406] 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.

[0407] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0408] 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.

[0409] [Third Embodiment]

[0410] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0411] 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.

[0412] 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).

[0413] 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.

[0414] 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.

[0415] 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).

[0416] 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.

[0417] 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.

[0418] 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.

[0419] 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.

[0420] 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.

[0421] 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".

[0422] This invention is implemented as a system for optimizing personnel allocation within an organization. Specifically, servers, terminals, and users each play their respective roles and cooperate to operate the system.

[0423] The server collects and integrates personal, departmental, and managerial data. This includes retrieving information from internal HR systems and external databases. The server normalizes the retrieved data and, where necessary, uses natural language processing techniques to extract keywords and skills from the text data.

[0424] The terminal uses integrated data provided by the server to operate the AI ​​agent. The terminal utilizes scoring algorithms to evaluate roles that can contribute to an individual's career growth and placements that align with the organization's strategy. The terminal generates placement scenarios corresponding to individual, organizational, and HR perspectives, and integrates these to create the optimal placement plan.

[0425] Users review deployment proposals via their terminals and evaluate the advantages and risks of the proposed scenarios. Based on feedback from the system, users refine the deployment proposals and make a final decision. This decision is recorded on the server and used as data to evaluate post-deployment performance.

[0426] As a concrete example, suppose an employee already possesses sufficient technical skills but lacks leadership experience. This employee is proposed to take on the role of sub-leader for a new project within their department. The server provides data demonstrating that this proposal contributes to the individual's leadership development and strengthens the department's capabilities. If the user approves the proposal, the employee is assigned to the sub-leader position, and the impact of the placement is monitored to support their future career growth.

[0427] Thus, the present invention enables personnel allocation that simultaneously meets the needs of individuals and organizations, and promotes the effective operation of the entire organization.

[0428] The following describes the processing flow.

[0429] Step 1:

[0430] The server collects personal data, departmental data, and job title data from internal and external data sources and stores it in a database. This includes converting data provided in different formats and imputing missing values.

[0431] Step 2:

[0432] The server normalizes the collected data and uses natural language processing to extract keywords and skills from the text data. This ensures a consistent data structure for subsequent processing.

[0433] Step 3:

[0434] The terminal uses data provided by the server to execute a scoring algorithm and evaluate roles that could potentially contribute to each individual's career growth. This involves quantifying the degree of suitability based on the individual's skills and preferences.

[0435] Step 4:

[0436] The terminal generates deployment scenarios aimed at optimizing the entire organization, based on departmental needs and project requirements. These scenarios, evaluated from multiple perspectives, are also analyzed to determine whether they contribute to achieving business objectives.

[0437] Step 5:

[0438] The user reviews the deployment scenario presented through the device and evaluates the validity of the proposed content. If necessary, the user inputs feedback into the device and adjusts the scenario.

[0439] Step 6:

[0440] The server executes the user-approved assignment and records the assignment change in the system. The server then sends a notification of the assignment change to the relevant employees.

[0441] Step 7:

[0442] The server monitors deployment performance data in real time and uses the collected feedback to improve the machine learning model. This allows the system to continuously improve deployment accuracy step by step.

[0443] (Example 1)

[0444] 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."

[0445] Optimizing talent allocation within an organization requires considering multiple perspectives while simultaneously fostering individual growth and strengthening the organization as a whole. However, traditional methods suffer from inconsistent data collection, inaccurate scoring, and difficulties in properly evaluating and providing feedback on placement proposals, making the realization of optimal allocation a challenge.

[0446] 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.

[0447] In this invention, the server includes means for collecting information and storing it in a memory device, means for evaluating appropriate roles that contribute to individual growth and group strengthening based on the information, and means for generating placement procedures based on individual perspectives, group perspectives, and group management perspectives from the evaluation results. This makes it possible to perform multifaceted evaluations based on the collected information and to present optimal personnel placement plans.

[0448] "Information" refers to all data related to the characteristics, skills, roles, performance, and positioning within an individual or group.

[0449] "Storage device" refers to a broad range of recording media, including computer hardware and software for storing digital data over long periods of time.

[0450] "Growth" refers to the process by which an individual improves their abilities and skills, and strives for self-realization and career development.

[0451] A "group" refers to a collection of individuals organized to achieve a specific purpose.

[0452] "Means of evaluating roles" refer to algorithms and models used to identify appropriate duties and positions based on individual characteristics and group requirements.

[0453] "Placement procedures" refer to information that outlines a series of steps for determining what role an individual should play within a group.

[0454] "Perspective" refers to a framework or angle for judging and evaluating things from a specific viewpoint.

[0455] This invention is implemented as a system that optimizes personnel allocation within an organization by utilizing information technology. An embodiment of this system is described in detail below.

[0456] First, the server collects information related to the characteristics and roles of individuals and groups from multiple sources and stores it in memory. This includes data acquisition from the company's internal HR information system and external databases. The server saves the acquired information to the database using tools such as Python and SQL, and performs normalization processing to ensure data consistency. The server also utilizes natural language processing technology, using libraries such as spaCy and NLTK to extract keywords and skills from employee resumes and evaluation reports.

[0457] Next, the terminal activates an AI agent based on data provided by the server, using a scoring algorithm to match individual characteristics with group requirements. This process leverages generative AI models to identify roles that contribute to individual career growth and align with the group's strategy. The terminal generates placement procedures based on individual, group, and group management perspectives, and integrates them to present the optimal placement plan.

[0458] Users review the proposed placements presented via their terminals and evaluate the merits and risks of each proposal based on feedback from the system. They then consider how the placement will contribute to individual growth and the group's benefit, ultimately deciding on the final placement. This decision is recorded on the server, and continuous performance monitoring is conducted after the placement is implemented.

[0459] As a concrete example, consider a situation where an employee possesses high technical skills but lacks leadership experience. This employee is proposed to take on the role of sub-leader for a new project within their department. The server provides data demonstrating that this proposal contributes to developing the individual's management abilities and strengthening the group's capabilities, and the user approves the proposal, resulting in the employee being assigned as sub-leader.

[0460] An example of a prompt message would be, "Please suggest an appropriate placement for a certain employee. This employee has strong technical skills but lacks leadership experience." The system generates personnel placement suggestions based on this information input.

[0461] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0462] Step 1:

[0463] The server collects information from the company's internal HR information system and external databases. The input data includes individual characteristics (skills, experience, etc.) and group requirements (position, required skills, etc.). The server retrieves this data, performs normalization processing to ensure consistency, and stores it in a unified information format as output.

[0464] Step 2:

[0465] The server uses natural language processing techniques to extract keywords and skills from resumes and performance reports. Text data is provided as input, and the server extracts the necessary information using libraries such as NLTK and spaCy. The output is stored as structured data and used for subsequent processing.

[0466] Step 3:

[0467] The terminal operates the AI ​​agent using integrated data provided by the server. The input for this step is organized information and extracted skill data. The terminal uses a generated AI model to score the appropriate role for each individual. The output is evaluation data as a result of the scoring.

[0468] Step 4:

[0469] The terminal generates placement procedures based on individual, group, and group management perspectives, using evaluation data. The input is the evaluation data obtained in the previous step, and the terminal uses a scoring algorithm to formulate the optimal placement plan. The output is multiple placement procedures and placement plans based on them.

[0470] Step 5:

[0471] The user reviews the proposed deployments presented via the terminal and evaluates the advantages and risks of each scenario. The input is the generated deployment proposals, and the user makes the final deployment decision based on the presented proposals. The output is the final deployment proposal approved by the user.

