system

Abstract

Provide a system. 【Solution means】 Means for collecting and managing the attribute information of employees, Means for analyzing the attribute information and determining the technical capabilities of employees, Means for selecting appropriate employees with the technical capabilities based on the requirement information of the project, Means for providing appropriate education information to the selected employees, Means for monitoring the performance of the employees and updating the education information, Means for collecting and managing the operation information of the working machine, Means for arranging the working machine for optimal work based on the operation information, Means for providing the operation skills required by the working machine, A system including the above.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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 project execution, frequent personnel changes and departures of employees often occur, and the selection of appropriate replacement personnel is required. However, it is difficult to quickly and accurately find employees who meet the requirements of each project, and the progress of the entire project may be delayed. In addition, when employees acquire new technologies, it is also a challenge to find appropriate training methods and reskilling opportunities. The present invention aims to efficiently solve these problems.

Means for Solving the Problems

[0005] This invention provides a system for evaluating individual technical capabilities by collecting and analyzing specific employee information. Furthermore, it includes a function to automatically select suitable employees based on project requirements and provide them with the necessary training information. This enables efficient personnel allocation for projects and supports employee growth. In addition, it promotes skill improvement within the organization by continuously monitoring employee performance and updating training information as needed.

[0006] "Employees" refers to individuals who perform tasks within a specific organization or project.

[0007] "Specific information" refers to information that includes employee skills, experience, and performance data.

[0008] "Technical competence" is an indicator that shows the level of specialized knowledge and skills possessed by an employee.

[0009] "Project requirements information" refers to information about the skills and conditions necessary for a particular project to be completed.

[0010] "Educational information" refers to educational programs and materials necessary for employees to acquire new skills.

[0011] "Performance" refers to the results and performance achieved by an employee in a project or task. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This 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.

Embodiments for Carrying out the Invention

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

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

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

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

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

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

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

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

[0022] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0029] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

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

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

[0033] This invention is a system for evaluating the technical capabilities of employees within a specific organization and selecting the most suitable personnel for a project. In this system, servers, terminals, and users each play their respective roles in data collection, analysis, recommendation, and provision of educational information.

[0034] Data collection

[0035] Users use a terminal to input their skills, past project experience, and desired training areas. The terminal sends this data to a server, which stores the information in a database. The server also periodically collects performance data from the company's evaluation system and updates the database.

[0036] Data analysis and talent recommendation

[0037] The server analyzes the collected data to clarify the skill sets of employees. This analysis utilizes generative AI to convert text-based information into structured data. Next, when project requirements information is entered, the server selects the most suitable employees based on those requirements and presents a recommendation list to the user.

[0038] Provision of educational information

[0039] Furthermore, the server selects the necessary educational information for employees to acquire new skills and provides it to users via their terminals. Users can review the recommended educational programs and complete the necessary procedures for registration and further details.

[0040] Performance Monitoring

[0041] During and after the completed project, the server monitors employee performance and periodically evaluates skill improvements. Based on the evaluation results, training information is updated as needed, and areas for improvement are provided as feedback to the user.

[0042] This system enables rapid and effective personnel allocation and continuous skill development. For example, if Python skills are required for a data analysis project, the server can cluster multiple candidates with the relevant skills and recommend the most suitable person. Furthermore, if training in the latest machine learning techniques is needed, it can instantly present appropriate training courses, effectively supporting career development.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] Users input their skills, past project experience, and desired training areas using a terminal. The terminal then transmits this information to a server.

[0046] Step 2:

[0047] The server stores the received data in a database. Furthermore, it automatically retrieves employee performance data from the company's evaluation system on a regular basis, keeping the database constantly updated.

[0048] Step 3:

[0049] The server uses generative AI to analyze employees' skill sets based on information stored in the database. It extracts structured skill information from text data and evaluates individual technical capabilities.

[0050] Step 4:

[0051] The user enters project requirements information using a terminal. The terminal sends this information to the server, which provides basic information to identify the necessary skills and experience.

[0052] Step 5:

[0053] The server selects the most suitable employee based on project requirements information and analyzed skill information. It generates a list of candidates with the appropriate skill sets and presents it to the user as a recommendation list.

[0054] Step 6:

[0055] The server selects the necessary educational information for the chosen employees and makes it accessible to users through their terminals. This provides employees with an educational program that allows them to learn the necessary skills.

[0056] Step 7:

[0057] The server monitors employee performance during and after project execution and evaluates skill growth. Based on the evaluation results, it updates training information and provides feedback to users.

[0058] (Example 1)

[0059] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0060] There is a need for a system that can accurately assess employees' technical capabilities and efficiently provide optimal project assignments and training opportunities. However, conventional methods tend to rely heavily on subjective aspects in employee skill assessment, and there are problems with the selection and provision of appropriate training information.

[0061] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0062] In this invention, the server includes a device for collecting and managing employee-specific information, a device for analyzing employee technical capabilities using a generated AI model, and a device for selecting and recommending appropriate employees. This enables objective and rapid determination of technical capabilities and the provision of optimal personnel placement and training opportunities.

[0063] "Employee" refers to a worker or person in charge who performs duties within a specific organization.

[0064] "Specific information" refers to information that includes attributes and data necessary for analysis, such as an employee's skills, experience, and work history.

[0065] A "generative AI model" refers to an algorithm or program that uses artificial intelligence to analyze and evaluate employee information and generate structured data.

[0066] "Project requirements information" refers to detailed information including the skill sets, experience, and other conditions required for a specific project.

[0067] "Recommending equipment" refers to an automated system that selects appropriate employees based on analysis results and assigns them to projects.

[0068] "Educational information" refers to information about training programs and learning resources that employees need to acquire new skills.

[0069] A "performance monitoring system" refers to a system for collecting and evaluating employee performance data during and after a project.

[0070] A "feedback device" refers to a tool used to communicate areas for improvement and educational information to employees based on performance evaluations.

[0071] "Clustering" refers to classifying employees into groups based on their characteristics and skills, using groups that share similar traits.

[0072] A "prompt" refers to a set of instructions or questions intended for input into a generation AI or other automation system.

[0073] This invention provides a system for efficiently evaluating the technical capabilities of employees within a specific organization and for optimal project assignment and provision of training information.

[0074] Data collection

[0075] Users input their skills and past project experience into the terminal. This involves using forms, selecting options, and registering the necessary information using text boxes. This data is transmitted from the terminal to the server via a security protocol (e.g., SSL / TLS) and stored in a database.

[0076] Data Analysis

[0077] The server analyzes the collected data using a generative AI model. This process utilizes machine learning algorithms to convert text information into structured data. Specifically, the hardware configuration requires a group of computers with high-performance processors and sufficient memory.

[0078] Talent Recommendation

[0079] Once project requirements are entered, the server uses an algorithm to select the most suitable employee. An example of a prompt might be, "Recommend an employee who can use Python for the data science project."

[0080] Provision of educational information

[0081] The server selects appropriate educational information for employees and provides it to users via terminals. For example, it lists resources for learning new technologies and techniques, and allows users to view detailed information and register online.

[0082] Performance Monitoring

[0083] During and after the project, the server regularly monitors employee performance. This enables a mechanism to evaluate skill improvements and provide feedback to users.

[0084] This invention makes it possible to conduct rapid and objective competency assessments, and to provide appropriate personnel placement and training opportunities.

[0085] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0086] Step 1:

[0087] Users input their skills and past project experience into a terminal. This input includes skill sets, years of experience, and specific project names. After this information is stored on the terminal, it is sent to the server in an encrypted state. By transmitting the input data while maintaining security, accurate information can be collected.

[0088] Step 2:

[0089] The server analyzes the received data and stores it in a database. Here, a generative AI model is used to extract structured data from text data. This analysis process clearly displays each employee's skills and characteristics as numerical data. Specifically, the information is organized using high-speed database operations.

[0090] Step 3:

[0091] The server periodically collects performance evaluation data from other internal systems and updates the existing database. This information includes each employee's evaluation results and achievement of performance targets. Based on the input performance data, a system operates to quantitatively evaluate each employee's performance.

[0092] Step 4:

[0093] The user enters project requirements information into a terminal. These requirements include necessary skills and years of experience. The terminal sends this information to a server, which uses a generated AI model to select suitable employees that meet the requirements. By entering detailed project requirements, the system recommends the most suitable personnel.

[0094] Step 5:

[0095] The server creates a list of selected employees and provides the user with the recommendation results. The output includes a list of candidates and each employee's skill score. The user can review this output on their terminal and further refine their recommendations using prompts.

[0096] Step 6:

[0097] The server selects the training programs that each employee should participate in and presents them to the user via their terminal. This training information includes skills improvement programs and training on the latest technologies. Users can also view detailed information and register to participate.

[0098] Step 7:

[0099] During and after the project, the server monitors employee performance and evaluates the results. The evaluation results are stored in a database, and training programs are recommended again as needed. This cycle ensures continuous skill improvement and talent development.

[0100] (Application Example 1)

[0101] 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."

[0102] In modern manufacturing facilities, not only employee capabilities but also the optimal operational placement of machinery is required. However, efficiently integrating this data and providing optimal work placement and necessary training information is difficult. In particular, understanding the performance of individual machines and recommending appropriate operations based on that information relies heavily on manual work and experience, making it inefficient and prone to errors. Furthermore, there is a lack of up-to-date training information to improve employee skills.

[0103] 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.

[0104] In this invention, the server includes means for collecting and managing employee attribute information, means for collecting and managing operational information of work machines, and means for assigning work machines to optimal tasks based on the operational information. This enables centralized management of information on work machines and employees, realization of optimal work assignments, and effective support for improving work efficiency and employee skill acquisition.

[0105] "Employee attribute information" refers to information about the skills, qualifications, and past experience of personnel within an organization.

[0106] "Means of management" refers to methods and devices for organizing collected information and storing it in a way that allows for easy retrieval and reference.

[0107] "Technical competence" is a measure used to evaluate the level of technical skills and knowledge possessed by an employee.

[0108] "Project requirements information" refers to information that describes the skills and conditions necessary to perform a specific task or project.

[0109] "Educational information" refers to information about educational programs and materials necessary for employees to acquire new skills or improve their current skills.

[0110] "Operational information for work machinery" refers to information regarding the operating status, performance, and placement of machinery and robots used in factories and other industrial settings.

[0111] "Assigning to a task" is the process of assigning the most suitable machinery or personnel to a specific task or operation.

[0112] "Operational skills" refer to the technical skills necessary to effectively operate a particular machine or device.

[0113] To realize this invention, the system consists of a server, terminals, and users. The server first has the function of collecting and managing employee attribute information and operational information of work machinery. This information is obtained from sensing devices and management systems as needed and stored in a database. The sensing devices used include, for example, IoT sensors.

