system

Abstract

We provide the system. [Solution] Methods for collecting personnel information from a database, A method for analyzing collected personnel information to evaluate each employee's skill set, A means to generate optimal personnel allocation and training plans based on evaluation results, A means of presenting the generated layout and plan to the user, A means of receiving user feedback and improving the accuracy of the system, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, 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 conventional personnel operations, there is a problem that since it depends on individual judgments, it is prone to bias and it is difficult to make objective and strategic decisions. In particular, it is difficult to perform personnel placement and cultivation in an organization in a data-driven manner, and the optimization of personnel capabilities has not been sufficiently carried out.

Means for Solving the Problems

[0005] This invention provides a system that utilizes human resource information collected from a database and analyzes it using AI. This makes it possible to objectively evaluate each employee's skill set and generate optimal personnel placement and training plans. The generated placements and plans are presented to the user, and the system's accuracy is improved using user feedback. This enables objective and data-driven decision-making and improves the overall human resource capabilities of the organization.

[0006] A "database" is a system for systematically accumulating and managing personnel information, designed to enable efficient information retrieval and updating.

[0007] "Analysis" is the process of identifying patterns and features within collected data and deriving meaning from them.

[0008] A "skill set" refers to a collection of knowledge, skills, and abilities necessary to perform a specific job or role.

[0009] "Evaluation" is the act of analyzing information about an individual or event and judging its value or performance.

[0010] "Personnel placement" is the process of assigning individuals to appropriate positions within an organization based on their skills and aptitudes.

[0011] A "development plan" is a systematic education and training program designed to enhance the necessary skills and knowledge based on individual abilities and career goals.

[0012] A "user" refers to a person or organization that operates the system and utilizes its results.

[0013] "Feedback" refers to the opinions and evaluations that users provide regarding system suggestions and performance.

[0014] "Improving accuracy" refers to improving the system's performance and enhancing its ability to produce more appropriate and accurate results.

Brief Description of the Drawings

[0015] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Modes for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] The system of the present invention integrates the processes of collecting, analyzing, generating suggestions for, and providing feedback on personnel information, thereby realizing data-driven human resource management. Specific embodiments of each element are described below.

[0037] Data collection methods

[0038] The server connects to the company's internal database via the network and periodically retrieves relevant data such as position data, individual skill information, and past work performance.

[0039] The server collects additional training and evaluation data from external learning management systems (LMS) and performance management tools via APIs.

[0040] Forms of data analysis

[0041] The server cleanses the collected data and runs AI-based algorithms to evaluate each employee's skill set as a quantified profile.

[0042] The server generates analytical reports, which are used for skill gap analysis and determining suitability for existing roles.

[0043] Proposal generation methods

[0044] Based on the analysis results, the server formulates appropriate personnel placement combinations within the organization. This includes proposals for placement in new projects and training plans for skill improvement in current positions.

[0045] The server delivers the generated suggestions to the terminal via a dashboard that visualizes them, and presents them to the user.

[0046] Forms of feedback and precision improvement

[0047] Users can provide feedback on the proposed content via their devices, offering information on the effectiveness and areas for improvement of the implementation.

[0048] The server collects feedback information and adjusts the analysis algorithm to improve the accuracy of subsequent evaluations and recommendations. This feedback loop allows for progressive customization tailored to the organization's specific needs.

[0049] As a specific example

[0050] In one company, a transfer of personnel to the data analytics department is necessary, and a server analyzes employee data within the organization to suggest the most suitable candidates. These candidates include specific training plans based on the analysis results (e.g., machine learning training courses). The suggested placement and plan are notified to the user via a terminal, and the user submits feedback while approving or modifying the suggestion. This creates a cycle that improves the quality of future suggestions, resulting in an objective, data-driven HR strategy.

[0051] The following describes the processing flow.

[0052] Step 1:

[0053] The server connects to the company's internal HR database and an external learning management system to collect necessary personnel information. This includes skills information, past work performance, and training history. After collection, the data is formatted into a standardized format.

[0054] Step 2:

[0055] The server processes the acquired data through a data cleansing process to remove duplicates and missing data. Then, an AI model is used to quantitatively evaluate each employee's skill set, which is stored as a profile.

[0056] Step 3:

[0057] The server performs analysis and conducts a skills gap analysis based on each employee's skills and the requirements of their position within the organization. This identifies the areas where skills need to be strengthened.

[0058] Step 4:

[0059] Based on the analysis above, the server generates proposals for optimal personnel placement and development plans within the organization. These proposals include placements in new positions and necessary skills training plans.

[0060] Step 5:

[0061] The server displays the generated suggestions on the terminal in a dashboard format and provides them to the user. The user can review the content of the suggestions in an easy-to-read format.

[0062] Step 6:

[0063] Users make decisions based on the proposals and provide feedback via their devices. This feedback may include evaluations of the proposal's suitability and additional requests.

[0064] Step 7:

[0065] The server receives feedback and makes adjustments to reflect it in the analysis algorithm. Based on the new feedback information, it continues to learn in order to improve the accuracy of the next data analysis and suggestions. In this way, the system continues to evolve.

[0066] (Example 1)

[0067] 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."

[0068] In information technology, there is a need for efficient and accurate personnel allocation and training planning. However, conventional methods have faced challenges in optimizing personnel utilization due to insufficient accuracy in data aggregation and analysis processes. Furthermore, there has been a lack of mechanisms for continuously improving systems by effectively utilizing user feedback.

[0069] 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.

[0070] In this invention, the server includes means for collecting information from information sources, means for cleansing the collected information and inputting it into an analysis model to perform a quantified evaluation, and means for formulating an appropriate deployment and development plan based on the analysis results. This enables accurate data-driven evaluation and the formulation of deployment plans.

[0071] "Information" refers to a collection of data and knowledge used for a specific purpose.

[0072] "Information source" refers to the data provider or database from which specific information is collected.

[0073] "Means of collection" refers to the processes and equipment used to obtain necessary information from specific sources.

[0074] "Cleansing" refers to the process of improving the quality of information by removing duplicates and errors from collected data and supplementing missing data.

[0075] An "analytical model" refers to an algorithm or system used to derive specific conclusions or predictions based on collected information.

[0076] "Quantified evaluation" refers to the results of expressing information numerically in order to quantitatively evaluate it based on an analytical model.

[0077] "Allocation" refers to activities or plans that demonstrate the appropriate allocation of personnel and resources within an organization.

[0078] A "development plan" refers to a specific program or plan designed to improve the abilities and skills of employees.

[0079] This invention utilizes a combination of software and hardware to realize a data-driven human resources management system.

[0080] The server connects to internal databases and external information sources via the network. The hardware used for this requires a server computer with a high-speed processor and sufficient memory. The software used includes a database management system (DBMS) for data collection and communication with external learning management systems (LMS) and performance management tools via APIs.

[0081] The server cleanses the collected data using Python's Pandas library, among others. This cleansing process removes duplicate data and imputes missing data. Next, the data is fed into an AI-based analysis model. This analysis uses machine learning libraries such as Scikit-learn to generate profiles that quantify each employee's skill set.

[0082] Furthermore, the server develops optimal personnel allocation and training plans based on the analysis results. This involves utilizing open-source optimization libraries to solve optimization problems that match the project needs within the organization.

[0083] The visualized analysis results are provided as a web application using frameworks such as Django or Flask, and presented to the user via their device. Through this, users can review the details of the suggestions and submit feedback as needed.

[0084] User feedback is used to improve accuracy in the next analysis cycle. The collected feedback data is analyzed on the server and used to fine-tune the analysis model. Through this feedback loop, the system can be customized to meet the specific needs of the organization.

[0085] For example, if a company is considering transferring personnel to its data analysis department, the server will analyze employee data within the organization and propose suitable candidates and their training plans. For instance, a suggestion might include "taking a basic machine learning course." An example of a prompt to the generating AI model would be, "Generate optimal personnel placement suggestions using internal data. Include appropriate training plans based on past performance and skill sets."

[0086] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0087] Step 1:

[0088] The server collects data from internal databases and external sources. Inputs include the internal employee database and learning history and performance evaluation data obtained via external APIs. This information is retrieved using SQL queries and API requests and stored in temporary storage as CSV or JSON data.

[0089] Step 2:

[0090] The server cleanses the collected data. The input is the data collected in step 1. Specifically, it uses the Python Pandas library to remove duplicate data and impute missing values. This outputs a clean dataset suitable for analysis.

[0091] Step 3:

[0092] The server uses the cleansed data to quantify the skill set. The input is the dataset processed in step 2. Using Scikit-learn, it generates a machine learning model to numerically evaluate each employee's skills. This analysis process outputs a quantified profile for each employee.

[0093] Step 4:

[0094] The server performs a skills gap analysis and assesses job suitability based on a quantified skills profile. The input is the quantified profile obtained in step 3. The script compares the skillset with the job requirements and outputs a report containing the gap analysis results and suitability assessment.

[0095] Step 5:

[0096] The server develops optimal personnel placement and training plans. The input is the analysis report generated in step 4. Using an open-source optimization library, it optimizes personnel placement based on the organizational role needs. This outputs proposed placements and training plans.

[0097] Step 6:

[0098] The server visualizes the formulated proposal and presents it to the user via the terminal. The input is the proposal from step 5. Using a web framework such as Django or Flask, the proposal content is displayed on a dashboard, allowing the user to review it via their terminal.

[0099] Step 7:

[0100] Users review the proposed placement and training plans and provide feedback. This feedback is entered via a user's device. They submit their opinions and requests for revisions to the proposal through a feedback form and send it to the server.

[0101] Step 8:

[0102] The server analyzes user feedback and improves the generated AI model. The input is the feedback collected in step 7. The server analyzes the feedback data and adjusts the parameters of the analysis model to improve accuracy. This creates a feedback loop that improves the quality of future suggestions.

[0103] (Application Example 1)

[0104] 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."

[0105] This invention relates to a system for optimizing human resource allocation across an entire city. While human resource allocation within individual companies and organizations has traditionally been effective, there has been a lack of means to efficiently manage a wide range of technical skill sets and allocate personnel suited to the required roles across public institutions and private companies throughout a city. As a result, situations sometimes arose where there was a shortage or surplus of personnel with specific skills.

[0106] 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.

[0107] In this invention, the server includes means for collecting personnel information from an information storage device, means for analyzing the collected personnel information and evaluating the skill set of each worker, and means for aggregating the skill needs of the entire city and proposing the necessary personnel on a smart device. This enables optimal personnel allocation throughout the entire city.

[0108] "Personnel information" refers to data that includes each worker's skill set, work history, and work performance.

[0109] "Information storage device" refers to a database or cloud storage for storing personnel information.

[0110] "Analysis" is the process of deriving specific results using calculations and algorithms based on collected data.

[0111] A "skill set" is a collection of information about the skills and abilities possessed by a particular worker.

[0112] A "growth plan" is a plan that includes specific measures and training plans to improve workers' job skills.

[0113] "Users" refers to administrators and stakeholders who use the system to receive proposals for personnel allocation and growth plans.

[0114] "Response" refers to the feedback and evaluation information that users provide to the system.

[0115] "City-wide skills needs" refer to the technical skills and job requirements demanded by public institutions and private companies in a specific city or region.

[0116] A "smart device" is a device that has information processing capabilities via the internet, such as a smartphone or tablet.

[0117] The system for implementing this invention combines a data processing device and a smart device. The server collects personnel information from an information storage device and executes a program to analyze that data. For data collection, it utilizes internal databases and cloud systems, and acquires external data via APIs as needed. The server uses the Python programming language and libraries (e.g., requests, scikit-learn) to perform data processing such as normalization, dimensionality reduction, and clustering.

[0118] The analyzed data is delivered to smart devices such as smartphones and tablets, and users are presented with optimal personnel allocation suggestions based on the city's overall skill needs. At this stage, a generated AI model is used to improve the accuracy of the suggestions. Users can review, modify, and approve the suggestions on the user interface. User responses are sent to the server as a feedback loop and are used to improve the accuracy of the analysis algorithm.

[0119] As a concrete example, in a major city, the system can detect a shortage of public transportation engineers and, through analysis, propose and deploy appropriate engineers from other relevant companies, thereby quickly resolving the problem. The generative AI model supporting this process uses instruction statements such as, "Analyze the latest personnel data and propose the optimal personnel deployment based on the skills needed in the city. Please also consider feedback in your proposal."

[0120] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0121] Step 1:

[0122] The server first collects personnel information from the information storage device. It periodically retrieves necessary skills information, work history, and work results from databases and external APIs, and uses this information to prepare for the next analysis phase. At this stage, the input is raw data from the information storage device, and the output is data formatted for analysis.

[0123] Step 2:

[0124] The server performs data cleansing based on the collected data. Using libraries such as Python's pandas library, invalid data and missing values ​​are removed, and only the necessary data is extracted. Here, the formatted data is further normalized to a format suitable for analysis. The input to this process is the formatted raw data, and the output is validated data from which invalid data and duplicates have been removed.