[0472] Step 6:

[0473] The server records the implementation of deployment plans after their decision and monitors ongoing results. Inputs are deployment implementation status and subsequent performance data. The server analyzes this data and records it to evaluate organizational and individual goal achievement. Outputs are feedback data for improving future deployment plans.

[0474] (Application Example 1)

[0475] 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."

[0476] Optimizing machine placement in a factory is extremely complex, as assigning tasks based on the efficiency and required skill level of each machine is essential. Traditional methods often struggle with efficient machine placement and operation, potentially leading to decreased overall productivity. This challenge involves real-time placement optimization and measuring its effectiveness.

[0477] 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.

[0478] In this invention, the server includes means for collecting personal information, functional department information, and executive information and recording it in an information storage device; means for evaluating appropriate tasks that contribute to individual growth and organizational efficiency based on the said information; and means for creating placement scenarios based on individual, organizational, and personnel perspectives from the evaluation results. This enables the optimization of factory machinery placement, efficient operation, and real-time monitoring.

[0479] "Personal information" refers to data about individual personnel, including information such as characteristics, skills, and experience.

[0480] "Functional department information" refers to information about the functions and roles of each department within an organization.

[0481] "Position information" refers to information about individuals holding specific positions within an organization, including their job duties and responsibilities.

[0482] An "information storage device" refers to a computer system or database used to collect and store data.

[0483] "Means of evaluation" refer to methods and techniques for quantifying and analyzing the efficiency and suitability of individuals and departments using data.

[0484] A "placement scenario" refers to a plan or simulation of the work assignments of individuals and departments, generated based on data.

[0485] "Factory machinery" refers to automated mechanical devices used in manufacturing processes that automatically perform specific tasks.

[0486] "Real-time monitoring" refers to continuously observing the current situation and data, and analyzing them immediately.

[0487] The embodiments for carrying out the invention are described below.

[0488] The server utilizes the necessary hardware and software to collect and integrate various data within the factory. Specifically, it stores personal information, functional department information, and executive information in a database, and extracts necessary keywords and skills using data normalization and natural language processing techniques. Analysis libraries such as Pandas and NumPy are used for data processing. Furthermore, an AI agent performs scoring based on the data and applies machine learning models to generate placement scenarios.

[0489] The terminal uses data supplied from the server to evaluate the efficiency and required skill level of each machine. The evaluation uses machine learning libraries such as Scikit-learn to perform clustering and generate optimal placement suggestions for each robot. These placement suggestions are updated in real time, allowing for immediate feedback.

[0490] Users review the generated layout proposals via their devices and evaluate them from various perspectives. They consider the advantages and risks of the proposed scenarios and make a final decision on the layout. Furthermore, monitoring how the optimized layout improves productivity can help address future challenges.

[0491] As a concrete example, if a production line in a factory is congested, the server collects and analyzes data and, as an agent, proposes a new layout plan to improve efficiency. For instance, a prompt such as, "We are considering the optimal placement of robots in the factory. Please create an optimal placement plan that shows the impact of each robot's task efficiency and skill level on overall efficiency," can be used to find the optimal placement for the AI ​​model. In this way, it is possible to provide a new method for improving the overall efficiency of the factory.

[0492] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0493] Step 1:

[0494] The server collects personal information, functional department information, and executive information from various machines within the factory and stores it in a database. This process integrates information from various data sources to create an initial dataset. The server then standardizes and normalizes the data format, preparing it for subsequent analysis.

[0495] Step 2:

[0496] The server extracts keywords and skills from the collected data using natural language processing. The input is the integrated data prepared in step 1, and the output is a list of important skills and attributes. This process involves using a natural language processing library to extract semantic information from the data.

[0497] Step 3:

[0498] The server evaluates the extracted skill information using a scoring algorithm, quantifying the efficiency and suitability of each machine. This allows the suitability of each machine to be expressed numerically. The input is a list of keywords and skills, and the output is the score for each machine. This score is used later to generate deployment scenarios.

[0499] Step 4:

[0500] The terminals use score data supplied from the server to perform clustering and create placement scenarios. The input is score data, and the output is a placement scenario. Machine learning libraries such as Scikit-learn are used for clustering, and calculations are performed to optimize the placement of each machine.

[0501] Step 5:

[0502] The terminal presents the generated deployment scenarios to the user and collects feedback in real time. The user reviews the scenarios and evaluates the benefits and risks. The feedback shapes the final deployment plan. In this step, the system uses a user interface to help guide the user to the most satisfactory deployment.

[0503] Step 6:

[0504] The user reviews the evaluated layout options and determines the optimal placement. The determined placement is recorded by the server and used for later analysis and monitoring. The final output is the determined machine layout. A means is established that reflects the user's perspective and contributes to improving efficiency across the entire organization.

[0505] 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.

[0506] This invention aims to personalize the presentation and adjustment of personnel placement suggestions by incorporating an emotion engine into a system that optimizes personnel allocation within an organization, thereby recognizing the user's emotions. Specifically, the server, terminal, user, and emotion engine each play their respective roles and work together to operate the system.

[0507] The server collects personal data, departmental data, and job title data and stores it in a database. The collected data is normalized, and keywords and skills are extracted using natural language processing techniques to form a unified data structure.

[0508] The terminal operates an AI agent based on data provided by the server, scoring roles that contribute to individual growth and organizational strengthening. The terminal generates placement scenarios from individual, organizational, and HR perspectives, and integrates them to present the optimal placement plan.

[0509] The emotion engine recognizes the user's emotional response in real time to the layout suggestions presented through the device. This uses facial expression analysis and voice analysis technologies to determine the user's satisfaction level, stress level, and other factors. The device uses this emotional data to present layout suggestions in a way that is appropriate for the user, and makes adjustments as needed.

[0510] Users review the layout proposals on their devices and evaluate the suggestions based on feedback information analyzed by the sentiment engine. User feedback is recorded on the server and used to adjust the layout proposals and improve the system.

[0511] For example, suppose a scenario is proposed in which an employee is assigned to lead a new project. If the user expresses anxiety or resistance to this proposal, the emotion engine will feed that information back to the server via the device. Based on that emotion data, the server will re-propose suggestions that include training opportunities to enhance skills, helping the user comfortably adapt to the new role.

[0512] Thus, the present invention is a system that aims to improve the efficiency of organizational management and enhance employee satisfaction by optimizing personnel allocation while taking user emotions into consideration.

[0513] The following describes the processing flow.

[0514] Step 1:

[0515] The server collects personal data, departmental data, and job title data from internal systems and external sources and stores it in a database. The collected data is normalized by format conversion and imputation of missing values ​​to create a consistent dataset.

[0516] Step 2:

[0517] The server applies natural language processing to normalized data, extracting keywords and skills from resumes and self-reported data. This prepares data that clarifies an individual's abilities and career aspirations.

[0518] Step 3:

[0519] The device utilizes data provided by the server and uses an AI algorithm to score suitable roles for each individual. This scoring is quantified by considering the individual's skills, career goals, and departmental needs.

[0520] Step 4:

[0521] The device generates placement scenarios from individual, organizational, and HR perspectives based on the generated scoring results. This allows for the creation of optimal placement plans from different viewpoints.

[0522] Step 5:

[0523] The emotion engine analyzes the user's emotions in real time as they view layout plans through their device. It uses facial recognition and voice analysis technologies to determine the user's emotional state and provides immediate feedback based on that data.