[0114] The server then uses a generative AI model to analyze the collected information. This process utilizes frameworks such as TENSORFLOW® and PyTorch to analyze the data using machine learning techniques and determine the optimal work arrangement and required skills. Based on these results, it provides information on the placement of work machines and employee training. For example, when starting a new product assembly line, it can instantly recommend the optimal combination of work machines and employees.

[0115] Furthermore, the server provides educational information to employees and operators. This information is provided as links to online courses and details of training programs. Through the terminal, users can view this information and obtain more detailed information as needed. The server can also utilize a generated AI model with prompt text as input to make more accurate recommendations. An example of a prompt text is, "Based on the work data of the factory robots, suggest the optimal task assignment for the next product production line."

[0116] Specifically, for example, in the assembly of product A, the server might assign welding tasks, which robot X excels at, while recommending online training to improve problem-solving skills to personnel Y. In this way, it is possible to constantly strive for optimal work efficiency and skill improvement.

[0117] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0118] Step 1:

[0119] The server receives employee attribute information and work machine operation information from sensing devices and management systems and stores it in a database. This input information includes employee skills, qualifications, experience, and work machine operating status and performance data. The information is acquired from IoT sensors and employee information systems and stored in the database to prepare for later analysis.

[0120] Step 2:

[0121] The server analyzes data by running a generative AI model based on the information stored in the database. Specifically, it uses TensorFlow and PyTorch to analyze employee skill sets and machine performance data. This analysis includes structuring text data to evaluate skills and calculating optimal work assignments using machine learning algorithms. The analysis results output the optimal tasks for employees and machines.

[0122] Step 3:

[0123] Based on the analysis results, the server places the work equipment in the optimal work area and provides the corresponding employees with the necessary training information. It generates work instructions according to the outputted task placement information and sends them to the terminal. Employees are also provided with recommendations for training programs and online courses to improve their necessary skills. This information includes specific links and detailed information.

[0124] Step 4:

[0125] The terminal displays work instructions and training information received from the server to the user. The user reviews the provided information and chooses to modify their work or participate in training programs as needed. Specifically, they can access online courses by clicking on the received links.

[0126] Step 5:

[0127] Users perform tasks based on the work instructions they receive and send progress and completion reports from their terminals to the server. The server monitors work performance based on this information and updates training information as needed. Specifically, it utilizes a generative AI model to provide users with feedback tailored to their current skill level.

[0128] 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.

[0129] This invention combines a system that collects and manages employee identification information and selects the most suitable personnel based on project requirements with an emotion engine that recognizes user emotions. In this system, the server, terminals, and users cooperate to analyze and manage data, provide educational information, and provide feedback based on emotion recognition.

[0130] Data collection and emotion recognition

[0131] Users input information about their skills, experience, and projects through their device. During this process, an emotion engine analyzes the user's emotional state in real time and collects it as emotion data. The device then sends this information to a server, which stores it in a database.

[0132] Data analysis and talent recommendation

[0133] The server analyzes collected data and emotional data to evaluate employees' skill sets and emotional states. It selects the most suitable employees that match the project requirements and presents a recommendation list to the user. The list may be adjusted to take emotional states into account.

[0134] Provision and optimization of educational information

[0135] The server selects appropriate educational information for the chosen employees and provides it to the users through their terminals. The emotion engine optimizes the content and timing of the educational information delivery according to the user's emotional state, supporting effective reskilling.

[0136] Performance monitoring and emotional feedback

[0137] During and after a project, the server monitors employee performance data and evaluates it in conjunction with emotional data. The evaluation results are fed back to the user, prompting them to take new actions based on their emotional state. For example, if an employee is experiencing stress during a project, additional support or training may be suggested.

[0138] This system enables flexible and accurate personnel placement and training that takes emotions into consideration, simultaneously improving the efficiency of human resource utilization within companies and increasing employee satisfaction. Specifically, for example, in a data analysis project, if sentiment analysis determines that a user lacks motivation for a task, the server can suggest relevant training or actions that will lead to increased motivation.

[0139] The following describes the processing flow.

[0140] Step 1:

[0141] The user uses a terminal to input their skills, past project experience, and desired training areas. During this process, the terminal utilizes an emotion engine to recognize the user's emotional state from their facial expressions and tone of voice, generating emotional data. This generated data is then sent to a server.

[0142] Step 2:

[0143] The server stores skill and sentiment data sent from terminals in a database. Furthermore, it continuously updates the database by retrieving employee evaluation data from other internal systems.

[0144] Step 3:

[0145] The server analyzes the collected data using generative AI. It analyzes the user's skill set and emotional data, and evaluates the appropriate technical abilities for each field. It also determines the user's current mental state based on the emotional data.

[0146] Step 4:

[0147] The user enters project requirements information using a terminal. The terminal sends this information to a server, which provides background information related to the required skills and aptitudes.

[0148] Step 5:

[0149] The server selects the most suitable employee based on project requirements information and analyzed skills and sentiment data. In doing so, it considers not only technical abilities but also the employee's emotional state to recommend the most suitable person for the project. The results are presented to the user via a terminal.

[0150] Step 6:

[0151] The server selects and provides necessary educational information to the chosen employees. Based on an emotion engine, the content and timing of the educational information are adjusted according to the user's emotional state. Users can view the educational information and obtain details on their devices.

[0152] Step 7:

[0153] During and after the project, the server monitors and evaluates employee performance and emotional data. Based on the evaluation results, it updates educational information as needed and provides feedback to users through their terminals. This feedback allows users to decide on their next actions based on their emotional state.

[0154] (Example 2)

[0155] 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".

[0156] In modern project management, appropriate personnel placement and training that consider not only employees' skills and experience but also their emotional state are crucial. However, traditional systems have not adequately collected and analyzed emotional information, which can lead to decreased employee motivation and stress negatively impacting project outcomes. Furthermore, the provision of training information has not been optimized according to individual emotional states, making it difficult to effectively utilize human resources.

[0157] 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.

[0158] In this invention, the server includes means for collecting and managing employee identification information and emotional information; means for analyzing the identification information and emotional information to determine the employee's technical ability and emotional state; and means for selecting appropriate employees with the aforementioned technical ability and emotional state based on project requirements information, and adjusting the recommendation list according to the emotional state. This enables flexible and accurate personnel placement and training that also takes emotional factors into consideration.

[0159] "Specific information" refers to individual data such as an employee's skills and experience, and is necessary to identify individual employees and manage their respective abilities and past performance.

[0160] "Emotional information" refers to data about the emotional state of employees, including stress, excitement, and feelings of security, and is used to evaluate the psychological impact on a project.

[0161] "Analysis" refers to the process of processing collected data to determine the abilities and emotional state of employees, and is the act of analyzing and evaluating data.

[0162] "Technical competence" refers to the specialized knowledge and skills that an employee needs to meet the requirements of a specific project.

[0163] "Emotional state" refers to the psychological and emotional conditions that employees experience while performing their duties, and is a factor that can potentially affect project performance.

[0164] A "recommendation list" refers to a list created to suggest the most suitable employee based on project requirements and employee suitability.

[0165] "Educational information" refers to learning materials and training programs provided to support employees in improving their abilities and acquiring skills.

[0166] "Performance" refers to the results and achievements that employees have accomplished in a project, and is used as an indicator or standard.

[0167] "Monitoring" refers to the process of continuously tracking employee performance and emotional state to detect changes or anomalies.

[0168] This invention is a system that collects and manages employee identification and emotional information, selects the most suitable personnel according to the requirements of a specific project, and provides educational support. This system functions through the coordinated operation of servers, terminals, and users.

[0169] The server is responsible for central data management, storing specific and emotional information in the database. This includes employee skill sets, experience, and emotional information. The server uses a generative AI model to analyze this information. The AI ​​model processes prompt statements as input and performs data analysis. This analysis evaluates the employee's technical skills and emotional state.

[0170] The terminal provides an interface for users to input information. Through an emotion engine, the terminal acquires emotional information in real time as the user inputs information and collects it as data. For example, if the system analyzes that a user is experiencing stress while inputting skills for a specific project, that information is also collected simultaneously.

[0171] Users can input project requirements information via their terminal, including necessary skills, experience, and goals. Emotional states are taken into consideration, and the server recommends the most suitable candidates. When providing training information, the timing and content are optimized based on the employee's emotional state.

[0172] For example, in a data analysis project, the AI ​​model might be prompted with the message, "Select the most suitable employee to meet the project requirements and create feedback based on sentiment analysis." The AI ​​then generates a list of recommended employees and provides sentiment-based feedback.

[0173] In this way, the system enables flexible and highly efficient personnel allocation and training support that even takes emotional information into account, achieving both employee motivation maintenance and the success of corporate projects.

[0174] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0175] Step 1:

[0176] Users input information about their skills, experience, and projects through a terminal. The input data is collected by the terminal, while an emotion engine analyzes the user's emotional state in real time. Input includes skill information and project requirements, and output generates skill data and emotion data. The terminal packages this data and sends it to the server.

[0177] Step 2:

[0178] The server stores skill and sentiment data received from the terminals in a database. Here, the received data is not simply stored as is, but is indexed to enable efficient access and analysis. The inputs are skill and sentiment data, and the output is organized database entries.

[0179] Step 3:

[0180] The server uses information from the database to input prompts into the generative AI model. For example, a prompt such as "Create an optimal recommendation list based on the employee's specific skills and emotional state" might be used. The generative AI model analyzes the prompt and evaluates the employee's technical abilities and emotional state. The input here is the prompt and related information from the database, and the output is the evaluation result.

[0181] Step 4:

[0182] The server generates a list of recommended employees suitable for the project based on the evaluation results. This list may be prioritized based on emotional state. The input is the evaluation results, and the output is the adjusted recommendation list. The server sends this list to the terminal and presents it to the user.

[0183] Step 5:

[0184] The server selects appropriate educational information for the chosen employee, taking their emotional state into account, and sends it to the terminal at an optimized timing. For example, if an employee is determined to be highly stressed, educational materials on relaxation techniques will be provided. The input is a database of emotional states and educational materials, and the output is optimized educational information. The user receives this information through the terminal interface.

[0185] Step 6:

[0186] During and after project execution, the server collects employee performance data and combines it with previously collected sentiment data to provide an overall performance evaluation. Based on the evaluation, additional support and training are automatically suggested as needed. The inputs are performance data and sentiment data, and the output is feedback and support suggestions. The terminal notifies the user of this feedback and prompts them to take the next steps.

[0187] (Application Example 2)

[0188] 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".

[0189] In technology projects, including those involving autonomous vehicles, optimizing employee selection and training is a critical challenge. Traditional methods focused solely on employees' technical skills, neglecting their emotional states, which could negatively impact productivity and workplace efficiency. Furthermore, the lack of means to provide appropriate rest and support based on emotional states led to decreased employee satisfaction and performance.