[0125] Step 3:

[0126] The server feeds normalized data into an AI algorithm for analysis. Using scikit-learn, dimensionality reduction is performed using principal component analysis (PCA), and then the data is grouped using a clustering method (e.g., KMeans). The input here is validated data, and the output is cluster data showing how each worker's skills are classified.

[0127] Step 4:

[0128] The server utilizes a generated AI model based on clustering results to produce proposals for talent allocation across the entire city. These proposals include allocation strategies tailored to specific skill needs. The input is cluster data, and the output is a proposal document detailing the optimal allocation and the reasons for it.

[0129] Step 5:

[0130] The terminal receives a proposal document from the server and displays it on the smart device's screen. The user can review this proposal and make revisions or approvals as needed. The input here is the proposal document, and the output is the user's response data.

[0131] Step 6:

[0132] The user's response is sent to the server and used for analysis as part of a feedback loop. The server readjusts the AI ​​algorithm based on this response to improve the accuracy of future suggestions. The input to this process is the user response data, and the output is the algorithm adjusted to reflect the feedback.

[0133] 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.

[0134] The system of the present invention combines an emotion engine with the management and suggestion of personnel information to provide flexible personnel placement and training plans that reflect the user's emotional state. Specific embodiments for carrying out the present invention are described below.

[0135] Data collection methods

[0136] The server connects to the company's talent database via the network and regularly collects necessary information, including employee skills, work performance, and training history.

[0137] The server comprehensively acquires personnel information, utilizing additional data from external systems as well.

[0138] Forms of collecting and analyzing emotional data

[0139] The device activates an emotion engine when the user provides feedback, detecting the emotional state from facial expressions, voice, and text data. This data indicates the user's satisfaction level and their feelings towards the suggestions.

[0140] The server analyzes emotional data obtained from the emotion engine and integrates it with conventional feedback data to make an overall decision.

[0141] Proposal generation methods

[0142] The server optimizes talent allocation and training plans within the organization based on employee skill information and emotional feedback from users. In this process, it dynamically adjusts suggestions based on emotional data, enabling suggestions that enhance user satisfaction.

[0143] The server generates visualized suggestions on the dashboard and presents them to the user via the terminal.

[0144] Presentation to the user and the form of the feedback loop

[0145] Users review proposed personnel placement and training plans and send emotion-based feedback via their devices.

[0146] The server evaluates the feedback, incorporates it into the system, and fine-tunes the AI ​​algorithm along with the new sentiment data.

[0147] As a specific example

[0148] When a company needs to assemble a new project team, the server analyzes the skills of employees within the organization and creates an initial personnel placement plan. Users can receive this proposal and provide feedback through facial expressions and voice via the emotion engine. For example, if a user shows positive emotion towards a proposal, the server reflects this in its analysis, identifies which parts of the proposal were good, and further improves the accuracy of future proposals. In this way, a feedback and proposal cycle utilizing emotional data is realized, contributing to improved HR strategies and employee satisfaction across the entire organization.

[0149] The following describes the processing flow.

[0150] Step 1:

[0151] The server collects data such as employee skills, work performance, and training history from the company's internal talent database and external systems. The collected data is formatted in a standardized format, and a detailed profile is created for each employee.

[0152] Step 2:

[0153] The device activates an emotion engine when the user provides feedback, and uses a facial recognition camera and voice recognition microphone to analyze the user's emotions in real time. As a result, it quantifies the user's response to the suggestion and records whether their emotional state is positive or negative.

[0154] Step 3:

[0155] The server integrates emotional data with employee skill assessment data and runs an analysis algorithm. This integrated dataset is used to create personnel placement and training plans, with dynamic adjustments made to take user emotions into account.

[0156] Step 4:

[0157] The server visualizes the generated personnel placement and training plans on a graphical dashboard and presents them to the user via their terminal. Users can then systematically and visually review the information.

[0158] Step 5:

[0159] Users provide feedback on the proposed placement and training plans. During this process, they send specific opinions and evaluations, along with emotional responses captured by the emotion engine, to the server via their device.

[0160] Step 6:

[0161] The server analyzes and compares user feedback and sentiment data to adjust algorithms and improve the accuracy of future recommendations. This results in more appropriate planning that reflects the user's emotional needs.

[0162] (Example 2)

[0163] 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".

[0164] In modern organizations, optimizing personnel placement and development plans that take into account employees' skills and emotional states is essential. However, existing systems often make decisions based solely on skill information, making it difficult to implement flexible personnel placement that reflects employees' emotional states. This makes it difficult to improve employee satisfaction and optimize overall organizational efficiency.

[0165] 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.

[0166] In this invention, the server includes means for collecting personnel information within an organization from an information aggregation device, means for analyzing the collected information to evaluate the skill composition and emotional data of each person, and means for generating an optimal personnel allocation and training plan based on the evaluation results and emotional data, and visualizing the generated plan and presenting it to the user. This makes it possible to provide flexible personnel allocation and training plans that take into account the emotional state of employees.

[0167] An "information aggregation device" is a device used to collect and integrate necessary information from various databases and systems both inside and outside an organization.

[0168] "Skill composition" refers to a systematic combination of skills and knowledge necessary for an individual or group to perform a specific task or activity.

[0169] A "development plan" is a set of guidelines that includes specific goals, methods, and progress management to promote employee skill development and career growth.

[0170] "Emotional data" refers to information that indicates a user's emotional state, and includes data obtained from facial expressions, voice, text, etc.

[0171] "Visualization" is a technique for representing information and data in visual forms such as graphs, charts, and diagrams to facilitate understanding.

[0172] "Readjustment" is the process of improving existing models and systems based on new information and feedback to enhance their performance and adaptability.

[0173] This invention aims to construct a system for managing and optimizing human resource information within an organization. Specific embodiments are described below.

[0174] The server connects to an information aggregation device via the network and periodically collects personnel information. This information covers a wide range of topics, including work performance, skill history, and training records. External data can also be obtained from external certification bodies and industry information providers.

[0175] The device activates an emotion engine upon receiving user feedback, using the camera and microphone to acquire facial and audio data. This emotion data is analyzed by image analysis software (e.g., OpenCV), speech recognition tools (e.g., Google® Speech-to-Text), and natural language processing tools (e.g., NLTK). This allows the user's emotional state to be detected and monitored in real time.

[0176] Based on the collected skill configurations and emotional data, the server generates optimal staffing and training plans using a generative AI model (e.g., TENSORFLOW®). The generated suggestions are visually represented using a visualization tool (e.g., Tableau) and presented to the user via a terminal.

[0177] For example, in selecting a leader for a new project, the server analyzes candidates' technical skills, project experience, and recent performance to suggest the most suitable person. Users can review this suggestion on their device and express positive emotions as feedback. The server then re-evaluates the feedback and incorporates it into future suggestions to improve their accuracy.

[0178] A concrete example of a prompt message would be: "When using this system, detect how employees feel about the suggestions and provide an effective training plan based on that."

[0179] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0180] Step 1:

[0181] The server takes data from the information aggregation device as input and retrieves personnel information. Specifically, it uses an API to collect employee skills, work performance, and training history. The collected data is output to temporary storage in flat file format. The data processing performed in this step is to organize and ensure consistency of the data.

[0182] Step 2:

[0183] The server retrieves additional information from external data providers. This includes collecting certification information and industry trend data via external APIs. Input is data from external systems, which is then integrated with existing internal data for output. Data processing involves normalizing and integrating data from different sources.

[0184] Step 3:

[0185] The device activates its emotion engine upon receiving input from the user. For example, a user might fill out a survey or feedback form, which is then captured as emotion data via the camera and microphone. The input includes facial expressions, voice, and text information, which are output as emotional states. This process involves detecting emotions using image analysis and speech recognition.

[0186] Step 4:

[0187] The server analyzes the collected skill set and emotional data. This analysis utilizes a generative AI model, performing optimization processes including skill matching and emotional feedback. The input is the data obtained in steps 1 and 3, and the output is a proposed personnel placement and training plan. Data calculations include model-based prediction and optimization.

[0188] Step 5:

[0189] The server visualizes the generated proposals and presents them to the user via the terminal. Specifically, it uses visualization tools to convert the data into graphs and charts and outputs them to the user interface. The input is the proposal data generated in step 4, and the output is the visualized plan presented to the user.

[0190] Step 6:

[0191] Users review the suggestions presented through their terminals and provide feedback. This feedback process involves users expressing their feelings based on their emotions and inputting them into the terminal. The input is captured as user feedback, received and analyzed by the server, and stored as output to improve the accuracy of future suggestions. Data processing includes evaluating the feedback data and incorporating it into the system.

[0192] (Application Example 2)

[0193] 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".

[0194] In modern workplaces, it is common practice to formulate placement and training plans without considering employees' emotional states. This can lead to problems in the work environment, such as decreased efficiency and lower employee motivation. In particular, in field-based jobs like security, personnel placement that reflects real-time emotional states is essential.

[0195] 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.

[0196] In this invention, the server includes means for collecting information from a device that stores information for management, means for evaluating individual characteristics based on emotional data analyzed using an emotion analysis device, means for receiving feedback from users and improving the accuracy of the system, and means for acquiring emotional data using a display device worn by on-site personnel. This makes it possible to propose optimal placement and training plans based on the emotional state of employees.

[0197] A "device for storing information for management purposes" is an electronic device used to safely and efficiently store and manage various types of data held by an organization.

[0198] An "emotion analysis device" is a device equipped with the function of identifying and evaluating a person's emotional state through data such as facial expressions, voice, and text.

[0199] "Means of evaluating individual characteristics" refers to the process of analyzing each individual's skills and emotional state and making evaluations based on that analysis.

[0200] "Means of receiving feedback from users and improving the accuracy of the system" refers to methods of collecting feedback from users and using that data to improve the accuracy of the system and the quality of the proposed solutions.

[0201] A "display device worn by on-site personnel" is a device that workers carry or wear and use to display information in real time.

[0202] "Means of acquiring emotional data" refers to the process of collecting information related to an individual's emotions in real time using sensors and analytical algorithms.

[0203] To realize this invention, the server is operated using a combination of multiple hardware and software components. First, the server periodically collects personnel data from a device that stores the information to be managed. This data includes employees' skills, performance, and training history. Next, the emotion analysis device evaluates individual characteristics based on data acquired from a display device worn by the field staff. During this process, real-time emotion analysis is performed, and emotional data is analyzed using sensors and analysis algorithms (e.g., facial recognition software and voice emotion analysis tools).

[0204] Subsequently, the server uses these evaluation results to generate an optimal placement and training plan. The generated placement is visually displayed on the management screen, which users can verify through the display device. Furthermore, the system receives feedback from users, and the accuracy of the system is improved based on the feedback data.

[0205] As a concrete example, in a security monitoring center, when employee A wears smart glasses while working, the server analyzes their emotional state in real time. This information is displayed on the administrator's dashboard, suggesting the placement with the lowest stress level and highest performance. This is expected to improve monitoring efficiency.

[0206] An example of a prompt would be, "Using current employee sentiment data, suggest the optimal staffing and tasks to maximize their performance." In this way, sophisticated suggestions can be made using generative AI models.

[0207] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0208] Step 1:

[0209] The server collects personnel data from devices that store information for management purposes. Inputs include employee skills, work performance, and training history, while output is structured data stored in a database. This data forms the basis for subsequent analysis and is updated in real time via the network.

[0210] Step 2:

[0211] The terminal transmits emotional data acquired through a display device worn by the field staff to the server. The input is emotional data from the staff member's facial expressions and voice, and the output is information converted from that data into an analyzable format. Sensor devices and real-time data streaming technology are utilized here.

[0212] Step 3:

[0213] The server uses an emotion analysis device to analyze emotional data obtained from terminals and evaluate individual characteristics. The input consists of emotional data and personnel data, and the output is an evaluation result reflecting each individual's emotional state and characteristics. A generative AI model estimates the emotional state, and individual evaluations are formed based on this.

[0214] Step 4:

[0215] The server generates optimal personnel placement and development plans based on evaluation results. The inputs are employee evaluation results and organizational placement conditions, and the output is a draft of an efficient placement and development plan. The generated draft is then refined by an algorithm to optimize it for the organization's objectives.

[0216] Step 5:

[0217] The server visually displays the generated deployment and growth plans on the management screen. The inputs are the deployment and plan proposals, and the output is the visualized information presented to the user. Dashboard software is utilized, enabling users to make decisions with a clear understanding.

[0218] Step 6:

[0219] Users provide feedback to the system via their terminals regarding the information presented. The input is the user's feedback, and the output is the data stored in the system as feedback. User opinions influence the accuracy of future suggestions and serve as the basis for improvement.