[0524] Step 6:

[0525] Based on feedback from the emotion engine, the device adjusts the presentation method and content of layout suggestions according to the user's emotional state. It also suggests additional information and support options necessary to alleviate the user's anxiety.

[0526] Step 7:

[0527] The user reviews the adjusted layout proposal and makes a final approval or requests revisions. The feedback is sent to the server and recorded as data for future system improvements.

[0528] Step 8:

[0529] The server executes the approved deployment, records the new assignment in the database, and notifies the relevant employees of the change. This accumulates data that enables post-deployment monitoring and improvement.

[0530] (Example 2)

[0531] 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."

[0532] In modern organizational management, optimizing personnel allocation is a crucial issue, but traditional methods struggle to provide allocation proposals that adequately consider individual emotions and the dynamic changes within the organization. Furthermore, the lack of mechanisms for presenting allocation proposals based on skills and emotions, and for providing feedback based on responses, makes it difficult to make optimal allocation decisions.

[0533] 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.

[0534] This invention includes means for a server to acquire personal data, departmental information, and administrator information and store them in a storage device; means for evaluating appropriate roles that contribute to individual growth and organizational strengthening based on the said information; means for creating placement plans based on individual, organizational, and personnel perspectives from the evaluation results; and means for adjusting the proposal method based on emotional responses and re-presenting improved placement plans. This enables the presentation of placement plans that take individual emotions into consideration, and allows for dynamic and flexible personnel allocation within the organization.

[0535] "Personal data" refers to information about individual employees, including their name, age, skills, and experience.

[0536] "Departmental information" refers to information about each department within an organization, including data such as the department's role, goals, and resources.

[0537] "Administrator information" refers to information about the person in charge of managing a department or team, and includes data such as the person's name, job responsibilities, and performance evaluation.

[0538] A "storage device" is a physical or virtual device used to store data, including hard disks and cloud storage.

[0539] An "appropriate role" refers to the duties and responsibilities that are best suited to each individual employee or organization, and is a role that allows for the maximum utilization of an individual's skills and potential for growth.

[0540] "Evaluation" is the process of determining suitability based on an individual's abilities and the organization's needs through data collection and analysis.

[0541] A "staffing plan" is a blueprint for personnel placement that takes into account individual aptitudes, organizational needs, and human resources perspectives, and is a plan for efficient and effective organizational management.

[0542] "Emotional response" refers to the emotional feedback a user gives to a proposed layout, and includes feelings such as satisfaction, anxiety, and resistance.

[0543] "Adjusting the presentation method" means modifying the way placement options are presented based on user emotional responses and feedback, and optimizing the approach to encourage better acceptance.

[0544] This invention is a system for optimizing personnel allocation within an organization. By incorporating an emotion engine, it recognizes the user's emotions and personalizes the presentation and adjustment of placement proposals. The system mainly consists of a server, terminals, users, and the emotion engine.

[0545] The server first collects personal data, departmental information, and administrator information within the organization and stores it in storage. A database management system (such as MySQL or PostgreSQL) is used for this purpose. The server normalizes the data using programming tools such as Python, and extracts keywords and skills using natural language processing techniques (such as NLTK or spaCy) to form a unified data structure.

[0546] The terminal operates an AI agent based on data supplied from the server. It utilizes a generated AI model (using TensorFlow or PyTorch) to evaluate the optimal role based on the individual and organizational circumstances. The deployment plan generated based on this evaluation is integrated, taking into account individual, organizational, and HR perspectives, and presented to the user as an optimal deployment proposal.

[0547] The emotion engine uses facial expression and voice analysis technologies to recognize in real time how users react to placement suggestions. Facial expression analysis utilizes technologies such as OpenCV and DeepFace, and the results are used to analyze user satisfaction levels, stress levels, and other factors.

[0548] Users review placement suggestions provided via their devices and evaluate the suggestions based on feedback information analyzed by the sentiment engine. This evaluation is recorded on the server and used to improve placement and model accuracy in the future.

[0549] As a concrete example, suppose an employee is selected as a leader for a new project, and the emotion engine detects the user's anxiety. Based on this information, the server can consider providing alternative solutions, such as training opportunities to improve skills, and present these to the user on their device.

[0550] An example of a prompt message is, "Generate the optimal feedback proposal for employee A's new project leader scenario, taking into account the employee's emotional response." This system enables organizations to achieve dynamic and flexible personnel allocation and efficient operations that reflect individual emotions.

[0551] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0552] Step 1:

[0553] The server retrieves personal data, departmental information, and administrator information from external systems and internal databases. In this process, information is collected via APIs and database connections and stored in storage. Input is raw data, and output is structured database entries.

[0554] Step 2:

[0555] The server normalizes the acquired data using Python's pandas library and SQL. Normalization ensures data consistency by unifying data formats and imputing missing values. The input data is in various formats, and the normalized output data forms the basis for subsequent processing.

[0556] Step 3:

[0557] The server extracts keywords and skills from the data using natural language processing techniques. Text data is analyzed using libraries such as NLTK and spaCy, and the required information is extracted. In this process, natural language text is provided as input, and skill sets and attribute information are generated as output.

[0558] Step 4:

[0559] The terminal runs a generated AI model based on normalized data supplied from the server to evaluate roles from both an individual and organizational perspective. The machine learning model operates using TensorFlow and PyTorch. As part of the data processing, scored evaluation results are output from the input data, and the appropriate role for each employee is calculated.

[0560] Step 5:

[0561] The terminal creates a placement plan based on the evaluation results. The plan integrates individual, organizational, and HR perspectives. The generated placement proposal is presented as output, visualizing appropriate placement within the organization. Specifically, role evaluations are used as input, and the integrated placement proposal is the output.

[0562] Step 6:

[0563] The emotion engine analyzes the user's emotional response to placement suggestions in real time. It uses facial expression analysis technologies such as OpenCV and DeepFace to analyze the user's facial expressions and voice. The input is real-time data obtained from the user, and emotional evaluations such as satisfaction level and stress level are generated as output.

[0564] Step 7:

[0565] The user reviews the placement suggestions provided via the terminal and provides feedback. Based on the sentiment evaluation and feedback from the sentiment engine, the server records information to adjust the next suggestion. The input is the user's reactions and opinions, and the adjustment information based on them is used as the output.

[0566] (Application Example 2)

[0567] 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."

[0568] In caregiving settings, optimizing the work environment is crucial to reducing the decline in work efficiency and staff stress caused by inappropriate staffing, and to providing high-quality care services. However, conventional methods make it difficult to flexibly adjust staffing based on staff emotions and individual growth, and have not been able to sufficiently improve staff satisfaction.

[0569] 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.

[0570] This invention includes a server that collects personal information, departmental information, and managerial information and stores it in a storage device; a server that quantifies and evaluates appropriate roles that contribute to individual growth and organizational strengthening based on the said information; and a server that creates a staffing plan based on individual, organizational, and human resource management perspectives from the results of the said quantification evaluation. This makes it possible to optimize the work environment within actual care facilities based on the emotions and skills of the staff, enabling more efficient and satisfying service provision.

[0571] "Personal information" refers to information used to identify a specific individual, and includes names, ages, skills, and past achievements.

[0572] "Departmental information" refers to information about groups within an organization that handle different tasks or functions, and includes the roles and members of each department.

[0573] "Position information" refers to information about individuals within an organization who hold specific roles or positions, including job titles, responsibilities, and past performance.

[0574] A "storage device" is a device for permanently or temporarily storing information, and includes hard disks, SSDs, and cloud storage.