[0190] 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.

[0191] In this invention, the server includes means for collecting and managing employee-specific information, means for analyzing employee technical skills and emotional state, means for selecting the most suitable employee considering their emotional state, and means for providing educational information optimized based on their emotional state. This enables flexible and accurate personnel placement that considers not only technical skills but also emotional aspects, thereby improving employee performance and optimizing the work environment.

[0192] "Employee identification information" refers to individual information about employees, such as their skills, experience, and emotional state, and is data used to ensure appropriate personnel placement within a project.

[0193] "Analysis methods" refer to processes and tools used to analyze collected data and evaluate employees' technical skills and emotional states.

[0194] "Emotional state" refers to information that indicates an employee's current emotions and psychological state, and is a factor that influences the progress of a project.

[0195] "Educational information" refers to knowledge and training content provided to employees with the aim of improving their skills and optimizing their performance.

[0196] "Optimized educational information" refers to educational information that is tailored to the emotional state and technical skills of employees and is provided in a way that best suits their needs.

[0197] "Rest or relaxation activities" refer to activities aimed at reducing employee stress and refreshing them, and are proposed based on the employee's emotional state.

[0198] "Clustering" is a term that describes the process of grouping employees based on specific information and emotional data, and combining those with similar characteristics.

[0199] The system for realizing this invention aims to optimize personnel allocation and training by collecting specific information on employees and analyzing their technical skills and emotional state.

[0200] The core of the system is a server that manages a series of processes including data collection, analysis, evaluation, and education delivery. The server receives information transmitted from each employee's smartphone or tablet and stores it in a database. In terms of specific hardware, it uses general server equipment that provides cloud services. In addition, it leverages Google Cloud Platform's natural language processing API to analyze sentiment data in detail.

[0201] Users input their skill information and project data into the device. The device incorporates voice input and text input interfaces, and the entered information is transferred to the server in real time. The device's operating system and applications utilize a general-purpose smartphone OS and application framework.

[0202] The server uses data analysis tools such as Python and TensorFlow to perform employee skill assessments and sentiment analysis based on the collected information. Based on the results, it automatically selects employees suitable for project requirements and prepares to provide optimized training information.

[0203] For example, in an autonomous vehicle maintenance project, if there is an employee who is highly skilled but experiencing stress, the server will notify the terminal with specific actions in the format of "Suggest additional technical training and a 5-minute break."

[0204] An example of a prompt sentence for a generated AI model is, "Suggest a simple relaxation exercise to help staff members who are feeling stressed about vehicle maintenance reset their mood." In this way, the system can provide personalized feedback to each employee.

[0205] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0206] Step 1:

[0207] Users input information about their skills, experience, and projects using smartphones or tablets. This includes text and voice input. The input information is formatted by the device and sent to the server via the internet. During this process, the device uses an emotion engine to analyze the emotional state from the voice data and sends the results along with the data to the server.

[0208] Step 2:

[0209] The server stores data received from terminals in a database. The data is organized by user and includes both skill information and emotional status. The database is managed using query languages ​​such as SQL, enabling efficient data storage and retrieval.

[0210] Step 3:

[0211] The server uses Python and TensorFlow to analyze data and evaluate each user's technical skills and emotional state. This analysis quantifies skill sets and applies a clustering algorithm based on emotional data. The analysis results are output as evaluation profiles generated for each user.

[0212] Step 4:

[0213] The server selects the most suitable employee based on the project requirements. Using an AI model within the server, the selection process considers a balance of skills and emotional state to create a list of optimal users. The selection results are sent to the terminal as a recommendation list.

[0214] Step 5:

[0215] The user's device provides appropriate educational information based on the received recommendation list. The educational content and timing are optimized according to the user's emotional state. For example, if the user is experiencing high stress, a relaxation video might be suggested. This information is displayed on the device screen, prompting the user to take specific action.

[0216] Step 6:

[0217] The server continuously collects employee performance and sentiment data from the database throughout the project, providing situation-based feedback. Based on each user's performance data, it generates new educational information and continuously improves to provide even more effective education and support.

[0218] Step 7:

[0219] The device generates reports as needed, providing users with regular feedback. These reports include a history of performance and emotional state, as well as suggestions for future improvements and next steps. The generated reports serve as valuable resources for users to refer to and decide on their next actions.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] [Second Embodiment]

[0224] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0225] 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.

[0226] 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).

[0227] 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.

[0228] 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.

[0229] 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).

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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".

[0236] This invention is a system for evaluating the technical capabilities of employees within a specific organization and selecting the most suitable personnel for a project. In this system, servers, terminals, and users each play their respective roles in data collection, analysis, recommendation, and provision of educational information.

[0237] Data collection

[0238] Users use a terminal to input their skills, past project experience, and desired training areas. The terminal sends this data to a server, which stores the information in a database. The server also periodically collects performance data from the company's evaluation system and updates the database.

[0239] Data analysis and talent recommendation

[0240] The server analyzes the collected data to clarify the skill sets of employees. This analysis utilizes generative AI to convert text-based information into structured data. Next, when project requirements information is entered, the server selects the most suitable employees based on those requirements and presents a recommendation list to the user.

[0241] Provision of educational information

[0242] Furthermore, the server selects the necessary educational information for employees to acquire new skills and provides it to users via their terminals. Users can review the recommended educational programs and complete the necessary procedures for registration and further details.

[0243] Performance Monitoring

[0244] During and after the completed project, the server monitors employee performance and periodically evaluates skill improvements. Based on the evaluation results, training information is updated as needed, and areas for improvement are provided as feedback to the user.

[0245] This system enables rapid and effective personnel allocation and continuous skill development. For example, if Python skills are required for a data analysis project, the server can cluster multiple candidates with the relevant skills and recommend the most suitable person. Furthermore, if training in the latest machine learning techniques is needed, it can instantly present appropriate training courses, effectively supporting career development.

[0246] The following describes the processing flow.

[0247] Step 1:

[0248] Users input their skills, past project experience, and desired training areas using a terminal. The terminal then transmits this information to a server.

[0249] Step 2:

[0250] The server stores the received data in a database. Furthermore, it automatically retrieves employee performance data from the company's evaluation system on a regular basis, keeping the database constantly updated.

[0251] Step 3:

[0252] The server uses generative AI to analyze employees' skill sets based on information stored in the database. It extracts structured skill information from text data and evaluates individual technical capabilities.

[0253] Step 4:

[0254] The user enters project requirements information using a terminal. The terminal sends this information to the server, which provides basic information to identify the necessary skills and experience.

[0255] Step 5:

[0256] The server selects the most suitable employee based on project requirements information and analyzed skill information. It generates a list of candidates with the appropriate skill sets and presents it to the user as a recommendation list.

[0257] Step 6:

[0258] The server selects the necessary educational information for the chosen employees and makes it accessible to users through their terminals. This provides employees with an educational program that allows them to learn the necessary skills.

[0259] Step 7:

[0260] The server monitors employee performance during and after project execution and evaluates skill growth. Based on the evaluation results, it updates training information and provides feedback to users.

[0261] (Example 1)

[0262] 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."

[0263] There is a need for a system that can accurately assess employees' technical capabilities and efficiently provide optimal project assignments and training opportunities. However, conventional methods tend to rely heavily on subjective aspects in employee skill assessment, and there are problems with the selection and provision of appropriate training information.

[0264] 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.

[0265] In this invention, the server includes a device for collecting and managing employee-specific information, a device for analyzing employee technical capabilities using a generated AI model, and a device for selecting and recommending appropriate employees. This enables objective and rapid determination of technical capabilities and the provision of optimal personnel placement and training opportunities.

[0266] "Employee" refers to a worker or person in charge who performs duties within a specific organization.

[0267] "Specific information" refers to information that includes attributes and data necessary for analysis, such as an employee's skills, experience, and work history.

[0268] A "generative AI model" refers to an algorithm or program that uses artificial intelligence to analyze and evaluate employee information and generate structured data.

[0269] "Project requirements information" refers to detailed information including the skill sets, experience, and other conditions required for a specific project.

[0270] "Recommending equipment" refers to an automated system that selects appropriate employees based on analysis results and assigns them to projects.

[0271] "Educational information" refers to information about training programs and learning resources that employees need to acquire new skills.

[0272] A "performance monitoring system" refers to a system for collecting and evaluating employee performance data during and after a project.

[0273] A "feedback device" refers to a tool used to communicate areas for improvement and educational information to employees based on performance evaluations.

[0274] "Clustering" refers to classifying employees into groups based on their characteristics and skills, using groups that share similar traits.

[0275] A "prompt" refers to a set of instructions or questions intended for input into a generation AI or other automation system.

[0276] This invention provides a system for efficiently evaluating the technical capabilities of employees within a specific organization and for optimal project assignment and provision of training information.

[0277] Data collection

[0278] The user inputs their skills and past project experience into the terminal. For this, a form is used, and the necessary information is registered using options and text boxes. Such data is sent by the terminal to the server through a security protocol (e.g., SSL / TLS) and stored in the database.

[0279] Data analysis

[0280] The server analyzes the collected data using a generative AI model. In this process, machine learning algorithms are used to convert text information into structured data. As a specific hardware configuration, a cluster of computers with high-performance processors and sufficient memory is required.

[0281] Personnel recommendation

[0282] When project requirement information is input, the server selects the most suitable employees using an algorithm. As an example of a prompt sentence, an instruction in the form of "Please recommend employees who can use Python in a data science project" is used.

[0283] Provision of educational information

[0284] The server selects appropriate educational information for the employees and provides it to the user via the terminal. For example, resources for acquiring new technologies and techniques are listed, allowing for online confirmation of detailed information and participation procedures.

[0285] Performance monitoring

[0286] During and after the implementation of the project, the server regularly monitors the performance of the employees. Thereby, the improvement of skills is evaluated, and a mechanism for providing feedback to the user operates.

[0287] According to this invention, a quick and objective ability evaluation can be performed, and it becomes possible to provide appropriate personnel placement and educational opportunities.

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

[0289] Step 1:

[0290] Users input their skills and past project experience into a terminal. This input includes skill sets, years of experience, and specific project names. After this information is stored on the terminal, it is sent to the server in an encrypted state. By transmitting the input data while maintaining security, accurate information can be collected.

[0291] Step 2:

[0292] The server analyzes the received data and stores it in a database. Here, a generative AI model is used to extract structured data from text data. This analysis process clearly displays each employee's skills and characteristics as numerical data. Specifically, the information is organized using high-speed database operations.

[0293] Step 3:

[0294] The server periodically collects performance evaluation data from other internal systems and updates the existing database. This information includes each employee's evaluation results and achievement of performance targets. Based on the input performance data, a system operates to quantitatively evaluate each employee's performance.