[0220] Step 7:

[0221] The server readjusts the AI ​​model to improve the system's accuracy based on the collected feedback data. The input is the feedback data and past analysis results, and the output is the improved proposed algorithm. This enables more accurate and appropriate personnel allocation.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] [Second Embodiment]

[0226] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0227] 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.

[0228] 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).

[0229] 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.

[0230] 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.

[0231] 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).

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] 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".

[0238] The system of the present invention integrates the processes of collecting, analyzing, generating suggestions for, and providing feedback on personnel information, thereby realizing data-driven human resource management. Specific embodiments of each element are described below.

[0239] Data collection methods

[0240] The server connects to the company's internal database via the network and periodically retrieves relevant data such as position data, individual skill information, and past work performance.

[0241] The server collects additional training and evaluation data from external learning management systems (LMS) and performance management tools via APIs.

[0242] Forms of data analysis

[0243] The server cleanses the collected data and runs AI-based algorithms to evaluate each employee's skill set as a quantified profile.

[0244] The server generates analytical reports, which are used for skill gap analysis and determining suitability for existing roles.

[0245] Proposal generation methods

[0246] Based on the analysis results, the server formulates appropriate personnel placement combinations within the organization. This includes proposals for placement in new projects and training plans for skill improvement in current positions.

[0247] The server delivers the generated suggestions to the terminal via a dashboard that visualizes them, and presents them to the user.

[0248] Forms of feedback and precision improvement

[0249] Users can provide feedback on the proposed content via their devices, offering information on the effectiveness and areas for improvement of the implementation.

[0250] The server collects feedback information and adjusts the analysis algorithm to improve the accuracy of subsequent evaluations and recommendations. This feedback loop allows for progressive customization tailored to the organization's specific needs.

[0251] As a specific example

[0252] In one company, a transfer of personnel to the data analytics department is necessary, and a server analyzes employee data within the organization to suggest the most suitable candidates. These candidates include specific training plans based on the analysis results (e.g., machine learning training courses). The suggested placement and plan are notified to the user via a terminal, and the user submits feedback while approving or modifying the suggestion. This creates a cycle that improves the quality of future suggestions, resulting in an objective, data-driven HR strategy.

[0253] The following describes the processing flow.

[0254] Step 1:

[0255] The server connects to the company's internal HR database and an external learning management system to collect necessary personnel information. This includes skills information, past work performance, and training history. After collection, the data is formatted into a standardized format.

[0256] Step 2:

[0257] The server processes the acquired data through a data cleansing process to remove duplicates and missing data. Then, an AI model is used to quantitatively evaluate each employee's skill set, which is stored as a profile.

[0258] Step 3:

[0259] The server performs analysis and conducts a skills gap analysis based on each employee's skills and the requirements of their position within the organization. This identifies the areas where skills need to be strengthened.

[0260] Step 4:

[0261] Based on the analysis above, the server generates proposals for optimal personnel placement and development plans within the organization. These proposals include placements in new positions and necessary skills training plans.

[0262] Step 5:

[0263] The server displays the generated suggestions on the terminal in a dashboard format and provides them to the user. The user can review the content of the suggestions in an easy-to-read format.

[0264] Step 6:

[0265] Users make decisions based on the proposals and provide feedback via their devices. This feedback may include evaluations of the proposal's suitability and additional requests.

[0266] Step 7:

[0267] The server receives feedback and makes adjustments to reflect it in the analysis algorithm. Based on the new feedback information, it continues to learn in order to improve the accuracy of the next data analysis and suggestions. In this way, the system continues to evolve.

[0268] (Example 1)

[0269] 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."

[0270] In information technology, there is a need for efficient and accurate personnel allocation and training planning. However, conventional methods have faced challenges in optimizing personnel utilization due to insufficient accuracy in data aggregation and analysis processes. Furthermore, there has been a lack of mechanisms for continuously improving systems by effectively utilizing user feedback.

[0271] 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.

[0272] In this invention, the server includes means for collecting information from information sources, means for cleansing the collected information and inputting it into an analysis model to perform a quantified evaluation, and means for formulating an appropriate deployment and development plan based on the analysis results. This enables accurate data-driven evaluation and the formulation of deployment plans.

[0273] "Information" refers to a collection of data and knowledge used for a specific purpose.

[0274] "Information source" refers to the data provider or database from which specific information is collected.

[0275] "Means of collection" refers to the processes and equipment used to obtain necessary information from specific sources.

[0276] "Cleansing" refers to the process of improving the quality of information by removing duplicates and errors from collected data and supplementing missing data.

[0277] An "analytical model" refers to an algorithm or system used to derive specific conclusions or predictions based on collected information.

[0278] "Quantified evaluation" refers to the results of expressing information numerically in order to quantitatively evaluate it based on an analytical model.

[0279] "Allocation" refers to activities or plans that demonstrate the appropriate allocation of personnel and resources within an organization.

[0280] A "development plan" refers to a specific program or plan designed to improve the abilities and skills of employees.

[0281] This invention utilizes a combination of software and hardware to realize a data-driven human resources management system.

[0282] The server connects to the in-house database and external information sources via the network. For the hardware used in this process, a server computer equipped with a high-speed processor and sufficient memory is required. As the software to be used, data collection is performed using a database management system (DBMS), and communication is carried out with external learning management systems (LMS) and performance management tools via APIs.

[0283] The server cleans the collected data via libraries such as the Pandas library in Python. In the cleaning process, duplicate data is removed and missing data is supplemented. Next, the data is input into an AI-based analysis model. For this analysis, machine learning libraries such as Scikit-learn are used to generate a profile that quantifies the skill set of each employee.

[0284] Furthermore, the server formulates an optimal personnel allocation and training plan based on the analysis results. For this, open-source optimization libraries are utilized to solve optimization problems that match the project needs within the organization.

[0285] The visualized analysis results are provided as a web application using frameworks such as Django and Flask and presented to users via terminals. Through this, users can check the details of the proposals and send feedback if necessary.

[0286] The feedback from users is utilized to improve the accuracy in the next analysis cycle. The collected feedback data is analyzed within the server and used to fine-tune the analysis model. Through this feedback loop, the system can be customized according to the specific needs of the organization.

[0287] For example, if a company is considering transferring personnel to its data analysis department, the server will analyze employee data within the organization and propose suitable candidates and their training plans. For instance, a suggestion might include "taking a basic machine learning course." An example of a prompt to the generating AI model would be, "Generate optimal personnel placement suggestions using internal data. Include appropriate training plans based on past performance and skill sets."

[0288] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0289] Step 1:

[0290] The server collects data from internal databases and external sources. Inputs include the internal employee database and learning history and performance evaluation data obtained via external APIs. This information is retrieved using SQL queries and API requests and stored in temporary storage as CSV or JSON data.

[0291] Step 2:

[0292] The server cleanses the collected data. The input is the data collected in step 1. Specifically, it uses the Python Pandas library to remove duplicate data and impute missing values. This outputs a clean dataset suitable for analysis.

[0293] Step 3:

[0294] The server uses the cleansed data to quantify the skill set. The input is the dataset processed in step 2. Using Scikit-learn, it generates a machine learning model to numerically evaluate each employee's skills. This analysis process outputs a quantified profile for each employee.

[0295] Step 4:

[0296] The server performs a skills gap analysis and assesses job suitability based on a quantified skills profile. The input is the quantified profile obtained in step 3. The script compares the skillset with the job requirements and outputs a report containing the gap analysis results and suitability assessment.

[0297] Step 5:

[0298] The server develops optimal personnel placement and training plans. The input is the analysis report generated in step 4. Using an open-source optimization library, it optimizes personnel placement based on the organizational role needs. This outputs proposed placements and training plans.

[0299] Step 6:

[0300] The server visualizes the formulated proposal and presents it to the user via the terminal. The input is the proposal from step 5. Using a web framework such as Django or Flask, the proposal content is displayed on a dashboard, allowing the user to review it via their terminal.

[0301] Step 7:

[0302] Users review the proposed placement and training plans and provide feedback. This feedback is entered via a user's device. They submit their opinions and requests for revisions to the proposal through a feedback form and send it to the server.

[0303] Step 8:

[0304] The server analyzes user feedback and improves the generated AI model. The input is the feedback collected in step 7. The server analyzes the feedback data and adjusts the parameters of the analysis model to improve accuracy. This creates a feedback loop that improves the quality of future suggestions.

[0305] (Application Example 1)

[0306] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0307] The present invention relates to a system for optimizing the allocation of human resources across an entire city. Conventionally, the allocation of human resources within individual companies or organizations has been effectively carried out. However, in public institutions and private companies across the entire city, there has been a lack of means to efficiently manage a wide range of technical skill sets and allocate human resources suitable for the required roles. As a result, there have been situations where there is a shortage or an overabundance of human resources with specific skills.

[0308] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0309] In this invention, the server includes means for collecting human resource information from an information storage device, means for analyzing the collected human resource information to evaluate the skill sets of each worker, and means for aggregating the skill needs of the entire city and proposing the necessary human resources on a smart device. As a result, optimal human resource allocation across the entire city becomes possible.

[0310] "Human resource information" is data including the skill sets, work histories, business achievements, etc. related to each worker.

[0311] "Information storage device" refers to a database or cloud storage for storing human resource information.

[0312] "Analysis" is a process of deriving specific results using calculations and algorithms based on the collected data.

[0313] "Skill set" is a collection of information regarding the technologies and abilities possessed by a specific worker.

[0314] A "growth plan" is a plan that includes specific measures and training plans to improve workers' job skills.

[0315] "Users" refers to administrators and stakeholders who use the system to receive proposals for personnel allocation and growth plans.

[0316] "Response" refers to the feedback and evaluation information that users provide to the system.

[0317] "City-wide skills needs" refer to the technical skills and job requirements demanded by public institutions and private companies in a specific city or region.

[0318] A "smart device" is a device that has information processing capabilities via the internet, such as a smartphone or tablet.

[0319] The system for implementing this invention combines a data processing device and a smart device. The server collects personnel information from an information storage device and executes a program to analyze that data. For data collection, it utilizes internal databases and cloud systems, and acquires external data via APIs as needed. The server uses the Python programming language and libraries (e.g., requests, scikit-learn) to perform data processing such as normalization, dimensionality reduction, and clustering.

[0320] The analyzed data is delivered to smart devices such as smartphones and tablets, and users are presented with optimal personnel allocation suggestions based on the city's overall skill needs. At this stage, a generated AI model is used to improve the accuracy of the suggestions. Users can review, modify, and approve the suggestions on the user interface. User responses are sent to the server as a feedback loop and are used to improve the accuracy of the analysis algorithm.

[0321] As a concrete example, in a major city, the system can detect a shortage of public transportation engineers and, through analysis, propose and deploy appropriate engineers from other relevant companies, thereby quickly resolving the problem. The generative AI model supporting this process uses instruction statements such as, "Analyze the latest personnel data and propose the optimal personnel deployment based on the skills needed in the city. Please also consider feedback in your proposal."

[0322] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0323] Step 1:

[0324] The server first collects personnel information from the information storage device. It periodically retrieves necessary skills information, work history, and work results from databases and external APIs, and uses this information to prepare for the next analysis phase. At this stage, the input is raw data from the information storage device, and the output is data formatted for analysis.

[0325] Step 2:

[0326] The server performs data cleansing based on the collected data. Using libraries such as Python's pandas library, invalid data and missing values ​​are removed, and only the necessary data is extracted. Here, the formatted data is further normalized to a format suitable for analysis. The input to this process is the formatted raw data, and the output is validated data from which invalid data and duplicates have been removed.

[0327] Step 3:

[0328] The server feeds normalized data into an AI algorithm for analysis. Using scikit-learn, dimensionality reduction is performed using principal component analysis (PCA), and then the data is grouped using a clustering method (e.g., KMeans). The input here is validated data, and the output is cluster data showing how each worker's skills are classified.

[0329] Step 4:

[0330] The server utilizes a generated AI model based on clustering results to produce proposals for talent allocation across the entire city. These proposals include allocation strategies tailored to specific skill needs. The input is cluster data, and the output is a proposal document detailing the optimal allocation and the reasons for it.

[0331] Step 5:

[0332] The terminal receives a proposal document from the server and displays it on the smart device's screen. The user can review this proposal and make revisions or approvals as needed. The input here is the proposal document, and the output is the user's response data.

[0333] Step 6:

[0334] The user's response is sent to the server and used for analysis as part of a feedback loop. The server readjusts the AI ​​algorithm based on this response to improve the accuracy of future suggestions. The input to this process is the user response data, and the output is the algorithm adjusted to reflect the feedback.

[0335] 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.

[0336] The system of the present invention combines an emotion engine with the management and suggestion of personnel information to provide flexible personnel placement and training plans that reflect the user's emotional state. Specific embodiments for carrying out the present invention are described below.

[0337] Data collection methods

[0338] The server connects to the company's talent database via the network and regularly collects necessary information, including employee skills, work performance, and training history.

[0339] The server comprehensively acquires personnel information, utilizing additional data from external systems as well.