[0575] "Quantitative evaluation" is a method of quantitatively evaluating a target element and expressing it as a numerical value.

[0576] A "staffing plan" is a plan for assigning staff and resources to the most optimal locations and roles based on specific conditions.

[0577] "Emotional response" refers to the changes and expressions of emotions that an individual shows in response to presented information or situations, and can be detected from facial expressions, voice, etc.

[0578] A "generative AI model" is an artificial intelligence algorithm trained to generate new information or suggestions based on given data.

[0579] "Work environment" refers to the physical or interpersonal conditions and circumstances necessary for an employee to perform their duties.

[0580] The system for realizing this invention is designed for the collaborative operation of a server, terminal, and user. Details are described below.

[0581] The server efficiently collects personal information, departmental information, and job title information, and stores this information in storage. This allows for quick retrieval of necessary information for analysis. Specifically, the server requires a high-performance storage device, and it is desirable to use SSDs or cloud storage, for example. Furthermore, natural language processing technology is used for data analysis to extract keywords and skills. Google's Cloud Natural Language API is utilized for this purpose.

[0582] The terminal quantifies and evaluates appropriate roles that contribute to individual growth and organizational strengthening, based on information received from the server. The terminal uses an AI model to create placement plans from individual, organizational, and talent management perspectives. This AI model performs machine learning using TensorFlow and Scikit-learn. Furthermore, the terminal uses the Google Cloud Vision API to capture and evaluate users' emotional responses in real time. Based on the emotional data obtained, the terminal uses a generative AI model to revise placement suggestions, helping to optimize the work environment.

[0583] Users review placement suggestions via their devices and send feedback to the server. If a user feels anxious about a new role, for example, that emotional response is immediately fed back to the system. This allows the system to quickly optimize placement suggestions using a generative AI model, making adjustments to ensure the user can comfortably embrace their new role.

[0584] As a concrete example, suppose a caregiver is proposed to be assigned to the night shift. If the caregiver shows signs of stress, the system will detect this emotion and provide a new work shift proposal that takes this into consideration.

[0585] Example of a prompt:

[0586] "Please create a staffing plan that takes into account employee emotional data and skills. Employee A has expressed anxiety regarding night shifts. Please propose an alternative plan."

[0587] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0588] Step 1:

[0589] The server collects personal information, departmental information, and job title information and stores it in storage. It receives individual datasets from each information source as input. This data undergoes data normalization and formatting, and keywords and skills are extracted using natural language processing techniques. The formatted information is then stored in storage as output. Operationally, it analyzes data using a Python script and refines the information using the Google Cloud Natural Language API.

[0590] Step 2:

[0591] The terminal receives information processed by the server and performs a numerical evaluation. It receives formatted personal information and skill data as input. Based on this data, it uses TensorFlow to numerically evaluate an individual's growth potential and contribution. The output is a numerical evaluation of each individual's role. A dedicated AI model runs to calculate the evaluation score.

[0592] Step 3:

[0593] The terminal creates a deployment plan based on the evaluation results. The role evaluation scores generated in step 2 are used as input. An AI model is used to analyze the data and derive optimal deployment proposals from individual, organizational, and human resource management perspectives. The output is a deployment proposal that integrates these perspectives. The system uses Scikit-learn's clustering algorithm to create deployment scenarios.

[0594] Step 4:

[0595] The device presents the generated layout plan to the user and collects the user's emotional response. The input is the presented layout plan itself. The user reviews the layout plan on the device and generates emotional response data via the webcam and microphone. The output is emotional evaluation data. In terms of operation, it uses the Google Cloud Vision API for facial expression analysis and the Natural Language API for voice input analysis.

[0596] Step 5:

[0597] The server modifies the layout plan based on user emotional responses and feedback data. It takes emotional evaluation data and user feedback as input. Using a generative AI model, it processes this data and reconstructs the layout plan as needed. The output is the newly modified layout plan. Its operation involves recalculating based on the collected data to generate the optimal layout suggestion to communicate to the user.

[0598] Step 6:

[0599] The user reviews the new layout plan via their terminal and provides final feedback. The input is the revised layout plan. The user evaluates the final layout plan and sends as detailed feedback as possible to the server. The output is the final record including the feedback. The user is prompted to log in again to review the layout plan and submit feedback.

[0600] 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.

[0601] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0602] 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.

[0603] [Fourth Embodiment]

[0604] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0605] 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.

[0606] 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).

[0607] 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.

[0608] 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.

[0609] 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).

[0610] 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.

[0611] 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.

[0612] 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.

[0613] 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.

[0614] 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.

[0615] 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.

[0616] 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".

[0617] This invention is implemented as a system for optimizing personnel allocation within an organization. Specifically, servers, terminals, and users each play their respective roles and cooperate to operate the system.

[0618] The server collects and integrates personal, departmental, and managerial data. This includes retrieving information from internal HR systems and external databases. The server normalizes the retrieved data and, where necessary, uses natural language processing techniques to extract keywords and skills from the text data.

[0619] The terminal uses integrated data provided by the server to operate the AI ​​agent. The terminal utilizes scoring algorithms to evaluate roles that can contribute to an individual's career growth and placements that align with the organization's strategy. The terminal generates placement scenarios corresponding to individual, organizational, and HR perspectives, and integrates these to create the optimal placement plan.

[0620] Users review deployment proposals via their terminals and evaluate the advantages and risks of the proposed scenarios. Based on feedback from the system, users refine the deployment proposals and make a final decision. This decision is recorded on the server and used as data to evaluate post-deployment performance.

[0621] As a concrete example, suppose an employee already possesses sufficient technical skills but lacks leadership experience. This employee is proposed to take on the role of sub-leader for a new project within their department. The server provides data demonstrating that this proposal contributes to the individual's leadership development and strengthens the department's capabilities. If the user approves the proposal, the employee is assigned to the sub-leader position, and the impact of the placement is monitored to support their future career growth.

[0622] Thus, the present invention enables personnel allocation that simultaneously meets the needs of individuals and organizations, and promotes the effective operation of the entire organization.

[0623] The following describes the processing flow.

[0624] Step 1:

[0625] The server collects personal data, departmental data, and job title data from internal and external data sources and stores it in a database. This includes converting data provided in different formats and imputing missing values.

[0626] Step 2:

[0627] The server normalizes the collected data and uses natural language processing to extract keywords and skills from the text data. This ensures a consistent data structure for subsequent processing.

[0628] Step 3:

[0629] The terminal uses data provided by the server to execute a scoring algorithm and evaluate roles that could potentially contribute to each individual's career growth. This involves quantifying the degree of suitability based on the individual's skills and preferences.

[0630] Step 4:

[0631] The terminal generates deployment scenarios aimed at optimizing the entire organization, based on departmental needs and project requirements. These scenarios, evaluated from multiple perspectives, are also analyzed to determine whether they contribute to achieving business objectives.

[0632] Step 5:

[0633] The user reviews the deployment scenario presented through the device and evaluates the validity of the proposed content. If necessary, the user inputs feedback into the device and adjusts the scenario.

[0634] Step 6:

[0635] The server executes the user-approved assignment and records the assignment change in the system. The server then sends a notification of the assignment change to the relevant employees.

[0636] Step 7:

[0637] The server monitors deployment performance data in real time and uses the collected feedback to improve the machine learning model. This allows the system to continuously improve deployment accuracy step by step.

[0638] (Example 1)

[0639] 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".