[0295] Step 4:

[0296] The user enters project requirements information into a terminal. These requirements include necessary skills and years of experience. The terminal sends this information to a server, which uses a generated AI model to select suitable employees that meet the requirements. By entering detailed project requirements, the system recommends the most suitable personnel.

[0297] Step 5:

[0298] The server creates a list of selected employees and provides the user with the recommendation results. The output includes a list of candidates and each employee's skill score. The user can review this output on their terminal and further refine their recommendations using prompts.

[0299] Step 6:

[0300] The server selects the training programs that each employee should participate in and presents them to the user via their terminal. This training information includes skills improvement programs and training on the latest technologies. Users can also view detailed information and register to participate.

[0301] Step 7:

[0302] During and after the project, the server monitors employee performance and evaluates the results. The evaluation results are stored in a database, and training programs are recommended again as needed. This cycle ensures continuous skill improvement and talent development.

[0303] (Application Example 1)

[0304] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0305] In modern manufacturing facilities, not only employee capabilities but also the optimal operational placement of machinery is required. However, efficiently integrating this data and providing optimal work placement and necessary training information is difficult. In particular, understanding the performance of individual machines and recommending appropriate operations based on that information relies heavily on manual work and experience, making it inefficient and prone to errors. Furthermore, there is a lack of up-to-date training information to improve employee skills.

[0306] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0307] In this invention, the server includes means for collecting and managing the attribute information of employees, means for collecting and managing the operation information of work machines, and means for arranging work machines for optimal work based on the operation information. Thereby, it becomes possible to centrally manage the information of work machines and employees, realize optimal work arrangements, and effectively support the improvement of work efficiency and the skill acquisition of employees.

[0308] "The attribute information of employees" refers to information regarding skills, qualifications, and past experiences of personnel within the organization.

[0309] "Means for managing" refers to a method or device for organizing the collected information and storing it so that it can be easily searched and referenced.

[0310] "Technical ability" is a measure for evaluating the level of technical skills and knowledge possessed by employees.

[0311] "Requirement information of the project" refers to information describing the skills and conditions required to perform a specific task or project.

[0312] "Educational information" is information regarding educational programs and materials necessary for employees to acquire new skills or improve their current skills.

[0313] "Operation information of work machines" refers to information regarding the operating status, performance, and arrangement of machines and robots used in factories and the like.

[0314] "Arranging for work" is a process of assigning optimal machines and employees to a specific task or job.

[0315] "Operation skill" refers to the technical skills necessary to effectively operate a specific machine or device.

[0316] To realize this invention, the system consists of a server, terminals, and users. The server first has the function of collecting and managing employee attribute information and operational information of work machinery. This information is obtained from sensing devices and management systems as needed and stored in a database. The sensing devices used include, for example, IoT sensors.

[0317] The server then uses a generative AI model to analyze the collected information. This process utilizes frameworks such as TensorFlow and PyTorch to analyze the data using machine learning techniques and determine the optimal work arrangement and required skills. Based on these results, it provides information on the placement of work machines and employee training. For example, when starting a new product assembly line, it can instantly recommend the optimal combination of work machines and employees.

[0318] Furthermore, the server provides educational information to employees and operators. This information is provided as links to online courses and details of training programs. Through the terminal, users can view this information and obtain more detailed information as needed. The server can also utilize a generated AI model with prompt text as input to make more accurate recommendations. An example of a prompt text is, "Based on the work data of the factory robots, suggest the optimal task assignment for the next product production line."

[0319] Specifically, for example, in the assembly of product A, the server might assign welding tasks, which robot X excels at, while recommending online training to improve problem-solving skills to personnel Y. In this way, it is possible to constantly strive for optimal work efficiency and skill improvement.

[0320] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0321] Step 1:

[0322] The server receives employee attribute information and work machine operation information from sensing devices and management systems and stores it in a database. This input information includes employee skills, qualifications, experience, and work machine operating status and performance data. The information is acquired from IoT sensors and employee information systems and stored in the database to prepare for later analysis.

[0323] Step 2:

[0324] The server analyzes data by running a generative AI model based on the information stored in the database. Specifically, it uses TensorFlow and PyTorch to analyze employee skill sets and machine performance data. This analysis includes structuring text data to evaluate skills and calculating optimal work assignments using machine learning algorithms. The analysis results output the optimal tasks for employees and machines.

[0325] Step 3:

[0326] Based on the analysis results, the server places the work equipment in the optimal work area and provides the corresponding employees with the necessary training information. It generates work instructions according to the outputted task placement information and sends them to the terminal. Employees are also provided with recommendations for training programs and online courses to improve their necessary skills. This information includes specific links and detailed information.

[0327] Step 4:

[0328] The terminal displays work instructions and training information received from the server to the user. The user reviews the provided information and chooses to modify their work or participate in training programs as needed. Specifically, they can access online courses by clicking on the received links.

[0329] Step 5:

[0330] Users perform tasks based on the work instructions they receive and send progress and completion reports from their terminals to the server. The server monitors work performance based on this information and updates training information as needed. Specifically, it utilizes a generative AI model to provide users with feedback tailored to their current skill level.

[0331] 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.

[0332] This invention combines a system that collects and manages employee identification information and selects the most suitable personnel based on project requirements with an emotion engine that recognizes user emotions. In this system, the server, terminals, and users cooperate to analyze and manage data, provide educational information, and provide feedback based on emotion recognition.

[0333] Data collection and emotion recognition

[0334] Users input information about their skills, experience, and projects through their device. During this process, an emotion engine analyzes the user's emotional state in real time and collects it as emotion data. The device then sends this information to a server, which stores it in a database.

[0335] Data analysis and talent recommendation

[0336] The server analyzes collected data and emotional data to evaluate employees' skill sets and emotional states. It selects the most suitable employees that match the project requirements and presents a recommendation list to the user. The list may be adjusted to take emotional states into account.

[0337] Provision and optimization of educational information

[0338] The server selects appropriate educational information for the chosen employees and provides it to the users through their terminals. The emotion engine optimizes the content and timing of the educational information delivery according to the user's emotional state, supporting effective reskilling.

[0339] Performance monitoring and emotional feedback

[0340] During and after a project, the server monitors employee performance data and evaluates it in conjunction with emotional data. The evaluation results are fed back to the user, prompting them to take new actions based on their emotional state. For example, if an employee is experiencing stress during a project, additional support or training may be suggested.

[0341] This system enables flexible and accurate personnel placement and training that takes emotions into consideration, simultaneously improving the efficiency of human resource utilization within companies and increasing employee satisfaction. Specifically, for example, in a data analysis project, if sentiment analysis determines that a user lacks motivation for a task, the server can suggest relevant training or actions that will lead to increased motivation.

[0342] The following describes the processing flow.

[0343] Step 1:

[0344] The user uses a terminal to input their skills, past project experience, and desired training areas. During this process, the terminal utilizes an emotion engine to recognize the user's emotional state from their facial expressions and tone of voice, generating emotional data. This generated data is then sent to a server.

[0345] Step 2:

[0346] The server stores skill and sentiment data sent from terminals in a database. Furthermore, it continuously updates the database by retrieving employee evaluation data from other internal systems.

[0347] Step 3:

[0348] The server analyzes the collected data using generative AI. It analyzes the user's skill set and emotional data, and evaluates the appropriate technical abilities for each field. It also determines the user's current mental state based on the emotional data.

[0349] Step 4:

[0350] The user enters project requirements information using a terminal. The terminal sends this information to a server, which provides background information related to the required skills and aptitudes.

[0351] Step 5:

[0352] The server selects the most suitable employee based on project requirements information and analyzed skills and sentiment data. In doing so, it considers not only technical abilities but also the employee's emotional state to recommend the most suitable person for the project. The results are presented to the user via a terminal.

[0353] Step 6:

[0354] The server selects and provides necessary educational information to the chosen employees. Based on an emotion engine, the content and timing of the educational information are adjusted according to the user's emotional state. Users can view the educational information and obtain details on their devices.

[0355] Step 7:

[0356] During and after the project, the server monitors and evaluates employee performance and emotional data. Based on the evaluation results, it updates educational information as needed and provides feedback to users through their terminals. This feedback allows users to decide on their next actions based on their emotional state.

[0357] (Example 2)

[0358] 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".

[0359] In modern project management, appropriate personnel placement and training that consider not only employees' skills and experience but also their emotional state are crucial. However, traditional systems have not adequately collected and analyzed emotional information, which can lead to decreased employee motivation and stress negatively impacting project outcomes. Furthermore, the provision of training information has not been optimized according to individual emotional states, making it difficult to effectively utilize human resources.

[0360] 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.

[0361] In this invention, the server includes means for collecting and managing employee identification information and emotional information; means for analyzing the identification information and emotional information to determine the employee's technical ability and emotional state; and means for selecting appropriate employees with the aforementioned technical ability and emotional state based on project requirements information, and adjusting the recommendation list according to the emotional state. This enables flexible and accurate personnel placement and training that also takes emotional factors into consideration.

[0362] "Specific information" refers to individual data such as an employee's skills and experience, and is necessary to identify individual employees and manage their respective abilities and past performance.

[0363] "Emotional information" refers to data about the emotional state of employees, including stress, excitement, and feelings of security, and is used to evaluate the psychological impact on a project.

[0364] "Analysis" refers to the process of processing collected data to determine the abilities and emotional state of employees, and is the act of analyzing and evaluating data.

[0365] "Technical competence" refers to the specialized knowledge and skills that an employee needs to meet the requirements of a specific project.

[0366] "Emotional state" refers to the psychological and emotional conditions that employees experience while performing their duties, and is a factor that can potentially affect project performance.

[0367] A "recommendation list" refers to a list created to suggest the most suitable employee based on project requirements and employee suitability.

[0368] "Educational information" refers to learning materials and training programs provided to support employees in improving their abilities and acquiring skills.

[0369] "Performance" refers to the results and achievements that employees have accomplished in a project, and is used as an indicator or standard.

[0370] "Monitoring" refers to the process of continuously tracking employee performance and emotional state to detect changes or anomalies.

[0371] This invention is a system that collects and manages employee identification and emotional information, selects the most suitable personnel according to the requirements of a specific project, and provides educational support. This system functions through the coordinated operation of servers, terminals, and users.

[0372] The server is responsible for central data management, storing specific and emotional information in the database. This includes employee skill sets, experience, and emotional information. The server uses a generative AI model to analyze this information. The AI ​​model processes prompt statements as input and performs data analysis. This analysis evaluates the employee's technical skills and emotional state.

[0373] The terminal provides an interface for users to input information. Through an emotion engine, the terminal acquires emotional information in real time as the user inputs information and collects it as data. For example, if the system analyzes that a user is experiencing stress while inputting skills for a specific project, that information is also collected simultaneously.