[0340] Forms of collecting and analyzing emotional data

[0341] The device activates an emotion engine when the user provides feedback, detecting the emotional state from facial expressions, voice, and text data. This data indicates the user's satisfaction level and their feelings towards the suggestions.

[0342] The server analyzes emotional data obtained from the emotion engine and integrates it with conventional feedback data to make an overall decision.

[0343] Proposal generation methods

[0344] The server optimizes talent allocation and training plans within the organization based on employee skill information and emotional feedback from users. In this process, it dynamically adjusts suggestions based on emotional data, enabling suggestions that enhance user satisfaction.

[0345] The server generates visualized suggestions on the dashboard and presents them to the user via the terminal.

[0346] Presentation to the user and the form of the feedback loop

[0347] Users review proposed personnel placement and training plans and send emotion-based feedback via their devices.

[0348] The server evaluates the feedback, incorporates it into the system, and fine-tunes the AI ​​algorithm along with the new sentiment data.

[0349] As a specific example

[0350] When a company needs to assemble a new project team, the server analyzes the skills of employees within the organization and creates an initial personnel placement plan. Users can receive this proposal and provide feedback through facial expressions and voice via the emotion engine. For example, if a user shows positive emotion towards a proposal, the server reflects this in its analysis, identifies which parts of the proposal were good, and further improves the accuracy of future proposals. In this way, a feedback and proposal cycle utilizing emotional data is realized, contributing to improved HR strategies and employee satisfaction across the entire organization.

[0351] The following describes the processing flow.

[0352] Step 1:

[0353] The server collects data such as employee skills, work performance, and training history from the company's internal talent database and external systems. The collected data is formatted in a standardized format, and a detailed profile is created for each employee.

[0354] Step 2:

[0355] The device activates an emotion engine when the user provides feedback, and uses a facial recognition camera and voice recognition microphone to analyze the user's emotions in real time. As a result, it quantifies the user's response to the suggestion and records whether their emotional state is positive or negative.

[0356] Step 3:

[0357] The server integrates emotional data with employee skill assessment data and runs an analysis algorithm. This integrated dataset is used to create personnel placement and training plans, with dynamic adjustments made to take user emotions into account.

[0358] Step 4:

[0359] The server visualizes the generated personnel placement and training plans on a graphical dashboard and presents them to the user via their terminal. Users can then systematically and visually review the information.

[0360] Step 5:

[0361] Users provide feedback on the proposed placement and training plans. During this process, they send specific opinions and evaluations, along with emotional responses captured by the emotion engine, to the server via their device.

[0362] Step 6:

[0363] The server analyzes and compares user feedback and sentiment data to adjust algorithms and improve the accuracy of future recommendations. This results in more appropriate planning that reflects the user's emotional needs.

[0364] (Example 2)

[0365] 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".

[0366] In modern organizations, optimizing personnel placement and development plans that take into account employees' skills and emotional states is essential. However, existing systems often make decisions based solely on skill information, making it difficult to implement flexible personnel placement that reflects employees' emotional states. This makes it difficult to improve employee satisfaction and optimize overall organizational efficiency.

[0367] 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.

[0368] In this invention, the server includes means for collecting personnel information within an organization from an information aggregation device, means for analyzing the collected information to evaluate the skill composition and emotional data of each person, and means for generating an optimal personnel allocation and training plan based on the evaluation results and emotional data, and visualizing the generated plan and presenting it to the user. This makes it possible to provide flexible personnel allocation and training plans that take into account the emotional state of employees.

[0369] An "information aggregation device" is a device used to collect and integrate necessary information from various databases and systems both inside and outside an organization.

[0370] "Skill composition" refers to a systematic combination of skills and knowledge necessary for an individual or group to perform a specific task or activity.

[0371] A "development plan" is a set of guidelines that includes specific goals, methods, and progress management to promote employee skill development and career growth.

[0372] "Emotional data" refers to information that indicates a user's emotional state, and includes data obtained from facial expressions, voice, text, etc.

[0373] "Visualization" is a technique for representing information and data in visual forms such as graphs, charts, and diagrams to facilitate understanding.

[0374] "Readjustment" is the process of improving existing models and systems based on new information and feedback to enhance their performance and adaptability.

[0375] This invention aims to construct a system for managing and optimizing human resource information within an organization. Specific embodiments are described below.

[0376] The server connects to an information aggregation device via the network and periodically collects personnel information. This information covers a wide range of topics, including work performance, skill history, and training records. External data can also be obtained from external certification bodies and industry information providers.

[0377] The device activates an emotion engine upon receiving user feedback, using the camera and microphone to acquire facial and audio data. This emotion data is analyzed by image analysis software (e.g., OpenCV), speech recognition tools (e.g., Google Speech-to-Text), and natural language processing tools (e.g., NLTK). This allows the user's emotional state to be detected and monitored in real time.

[0378] The server generates optimal staffing and training plans using a generative AI model (e.g., TensorFlow) based on collected skill configurations and emotional data. The generated suggestions are visually represented using a visualization tool (e.g., Tableau) and presented to the user via a terminal.

[0379] For example, in selecting a leader for a new project, the server analyzes candidates' technical skills, project experience, and recent performance to suggest the most suitable person. Users can review this suggestion on their device and express positive emotions as feedback. The server then re-evaluates the feedback and incorporates it into future suggestions to improve their accuracy.

[0380] A concrete example of a prompt message would be: "When using this system, detect how employees feel about the suggestions and provide an effective training plan based on that."

[0381] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0382] Step 1:

[0383] The server takes data from the information aggregation device as input and retrieves personnel information. Specifically, it uses an API to collect employee skills, work performance, and training history. The collected data is output to temporary storage in flat file format. The data processing performed in this step is to organize and ensure consistency of the data.

[0384] Step 2:

[0385] The server retrieves additional information from external data providers. This includes collecting certification information and industry trend data via external APIs. Input is data from external systems, which is then integrated with existing internal data for output. Data processing involves normalizing and integrating data from different sources.

[0386] Step 3:

[0387] The device activates its emotion engine upon receiving input from the user. For example, a user might fill out a survey or feedback form, which is then captured as emotion data via the camera and microphone. The input includes facial expressions, voice, and text information, which are output as emotional states. This process involves detecting emotions using image analysis and speech recognition.

[0388] Step 4:

[0389] The server analyzes the collected skill set and emotional data. This analysis utilizes a generative AI model, performing optimization processes including skill matching and emotional feedback. The input is the data obtained in steps 1 and 3, and the output is a proposed personnel placement and training plan. Data calculations include model-based prediction and optimization.

[0390] Step 5:

[0391] The server visualizes the generated proposals and presents them to the user via the terminal. Specifically, it uses visualization tools to convert the data into graphs and charts and outputs them to the user interface. The input is the proposal data generated in step 4, and the output is the visualized plan presented to the user.

[0392] Step 6:

[0393] Users review the suggestions presented through their terminals and provide feedback. This feedback process involves users expressing their feelings based on their emotions and inputting them into the terminal. The input is captured as user feedback, received and analyzed by the server, and stored as output to improve the accuracy of future suggestions. Data processing includes evaluating the feedback data and incorporating it into the system.

[0394] (Application Example 2)

[0395] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0396] In modern workplaces, it is common practice to formulate placement and training plans without considering employees' emotional states. This can lead to problems in the work environment, such as decreased efficiency and lower employee motivation. In particular, in field-based jobs like security, personnel placement that reflects real-time emotional states is essential.

[0397] 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.

[0398] In this invention, the server includes means for collecting information from a device that stores information for management, means for evaluating individual characteristics based on emotional data analyzed using an emotion analysis device, means for receiving feedback from users and improving the accuracy of the system, and means for acquiring emotional data using a display device worn by on-site personnel. This makes it possible to propose optimal placement and training plans based on the emotional state of employees.

[0399] A "device for storing information for management purposes" is an electronic device used to safely and efficiently store and manage various types of data held by an organization.

[0400] An "emotion analysis device" is a device equipped with the function of identifying and evaluating a person's emotional state through data such as facial expressions, voice, and text.

[0401] "Means of evaluating individual characteristics" refers to the process of analyzing each individual's skills and emotional state and making evaluations based on that analysis.

[0402] "Means of receiving feedback from users and improving the accuracy of the system" refers to methods of collecting feedback from users and using that data to improve the accuracy of the system and the quality of the proposed solutions.

[0403] A "display device worn by on-site personnel" is a device that workers carry or wear and use to display information in real time.

[0404] "Means of acquiring emotional data" refers to the process of collecting information related to an individual's emotions in real time using sensors and analytical algorithms.

[0405] To realize this invention, the server is operated using a combination of multiple hardware and software components. First, the server periodically collects personnel data from a device that stores the information to be managed. This data includes employees' skills, performance, and training history. Next, the emotion analysis device evaluates individual characteristics based on data acquired from a display device worn by the field staff. During this process, real-time emotion analysis is performed, and emotional data is analyzed using sensors and analysis algorithms (e.g., facial recognition software and voice emotion analysis tools).

[0406] Subsequently, the server uses these evaluation results to generate an optimal placement and training plan. The generated placement is visually displayed on the management screen, which users can verify through the display device. Furthermore, the system receives feedback from users, and the accuracy of the system is improved based on the feedback data.

[0407] As a concrete example, in a security monitoring center, when employee A wears smart glasses while working, the server analyzes their emotional state in real time. This information is displayed on the administrator's dashboard, suggesting the placement with the lowest stress level and highest performance. This is expected to improve monitoring efficiency.

[0408] An example of a prompt would be, "Using current employee sentiment data, suggest the optimal staffing and tasks to maximize their performance." In this way, sophisticated suggestions can be made using generative AI models.

[0409] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0410] Step 1:

[0411] The server collects personnel data from devices that store information for management purposes. Inputs include employee skills, work performance, and training history, while output is structured data stored in a database. This data forms the basis for subsequent analysis and is updated in real time via the network.

[0412] Step 2:

[0413] The terminal transmits emotional data acquired through a display device worn by the field staff to the server. The input is emotional data from the staff member's facial expressions and voice, and the output is information converted from that data into an analyzable format. Sensor devices and real-time data streaming technology are utilized here.

[0414] Step 3:

[0415] The server uses an emotion analysis device to analyze emotional data obtained from terminals and evaluate individual characteristics. The input consists of emotional data and personnel data, and the output is an evaluation result reflecting each individual's emotional state and characteristics. A generative AI model estimates the emotional state, and individual evaluations are formed based on this.

[0416] Step 4:

[0417] The server generates optimal personnel placement and development plans based on evaluation results. The inputs are employee evaluation results and organizational placement conditions, and the output is a draft of an efficient placement and development plan. The generated draft is then refined by an algorithm to optimize it for the organization's objectives.

[0418] Step 5:

[0419] The server visually displays the generated deployment and growth plans on the management screen. The inputs are the deployment and plan proposals, and the output is the visualized information presented to the user. Dashboard software is utilized, enabling users to make decisions with a clear understanding.

[0420] Step 6:

[0421] Users provide feedback to the system via their terminals regarding the information presented. The input is the user's feedback, and the output is the data stored in the system as feedback. User opinions influence the accuracy of future suggestions and serve as the basis for improvement.

[0422] Step 7:

[0423] The server readjusts the AI ​​model to improve the system's accuracy based on the collected feedback data. The input is the feedback data and past analysis results, and the output is the improved proposed algorithm. This enables more accurate and appropriate personnel allocation.

[0424] 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.

[0425] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0426] 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.

[0427] [Third Embodiment]

[0428] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0429] 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.

[0430] 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).

[0431] 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.

[0432] 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.

[0433] 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).

[0434] 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.

[0435] 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.

[0436] 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.

[0437] 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.

[0438] 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.

[0439] 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".

[0440] The system of the present invention integrates the processes of collecting, analyzing, generating suggestions for, and providing feedback on personnel information, thereby realizing data-driven human resource management. Specific embodiments of each element are described below.

[0441] Data collection methods

[0442] The server connects to the company's internal database via the network and periodically retrieves relevant data such as position data, individual skill information, and past work performance.

[0443] The server collects additional training and evaluation data from external learning management systems (LMS) and performance management tools via APIs.

[0444] Forms of data analysis

[0445] The server cleanses the collected data and runs AI-based algorithms to evaluate each employee's skill set as a quantified profile.

[0446] The server generates analytical reports, which are used for skill gap analysis and determining suitability for existing roles.

[0447] Proposal generation methods

[0448] Based on the analysis results, the server formulates appropriate personnel placement combinations within the organization. This includes proposals for placement in new projects and training plans for skill improvement in current positions.

[0449] The server delivers the generated suggestions to the terminal via a dashboard that visualizes them, and presents them to the user.

[0450] Forms of feedback and precision improvement

[0451] Users can provide feedback on the proposed content via their devices, offering information on the effectiveness and areas for improvement of the implementation.