[0640] Optimizing talent allocation within an organization requires considering multiple perspectives while simultaneously fostering individual growth and strengthening the organization as a whole. However, traditional methods suffer from inconsistent data collection, inaccurate scoring, and difficulties in properly evaluating and providing feedback on placement proposals, making the realization of optimal allocation a challenge.

[0641] 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.

[0642] In this invention, the server includes means for collecting information and storing it in a memory device, means for evaluating appropriate roles that contribute to individual growth and group strengthening based on the information, and means for generating placement procedures based on individual perspectives, group perspectives, and group management perspectives from the evaluation results. This makes it possible to perform multifaceted evaluations based on the collected information and to present optimal personnel placement plans.

[0643] "Information" refers to all data related to the characteristics, skills, roles, performance, and positioning within an individual or group.

[0644] "Storage device" refers to a broad range of recording media, including computer hardware and software for storing digital data over long periods of time.

[0645] "Growth" refers to the process by which an individual improves their abilities and skills, and strives for self-realization and career development.

[0646] A "group" refers to a collection of individuals organized to achieve a specific purpose.

[0647] "Means of evaluating roles" refer to algorithms and models used to identify appropriate duties and positions based on individual characteristics and group requirements.

[0648] "Placement procedures" refer to information that outlines a series of steps for determining what role an individual should play within a group.

[0649] "Perspective" refers to a framework or angle for judging and evaluating things from a specific viewpoint.

[0650] This invention is implemented as a system that optimizes personnel allocation within an organization by utilizing information technology. An embodiment of this system is described in detail below.

[0651] First, the server collects information related to the characteristics and roles of individuals and groups from multiple sources and stores it in memory. This includes data acquisition from the company's internal HR information system and external databases. The server saves the acquired information to the database using tools such as Python and SQL, and performs normalization processing to ensure data consistency. The server also utilizes natural language processing technology, using libraries such as spaCy and NLTK to extract keywords and skills from employee resumes and evaluation reports.

[0652] Next, the terminal activates an AI agent based on data provided by the server, using a scoring algorithm to match individual characteristics with group requirements. This process leverages generative AI models to identify roles that contribute to individual career growth and align with the group's strategy. The terminal generates placement procedures based on individual, group, and group management perspectives, and integrates them to present the optimal placement plan.

[0653] Users review the proposed placements presented via their terminals and evaluate the merits and risks of each proposal based on feedback from the system. They then consider how the placement will contribute to individual growth and the group's benefit, ultimately deciding on the final placement. This decision is recorded on the server, and continuous performance monitoring is conducted after the placement is implemented.

[0654] As a concrete example, consider a situation where an employee possesses high technical skills but lacks leadership experience. This employee is proposed to take on the role of sub-leader for a new project within their department. The server provides data demonstrating that this proposal contributes to developing the individual's management abilities and strengthening the group's capabilities, and the user approves the proposal, resulting in the employee being assigned as sub-leader.

[0655] An example of a prompt message would be, "Please suggest an appropriate placement for a certain employee. This employee has strong technical skills but lacks leadership experience." The system generates personnel placement suggestions based on this information input.

[0656] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0657] Step 1:

[0658] The server collects information from the company's internal HR information system and external databases. The input data includes individual characteristics (skills, experience, etc.) and group requirements (position, required skills, etc.). The server retrieves this data, performs normalization processing to ensure consistency, and stores it in a unified information format as output.

[0659] Step 2:

[0660] The server uses natural language processing techniques to extract keywords and skills from resumes and performance reports. Text data is provided as input, and the server extracts the necessary information using libraries such as NLTK and spaCy. The output is stored as structured data and used for subsequent processing.

[0661] Step 3:

[0662] The terminal operates the AI ​​agent using integrated data provided by the server. The input for this step is organized information and extracted skill data. The terminal uses a generated AI model to score the appropriate role for each individual. The output is evaluation data as a result of the scoring.

[0663] Step 4:

[0664] The terminal generates placement procedures based on individual, group, and group management perspectives, using evaluation data. The input is the evaluation data obtained in the previous step, and the terminal uses a scoring algorithm to formulate the optimal placement plan. The output is multiple placement procedures and placement plans based on them.

[0665] Step 5:

[0666] The user reviews the proposed deployments presented via the terminal and evaluates the advantages and risks of each scenario. The input is the generated deployment proposals, and the user makes the final deployment decision based on the presented proposals. The output is the final deployment proposal approved by the user.

[0667] Step 6:

[0668] The server records the implementation of deployment plans after their decision and monitors ongoing results. Inputs are deployment implementation status and subsequent performance data. The server analyzes this data and records it to evaluate organizational and individual goal achievement. Outputs are feedback data for improving future deployment plans.

[0669] (Application Example 1)

[0670] 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".

[0671] Optimizing machine placement in a factory is extremely complex, as assigning tasks based on the efficiency and required skill level of each machine is essential. Traditional methods often struggle with efficient machine placement and operation, potentially leading to decreased overall productivity. This challenge involves real-time placement optimization and measuring its effectiveness.

[0672] 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.

[0673] In this invention, the server includes means for collecting personal information, functional department information, and executive information and recording it in an information storage device; means for evaluating appropriate tasks that contribute to individual growth and organizational efficiency based on the said information; and means for creating placement scenarios based on individual, organizational, and personnel perspectives from the evaluation results. This enables the optimization of factory machinery placement, efficient operation, and real-time monitoring.

[0674] "Personal information" refers to data about individual personnel, including information such as characteristics, skills, and experience.

[0675] "Functional department information" refers to information about the functions and roles of each department within an organization.

[0676] "Position information" refers to information about individuals holding specific positions within an organization, including their job duties and responsibilities.

[0677] An "information storage device" refers to a computer system or database used to collect and store data.

[0678] "Means of evaluation" refer to methods and techniques for quantifying and analyzing the efficiency and suitability of individuals and departments using data.

[0679] A "placement scenario" refers to a plan or simulation of the work assignments of individuals and departments, generated based on data.

[0680] "Factory machinery" refers to automated mechanical devices used in manufacturing processes that automatically perform specific tasks.

[0681] "Real-time monitoring" refers to continuously observing the current situation and data, and analyzing them immediately.

[0682] The embodiments for carrying out the invention are described below.

[0683] The server utilizes the necessary hardware and software to collect and integrate various data within the factory. Specifically, it stores personal information, functional department information, and executive information in a database, and extracts necessary keywords and skills using data normalization and natural language processing techniques. Analysis libraries such as Pandas and NumPy are used for data processing. Furthermore, an AI agent performs scoring based on the data and applies machine learning models to generate placement scenarios.

[0684] The terminal uses data supplied from the server to evaluate the efficiency and required skill level of each machine. The evaluation uses machine learning libraries such as Scikit-learn to perform clustering and generate optimal placement suggestions for each robot. These placement suggestions are updated in real time, allowing for immediate feedback.

[0685] Users review the generated layout proposals via their devices and evaluate them from various perspectives. They consider the advantages and risks of the proposed scenarios and make a final decision on the layout. Furthermore, monitoring how the optimized layout improves productivity can help address future challenges.

[0686] As a concrete example, if a production line in a factory is congested, the server collects and analyzes data and, as an agent, proposes a new layout plan to improve efficiency. For instance, a prompt such as, "We are considering the optimal placement of robots in the factory. Please create an optimal placement plan that shows the impact of each robot's task efficiency and skill level on overall efficiency," can be used to find the optimal placement for the AI ​​model. In this way, it is possible to provide a new method for improving the overall efficiency of the factory.