[0374] Users can input project requirements information via their terminal, including necessary skills, experience, and goals. Emotional states are taken into consideration, and the server recommends the most suitable candidates. When providing training information, the timing and content are optimized based on the employee's emotional state.

[0375] For example, in a data analysis project, the AI ​​model might be prompted with the message, "Select the most suitable employee to meet the project requirements and create feedback based on sentiment analysis." The AI ​​then generates a list of recommended employees and provides sentiment-based feedback.

[0376] In this way, the system enables flexible and highly efficient personnel allocation and training support that even takes emotional information into account, achieving both employee motivation maintenance and the success of corporate projects.

[0377] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0378] Step 1:

[0379] Users input information about their skills, experience, and projects through a terminal. The input data is collected by the terminal, while an emotion engine analyzes the user's emotional state in real time. Input includes skill information and project requirements, and output generates skill data and emotion data. The terminal packages this data and sends it to the server.

[0380] Step 2:

[0381] The server stores skill and sentiment data received from the terminals in a database. Here, the received data is not simply stored as is, but is indexed to enable efficient access and analysis. The inputs are skill and sentiment data, and the output is organized database entries.

[0382] Step 3:

[0383] The server uses information from the database to input prompts into the generative AI model. For example, a prompt such as "Create an optimal recommendation list based on the employee's specific skills and emotional state" might be used. The generative AI model analyzes the prompt and evaluates the employee's technical abilities and emotional state. The input here is the prompt and related information from the database, and the output is the evaluation result.

[0384] Step 4:

[0385] The server generates a list of recommended employees suitable for the project based on the evaluation results. This list may be prioritized based on emotional state. The input is the evaluation results, and the output is the adjusted recommendation list. The server sends this list to the terminal and presents it to the user.

[0386] Step 5:

[0387] The server selects appropriate educational information for the chosen employee, taking their emotional state into account, and sends it to the terminal at an optimized timing. For example, if an employee is determined to be highly stressed, educational materials on relaxation techniques will be provided. The input is a database of emotional states and educational materials, and the output is optimized educational information. The user receives this information through the terminal interface.

[0388] Step 6:

[0389] During and after project execution, the server collects employee performance data and combines it with previously collected sentiment data to provide an overall performance evaluation. Based on the evaluation, additional support and training are automatically suggested as needed. The inputs are performance data and sentiment data, and the output is feedback and support suggestions. The terminal notifies the user of this feedback and prompts them to take the next steps.

[0390] (Application Example 2)

[0391] 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."

[0392] In technology projects, including those involving autonomous vehicles, optimizing employee selection and training is a critical challenge. Traditional methods focused solely on employees' technical skills, neglecting their emotional states, which could negatively impact productivity and workplace efficiency. Furthermore, the lack of means to provide appropriate rest and support based on emotional states led to decreased employee satisfaction and performance.

[0393] 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.

[0394] In this invention, the server includes means for collecting and managing employee-specific information, means for analyzing employee technical skills and emotional state, means for selecting the most suitable employee considering their emotional state, and means for providing educational information optimized based on their emotional state. This enables flexible and accurate personnel placement that considers not only technical skills but also emotional aspects, thereby improving employee performance and optimizing the work environment.

[0395] "Employee identification information" refers to individual information about employees, such as their skills, experience, and emotional state, and is data used to ensure appropriate personnel placement within a project.

[0396] "Analysis methods" refer to processes and tools used to analyze collected data and evaluate employees' technical skills and emotional states.

[0397] "Emotional state" refers to information that indicates an employee's current emotions and psychological state, and is a factor that influences the progress of a project.

[0398] "Educational information" refers to knowledge and training content provided to employees with the aim of improving their skills and optimizing their performance.

[0399] "Optimized educational information" refers to educational information that is tailored to the emotional state and technical skills of employees and is provided in a way that best suits their needs.

[0400] "Rest or relaxation activities" refer to activities aimed at reducing employee stress and refreshing them, and are proposed based on the employee's emotional state.

[0401] "Clustering" is a term that describes the process of grouping employees based on specific information and emotional data, and combining those with similar characteristics.

[0402] The system for realizing this invention aims to optimize personnel allocation and training by collecting specific information on employees and analyzing their technical skills and emotional state.

[0403] The core of the system is a server that manages a series of processes including data collection, analysis, evaluation, and education delivery. The server receives information transmitted from each employee's smartphone or tablet and stores it in a database. In terms of specific hardware, it uses general server equipment that provides cloud services. In addition, it leverages Google Cloud Platform's natural language processing API to analyze sentiment data in detail.

[0404] Users input their skill information and project data into the device. The device incorporates voice input and text input interfaces, and the entered information is transferred to the server in real time. The device's operating system and applications utilize a general-purpose smartphone OS and application framework.

[0405] The server uses data analysis tools such as Python and TensorFlow to perform employee skill assessments and sentiment analysis based on the collected information. Based on the results, it automatically selects employees suitable for project requirements and prepares to provide optimized training information.

[0406] For example, in an autonomous vehicle maintenance project, if there is an employee who is highly skilled but experiencing stress, the server will notify the terminal with specific actions in the format of "Suggest additional technical training and a 5-minute break."

[0407] An example of a prompt sentence for a generated AI model is, "Suggest a simple relaxation exercise to help staff members who are feeling stressed about vehicle maintenance reset their mood." In this way, the system can provide personalized feedback to each employee.

[0408] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0409] Step 1:

[0410] Users input information about their skills, experience, and projects using smartphones or tablets. This includes text and voice input. The input information is formatted by the device and sent to the server via the internet. During this process, the device uses an emotion engine to analyze the emotional state from the voice data and sends the results along with the data to the server.

[0411] Step 2:

[0412] The server stores data received from terminals in a database. The data is organized by user and includes both skill information and emotional status. The database is managed using query languages ​​such as SQL, enabling efficient data storage and retrieval.

[0413] Step 3:

[0414] The server uses Python and TensorFlow to analyze data and evaluate each user's technical skills and emotional state. This analysis quantifies skill sets and applies a clustering algorithm based on emotional data. The analysis results are output as evaluation profiles generated for each user.

[0415] Step 4:

[0416] The server selects the most suitable employee based on the project requirements. Using an AI model within the server, the selection process considers a balance of skills and emotional state to create a list of optimal users. The selection results are sent to the terminal as a recommendation list.

[0417] Step 5:

[0418] The user's device provides appropriate educational information based on the received recommendation list. The educational content and timing are optimized according to the user's emotional state. For example, if the user is experiencing high stress, a relaxation video might be suggested. This information is displayed on the device screen, prompting the user to take specific action.

[0419] Step 6:

[0420] The server continuously collects employee performance and sentiment data from the database throughout the project, providing situation-based feedback. Based on each user's performance data, it generates new educational information and continuously improves to provide even more effective education and support.

[0421] Step 7:

[0422] The device generates reports as needed, providing users with regular feedback. These reports include a history of performance and emotional state, as well as suggestions for future improvements and next steps. The generated reports serve as valuable resources for users to refer to and decide on their next actions.

[0423] 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.

[0424] 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.

[0425] 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.

[0426] [Third Embodiment]

[0427] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0428] 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.

[0429] 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).

[0430] 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.

[0431] 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.

[0432] 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).

[0433] 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.

[0434] 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.

[0435] 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.

[0436] 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.

[0437] 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.

[0438] 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".

[0439] This invention is a system for evaluating the technical capabilities of employees within a specific organization and selecting the most suitable personnel for a project. In this system, servers, terminals, and users each play their respective roles in data collection, analysis, recommendation, and provision of educational information.

[0440] Data collection

[0441] Users use a terminal to input their skills, past project experience, and desired training areas. The terminal sends this data to a server, which stores the information in a database. The server also periodically collects performance data from the company's evaluation system and updates the database.

[0442] Data analysis and talent recommendation

[0443] The server analyzes the collected data to clarify the skill sets of employees. This analysis utilizes generative AI to convert text-based information into structured data. Next, when project requirements information is entered, the server selects the most suitable employees based on those requirements and presents a recommendation list to the user.

[0444] Provision of educational information

[0445] Furthermore, the server selects the necessary educational information for employees to acquire new skills and provides it to users via their terminals. Users can review the recommended educational programs and complete the necessary procedures for registration and further details.

[0446] Performance Monitoring

[0447] During and after the completed project, the server monitors employee performance and periodically evaluates skill improvements. Based on the evaluation results, training information is updated as needed, and areas for improvement are provided as feedback to the user.

[0448] This system enables rapid and effective personnel allocation and continuous skill development. For example, if Python skills are required for a data analysis project, the server can cluster multiple candidates with the relevant skills and recommend the most suitable person. Furthermore, if training in the latest machine learning techniques is needed, it can instantly present appropriate training courses, effectively supporting career development.

[0449] The following describes the processing flow.

[0450] Step 1:

[0451] Users input their skills, past project experience, and desired training areas using a terminal. The terminal then transmits this information to a server.

[0452] Step 2:

[0453] The server stores the received data in a database. Furthermore, it automatically retrieves employee performance data from the company's evaluation system on a regular basis, keeping the database constantly updated.

[0454] Step 3:

[0455] The server uses generative AI to analyze employees' skill sets based on information stored in the database. It extracts structured skill information from text data and evaluates individual technical capabilities.

[0456] Step 4:

[0457] The user enters project requirements information using a terminal. The terminal sends this information to the server, which provides basic information to identify the necessary skills and experience.

[0458] Step 5:

[0459] The server selects the most suitable employee based on project requirements information and analyzed skill information. It generates a list of candidates with the appropriate skill sets and presents it to the user as a recommendation list.

[0460] Step 6:

[0461] The server selects the necessary educational information for the chosen employees and makes it accessible to users through their terminals. This provides employees with an educational program that allows them to learn the necessary skills.

[0462] Step 7:

[0463] The server monitors employee performance during and after project execution and evaluates skill growth. Based on the evaluation results, it updates training information and provides feedback to users.

[0464] (Example 1)

[0465] 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."

[0466] There is a need for a system that can accurately assess employees' technical capabilities and efficiently provide optimal project assignments and training opportunities. However, conventional methods tend to rely heavily on subjective aspects in employee skill assessment, and there are problems with the selection and provision of appropriate training information.

[0467] 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.

[0468] In this invention, the server includes a device for collecting and managing employee-specific information, a device for analyzing employee technical capabilities using a generated AI model, and a device for selecting and recommending appropriate employees. This enables objective and rapid determination of technical capabilities and the provision of optimal personnel placement and training opportunities.

[0469] "Employee" refers to a worker or person in charge who performs duties within a specific organization.

[0470] "Specific information" refers to information that includes attributes and data necessary for analysis, such as an employee's skills, experience, and work history.