[0452] The server collects feedback information and adjusts the analysis algorithm to improve the accuracy of subsequent evaluations and recommendations. This feedback loop allows for progressive customization tailored to the organization's specific needs.

[0453] As a specific example

[0454] In one company, a transfer of personnel to the data analytics department is necessary, and a server analyzes employee data within the organization to suggest the most suitable candidates. These candidates include specific training plans based on the analysis results (e.g., machine learning training courses). The suggested placement and plan are notified to the user via a terminal, and the user submits feedback while approving or modifying the suggestion. This creates a cycle that improves the quality of future suggestions, resulting in an objective, data-driven HR strategy.

[0455] The following describes the processing flow.

[0456] Step 1:

[0457] The server connects to the company's internal HR database and an external learning management system to collect necessary personnel information. This includes skills information, past work performance, and training history. After collection, the data is formatted into a standardized format.

[0458] Step 2:

[0459] The server processes the acquired data through a data cleansing process to remove duplicates and missing data. Then, an AI model is used to quantitatively evaluate each employee's skill set, which is stored as a profile.

[0460] Step 3:

[0461] The server performs analysis and conducts a skills gap analysis based on each employee's skills and the requirements of their position within the organization. This identifies the areas where skills need to be strengthened.

[0462] Step 4:

[0463] Based on the analysis above, the server generates proposals for optimal personnel placement and development plans within the organization. These proposals include placements in new positions and necessary skills training plans.

[0464] Step 5:

[0465] The server displays the generated suggestions on the terminal in a dashboard format and provides them to the user. The user can review the content of the suggestions in an easy-to-read format.

[0466] Step 6:

[0467] Users make decisions based on the proposals and provide feedback via their devices. This feedback may include evaluations of the proposal's suitability and additional requests.

[0468] Step 7:

[0469] The server receives feedback and makes adjustments to reflect it in the analysis algorithm. Based on the new feedback information, it continues to learn in order to improve the accuracy of the next data analysis and suggestions. In this way, the system continues to evolve.

[0470] (Example 1)

[0471] 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."

[0472] In information technology, there is a need for efficient and accurate personnel allocation and training planning. However, conventional methods have faced challenges in optimizing personnel utilization due to insufficient accuracy in data aggregation and analysis processes. Furthermore, there has been a lack of mechanisms for continuously improving systems by effectively utilizing user feedback.

[0473] 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.

[0474] In this invention, the server includes means for collecting information from information sources, means for cleansing the collected information and inputting it into an analysis model to perform a quantified evaluation, and means for formulating an appropriate deployment and development plan based on the analysis results. This enables accurate data-driven evaluation and the formulation of deployment plans.

[0475] "Information" refers to a collection of data and knowledge used for a specific purpose.

[0476] "Information source" refers to the data provider or database from which specific information is collected.

[0477] "Means of collection" refers to the processes and equipment used to obtain necessary information from specific sources.

[0478] "Cleansing" refers to the process of improving the quality of information by removing duplicates and errors from collected data and supplementing missing data.

[0479] An "analytical model" refers to an algorithm or system used to derive specific conclusions or predictions based on collected information.

[0480] "Quantified evaluation" refers to the results of expressing information numerically in order to quantitatively evaluate it based on an analytical model.

[0481] "Allocation" refers to activities or plans that demonstrate the appropriate allocation of personnel and resources within an organization.

[0482] A "development plan" refers to a specific program or plan designed to improve the abilities and skills of employees.

[0483] This invention utilizes a combination of software and hardware to realize a data-driven human resources management system.

[0484] The server connects to internal databases and external information sources via the network. The hardware used for this requires a server computer with a high-speed processor and sufficient memory. The software used includes a database management system (DBMS) for data collection and communication with external learning management systems (LMS) and performance management tools via APIs.

[0485] The server cleanses the collected data using Python's Pandas library, among others. This cleansing process removes duplicate data and imputes missing data. Next, the data is fed into an AI-based analysis model. This analysis uses machine learning libraries such as Scikit-learn to generate profiles that quantify each employee's skill set.

[0486] Furthermore, the server develops optimal personnel allocation and training plans based on the analysis results. This involves utilizing open-source optimization libraries to solve optimization problems that match the project needs within the organization.

[0487] The visualized analysis results are provided as a web application using frameworks such as Django or Flask, and presented to the user via their device. Through this, users can review the details of the suggestions and submit feedback as needed.

[0488] User feedback is used to improve accuracy in the next analysis cycle. The collected feedback data is analyzed on the server and used to fine-tune the analysis model. Through this feedback loop, the system can be customized to meet the specific needs of the organization.

[0489] For example, if a company is considering transferring personnel to its data analysis department, the server will analyze employee data within the organization and propose suitable candidates and their training plans. For instance, a suggestion might include "taking a basic machine learning course." An example of a prompt to the generating AI model would be, "Generate optimal personnel placement suggestions using internal data. Include appropriate training plans based on past performance and skill sets."

[0490] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0491] Step 1:

[0492] The server collects data from internal databases and external sources. Inputs include the internal employee database and learning history and performance evaluation data obtained via external APIs. This information is retrieved using SQL queries and API requests and stored in temporary storage as CSV or JSON data.

[0493] Step 2:

[0494] The server cleanses the collected data. The input is the data collected in step 1. Specifically, it uses the Python Pandas library to remove duplicate data and impute missing values. This outputs a clean dataset suitable for analysis.

[0495] Step 3:

[0496] The server uses the cleansed data to quantify the skill set. The input is the dataset processed in step 2. Using Scikit-learn, it generates a machine learning model to numerically evaluate each employee's skills. This analysis process outputs a quantified profile for each employee.

[0497] Step 4:

[0498] The server performs a skills gap analysis and assesses job suitability based on a quantified skills profile. The input is the quantified profile obtained in step 3. The script compares the skillset with the job requirements and outputs a report containing the gap analysis results and suitability assessment.

[0499] Step 5:

[0500] The server develops optimal personnel placement and training plans. The input is the analysis report generated in step 4. Using an open-source optimization library, it optimizes personnel placement based on the organizational role needs. This outputs proposed placements and training plans.

[0501] Step 6:

[0502] The server visualizes the formulated proposal and presents it to the user via the terminal. The input is the proposal from step 5. Using a web framework such as Django or Flask, the proposal content is displayed on a dashboard, allowing the user to review it via their terminal.

[0503] Step 7:

[0504] Users review the proposed placement and training plans and provide feedback. This feedback is entered via a user's device. They submit their opinions and requests for revisions to the proposal through a feedback form and send it to the server.

[0505] Step 8:

[0506] The server analyzes user feedback and improves the generated AI model. The input is the feedback collected in step 7. The server analyzes the feedback data and adjusts the parameters of the analysis model to improve accuracy. This creates a feedback loop that improves the quality of future suggestions.

[0507] (Application Example 1)

[0508] 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."

[0509] This invention relates to a system for optimizing human resource allocation across an entire city. While human resource allocation within individual companies and organizations has traditionally been effective, there has been a lack of means to efficiently manage a wide range of technical skill sets and allocate personnel suited to the required roles across public institutions and private companies throughout a city. As a result, situations sometimes arose where there was a shortage or surplus of personnel with specific skills.

[0510] 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.

[0511] In this invention, the server includes means for collecting personnel information from an information storage device, means for analyzing the collected personnel information and evaluating the skill set of each worker, and means for aggregating the skill needs of the entire city and proposing the necessary personnel on a smart device. This enables optimal personnel allocation throughout the entire city.

[0512] "Personnel information" refers to data that includes each worker's skill set, work history, and work performance.

[0513] "Information storage device" refers to a database or cloud storage for storing personnel information.

[0514] "Analysis" is the process of deriving specific results using calculations and algorithms based on collected data.

[0515] A "skill set" is a collection of information about the skills and abilities possessed by a particular worker.

[0516] A "growth plan" is a plan that includes specific measures and training plans to improve workers' job skills.

[0517] "Users" refers to administrators and stakeholders who use the system to receive proposals for personnel allocation and growth plans.

[0518] "Response" refers to the feedback and evaluation information that users provide to the system.

[0519] "City-wide skills needs" refer to the technical skills and job requirements demanded by public institutions and private companies in a specific city or region.

[0520] A "smart device" is a device that has information processing capabilities via the internet, such as a smartphone or tablet.

[0521] The system for implementing this invention combines a data processing device and a smart device. The server collects personnel information from an information storage device and executes a program to analyze that data. For data collection, it utilizes internal databases and cloud systems, and acquires external data via APIs as needed. The server uses the Python programming language and libraries (e.g., requests, scikit-learn) to perform data processing such as normalization, dimensionality reduction, and clustering.

[0522] The analyzed data is delivered to smart devices such as smartphones and tablets, and users are presented with optimal personnel allocation suggestions based on the city's overall skill needs. At this stage, a generated AI model is used to improve the accuracy of the suggestions. Users can review, modify, and approve the suggestions on the user interface. User responses are sent to the server as a feedback loop and are used to improve the accuracy of the analysis algorithm.

[0523] As a concrete example, in a major city, the system can detect a shortage of public transportation engineers and, through analysis, propose and deploy appropriate engineers from other relevant companies, thereby quickly resolving the problem. The generative AI model supporting this process uses instruction statements such as, "Analyze the latest personnel data and propose the optimal personnel deployment based on the skills needed in the city. Please also consider feedback in your proposal."

[0524] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0525] Step 1:

[0526] The server first collects personnel information from the information storage device. It periodically retrieves necessary skills information, work history, and work results from databases and external APIs, and uses this information to prepare for the next analysis phase. At this stage, the input is raw data from the information storage device, and the output is data formatted for analysis.

[0527] Step 2:

[0528] The server performs data cleansing based on the collected data. Using libraries such as Python's pandas library, invalid data and missing values ​​are removed, and only the necessary data is extracted. Here, the formatted data is further normalized to a format suitable for analysis. The input to this process is the formatted raw data, and the output is validated data from which invalid data and duplicates have been removed.

[0529] Step 3:

[0530] The server feeds normalized data into an AI algorithm for analysis. Using scikit-learn, dimensionality reduction is performed using principal component analysis (PCA), and then the data is grouped using a clustering method (e.g., KMeans). The input here is validated data, and the output is cluster data showing how each worker's skills are classified.

[0531] Step 4:

[0532] The server utilizes a generated AI model based on clustering results to produce proposals for talent allocation across the entire city. These proposals include allocation strategies tailored to specific skill needs. The input is cluster data, and the output is a proposal document detailing the optimal allocation and the reasons for it.

[0533] Step 5:

[0534] The terminal receives a proposal document from the server and displays it on the smart device's screen. The user can review this proposal and make revisions or approvals as needed. The input here is the proposal document, and the output is the user's response data.

[0535] Step 6:

[0536] The user's response is sent to the server and used for analysis as part of a feedback loop. The server readjusts the AI ​​algorithm based on this response to improve the accuracy of future suggestions. The input to this process is the user response data, and the output is the algorithm adjusted to reflect the feedback.

[0537] 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.

[0538] The system of the present invention combines an emotion engine with the management and suggestion of personnel information to provide flexible personnel placement and training plans that reflect the user's emotional state. Specific embodiments for carrying out the present invention are described below.

[0539] Data collection methods

[0540] The server connects to the company's talent database via the network and regularly collects necessary information, including employee skills, work performance, and training history.

[0541] The server comprehensively acquires personnel information, utilizing additional data from external systems as well.

[0542] Forms of collecting and analyzing emotional data

[0543] The device activates an emotion engine when the user provides feedback, detecting the emotional state from facial expressions, voice, and text data. This data indicates the user's satisfaction level and their feelings towards the suggestions.

[0544] The server analyzes emotional data obtained from the emotion engine and integrates it with conventional feedback data to make an overall decision.

[0545] Proposal generation methods

[0546] The server optimizes talent allocation and training plans within the organization based on employee skill information and emotional feedback from users. In this process, it dynamically adjusts suggestions based on emotional data, enabling suggestions that enhance user satisfaction.

[0547] The server generates visualized suggestions on the dashboard and presents them to the user via the terminal.

[0548] Presentation to the user and the form of the feedback loop

[0549] Users review proposed personnel placement and training plans and send emotion-based feedback via their devices.

[0550] The server evaluates the feedback, incorporates it into the system, and fine-tunes the AI ​​algorithm along with the new sentiment data.

[0551] As a specific example

[0552] When a company needs to assemble a new project team, the server analyzes the skills of employees within the organization and creates an initial personnel placement plan. Users can receive this proposal and provide feedback through facial expressions and voice via the emotion engine. For example, if a user shows positive emotion towards a proposal, the server reflects this in its analysis, identifies which parts of the proposal were good, and further improves the accuracy of future proposals. In this way, a feedback and proposal cycle utilizing emotional data is realized, contributing to improved HR strategies and employee satisfaction across the entire organization.

[0553] The following describes the processing flow.