[0687] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0688] Step 1:

[0689] The server collects personal information, functional department information, and executive information from various machines within the factory and stores it in a database. This process integrates information from various data sources to create an initial dataset. The server then standardizes and normalizes the data format, preparing it for subsequent analysis.

[0690] Step 2:

[0691] The server extracts keywords and skills from the collected data using natural language processing. The input is the integrated data prepared in step 1, and the output is a list of important skills and attributes. This process involves using a natural language processing library to extract semantic information from the data.

[0692] Step 3:

[0693] The server evaluates the extracted skill information using a scoring algorithm, quantifying the efficiency and suitability of each machine. This allows the suitability of each machine to be expressed numerically. The input is a list of keywords and skills, and the output is the score for each machine. This score is used later to generate deployment scenarios.

[0694] Step 4:

[0695] The terminals use score data supplied from the server to perform clustering and create placement scenarios. The input is score data, and the output is a placement scenario. Machine learning libraries such as Scikit-learn are used for clustering, and calculations are performed to optimize the placement of each machine.

[0696] Step 5:

[0697] The terminal presents the generated deployment scenarios to the user and collects feedback in real time. The user reviews the scenarios and evaluates the benefits and risks. The feedback shapes the final deployment plan. In this step, the system uses a user interface to help guide the user to the most satisfactory deployment.

[0698] Step 6:

[0699] The user reviews the evaluated layout options and determines the optimal placement. The determined placement is recorded by the server and used for later analysis and monitoring. The final output is the determined machine layout. A means is established that reflects the user's perspective and contributes to improving efficiency across the entire organization.

[0700] 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.

[0701] This invention aims to personalize the presentation and adjustment of personnel placement suggestions by incorporating an emotion engine into a system that optimizes personnel allocation within an organization, thereby recognizing the user's emotions. Specifically, the server, terminal, user, and emotion engine each play their respective roles and work together to operate the system.

[0702] The server collects personal data, departmental data, and job title data and stores it in a database. The collected data is normalized, and keywords and skills are extracted using natural language processing techniques to form a unified data structure.

[0703] The terminal operates an AI agent based on data provided by the server, scoring roles that contribute to individual growth and organizational strengthening. The terminal generates placement scenarios from individual, organizational, and HR perspectives, and integrates them to present the optimal placement plan.

[0704] The emotion engine recognizes the user's emotional response in real time to the layout suggestions presented through the device. This uses facial expression analysis and voice analysis technologies to determine the user's satisfaction level, stress level, and other factors. The device uses this emotional data to present layout suggestions in a way that is appropriate for the user, and makes adjustments as needed.

[0705] Users review the layout proposals on their devices and evaluate the suggestions based on feedback information analyzed by the sentiment engine. User feedback is recorded on the server and used to adjust the layout proposals and improve the system.

[0706] For example, suppose a scenario is proposed in which an employee is assigned to lead a new project. If the user expresses anxiety or resistance to this proposal, the emotion engine will feed that information back to the server via the device. Based on that emotion data, the server will re-propose suggestions that include training opportunities to enhance skills, helping the user comfortably adapt to the new role.

[0707] Thus, the present invention is a system that aims to improve the efficiency of organizational management and enhance employee satisfaction by optimizing personnel allocation while taking user emotions into consideration.

[0708] The following describes the processing flow.

[0709] Step 1:

[0710] The server collects personal data, departmental data, and job title data from internal systems and external sources and stores it in a database. The collected data is normalized by format conversion and imputation of missing values ​​to create a consistent dataset.

[0711] Step 2:

[0712] The server applies natural language processing to normalized data, extracting keywords and skills from resumes and self-reported data. This prepares data that clarifies an individual's abilities and career aspirations.

[0713] Step 3:

[0714] The device utilizes data provided by the server and uses an AI algorithm to score suitable roles for each individual. This scoring is quantified by considering the individual's skills, career goals, and departmental needs.

[0715] Step 4:

[0716] The device generates placement scenarios from individual, organizational, and HR perspectives based on the generated scoring results. This allows for the creation of optimal placement plans from different viewpoints.

[0717] Step 5:

[0718] The emotion engine analyzes the user's emotions in real time as they view layout plans through their device. It uses facial recognition and voice analysis technologies to determine the user's emotional state and provides immediate feedback based on that data.

[0719] Step 6:

[0720] Based on feedback from the emotion engine, the device adjusts the presentation method and content of layout suggestions according to the user's emotional state. It also suggests additional information and support options necessary to alleviate the user's anxiety.

[0721] Step 7:

[0722] The user reviews the adjusted layout proposal and makes a final approval or requests revisions. The feedback is sent to the server and recorded as data for future system improvements.

[0723] Step 8:

[0724] The server executes the approved deployment, records the new assignment in the database, and notifies the relevant employees of the change. This accumulates data that enables post-deployment monitoring and improvement.

[0725] (Example 2)

[0726] 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".

[0727] In modern organizational management, optimizing personnel allocation is a crucial issue, but traditional methods struggle to provide allocation proposals that adequately consider individual emotions and the dynamic changes within the organization. Furthermore, the lack of mechanisms for presenting allocation proposals based on skills and emotions, and for providing feedback based on responses, makes it difficult to make optimal allocation decisions.

[0728] 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.

[0729] This invention includes means for a server to acquire personal data, departmental information, and administrator information and store them in a storage device; means for evaluating appropriate roles that contribute to individual growth and organizational strengthening based on the said information; means for creating placement plans based on individual, organizational, and personnel perspectives from the evaluation results; and means for adjusting the proposal method based on emotional responses and re-presenting improved placement plans. This enables the presentation of placement plans that take individual emotions into consideration, and allows for dynamic and flexible personnel allocation within the organization.

[0730] "Personal data" refers to information about individual employees, including their name, age, skills, and experience.

[0731] "Departmental information" refers to information about each department within an organization, including data such as the department's role, goals, and resources.

[0732] "Administrator information" refers to information about the person in charge of managing a department or team, and includes data such as the person's name, job responsibilities, and performance evaluation.

[0733] A "storage device" is a physical or virtual device used to store data, including hard disks and cloud storage.

[0734] An "appropriate role" refers to the duties and responsibilities that are best suited to each individual employee or organization, and is a role that allows for the maximum utilization of an individual's skills and potential for growth.

[0735] "Evaluation" is the process of determining suitability based on an individual's abilities and the organization's needs through data collection and analysis.

[0736] A "staffing plan" is a blueprint for personnel placement that takes into account individual aptitudes, organizational needs, and human resources perspectives, and is a plan for efficient and effective organizational management.

[0737] "Emotional response" refers to the emotional feedback a user gives to a proposed layout, and includes feelings such as satisfaction, anxiety, and resistance.

[0738] "Adjusting the presentation method" means modifying the way placement options are presented based on user emotional responses and feedback, and optimizing the approach to encourage better acceptance.

[0739] This invention is a system for optimizing personnel allocation within an organization. By incorporating an emotion engine, it recognizes the user's emotions and personalizes the presentation and adjustment of placement proposals. The system mainly consists of a server, terminals, users, and the emotion engine.

[0740] The server first collects personal data, departmental information, and administrator information within the organization and stores it in storage. A database management system (such as MySQL or PostgreSQL) is used for this purpose. The server normalizes the data using programming tools such as Python, and extracts keywords and skills using natural language processing techniques (such as NLTK or spaCy) to form a unified data structure.