[0471] A "generative AI model" refers to an algorithm or program that uses artificial intelligence to analyze and evaluate employee information and generate structured data.

[0472] "Project requirements information" refers to detailed information including the skill sets, experience, and other conditions required for a specific project.

[0473] "Recommending equipment" refers to an automated system that selects appropriate employees based on analysis results and assigns them to projects.

[0474] "Educational information" refers to information about training programs and learning resources that employees need to acquire new skills.

[0475] A "performance monitoring system" refers to a system for collecting and evaluating employee performance data during and after a project.

[0476] A "feedback device" refers to a tool used to communicate areas for improvement and educational information to employees based on performance evaluations.

[0477] "Clustering" refers to classifying employees into groups based on their characteristics and skills, using groups that share similar traits.

[0478] A "prompt" refers to a set of instructions or questions intended for input into a generation AI or other automation system.

[0479] This invention provides a system for efficiently evaluating the technical capabilities of employees within a specific organization and for optimal project assignment and provision of training information.

[0480] Data collection

[0481] Users input their skills and past project experience into the terminal. This involves using forms, selecting options, and registering the necessary information using text boxes. This data is transmitted from the terminal to the server via a security protocol (e.g., SSL / TLS) and stored in a database.

[0482] Data Analysis

[0483] The server analyzes the collected data using a generative AI model. This process utilizes machine learning algorithms to convert text information into structured data. Specifically, the hardware configuration requires a group of computers with high-performance processors and sufficient memory.

[0484] Talent Recommendation

[0485] Once project requirements are entered, the server uses an algorithm to select the most suitable employee. An example of a prompt might be, "Recommend an employee who can use Python for the data science project."

[0486] Provision of educational information

[0487] The server selects appropriate educational information for employees and provides it to users via terminals. For example, it lists resources for learning new technologies and techniques, and allows users to view detailed information and register online.

[0488] Performance Monitoring

[0489] During and after the project, the server regularly monitors employee performance. This enables a mechanism to evaluate skill improvements and provide feedback to users.

[0490] This invention makes it possible to conduct rapid and objective competency assessments, and to provide appropriate personnel placement and training opportunities.

[0491] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0492] Step 1:

[0493] Users input their skills and past project experience into a terminal. This input includes skill sets, years of experience, and specific project names. After this information is stored on the terminal, it is sent to the server in an encrypted state. By transmitting the input data while maintaining security, accurate information can be collected.

[0494] Step 2:

[0495] The server analyzes the received data and stores it in a database. Here, a generative AI model is used to extract structured data from text data. This analysis process clearly displays each employee's skills and characteristics as numerical data. Specifically, the information is organized using high-speed database operations.

[0496] Step 3:

[0497] The server periodically collects performance evaluation data from other internal systems and updates the existing database. This information includes each employee's evaluation results and achievement of performance targets. Based on the input performance data, a system operates to quantitatively evaluate each employee's performance.

[0498] Step 4:

[0499] The user enters project requirements information into a terminal. These requirements include necessary skills and years of experience. The terminal sends this information to a server, which uses a generated AI model to select suitable employees that meet the requirements. By entering detailed project requirements, the system recommends the most suitable personnel.

[0500] Step 5:

[0501] The server creates a list of selected employees and provides the user with the recommendation results. The output includes a list of candidates and each employee's skill score. The user can review this output on their terminal and further refine their recommendations using prompts.

[0502] Step 6:

[0503] The server selects the training programs that each employee should participate in and presents them to the user via their terminal. This training information includes skills improvement programs and training on the latest technologies. Users can also view detailed information and register to participate.

[0504] Step 7:

[0505] During and after the project, the server monitors employee performance and evaluates the results. The evaluation results are stored in a database, and training programs are recommended again as needed. This cycle ensures continuous skill improvement and talent development.

[0506] (Application Example 1)

[0507] 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."

[0508] In modern manufacturing facilities, not only employee capabilities but also the optimal operational placement of machinery is required. However, efficiently integrating this data and providing optimal work placement and necessary training information is difficult. In particular, understanding the performance of individual machines and recommending appropriate operations based on that information relies heavily on manual work and experience, making it inefficient and prone to errors. Furthermore, there is a lack of up-to-date training information to improve employee skills.

[0509] 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.

[0510] In this invention, the server includes means for collecting and managing employee attribute information, means for collecting and managing operational information of work machines, and means for assigning work machines to optimal tasks based on the operational information. This enables centralized management of information on work machines and employees, realization of optimal work assignments, and effective support for improving work efficiency and employee skill acquisition.

[0511] "Employee attribute information" refers to information about the skills, qualifications, and past experience of personnel within an organization.

[0512] "Means of management" refers to methods and devices for organizing collected information and storing it in a way that allows for easy retrieval and reference.

[0513] "Technical competence" is a measure used to evaluate the level of technical skills and knowledge possessed by an employee.

[0514] "Project requirements information" refers to information that describes the skills and conditions necessary to perform a specific task or project.

[0515] "Educational information" refers to information about educational programs and materials necessary for employees to acquire new skills or improve their current skills.

[0516] "Operational information for work machinery" refers to information regarding the operating status, performance, and placement of machinery and robots used in factories and other industrial settings.

[0517] "Assigning to a task" is the process of assigning the most suitable machinery or personnel to a specific task or operation.

[0518] "Operational skills" refer to the technical skills necessary to effectively operate a particular machine or device.

[0519] To realize this invention, the system consists of a server, terminals, and users. The server first has the function of collecting and managing employee attribute information and operational information of work machinery. This information is obtained from sensing devices and management systems as needed and stored in a database. The sensing devices used include, for example, IoT sensors.

[0520] The server then uses a generative AI model to analyze the collected information. This process utilizes frameworks such as TensorFlow and PyTorch to analyze the data using machine learning techniques and determine the optimal work arrangement and required skills. Based on these results, it provides information on the placement of work machines and employee training. For example, when starting a new product assembly line, it can instantly recommend the optimal combination of work machines and employees.

[0521] Furthermore, the server provides educational information to employees and operators. This information is provided as links to online courses and details of training programs. Through the terminal, users can view this information and obtain more detailed information as needed. The server can also utilize a generated AI model with prompt text as input to make more accurate recommendations. An example of a prompt text is, "Based on the work data of the factory robots, suggest the optimal task assignment for the next product production line."

[0522] Specifically, for example, in the assembly of product A, the server might assign welding tasks, which robot X excels at, while recommending online training to improve problem-solving skills to personnel Y. In this way, it is possible to constantly strive for optimal work efficiency and skill improvement.

[0523] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0524] Step 1:

[0525] The server receives employee attribute information and work machine operation information from sensing devices and management systems and stores it in a database. This input information includes employee skills, qualifications, experience, and work machine operating status and performance data. The information is acquired from IoT sensors and employee information systems and stored in the database to prepare for later analysis.

[0526] Step 2:

[0527] The server analyzes data by running a generative AI model based on the information stored in the database. Specifically, it uses TensorFlow and PyTorch to analyze employee skill sets and machine performance data. This analysis includes structuring text data to evaluate skills and calculating optimal work assignments using machine learning algorithms. The analysis results output the optimal tasks for employees and machines.

[0528] Step 3:

[0529] Based on the analysis results, the server places the work equipment in the optimal work area and provides the corresponding employees with the necessary training information. It generates work instructions according to the outputted task placement information and sends them to the terminal. Employees are also provided with recommendations for training programs and online courses to improve their necessary skills. This information includes specific links and detailed information.

[0530] Step 4:

[0531] The terminal displays work instructions and training information received from the server to the user. The user reviews the provided information and chooses to modify their work or participate in training programs as needed. Specifically, they can access online courses by clicking on the received links.

[0532] Step 5:

[0533] Users perform tasks based on the work instructions they receive and send progress and completion reports from their terminals to the server. The server monitors work performance based on this information and updates training information as needed. Specifically, it utilizes a generative AI model to provide users with feedback tailored to their current skill level.

[0534] 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.

[0535] This invention combines a system that collects and manages employee identification information and selects the most suitable personnel based on project requirements with an emotion engine that recognizes user emotions. In this system, the server, terminals, and users cooperate to analyze and manage data, provide educational information, and provide feedback based on emotion recognition.

[0536] Data collection and emotion recognition

[0537] Users input information about their skills, experience, and projects through their device. During this process, an emotion engine analyzes the user's emotional state in real time and collects it as emotion data. The device then sends this information to a server, which stores it in a database.

[0538] Data analysis and talent recommendation

[0539] The server analyzes collected data and emotional data to evaluate employees' skill sets and emotional states. It selects the most suitable employees that match the project requirements and presents a recommendation list to the user. The list may be adjusted to take emotional states into account.

[0540] Provision and optimization of educational information

[0541] The server selects appropriate educational information for the chosen employees and provides it to the users through their terminals. The emotion engine optimizes the content and timing of the educational information delivery according to the user's emotional state, supporting effective reskilling.

[0542] Performance monitoring and emotional feedback

[0543] During and after a project, the server monitors employee performance data and evaluates it in conjunction with emotional data. The evaluation results are fed back to the user, prompting them to take new actions based on their emotional state. For example, if an employee is experiencing stress during a project, additional support or training may be suggested.

[0544] This system enables flexible and accurate personnel placement and training that takes emotions into consideration, simultaneously improving the efficiency of human resource utilization within companies and increasing employee satisfaction. Specifically, for example, in a data analysis project, if sentiment analysis determines that a user lacks motivation for a task, the server can suggest relevant training or actions that will lead to increased motivation.

[0545] The following describes the processing flow.

[0546] Step 1:

[0547] The user uses a terminal to input their skills, past project experience, and desired training areas. During this process, the terminal utilizes an emotion engine to recognize the user's emotional state from their facial expressions and tone of voice, generating emotional data. This generated data is then sent to a server.

[0548] Step 2:

[0549] The server stores skill and sentiment data sent from terminals in a database. Furthermore, it continuously updates the database by retrieving employee evaluation data from other internal systems.

[0550] Step 3:

[0551] The server analyzes the collected data using generative AI. It analyzes the user's skill set and emotional data, and evaluates the appropriate technical abilities for each field. It also determines the user's current mental state based on the emotional data.

[0552] Step 4:

[0553] The user enters project requirements information using a terminal. The terminal sends this information to a server, which provides background information related to the required skills and aptitudes.

[0554] Step 5:

[0555] The server selects the most suitable employee based on project requirements information and analyzed skills and sentiment data. In doing so, it considers not only technical abilities but also the employee's emotional state to recommend the most suitable person for the project. The results are presented to the user via a terminal.

[0556] Step 6:

[0557] The server selects and provides necessary educational information to the chosen employees. Based on an emotion engine, the content and timing of the educational information are adjusted according to the user's emotional state. Users can view the educational information and obtain details on their devices.