[0554] Step 1:

[0555] The server collects data such as employee skills, work performance, and training history from the company's internal talent database and external systems. The collected data is formatted in a standardized format, and a detailed profile is created for each employee.

[0556] Step 2:

[0557] The device activates an emotion engine when the user provides feedback, and uses a facial recognition camera and voice recognition microphone to analyze the user's emotions in real time. As a result, it quantifies the user's response to the suggestion and records whether their emotional state is positive or negative.

[0558] Step 3:

[0559] The server integrates emotional data with employee skill assessment data and runs an analysis algorithm. This integrated dataset is used to create personnel placement and training plans, with dynamic adjustments made to take user emotions into account.

[0560] Step 4:

[0561] The server visualizes the generated personnel placement and training plans on a graphical dashboard and presents them to the user via their terminal. Users can then systematically and visually review the information.

[0562] Step 5:

[0563] Users provide feedback on the proposed placement and training plans. During this process, they send specific opinions and evaluations, along with emotional responses captured by the emotion engine, to the server via their device.

[0564] Step 6:

[0565] The server analyzes and compares user feedback and sentiment data to adjust algorithms and improve the accuracy of future recommendations. This results in more appropriate planning that reflects the user's emotional needs.

[0566] (Example 2)

[0567] 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."

[0568] In modern organizations, optimizing personnel placement and development plans that take into account employees' skills and emotional states is essential. However, existing systems often make decisions based solely on skill information, making it difficult to implement flexible personnel placement that reflects employees' emotional states. This makes it difficult to improve employee satisfaction and optimize overall organizational efficiency.

[0569] 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.

[0570] In this invention, the server includes means for collecting personnel information within an organization from an information aggregation device, means for analyzing the collected information to evaluate the skill composition and emotional data of each person, and means for generating an optimal personnel allocation and training plan based on the evaluation results and emotional data, and visualizing the generated plan and presenting it to the user. This makes it possible to provide flexible personnel allocation and training plans that take into account the emotional state of employees.

[0571] An "information aggregation device" is a device used to collect and integrate necessary information from various databases and systems both inside and outside an organization.

[0572] "Skill composition" refers to a systematic combination of skills and knowledge necessary for an individual or group to perform a specific task or activity.

[0573] A "development plan" is a set of guidelines that includes specific goals, methods, and progress management to promote employee skill development and career growth.

[0574] "Emotional data" refers to information that indicates a user's emotional state, and includes data obtained from facial expressions, voice, text, etc.

[0575] "Visualization" is a technique for representing information and data in visual forms such as graphs, charts, and diagrams to facilitate understanding.

[0576] "Readjustment" is the process of improving existing models and systems based on new information and feedback to enhance their performance and adaptability.

[0577] This invention aims to construct a system for managing and optimizing human resource information within an organization. Specific embodiments are described below.

[0578] The server connects to an information aggregation device via the network and periodically collects personnel information. This information covers a wide range of topics, including work performance, skill history, and training records. External data can also be obtained from external certification bodies and industry information providers.

[0579] The device activates an emotion engine upon receiving user feedback, using the camera and microphone to acquire facial and audio data. This emotion data is analyzed by image analysis software (e.g., OpenCV), speech recognition tools (e.g., Google Speech-to-Text), and natural language processing tools (e.g., NLTK). This allows the user's emotional state to be detected and monitored in real time.

[0580] The server generates optimal staffing and training plans using a generative AI model (e.g., TensorFlow) based on collected skill configurations and emotional data. The generated suggestions are visually represented using a visualization tool (e.g., Tableau) and presented to the user via a terminal.

[0581] For example, in selecting a leader for a new project, the server analyzes candidates' technical skills, project experience, and recent performance to suggest the most suitable person. Users can review this suggestion on their device and express positive emotions as feedback. The server then re-evaluates the feedback and incorporates it into future suggestions to improve their accuracy.

[0582] A concrete example of a prompt message would be: "When using this system, detect how employees feel about the suggestions and provide an effective training plan based on that."

[0583] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0584] Step 1:

[0585] The server takes data from the information aggregation device as input and retrieves personnel information. Specifically, it uses an API to collect employee skills, work performance, and training history. The collected data is output to temporary storage in flat file format. The data processing performed in this step is to organize and ensure consistency of the data.

[0586] Step 2:

[0587] The server retrieves additional information from external data providers. This includes collecting certification information and industry trend data via external APIs. Input is data from external systems, which is then integrated with existing internal data for output. Data processing involves normalizing and integrating data from different sources.

[0588] Step 3:

[0589] The device activates its emotion engine upon receiving input from the user. For example, a user might fill out a survey or feedback form, which is then captured as emotion data via the camera and microphone. The input includes facial expressions, voice, and text information, which are output as emotional states. This process involves detecting emotions using image analysis and speech recognition.

[0590] Step 4:

[0591] The server analyzes the collected skill set and emotional data. This analysis utilizes a generative AI model, performing optimization processes including skill matching and emotional feedback. The input is the data obtained in steps 1 and 3, and the output is a proposed personnel placement and training plan. Data calculations include model-based prediction and optimization.

[0592] Step 5:

[0593] The server visualizes the generated proposals and presents them to the user via the terminal. Specifically, it uses visualization tools to convert the data into graphs and charts and outputs them to the user interface. The input is the proposal data generated in step 4, and the output is the visualized plan presented to the user.

[0594] Step 6:

[0595] Users review the suggestions presented through their terminals and provide feedback. This feedback process involves users expressing their feelings based on their emotions and inputting them into the terminal. The input is captured as user feedback, received and analyzed by the server, and stored as output to improve the accuracy of future suggestions. Data processing includes evaluating the feedback data and incorporating it into the system.

[0596] (Application Example 2)

[0597] 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."

[0598] In modern workplaces, it is common practice to formulate placement and training plans without considering employees' emotional states. This can lead to problems in the work environment, such as decreased efficiency and lower employee motivation. In particular, in field-based jobs like security, personnel placement that reflects real-time emotional states is essential.

[0599] 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.

[0600] In this invention, the server includes means for collecting information from a device that stores information for management, means for evaluating individual characteristics based on emotional data analyzed using an emotion analysis device, means for receiving feedback from users and improving the accuracy of the system, and means for acquiring emotional data using a display device worn by on-site personnel. This makes it possible to propose optimal placement and training plans based on the emotional state of employees.

[0601] A "device for storing information for management purposes" is an electronic device used to safely and efficiently store and manage various types of data held by an organization.

[0602] An "emotion analysis device" is a device equipped with the function of identifying and evaluating a person's emotional state through data such as facial expressions, voice, and text.

[0603] "Means of evaluating individual characteristics" refers to the process of analyzing each individual's skills and emotional state and making evaluations based on that analysis.

[0604] "Means of receiving feedback from users and improving the accuracy of the system" refers to methods of collecting feedback from users and using that data to improve the accuracy of the system and the quality of the proposed solutions.

[0605] A "display device worn by on-site personnel" is a device that workers carry or wear and use to display information in real time.

[0606] "Means of acquiring emotional data" refers to the process of collecting information related to an individual's emotions in real time using sensors and analytical algorithms.

[0607] To realize this invention, the server is operated using a combination of multiple hardware and software components. First, the server periodically collects personnel data from a device that stores the information to be managed. This data includes employees' skills, performance, and training history. Next, the emotion analysis device evaluates individual characteristics based on data acquired from a display device worn by the field staff. During this process, real-time emotion analysis is performed, and emotional data is analyzed using sensors and analysis algorithms (e.g., facial recognition software and voice emotion analysis tools).

[0608] Subsequently, the server uses these evaluation results to generate an optimal placement and training plan. The generated placement is visually displayed on the management screen, which users can verify through the display device. Furthermore, the system receives feedback from users, and the accuracy of the system is improved based on the feedback data.

[0609] As a concrete example, in a security monitoring center, when employee A wears smart glasses while working, the server analyzes their emotional state in real time. This information is displayed on the administrator's dashboard, suggesting the placement with the lowest stress level and highest performance. This is expected to improve monitoring efficiency.

[0610] An example of a prompt would be, "Using current employee sentiment data, suggest the optimal staffing and tasks to maximize their performance." In this way, sophisticated suggestions can be made using generative AI models.

[0611] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0612] Step 1:

[0613] The server collects personnel data from devices that store information for management purposes. Inputs include employee skills, work performance, and training history, while output is structured data stored in a database. This data forms the basis for subsequent analysis and is updated in real time via the network.

[0614] Step 2:

[0615] The terminal transmits emotional data acquired through a display device worn by the field staff to the server. The input is emotional data from the staff member's facial expressions and voice, and the output is information converted from that data into an analyzable format. Sensor devices and real-time data streaming technology are utilized here.

[0616] Step 3:

[0617] The server uses an emotion analysis device to analyze emotional data obtained from terminals and evaluate individual characteristics. The input consists of emotional data and personnel data, and the output is an evaluation result reflecting each individual's emotional state and characteristics. A generative AI model estimates the emotional state, and individual evaluations are formed based on this.

[0618] Step 4:

[0619] The server generates optimal personnel placement and development plans based on evaluation results. The inputs are employee evaluation results and organizational placement conditions, and the output is a draft of an efficient placement and development plan. The generated draft is then refined by an algorithm to optimize it for the organization's objectives.

[0620] Step 5:

[0621] The server visually displays the generated deployment and growth plans on the management screen. The inputs are the deployment and plan proposals, and the output is the visualized information presented to the user. Dashboard software is utilized, enabling users to make decisions with a clear understanding.

[0622] Step 6:

[0623] Users provide feedback to the system via their terminals regarding the information presented. The input is the user's feedback, and the output is the data stored in the system as feedback. User opinions influence the accuracy of future suggestions and serve as the basis for improvement.

[0624] Step 7:

[0625] The server readjusts the AI ​​model to improve the system's accuracy based on the collected feedback data. The input is the feedback data and past analysis results, and the output is the improved proposed algorithm. This enables more accurate and appropriate personnel allocation.

[0626] 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.

[0627] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0628] 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.

[0629] [Fourth Embodiment]

[0630] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0631] 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.

[0632] 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).

[0633] 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.

[0634] 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.

[0635] 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).

[0636] 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.

[0637] 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.

[0638] 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.

[0639] 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.

[0640] 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.

[0641] 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.

[0642] 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".

[0643] The system of the present invention integrates the processes of collecting, analyzing, generating suggestions for, and providing feedback on personnel information, thereby realizing data-driven human resource management. Specific embodiments of each element are described below.

[0644] Data collection methods

[0645] The server connects to the company's internal database via the network and periodically retrieves relevant data such as position data, individual skill information, and past work performance.

[0646] The server collects additional training and evaluation data from external learning management systems (LMS) and performance management tools via APIs.

[0647] Forms of data analysis

[0648] The server cleanses the collected data and runs AI-based algorithms to evaluate each employee's skill set as a quantified profile.

[0649] The server generates analytical reports, which are used for skill gap analysis and determining suitability for existing roles.

[0650] Proposal generation methods

[0651] Based on the analysis results, the server formulates appropriate personnel placement combinations within the organization. This includes proposals for placement in new projects and training plans for skill improvement in current positions.

[0652] The server delivers the generated suggestions to the terminal via a dashboard that visualizes them, and presents them to the user.

[0653] Forms of feedback and precision improvement

[0654] Users can provide feedback on the proposed content via their devices, offering information on the effectiveness and areas for improvement of the implementation.

[0655] The server collects feedback information and adjusts the analysis algorithm to improve the accuracy of subsequent evaluations and recommendations. This feedback loop allows for progressive customization tailored to the organization's specific needs.

[0656] As a specific example

[0657] In one company, a transfer of personnel to the data analytics department is necessary, and a server analyzes employee data within the organization to suggest the most suitable candidates. These candidates include specific training plans based on the analysis results (e.g., machine learning training courses). The suggested placement and plan are notified to the user via a terminal, and the user submits feedback while approving or modifying the suggestion. This creates a cycle that improves the quality of future suggestions, resulting in an objective, data-driven HR strategy.

[0658] The following describes the processing flow.

[0659] Step 1:

[0660] The server connects to the company's internal HR database and an external learning management system to collect necessary personnel information. This includes skills information, past work performance, and training history. After collection, the data is formatted into a standardized format.

[0661] Step 2:

[0662] The server processes the acquired data through a data cleansing process to remove duplicates and missing data. Then, an AI model is used to quantitatively evaluate each employee's skill set, which is stored as a profile.

[0663] Step 3:

[0664] The server performs analysis and conducts a skills gap analysis based on each employee's skills and the requirements of their position within the organization. This identifies the areas where skills need to be strengthened.

[0665] Step 4:

[0666] Based on the analysis above, the server generates proposals for optimal personnel placement and development plans within the organization. These proposals include placements in new positions and necessary skills training plans.

[0667] Step 5:

[0668] The server displays the generated suggestions on the terminal in a dashboard format and provides them to the user. The user can review the content of the suggestions in an easy-to-read format.