[0741] The terminal operates an AI agent based on data supplied from the server. It utilizes a generated AI model (using TensorFlow or PyTorch) to evaluate the optimal role based on the individual and organizational circumstances. The deployment plan generated based on this evaluation is integrated, taking into account individual, organizational, and HR perspectives, and presented to the user as an optimal deployment proposal.

[0742] The emotion engine uses facial expression and voice analysis technologies to recognize in real time how users react to placement suggestions. Facial expression analysis utilizes technologies such as OpenCV and DeepFace, and the results are used to analyze user satisfaction levels, stress levels, and other factors.

[0743] Users review placement suggestions provided via their devices and evaluate the suggestions based on feedback information analyzed by the sentiment engine. This evaluation is recorded on the server and used to improve placement and model accuracy in the future.

[0744] As a concrete example, suppose an employee is selected as a leader for a new project, and the emotion engine detects the user's anxiety. Based on this information, the server can consider providing alternative solutions, such as training opportunities to improve skills, and present these to the user on their device.

[0745] An example of a prompt message is, "Generate the optimal feedback proposal for employee A's new project leader scenario, taking into account the employee's emotional response." This system enables organizations to achieve dynamic and flexible personnel allocation and efficient operations that reflect individual emotions.

[0746] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0747] Step 1:

[0748] The server retrieves personal data, departmental information, and administrator information from external systems and internal databases. In this process, information is collected via APIs and database connections and stored in storage. Input is raw data, and output is structured database entries.

[0749] Step 2:

[0750] The server normalizes the acquired data using Python's pandas library and SQL. Normalization ensures data consistency by unifying data formats and imputing missing values. The input data is in various formats, and the normalized output data forms the basis for subsequent processing.

[0751] Step 3:

[0752] The server extracts keywords and skills from the data using natural language processing techniques. Text data is analyzed using libraries such as NLTK and spaCy, and the required information is extracted. In this process, natural language text is provided as input, and skill sets and attribute information are generated as output.

[0753] Step 4:

[0754] The terminal runs a generated AI model based on normalized data supplied from the server to evaluate roles from both an individual and organizational perspective. The machine learning model operates using TensorFlow and PyTorch. As part of the data processing, scored evaluation results are output from the input data, and the appropriate role for each employee is calculated.

[0755] Step 5:

[0756] The terminal creates a placement plan based on the evaluation results. The plan integrates individual, organizational, and HR perspectives. The generated placement proposal is presented as output, visualizing appropriate placement within the organization. Specifically, role evaluations are used as input, and the integrated placement proposal is the output.

[0757] Step 6:

[0758] The emotion engine analyzes the user's emotional response to placement suggestions in real time. It uses facial expression analysis technologies such as OpenCV and DeepFace to analyze the user's facial expressions and voice. The input is real-time data obtained from the user, and emotional evaluations such as satisfaction level and stress level are generated as output.

[0759] Step 7:

[0760] The user reviews the placement suggestions provided via the terminal and provides feedback. Based on the sentiment evaluation and feedback from the sentiment engine, the server records information to adjust the next suggestion. The input is the user's reactions and opinions, and the adjustment information based on them is used as the output.

[0761] (Application Example 2)

[0762] 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".

[0763] In caregiving settings, optimizing the work environment is crucial to reducing the decline in work efficiency and staff stress caused by inappropriate staffing, and to providing high-quality care services. However, conventional methods make it difficult to flexibly adjust staffing based on staff emotions and individual growth, and have not been able to sufficiently improve staff satisfaction.

[0764] 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.

[0765] This invention includes a server that collects personal information, departmental information, and managerial information and stores it in a storage device; a server that quantifies and evaluates appropriate roles that contribute to individual growth and organizational strengthening based on the said information; and a server that creates a staffing plan based on individual, organizational, and human resource management perspectives from the results of the said quantification evaluation. This makes it possible to optimize the work environment within actual care facilities based on the emotions and skills of the staff, enabling more efficient and satisfying service provision.

[0766] "Personal information" refers to information used to identify a specific individual, and includes names, ages, skills, and past achievements.

[0767] "Departmental information" refers to information about groups within an organization that handle different tasks or functions, and includes the roles and members of each department.

[0768] "Position information" refers to information about individuals within an organization who hold specific roles or positions, including job titles, responsibilities, and past performance.

[0769] A "storage device" is a device for permanently or temporarily storing information, and includes hard disks, SSDs, and cloud storage.

[0770] "Quantitative evaluation" is a method of quantitatively evaluating a target element and expressing it as a numerical value.

[0771] A "staffing plan" is a plan for assigning staff and resources to the most optimal locations and roles based on specific conditions.

[0772] "Emotional response" refers to the changes and expressions of emotions that an individual shows in response to presented information or situations, and can be detected from facial expressions, voice, etc.

[0773] A "generative AI model" is an artificial intelligence algorithm trained to generate new information or suggestions based on given data.

[0774] "Work environment" refers to the physical or interpersonal conditions and circumstances necessary for an employee to perform their duties.

[0775] The system for realizing this invention is designed for the collaborative operation of a server, terminal, and user. Details are described below.

[0776] The server efficiently collects personal information, departmental information, and job title information, and stores this information in storage. This allows for quick retrieval of necessary information for analysis. Specifically, the server requires a high-performance storage device, and it is desirable to use SSDs or cloud storage, for example. Furthermore, natural language processing technology is used for data analysis to extract keywords and skills. Google's Cloud Natural Language API is utilized for this purpose.

[0777] The terminal quantifies and evaluates appropriate roles that contribute to individual growth and organizational strengthening, based on information received from the server. The terminal uses an AI model to create placement plans from individual, organizational, and talent management perspectives. This AI model performs machine learning using TensorFlow and Scikit-learn. Furthermore, the terminal uses the Google Cloud Vision API to capture and evaluate users' emotional responses in real time. Based on the emotional data obtained, the terminal uses a generative AI model to revise placement suggestions, helping to optimize the work environment.

[0778] Users review placement suggestions via their devices and send feedback to the server. If a user feels anxious about a new role, for example, that emotional response is immediately fed back to the system. This allows the system to quickly optimize placement suggestions using a generative AI model, making adjustments to ensure the user can comfortably embrace their new role.

[0779] As a concrete example, suppose a caregiver is proposed to be assigned to the night shift. If the caregiver shows signs of stress, the system will detect this emotion and provide a new work shift proposal that takes this into consideration.

[0780] Example of a prompt:

[0781] "Please create a staffing plan that takes into account employee emotional data and skills. Employee A has expressed anxiety regarding night shifts. Please propose an alternative plan."

[0782] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0783] Step 1:

[0784] The server collects personal information, departmental information, and job title information and stores it in storage. It receives individual datasets from each information source as input. This data undergoes data normalization and formatting, and keywords and skills are extracted using natural language processing techniques. The formatted information is then stored in storage as output. Operationally, it analyzes data using a Python script and refines the information using the Google Cloud Natural Language API.

[0785] Step 2:

[0786] The terminal receives information processed by the server and performs a numerical evaluation. It receives formatted personal information and skill data as input. Based on this data, it uses TensorFlow to numerically evaluate an individual's growth potential and contribution. The output is a numerical evaluation of each individual's role. A dedicated AI model runs to calculate the evaluation score.

[0787] Step 3:

[0788] The terminal creates a deployment plan based on the evaluation results. The role evaluation scores generated in step 2 are used as input. An AI model is used to analyze the data and derive optimal deployment proposals from individual, organizational, and human resource management perspectives. The output is a deployment proposal that integrates these perspectives. The system uses Scikit-learn's clustering algorithm to create deployment scenarios.