[0558] Step 7:

[0559] During and after the project, the server monitors and evaluates employee performance and emotional data. Based on the evaluation results, it updates educational information as needed and provides feedback to users through their terminals. This feedback allows users to decide on their next actions based on their emotional state.

[0560] (Example 2)

[0561] 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."

[0562] In modern project management, appropriate personnel placement and training that consider not only employees' skills and experience but also their emotional state are crucial. However, traditional systems have not adequately collected and analyzed emotional information, which can lead to decreased employee motivation and stress negatively impacting project outcomes. Furthermore, the provision of training information has not been optimized according to individual emotional states, making it difficult to effectively utilize human resources.

[0563] 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.

[0564] In this invention, the server includes means for collecting and managing employee identification information and emotional information; means for analyzing the identification information and emotional information to determine the employee's technical ability and emotional state; and means for selecting appropriate employees with the aforementioned technical ability and emotional state based on project requirements information, and adjusting the recommendation list according to the emotional state. This enables flexible and accurate personnel placement and training that also takes emotional factors into consideration.

[0565] "Specific information" refers to individual data such as an employee's skills and experience, and is necessary to identify individual employees and manage their respective abilities and past performance.

[0566] "Emotional information" refers to data about the emotional state of employees, including stress, excitement, and feelings of security, and is used to evaluate the psychological impact on a project.

[0567] "Analysis" refers to the process of processing collected data to determine the abilities and emotional state of employees, and is the act of analyzing and evaluating data.

[0568] "Technical competence" refers to the specialized knowledge and skills that an employee needs to meet the requirements of a specific project.

[0569] "Emotional state" refers to the psychological and emotional conditions that employees experience while performing their duties, and is a factor that can potentially affect project performance.

[0570] A "recommendation list" refers to a list created to suggest the most suitable employee based on project requirements and employee suitability.

[0571] "Educational information" refers to learning materials and training programs provided to support employees in improving their abilities and acquiring skills.

[0572] "Performance" refers to the results and achievements that employees have accomplished in a project, and is used as an indicator or standard.

[0573] "Monitoring" refers to the process of continuously tracking employee performance and emotional state to detect changes or anomalies.

[0574] This invention is a system that collects and manages employee identification and emotional information, selects the most suitable personnel according to the requirements of a specific project, and provides educational support. This system functions through the coordinated operation of servers, terminals, and users.

[0575] The server is responsible for central data management, storing specific and emotional information in the database. This includes employee skill sets, experience, and emotional information. The server uses a generative AI model to analyze this information. The AI ​​model processes prompt statements as input and performs data analysis. This analysis evaluates the employee's technical skills and emotional state.

[0576] The terminal provides an interface for users to input information. Through an emotion engine, the terminal acquires emotional information in real time as the user inputs information and collects it as data. For example, if the system analyzes that a user is experiencing stress while inputting skills for a specific project, that information is also collected simultaneously.

[0577] Users can input project requirements information via their terminal, including necessary skills, experience, and goals. Emotional states are taken into consideration, and the server recommends the most suitable candidates. When providing training information, the timing and content are optimized based on the employee's emotional state.

[0578] For example, in a data analysis project, the AI ​​model might be prompted with the message, "Select the most suitable employee to meet the project requirements and create feedback based on sentiment analysis." The AI ​​then generates a list of recommended employees and provides sentiment-based feedback.

[0579] In this way, the system enables flexible and highly efficient personnel allocation and training support that even takes emotional information into account, achieving both employee motivation maintenance and the success of corporate projects.

[0580] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0581] Step 1:

[0582] Users input information about their skills, experience, and projects through a terminal. The input data is collected by the terminal, while an emotion engine analyzes the user's emotional state in real time. Input includes skill information and project requirements, and output generates skill data and emotion data. The terminal packages this data and sends it to the server.

[0583] Step 2:

[0584] The server stores skill and sentiment data received from the terminals in a database. Here, the received data is not simply stored as is, but is indexed to enable efficient access and analysis. The inputs are skill and sentiment data, and the output is organized database entries.

[0585] Step 3:

[0586] The server uses information from the database to input prompts into the generative AI model. For example, a prompt such as "Create an optimal recommendation list based on the employee's specific skills and emotional state" might be used. The generative AI model analyzes the prompt and evaluates the employee's technical abilities and emotional state. The input here is the prompt and related information from the database, and the output is the evaluation result.

[0587] Step 4:

[0588] The server generates a list of recommended employees suitable for the project based on the evaluation results. This list may be prioritized based on emotional state. The input is the evaluation results, and the output is the adjusted recommendation list. The server sends this list to the terminal and presents it to the user.

[0589] Step 5:

[0590] The server selects appropriate educational information for the chosen employee, taking their emotional state into account, and sends it to the terminal at an optimized timing. For example, if an employee is determined to be highly stressed, educational materials on relaxation techniques will be provided. The input is a database of emotional states and educational materials, and the output is optimized educational information. The user receives this information through the terminal interface.

[0591] Step 6:

[0592] During and after project execution, the server collects employee performance data and combines it with previously collected sentiment data to provide an overall performance evaluation. Based on the evaluation, additional support and training are automatically suggested as needed. The inputs are performance data and sentiment data, and the output is feedback and support suggestions. The terminal notifies the user of this feedback and prompts them to take the next steps.

[0593] (Application Example 2)

[0594] 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."

[0595] In technology projects, including those involving autonomous vehicles, optimizing employee selection and training is a critical challenge. Traditional methods focused solely on employees' technical skills, neglecting their emotional states, which could negatively impact productivity and workplace efficiency. Furthermore, the lack of means to provide appropriate rest and support based on emotional states led to decreased employee satisfaction and performance.

[0596] 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.

[0597] In this invention, the server includes means for collecting and managing employee-specific information, means for analyzing employee technical skills and emotional state, means for selecting the most suitable employee considering their emotional state, and means for providing educational information optimized based on their emotional state. This enables flexible and accurate personnel placement that considers not only technical skills but also emotional aspects, thereby improving employee performance and optimizing the work environment.

[0598] "Employee identification information" refers to individual information about employees, such as their skills, experience, and emotional state, and is data used to ensure appropriate personnel placement within a project.

[0599] "Analysis methods" refer to processes and tools used to analyze collected data and evaluate employees' technical skills and emotional states.

[0600] "Emotional state" refers to information that indicates an employee's current emotions and psychological state, and is a factor that influences the progress of a project.

[0601] "Educational information" refers to knowledge and training content provided to employees with the aim of improving their skills and optimizing their performance.

[0602] "Optimized educational information" refers to educational information that is tailored to the emotional state and technical skills of employees and is provided in a way that best suits their needs.

[0603] "Rest or relaxation activities" refer to activities aimed at reducing employee stress and refreshing them, and are proposed based on the employee's emotional state.

[0604] "Clustering" is a term that describes the process of grouping employees based on specific information and emotional data, and combining those with similar characteristics.

[0605] The system for realizing this invention aims to optimize personnel allocation and training by collecting specific information on employees and analyzing their technical skills and emotional state.

[0606] The core of the system is a server that manages a series of processes including data collection, analysis, evaluation, and education delivery. The server receives information transmitted from each employee's smartphone or tablet and stores it in a database. In terms of specific hardware, it uses general server equipment that provides cloud services. In addition, it leverages Google Cloud Platform's natural language processing API to analyze sentiment data in detail.

[0607] Users input their skill information and project data into the device. The device incorporates voice input and text input interfaces, and the entered information is transferred to the server in real time. The device's operating system and applications utilize a general-purpose smartphone OS and application framework.

[0608] The server uses data analysis tools such as Python and TensorFlow to perform employee skill assessments and sentiment analysis based on the collected information. Based on the results, it automatically selects employees suitable for project requirements and prepares to provide optimized training information.

[0609] For example, in an autonomous vehicle maintenance project, if there is an employee who is highly skilled but experiencing stress, the server will notify the terminal with specific actions in the format of "Suggest additional technical training and a 5-minute break."

[0610] An example of a prompt sentence for a generated AI model is, "Suggest a simple relaxation exercise to help staff members who are feeling stressed about vehicle maintenance reset their mood." In this way, the system can provide personalized feedback to each employee.

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

[0612] Step 1:

[0613] Users input information about their skills, experience, and projects using smartphones or tablets. This includes text and voice input. The input information is formatted by the device and sent to the server via the internet. During this process, the device uses an emotion engine to analyze the emotional state from the voice data and sends the results along with the data to the server.

[0614] Step 2:

[0615] The server stores data received from terminals in a database. The data is organized by user and includes both skill information and emotional status. The database is managed using query languages ​​such as SQL, enabling efficient data storage and retrieval.

[0616] Step 3:

[0617] The server uses Python and TensorFlow to analyze data and evaluate each user's technical skills and emotional state. This analysis quantifies skill sets and applies a clustering algorithm based on emotional data. The analysis results are output as evaluation profiles generated for each user.

[0618] Step 4:

[0619] The server selects the most suitable employee based on the project requirements. Using an AI model within the server, the selection process considers a balance of skills and emotional state to create a list of optimal users. The selection results are sent to the terminal as a recommendation list.

[0620] Step 5:

[0621] The user's device provides appropriate educational information based on the received recommendation list. The educational content and timing are optimized according to the user's emotional state. For example, if the user is experiencing high stress, a relaxation video might be suggested. This information is displayed on the device screen, prompting the user to take specific action.

[0622] Step 6:

[0623] The server continuously collects employee performance and sentiment data from the database throughout the project, providing situation-based feedback. Based on each user's performance data, it generates new educational information and continuously improves to provide even more effective education and support.

[0624] Step 7:

[0625] The device generates reports as needed, providing users with regular feedback. These reports include a history of performance and emotional state, as well as suggestions for future improvements and next steps. The generated reports serve as valuable resources for users to refer to and decide on their next actions.

[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] This invention is a system for evaluating the technical capabilities of employees within a specific organization and selecting the most suitable personnel for a project. In this system, servers, terminals, and users each play their respective roles in data collection, analysis, recommendation, and provision of educational information.

[0644] Data collection

[0645] Users use a terminal to input their skills, past project experience, and desired training areas. The terminal sends this data to a server, which stores the information in a database. The server also periodically collects performance data from the company's evaluation system and updates the database.

[0646] Data analysis and talent recommendation

[0647] The server analyzes the collected data to clarify the skill sets of employees. This analysis utilizes generative AI to convert text-based information into structured data. Next, when project requirements information is entered, the server selects the most suitable employees based on those requirements and presents a recommendation list to the user.

[0648] Provision of educational information

[0649] Furthermore, the server selects the necessary educational information for employees to acquire new skills and provides it to users via their terminals. Users can review the recommended educational programs and complete the necessary procedures for registration and further details.