[0669] Step 6:

[0670] Users make decisions based on the proposals and provide feedback via their devices. This feedback may include evaluations of the proposal's suitability and additional requests.

[0671] Step 7:

[0672] The server receives feedback and makes adjustments to reflect it in the analysis algorithm. Based on the new feedback information, it continues to learn in order to improve the accuracy of the next data analysis and suggestions. In this way, the system continues to evolve.

[0673] (Example 1)

[0674] 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".

[0675] In information technology, there is a need for efficient and accurate personnel allocation and training planning. However, conventional methods have faced challenges in optimizing personnel utilization due to insufficient accuracy in data aggregation and analysis processes. Furthermore, there has been a lack of mechanisms for continuously improving systems by effectively utilizing user feedback.

[0676] 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.

[0677] In this invention, the server includes means for collecting information from information sources, means for cleansing the collected information and inputting it into an analysis model to perform a quantified evaluation, and means for formulating an appropriate deployment and development plan based on the analysis results. This enables accurate data-driven evaluation and the formulation of deployment plans.

[0678] "Information" refers to a collection of data and knowledge used for a specific purpose.

[0679] "Information source" refers to the data provider or database from which specific information is collected.

[0680] "Means of collection" refers to the processes and equipment used to obtain necessary information from specific sources.

[0681] "Cleansing" refers to the process of improving the quality of information by removing duplicates and errors from collected data and supplementing missing data.

[0682] An "analytical model" refers to an algorithm or system used to derive specific conclusions or predictions based on collected information.

[0683] "Quantified evaluation" refers to the results of expressing information numerically in order to quantitatively evaluate it based on an analytical model.

[0684] "Allocation" refers to activities or plans that demonstrate the appropriate allocation of personnel and resources within an organization.

[0685] A "development plan" refers to a specific program or plan designed to improve the abilities and skills of employees.

[0686] This invention utilizes a combination of software and hardware to realize a data-driven human resources management system.

[0687] The server connects to internal databases and external information sources via the network. The hardware used for this requires a server computer with a high-speed processor and sufficient memory. The software used includes a database management system (DBMS) for data collection and communication with external learning management systems (LMS) and performance management tools via APIs.

[0688] The server cleanses the collected data using Python's Pandas library, among others. This cleansing process removes duplicate data and imputes missing data. Next, the data is fed into an AI-based analysis model. This analysis uses machine learning libraries such as Scikit-learn to generate profiles that quantify each employee's skill set.

[0689] Furthermore, the server develops optimal personnel allocation and training plans based on the analysis results. This involves utilizing open-source optimization libraries to solve optimization problems that match the project needs within the organization.

[0690] The visualized analysis results are provided as a web application using frameworks such as Django or Flask, and presented to the user via their device. Through this, users can review the details of the suggestions and submit feedback as needed.

[0691] User feedback is used to improve accuracy in the next analysis cycle. The collected feedback data is analyzed on the server and used to fine-tune the analysis model. Through this feedback loop, the system can be customized to meet the specific needs of the organization.

[0692] For example, if a company is considering transferring personnel to its data analysis department, the server will analyze employee data within the organization and propose suitable candidates and their training plans. For instance, a suggestion might include "taking a basic machine learning course." An example of a prompt to the generating AI model would be, "Generate optimal personnel placement suggestions using internal data. Include appropriate training plans based on past performance and skill sets."

[0693] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0694] Step 1:

[0695] The server collects data from internal databases and external sources. Inputs include the internal employee database and learning history and performance evaluation data obtained via external APIs. This information is retrieved using SQL queries and API requests and stored in temporary storage as CSV or JSON data.

[0696] Step 2:

[0697] The server cleanses the collected data. The input is the data collected in step 1. Specifically, it uses the Python Pandas library to remove duplicate data and impute missing values. This outputs a clean dataset suitable for analysis.

[0698] Step 3:

[0699] The server uses the cleansed data to quantify the skill set. The input is the dataset processed in step 2. Using Scikit-learn, it generates a machine learning model to numerically evaluate each employee's skills. This analysis process outputs a quantified profile for each employee.

[0700] Step 4:

[0701] The server performs a skills gap analysis and assesses job suitability based on a quantified skills profile. The input is the quantified profile obtained in step 3. The script compares the skillset with the job requirements and outputs a report containing the gap analysis results and suitability assessment.

[0702] Step 5:

[0703] The server develops optimal personnel placement and training plans. The input is the analysis report generated in step 4. Using an open-source optimization library, it optimizes personnel placement based on the organizational role needs. This outputs proposed placements and training plans.

[0704] Step 6:

[0705] The server visualizes the formulated proposal and presents it to the user via the terminal. The input is the proposal from step 5. Using a web framework such as Django or Flask, the proposal content is displayed on a dashboard, allowing the user to review it via their terminal.

[0706] Step 7:

[0707] Users review the proposed placement and training plans and provide feedback. This feedback is entered via a user's device. They submit their opinions and requests for revisions to the proposal through a feedback form and send it to the server.

[0708] Step 8:

[0709] The server analyzes user feedback and improves the generated AI model. The input is the feedback collected in step 7. The server analyzes the feedback data and adjusts the parameters of the analysis model to improve accuracy. This creates a feedback loop that improves the quality of future suggestions.

[0710] (Application Example 1)

[0711] 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".

[0712] This invention relates to a system for optimizing human resource allocation across an entire city. While human resource allocation within individual companies and organizations has traditionally been effective, there has been a lack of means to efficiently manage a wide range of technical skill sets and allocate personnel suited to the required roles across public institutions and private companies throughout a city. As a result, situations sometimes arose where there was a shortage or surplus of personnel with specific skills.

[0713] 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.

[0714] In this invention, the server includes means for collecting personnel information from an information storage device, means for analyzing the collected personnel information and evaluating the skill set of each worker, and means for aggregating the skill needs of the entire city and proposing the necessary personnel on a smart device. This enables optimal personnel allocation throughout the entire city.

[0715] "Personnel information" refers to data that includes each worker's skill set, work history, and work performance.

[0716] "Information storage device" refers to a database or cloud storage for storing personnel information.

[0717] "Analysis" is the process of deriving specific results using calculations and algorithms based on collected data.

[0718] A "skill set" is a collection of information about the skills and abilities possessed by a particular worker.

[0719] A "growth plan" is a plan that includes specific measures and training plans to improve workers' job skills.

[0720] "Users" refers to administrators and stakeholders who use the system to receive proposals for personnel allocation and growth plans.

[0721] "Response" refers to the feedback and evaluation information that users provide to the system.

[0722] "City-wide skills needs" refer to the technical skills and job requirements demanded by public institutions and private companies in a specific city or region.

[0723] A "smart device" is a device that has information processing capabilities via the internet, such as a smartphone or tablet.

[0724] The system for implementing this invention combines a data processing device and a smart device. The server collects personnel information from an information storage device and executes a program to analyze that data. For data collection, it utilizes internal databases and cloud systems, and acquires external data via APIs as needed. The server uses the Python programming language and libraries (e.g., requests, scikit-learn) to perform data processing such as normalization, dimensionality reduction, and clustering.

[0725] The analyzed data is delivered to smart devices such as smartphones and tablets, and users are presented with optimal personnel allocation suggestions based on the city's overall skill needs. At this stage, a generated AI model is used to improve the accuracy of the suggestions. Users can review, modify, and approve the suggestions on the user interface. User responses are sent to the server as a feedback loop and are used to improve the accuracy of the analysis algorithm.

[0726] As a concrete example, in a major city, the system can detect a shortage of public transportation engineers and, through analysis, propose and deploy appropriate engineers from other relevant companies, thereby quickly resolving the problem. The generative AI model supporting this process uses instruction statements such as, "Analyze the latest personnel data and propose the optimal personnel deployment based on the skills needed in the city. Please also consider feedback in your proposal."

[0727] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0728] Step 1:

[0729] The server first collects personnel information from the information storage device. It periodically retrieves necessary skills information, work history, and work results from databases and external APIs, and uses this information to prepare for the next analysis phase. At this stage, the input is raw data from the information storage device, and the output is data formatted for analysis.

[0730] Step 2:

[0731] The server performs data cleansing based on the collected data. Using libraries such as Python's pandas library, invalid data and missing values ​​are removed, and only the necessary data is extracted. Here, the formatted data is further normalized to a format suitable for analysis. The input to this process is the formatted raw data, and the output is validated data from which invalid data and duplicates have been removed.

[0732] Step 3:

[0733] The server feeds normalized data into an AI algorithm for analysis. Using scikit-learn, dimensionality reduction is performed using principal component analysis (PCA), and then the data is grouped using a clustering method (e.g., KMeans). The input here is validated data, and the output is cluster data showing how each worker's skills are classified.

[0734] Step 4:

[0735] The server utilizes a generated AI model based on clustering results to produce proposals for talent allocation across the entire city. These proposals include allocation strategies tailored to specific skill needs. The input is cluster data, and the output is a proposal document detailing the optimal allocation and the reasons for it.

[0736] Step 5:

[0737] The terminal receives a proposal document from the server and displays it on the smart device's screen. The user can review this proposal and make revisions or approvals as needed. The input here is the proposal document, and the output is the user's response data.

[0738] Step 6:

[0739] The user's response is sent to the server and used for analysis as part of a feedback loop. The server readjusts the AI ​​algorithm based on this response to improve the accuracy of future suggestions. The input to this process is the user response data, and the output is the algorithm adjusted to reflect the feedback.

[0740] 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.

[0741] The system of the present invention combines an emotion engine with the management and suggestion of personnel information to provide flexible personnel placement and training plans that reflect the user's emotional state. Specific embodiments for carrying out the present invention are described below.

[0742] Data collection methods

[0743] The server connects to the company's talent database via the network and regularly collects necessary information, including employee skills, work performance, and training history.

[0744] The server comprehensively acquires personnel information, utilizing additional data from external systems as well.

[0745] Forms of collecting and analyzing emotional data

[0746] The device activates an emotion engine when the user provides feedback, detecting the emotional state from facial expressions, voice, and text data. This data indicates the user's satisfaction level and their feelings towards the suggestions.

[0747] The server analyzes emotional data obtained from the emotion engine and integrates it with conventional feedback data to make an overall decision.

[0748] Proposal generation methods

[0749] The server optimizes talent allocation and training plans within the organization based on employee skill information and emotional feedback from users. In this process, it dynamically adjusts suggestions based on emotional data, enabling suggestions that enhance user satisfaction.

[0750] The server generates visualized suggestions on the dashboard and presents them to the user via the terminal.

[0751] Presentation to the user and the form of the feedback loop

[0752] Users review proposed personnel placement and training plans and send emotion-based feedback via their devices.

[0753] The server evaluates the feedback, incorporates it into the system, and fine-tunes the AI ​​algorithm along with the new sentiment data.

[0754] As a specific example

[0755] When a company needs to assemble a new project team, the server analyzes the skills of employees within the organization and creates an initial personnel placement plan. Users can receive this proposal and provide feedback through facial expressions and voice via the emotion engine. For example, if a user shows positive emotion towards a proposal, the server reflects this in its analysis, identifies which parts of the proposal were good, and further improves the accuracy of future proposals. In this way, a feedback and proposal cycle utilizing emotional data is realized, contributing to improved HR strategies and employee satisfaction across the entire organization.

[0756] The following describes the processing flow.

[0757] Step 1:

[0758] The server collects data such as employee skills, work performance, and training history from the company's internal talent database and external systems. The collected data is formatted in a standardized format, and a detailed profile is created for each employee.

[0759] Step 2:

[0760] The device activates an emotion engine when the user provides feedback, and uses a facial recognition camera and voice recognition microphone to analyze the user's emotions in real time. As a result, it quantifies the user's response to the suggestion and records whether their emotional state is positive or negative.

[0761] Step 3:

[0762] The server integrates emotional data with employee skill assessment data and runs an analysis algorithm. This integrated dataset is used to create personnel placement and training plans, with dynamic adjustments made to take user emotions into account.

[0763] Step 4:

[0764] The server visualizes the generated personnel placement and training plans on a graphical dashboard and presents them to the user via their terminal. Users can then systematically and visually review the information.

[0765] Step 5:

[0766] Users provide feedback on the proposed placement and training plans. During this process, they send specific opinions and evaluations, along with emotional responses captured by the emotion engine, to the server via their device.

[0767] Step 6:

[0768] The server analyzes and compares user feedback and sentiment data to adjust algorithms and improve the accuracy of future recommendations. This results in more appropriate planning that reflects the user's emotional needs.

[0769] (Example 2)

[0770] 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".

[0771] In modern organizations, optimizing personnel placement and development plans that take into account employees' skills and emotional states is essential. However, existing systems often make decisions based solely on skill information, making it difficult to implement flexible personnel placement that reflects employees' emotional states. This makes it difficult to improve employee satisfaction and optimize overall organizational efficiency.