[0789] Step 4:

[0790] The device presents the generated layout plan to the user and collects the user's emotional response. The input is the presented layout plan itself. The user reviews the layout plan on the device and generates emotional response data via the webcam and microphone. The output is emotional evaluation data. In terms of operation, it uses the Google Cloud Vision API for facial expression analysis and the Natural Language API for voice input analysis.

[0791] Step 5:

[0792] The server modifies the layout plan based on user emotional responses and feedback data. It takes emotional evaluation data and user feedback as input. Using a generative AI model, it processes this data and reconstructs the layout plan as needed. The output is the newly modified layout plan. Its operation involves recalculating based on the collected data to generate the optimal layout suggestion to communicate to the user.

[0793] Step 6:

[0794] The user reviews the new layout plan via their terminal and provides final feedback. The input is the revised layout plan. The user evaluates the final layout plan and sends as detailed feedback as possible to the server. The output is the final record including the feedback. The user is prompted to log in again to review the layout plan and submit feedback.

[0795] 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.

[0796] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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."

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] The following is further disclosed regarding the embodiments described above.

[0817] (Claim 1)

[0818] A means of collecting personal data, departmental data, and job title data and storing them in a database,

[0819] Based on the aforementioned data, a means of scoring appropriate roles that contribute to individual growth and organizational strengthening,

[0820] A means for generating placement scenarios based on individual, organizational, and human resources perspectives from the aforementioned scoring results,

[0821] A means for integrating the aforementioned placement scenarios to present an optimal placement plan,

[0822] A system including means for evaluating the aforementioned layout proposal in real time and collecting feedback.

[0823] (Claim 2)

[0824] The system according to claim 1, wherein in the data collection and preprocessing, data normalization and format standardization are performed, and keywords and skills are extracted using natural language processing technology.

[0825] (Claim 3)

[0826] The system according to claim 1, which continuously updates the machine learning model and improves the accuracy of the model based on the aforementioned evaluation and feedback.

[0827] "Example 1"

[0828] (Claim 1)

[0829] A means for collecting information and storing it in a memory device,

[0830] Based on the aforementioned information, a means to evaluate the appropriate role that contributes to the growth of individuals and the strengthening of groups,

[0831] A means for generating placement procedures based on individual perspectives, group perspectives, and group management perspectives from the aforementioned evaluation results,

[0832] A means for integrating the aforementioned arrangement procedures to present an optimal arrangement plan,

[0833] A means for evaluating the aforementioned layout plan in real time and collecting opinions,

[0834] A system that includes means for continuously monitoring results and recording information after the aforementioned deployment plan has been decided.

[0835] (Claim 2)

[0836] The system according to claim 1, wherein in the collection and preprocessing of the aforementioned information, the information is normalized and its format is standardized, and keywords and skills are extracted using natural language processing technology.

[0837] (Claim 3)

[0838] The system according to claim 1, which continuously updates the machine learning model based on the aforementioned evaluation and opinions to improve the accuracy of the model.

[0839] "Application Example 1"

[0840] (Claim 1)

[0841] A means for collecting personal information, functional department information, and executive information, and recording it in an information storage device,

[0842] Based on the aforementioned information, a means of evaluating appropriate tasks that contribute to individual growth and organizational efficiency,

[0843] Based on the aforementioned evaluation results, a means of creating placement scenarios based on individual, organizational, and human resources perspectives is provided.

[0844] A means for integrating the aforementioned placement scenarios to propose an optimal placement plan,

[0845] A means to verify the aforementioned layout plan in real time and collect opinions,

[0846] Information analysis to optimize the placement of factory machinery, and a means to automatically assign tasks based on the operating efficiency of equipment and the required skill level.

[0847] A system that includes means for visualizing data using information processing means and suggesting efficient arrangement.

[0848] (Claim 2)

[0849] The system according to claim 1, wherein in the collection and preprocessing of the aforementioned information, data formatting is standardized and quantified, and characteristic information and technology are extracted using natural language processing techniques.

[0850] (Claim 3)

[0851] The system according to claim 1, which continuously updates machine learning and improves model accuracy based on the aforementioned verification and feedback.

[0852] "Example 2 of combining an emotion engine"

[0853] (Claim 1)

[0854] Means for acquiring personal data, departmental information, and administrator information and storing them in a storage device,

[0855] Based on the aforementioned information, a means of evaluating appropriate roles that contribute to individual growth and organizational strengthening,

[0856] A means for creating a placement plan based on individual, organizational, and personnel perspectives from the aforementioned evaluation results,

[0857] A means for integrating the aforementioned placement plans to provide an optimal placement proposal,

[0858] A means for determining emotional responses to the aforementioned placement proposal in real time, and for collecting evaluations and opinions,

[0859] A system including means for adjusting the proposal method based on the aforementioned emotional response and re-presenting an improved layout plan.

[0860] (Claim 2)

[0861] The system according to claim 1, wherein in the acquisition and preprocessing of the aforementioned information, the information is standardized and its format is unified, and features and skills are extracted using natural language processing technology.

[0862] (Claim 3)

[0863] The system according to claim 1, which continuously updates the generated AI model based on the aforementioned evaluation and opinions to improve the accuracy of the model.

[0864] "Application example 2 when combining with an emotional engine"

[0865] (Claim 1)

[0866] Means for collecting and storing personal information, departmental information, and executive information on a storage device,

[0867] Based on the aforementioned information, a means of quantifying and evaluating appropriate roles that contribute to individual growth and organizational strengthening,

[0868] A means for creating a placement plan based on individual, organizational, and human resource management perspectives from the aforementioned quantified evaluation results,

[0869] A means for integrating the aforementioned placement plans to propose the optimal placement,

[0870] A means for evaluating the aforementioned placement proposal in real time and collecting emotional responses,

[0871] A means of optimizing the work environment by using an AI model generated from the aforementioned emotional responses to revise placement suggestions,

[0872] A system that includes means for receiving feedback on the aforementioned revised layout proposal and saving it again to a storage device.

[0873] (Claim 2)

[0874] The system according to claim 1, which, in the collection and preprocessing of the aforementioned information, unifies and standardizes the format of the information, extracts keywords and skills using natural language processing technology, and generates appropriate placement proposals based on those skills.

[0875] (Claim 3)

[0876] The system according to claim 1, which continuously trains a machine learning model based on the aforementioned evaluation and emotional response to improve the accuracy of placement suggestions and to improve the system to enable individual adaptation. [Explanation of Symbols]

[0877] 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 for collecting personal information, functional department information, and executive information, and recording it in an information storage device, Based on the aforementioned information, a means of evaluating appropriate tasks that contribute to individual growth and organizational efficiency, Based on the aforementioned evaluation results, a means of creating placement scenarios based on individual, organizational, and human resources perspectives is provided. A means for integrating the aforementioned placement scenarios to propose an optimal placement plan, A means to verify the aforementioned layout plan in real time and collect opinions, Information analysis to optimize the placement of factory machinery, and a means to automatically assign tasks based on the operating efficiency of equipment and the required skill level. A system that includes means for visualizing data using information processing means and suggesting efficient arrangement.

2. The system according to claim 1, wherein in the collection and preprocessing of the aforementioned information, data formatting is standardized and quantified, and characteristic information and technology are extracted using natural language processing techniques.

3. The system according to claim 1, which continuously updates machine learning and improves model accuracy based on the aforementioned verification and opinions.

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