[0772] 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.

[0773] In this invention, the server includes means for collecting personnel information within an organization from an information aggregation device, means for analyzing the collected information to evaluate the skill composition and emotional data of each person, and means for generating an optimal personnel allocation and training plan based on the evaluation results and emotional data, and visualizing the generated plan and presenting it to the user. This makes it possible to provide flexible personnel allocation and training plans that take into account the emotional state of employees.

[0774] An "information aggregation device" is a device used to collect and integrate necessary information from various databases and systems both inside and outside an organization.

[0775] "Skill composition" refers to a systematic combination of skills and knowledge necessary for an individual or group to perform a specific task or activity.

[0776] A "development plan" is a set of guidelines that includes specific goals, methods, and progress management to promote employee skill development and career growth.

[0777] "Emotional data" refers to information that indicates a user's emotional state, and includes data obtained from facial expressions, voice, text, etc.

[0778] "Visualization" is a technique for representing information and data in visual forms such as graphs, charts, and diagrams to facilitate understanding.

[0779] "Readjustment" is the process of improving existing models and systems based on new information and feedback to enhance their performance and adaptability.

[0780] This invention aims to construct a system for managing and optimizing human resource information within an organization. Specific embodiments are described below.

[0781] The server connects to an information aggregation device via the network and periodically collects personnel information. This information covers a wide range of topics, including work performance, skill history, and training records. External data can also be obtained from external certification bodies and industry information providers.

[0782] The device activates an emotion engine upon receiving user feedback, using the camera and microphone to acquire facial and audio data. This emotion data is analyzed by image analysis software (e.g., OpenCV), speech recognition tools (e.g., Google Speech-to-Text), and natural language processing tools (e.g., NLTK). This allows the user's emotional state to be detected and monitored in real time.

[0783] The server generates optimal staffing and training plans using a generative AI model (e.g., TensorFlow) based on collected skill configurations and emotional data. The generated suggestions are visually represented using a visualization tool (e.g., Tableau) and presented to the user via a terminal.

[0784] For example, in selecting a leader for a new project, the server analyzes candidates' technical skills, project experience, and recent performance to suggest the most suitable person. Users can review this suggestion on their device and express positive emotions as feedback. The server then re-evaluates the feedback and incorporates it into future suggestions to improve their accuracy.

[0785] A concrete example of a prompt message would be: "When using this system, detect how employees feel about the suggestions and provide an effective training plan based on that."

[0786] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0787] Step 1:

[0788] The server takes data from the information aggregation device as input and retrieves personnel information. Specifically, it uses an API to collect employee skills, work performance, and training history. The collected data is output to temporary storage in flat file format. The data processing performed in this step is to organize and ensure consistency of the data.

[0789] Step 2:

[0790] The server retrieves additional information from external data providers. This includes collecting certification information and industry trend data via external APIs. Input is data from external systems, which is then integrated with existing internal data for output. Data processing involves normalizing and integrating data from different sources.

[0791] Step 3:

[0792] The device activates its emotion engine upon receiving input from the user. For example, a user might fill out a survey or feedback form, which is then captured as emotion data via the camera and microphone. The input includes facial expressions, voice, and text information, which are output as emotional states. This process involves detecting emotions using image analysis and speech recognition.

[0793] Step 4:

[0794] The server analyzes the collected skill set and emotional data. This analysis utilizes a generative AI model, performing optimization processes including skill matching and emotional feedback. The input is the data obtained in steps 1 and 3, and the output is a proposed personnel placement and training plan. Data calculations include model-based prediction and optimization.

[0795] Step 5:

[0796] The server visualizes the generated proposals and presents them to the user via the terminal. Specifically, it uses visualization tools to convert the data into graphs and charts and outputs them to the user interface. The input is the proposal data generated in step 4, and the output is the visualized plan presented to the user.

[0797] Step 6:

[0798] Users review the suggestions presented through their terminals and provide feedback. This feedback process involves users expressing their feelings based on their emotions and inputting them into the terminal. The input is captured as user feedback, received and analyzed by the server, and stored as output to improve the accuracy of future suggestions. Data processing includes evaluating the feedback data and incorporating it into the system.

[0799] (Application Example 2)

[0800] 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".

[0801] In modern workplaces, it is common practice to formulate placement and training plans without considering employees' emotional states. This can lead to problems in the work environment, such as decreased efficiency and lower employee motivation. In particular, in field-based jobs like security, personnel placement that reflects real-time emotional states is essential.

[0802] 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.

[0803] In this invention, the server includes means for collecting information from a device that stores information for management, means for evaluating individual characteristics based on emotional data analyzed using an emotion analysis device, means for receiving feedback from users and improving the accuracy of the system, and means for acquiring emotional data using a display device worn by on-site personnel. This makes it possible to propose optimal placement and training plans based on the emotional state of employees.

[0804] A "device for storing information for management purposes" is an electronic device used to safely and efficiently store and manage various types of data held by an organization.

[0805] An "emotion analysis device" is a device equipped with the function of identifying and evaluating a person's emotional state through data such as facial expressions, voice, and text.

[0806] "Means of evaluating individual characteristics" refers to the process of analyzing each individual's skills and emotional state and making evaluations based on that analysis.

[0807] "Means of receiving feedback from users and improving the accuracy of the system" refers to methods of collecting feedback from users and using that data to improve the accuracy of the system and the quality of the proposed solutions.

[0808] A "display device worn by on-site personnel" is a device that workers carry or wear and use to display information in real time.

[0809] "Means of acquiring emotional data" refers to the process of collecting information related to an individual's emotions in real time using sensors and analytical algorithms.

[0810] To realize this invention, the server is operated using a combination of multiple hardware and software components. First, the server periodically collects personnel data from a device that stores the information to be managed. This data includes employees' skills, performance, and training history. Next, the emotion analysis device evaluates individual characteristics based on data acquired from a display device worn by the field staff. During this process, real-time emotion analysis is performed, and emotional data is analyzed using sensors and analysis algorithms (e.g., facial recognition software and voice emotion analysis tools).

[0811] Subsequently, the server uses these evaluation results to generate an optimal placement and training plan. The generated placement is visually displayed on the management screen, which users can verify through the display device. Furthermore, the system receives feedback from users, and the accuracy of the system is improved based on the feedback data.

[0812] As a concrete example, in a security monitoring center, when employee A wears smart glasses while working, the server analyzes their emotional state in real time. This information is displayed on the administrator's dashboard, suggesting the placement with the lowest stress level and highest performance. This is expected to improve monitoring efficiency.

[0813] An example of a prompt would be, "Using current employee sentiment data, suggest the optimal staffing and tasks to maximize their performance." In this way, sophisticated suggestions can be made using generative AI models.

[0814] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0815] Step 1:

[0816] The server collects personnel data from devices that store information for management purposes. Inputs include employee skills, work performance, and training history, while output is structured data stored in a database. This data forms the basis for subsequent analysis and is updated in real time via the network.

[0817] Step 2:

[0818] The terminal transmits emotional data acquired through a display device worn by the field staff to the server. The input is emotional data from the staff member's facial expressions and voice, and the output is information converted from that data into an analyzable format. Sensor devices and real-time data streaming technology are utilized here.

[0819] Step 3:

[0820] The server uses an emotion analysis device to analyze emotional data obtained from terminals and evaluate individual characteristics. The input consists of emotional data and personnel data, and the output is an evaluation result reflecting each individual's emotional state and characteristics. A generative AI model estimates the emotional state, and individual evaluations are formed based on this.

[0821] Step 4:

[0822] The server generates optimal personnel placement and development plans based on evaluation results. The inputs are employee evaluation results and organizational placement conditions, and the output is a draft of an efficient placement and development plan. The generated draft is then refined by an algorithm to optimize it for the organization's objectives.

[0823] Step 5:

[0824] The server visually displays the generated deployment and growth plans on the management screen. The inputs are the deployment and plan proposals, and the output is the visualized information presented to the user. Dashboard software is utilized, enabling users to make decisions with a clear understanding.

[0825] Step 6:

[0826] Users provide feedback to the system via their terminals regarding the information presented. The input is the user's feedback, and the output is the data stored in the system as feedback. User opinions influence the accuracy of future suggestions and serve as the basis for improvement.

[0827] Step 7:

[0828] The server readjusts the AI ​​model to improve the system's accuracy based on the collected feedback data. The input is the feedback data and past analysis results, and the output is the improved proposed algorithm. This enables more accurate and appropriate personnel allocation.

[0829] 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.

[0830] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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."

[0838] 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.

[0839] 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.

[0840] 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.

[0841] 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.

[0842] 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.

[0843] 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.

[0844] 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.

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] The following is further disclosed regarding the embodiments described above.

[0851] (Claim 1)

[0852] Methods for collecting personnel information from a database,

[0853] A method for analyzing collected personnel information to evaluate each employee's skill set,

[0854] A means to generate optimal personnel allocation and training plans based on evaluation results,

[0855] A means of presenting the generated layout and plan to the user,

[0856] A means of receiving user feedback and improving the accuracy of the system,

[0857] A system that includes this.

[0858] (Claim 2)

[0859] The system according to claim 1, comprising means for identifying skill gaps based on the analyzed skill set.

[0860] (Claim 3)

[0861] The system according to claim 1, comprising means for readjusting the analysis algorithm based on user feedback.

[0862] "Example 1"

[0863] (Claim 1)

[0864] The means of collecting information from sources,

[0865] A method for cleansing the collected information, inputting it into an analytical model, and performing a quantified evaluation,

[0866] Based on the analysis results, a means to formulate an appropriate placement and training plan,

[0867] A means for visualizing the generated proposal and presenting it via an output device,

[0868] A means of obtaining feedback and making adjustments to improve the analysis model,

[0869] A system that includes this.

[0870] (Claim 2)

[0871] The system according to claim 1, comprising means for identifying gaps using an analytical model.

[0872] (Claim 3)

[0873] The system according to claim 1, comprising means for readjusting the analysis model in response to feedback.

[0874] "Application Example 1"

[0875] (Claim 1)

[0876] A means of collecting personnel information from an information storage device,

[0877] A means of analyzing collected personnel information to evaluate the skill set of each worker,

[0878] A means to generate optimal personnel allocation and growth plans based on evaluation results,

[0879] A means of presenting the generated layout and plan to the user,

[0880] A means of receiving responses from users and improving the accuracy of the system,

[0881] A means of aggregating the skills needs of the entire city and proposing the necessary personnel on smart devices,

[0882] A system that includes this.

[0883] (Claim 2)

[0884] The system according to claim 1, comprising means for identifying skill differences based on the analyzed skill set.

[0885] (Claim 3)

[0886] The system according to claim 1, comprising means for readjusting the analysis procedure based on responses from users.

[0887] "Example 2 of combining an emotion engine"

[0888] (Claim 1)

[0889] A means of collecting personnel information within an organization from an information aggregation device,

[0890] A means of analyzing the collected information to evaluate the skill composition of each personnel,

[0891] A means for generating optimal staffing and training plans based on evaluation results and emotional data,

[0892] A means of presenting the generated layout and plan to the user,

[0893] A means of receiving emotion-based feedback from users to improve the accuracy of the system,

[0894] A means for detecting emotions from the user's facial expressions and voice using an emotion engine,

[0895] A means of visualizing the generated plan and presenting it to the user,

[0896] A system that includes this.

[0897] (Claim 2)

[0898] The system according to claim 1, comprising means for identifying skill differences based on analyzed skill configuration and emotional data.

[0899] (Claim 3)

[0900] The system according to claim 1, comprising means for readjusting the generated AI model based on user feedback.

[0901] "Application example 2 when combining with an emotional engine"

[0902] (Claim 1)

[0903] A means for collecting information from a device that stores information for management,

[0904] A means for evaluating individual characteristics based on emotional data analyzed using an emotion analysis device,

[0905] A means for generating an optimal placement and training plan based on the evaluation results,

[0906] A means of providing the generated layout and plan to the user,

[0907] A means of receiving feedback from users and improving the accuracy of the system,

[0908] A means of acquiring emotional data using a display device worn by the person in charge on site,

[0909] A system that includes this.

[0910] (Claim 2)

[0911] The system according to claim 1, comprising means for identifying differences in characteristics based on the analyzed characteristics.

[0912] (Claim 3)

[0913] The system according to claim 1, comprising means for readjusting the analytical calculation method based on user feedback. [Explanation of Symbols]

[0914] 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. Methods for collecting personnel information from a database, A method for analyzing collected personnel information to evaluate each employee's skill set, A means to generate optimal personnel allocation and training plans based on evaluation results, A means of presenting the generated layout and plan to the user, A means of receiving user feedback and improving the accuracy of the system, A system that includes this.

2. The system according to claim 1, comprising means for identifying skill gaps based on the analyzed skill set.

3. The system according to claim 1, comprising means for readjusting the analysis algorithm based on user feedback.

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