[0650] Performance Monitoring

[0651] During and after the completed project, the server monitors employee performance and periodically evaluates skill improvements. Based on the evaluation results, training information is updated as needed, and areas for improvement are provided as feedback to the user.

[0652] This system enables rapid and effective personnel allocation and continuous skill development. For example, if Python skills are required for a data analysis project, the server can cluster multiple candidates with the relevant skills and recommend the most suitable person. Furthermore, if training in the latest machine learning techniques is needed, it can instantly present appropriate training courses, effectively supporting career development.

[0653] The following describes the processing flow.

[0654] Step 1:

[0655] Users input their skills, past project experience, and desired training areas using a terminal. The terminal then transmits this information to a server.

[0656] Step 2:

[0657] The server stores the received data in a database. Furthermore, it automatically retrieves employee performance data from the company's evaluation system on a regular basis, keeping the database constantly updated.

[0658] Step 3:

[0659] The server uses generative AI to analyze employees' skill sets based on information stored in the database. It extracts structured skill information from text data and evaluates individual technical capabilities.

[0660] Step 4:

[0661] The user enters project requirements information using a terminal. The terminal sends this information to the server, which provides basic information to identify the necessary skills and experience.

[0662] Step 5:

[0663] The server selects the most suitable employee based on project requirements information and analyzed skill information. It generates a list of candidates with the appropriate skill sets and presents it to the user as a recommendation list.

[0664] Step 6:

[0665] The server selects the necessary educational information for the chosen employees and makes it accessible to users through their terminals. This provides employees with an educational program that allows them to learn the necessary skills.

[0666] Step 7:

[0667] The server monitors employee performance during and after project execution and evaluates skill growth. Based on the evaluation results, it updates training information and provides feedback to users.

[0668] (Example 1)

[0669] 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".

[0670] There is a need for a system that can accurately assess employees' technical capabilities and efficiently provide optimal project assignments and training opportunities. However, conventional methods tend to rely heavily on subjective aspects in employee skill assessment, and there are problems with the selection and provision of appropriate training information.

[0671] 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.

[0672] In this invention, the server includes a device for collecting and managing employee-specific information, a device for analyzing employee technical capabilities using a generated AI model, and a device for selecting and recommending appropriate employees. This enables objective and rapid determination of technical capabilities and the provision of optimal personnel placement and training opportunities.

[0673] "Employee" refers to a worker or person in charge who performs duties within a specific organization.

[0674] "Specific information" refers to information that includes attributes and data necessary for analysis, such as an employee's skills, experience, and work history.

[0675] A "generative AI model" refers to an algorithm or program that uses artificial intelligence to analyze and evaluate employee information and generate structured data.

[0676] "Project requirements information" refers to detailed information including the skill sets, experience, and other conditions required for a specific project.

[0677] "Recommending equipment" refers to an automated system that selects appropriate employees based on analysis results and assigns them to projects.

[0678] "Educational information" refers to information about training programs and learning resources that employees need to acquire new skills.

[0679] A "performance monitoring system" refers to a system for collecting and evaluating employee performance data during and after a project.

[0680] A "feedback device" refers to a tool used to communicate areas for improvement and educational information to employees based on performance evaluations.

[0681] "Clustering" refers to classifying employees into groups based on their characteristics and skills, using groups that share similar traits.

[0682] A "prompt" refers to a set of instructions or questions intended for input into a generation AI or other automation system.

[0683] This invention provides a system for efficiently evaluating the technical capabilities of employees within a specific organization and for optimal project assignment and provision of training information.

[0684] Data collection

[0685] Users input their skills and past project experience into the terminal. This involves using forms, selecting options, and registering the necessary information using text boxes. This data is transmitted from the terminal to the server via a security protocol (e.g., SSL / TLS) and stored in a database.

[0686] Data Analysis

[0687] The server analyzes the collected data using a generative AI model. This process utilizes machine learning algorithms to convert text information into structured data. Specifically, the hardware configuration requires a group of computers with high-performance processors and sufficient memory.

[0688] Talent Recommendation

[0689] Once project requirements are entered, the server uses an algorithm to select the most suitable employee. An example of a prompt might be, "Recommend an employee who can use Python for the data science project."

[0690] Provision of educational information

[0691] The server selects appropriate educational information for employees and provides it to users via terminals. For example, it lists resources for learning new technologies and techniques, and allows users to view detailed information and register online.

[0692] Performance Monitoring

[0693] During and after the project, the server regularly monitors employee performance. This enables a mechanism to evaluate skill improvements and provide feedback to users.

[0694] This invention makes it possible to conduct rapid and objective competency assessments, and to provide appropriate personnel placement and training opportunities.

[0695] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0696] Step 1:

[0697] Users input their skills and past project experience into a terminal. This input includes skill sets, years of experience, and specific project names. After this information is stored on the terminal, it is sent to the server in an encrypted state. By transmitting the input data while maintaining security, accurate information can be collected.

[0698] Step 2:

[0699] The server analyzes the received data and stores it in a database. Here, a generative AI model is used to extract structured data from text data. This analysis process clearly displays each employee's skills and characteristics as numerical data. Specifically, the information is organized using high-speed database operations.

[0700] Step 3:

[0701] The server periodically collects performance evaluation data from other internal systems and updates the existing database. This information includes each employee's evaluation results and achievement of performance targets. Based on the input performance data, a system operates to quantitatively evaluate each employee's performance.

[0702] Step 4:

[0703] The user enters project requirements information into a terminal. These requirements include necessary skills and years of experience. The terminal sends this information to a server, which uses a generated AI model to select suitable employees that meet the requirements. By entering detailed project requirements, the system recommends the most suitable personnel.

[0704] Step 5:

[0705] The server creates a list of selected employees and provides the user with the recommendation results. The output includes a list of candidates and each employee's skill score. The user can review this output on their terminal and further refine their recommendations using prompts.

[0706] Step 6:

[0707] The server selects the training programs that each employee should participate in and presents them to the user via their terminal. This training information includes skills improvement programs and training on the latest technologies. Users can also view detailed information and register to participate.

[0708] Step 7:

[0709] During and after the project, the server monitors employee performance and evaluates the results. The evaluation results are stored in a database, and training programs are recommended again as needed. This cycle ensures continuous skill improvement and talent development.

[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] In modern manufacturing facilities, not only employee capabilities but also the optimal operational placement of machinery is required. However, efficiently integrating this data and providing optimal work placement and necessary training information is difficult. In particular, understanding the performance of individual machines and recommending appropriate operations based on that information relies heavily on manual work and experience, making it inefficient and prone to errors. Furthermore, there is a lack of up-to-date training information to improve employee 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 and managing employee attribute information, means for collecting and managing operational information of work machines, and means for assigning work machines to optimal tasks based on the operational information. This enables centralized management of information on work machines and employees, realization of optimal work assignments, and effective support for improving work efficiency and employee skill acquisition.

[0715] "Employee attribute information" refers to information about the skills, qualifications, and past experience of personnel within an organization.

[0716] "Means of management" refers to methods and devices for organizing collected information and storing it in a way that allows for easy retrieval and reference.

[0717] "Technical competence" is a measure used to evaluate the level of technical skills and knowledge possessed by an employee.

[0718] "Project requirements information" refers to information that describes the skills and conditions necessary to perform a specific task or project.

[0719] "Educational information" refers to information about educational programs and materials necessary for employees to acquire new skills or improve their current skills.

[0720] "Operational information for work machinery" refers to information regarding the operating status, performance, and placement of machinery and robots used in factories and other industrial settings.

[0721] "Assigning to a task" is the process of assigning the most suitable machinery or personnel to a specific task or operation.

[0722] "Operational skills" refer to the technical skills necessary to effectively operate a particular machine or device.

[0723] To realize this invention, the system consists of a server, terminals, and users. The server first has the function of collecting and managing employee attribute information and operational information of work machinery. This information is obtained from sensing devices and management systems as needed and stored in a database. The sensing devices used include, for example, IoT sensors.

[0724] The server then uses a generative AI model to analyze the collected information. This process utilizes frameworks such as TensorFlow and PyTorch to analyze the data using machine learning techniques and determine the optimal work arrangement and required skills. Based on these results, it provides information on the placement of work machines and employee training. For example, when starting a new product assembly line, it can instantly recommend the optimal combination of work machines and employees.

[0725] Furthermore, the server provides educational information to employees and operators. This information is provided as links to online courses and details of training programs. Through the terminal, users can view this information and obtain more detailed information as needed. The server can also utilize a generated AI model with prompt text as input to make more accurate recommendations. An example of a prompt text is, "Based on the work data of the factory robots, suggest the optimal task assignment for the next product production line."

[0726] Specifically, for example, in the assembly of product A, the server might assign welding tasks, which robot X excels at, while recommending online training to improve problem-solving skills to personnel Y. In this way, it is possible to constantly strive for optimal work efficiency and skill improvement.

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

[0728] Step 1:

[0729] The server receives employee attribute information and work machine operation information from sensing devices and management systems and stores it in a database. This input information includes employee skills, qualifications, experience, and work machine operating status and performance data. The information is acquired from IoT sensors and employee information systems and stored in the database to prepare for later analysis.

[0730] Step 2:

[0731] The server analyzes data by running a generative AI model based on the information stored in the database. Specifically, it uses TensorFlow and PyTorch to analyze employee skill sets and machine performance data. This analysis includes structuring text data to evaluate skills and calculating optimal work assignments using machine learning algorithms. The analysis results output the optimal tasks for employees and machines.

[0732] Step 3:

[0733] Based on the analysis results, the server places the work equipment in the optimal work area and provides the corresponding employees with the necessary training information. It generates work instructions according to the outputted task placement information and sends them to the terminal. Employees are also provided with recommendations for training programs and online courses to improve their necessary skills. This information includes specific links and detailed information.

[0734] Step 4:

[0735] The terminal displays work instructions and training information received from the server to the user. The user reviews the provided information and chooses to modify their work or participate in training programs as needed. Specifically, they can access online courses by clicking on the received links.

[0736] Step 5:

[0737] Users perform tasks based on the work instructions they receive and send progress and completion reports from their terminals to the server. The server monitors work performance based on this information and updates training information as needed. Specifically, it utilizes a generative AI model to provide users with feedback tailored to their current skill level.

[0738] 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.

[0739] This invention combines a system that collects and manages employee identification information and selects the most suitable personnel based on project requirements with an emotion engine that recognizes user emotions. In this system, the server, terminals, and users cooperate to analyze and manage data, provide educational information, and provide feedback based on emotion recognition.

[0740] Data collection and emotion recognition

[0741] Users input information about their skills, experience, and projects through their device. During this process, an emotion engine analyzes the user's emotional state in real time and collects it as emotion data. The device then sends this information to a server, which stores it in a database.

[0742] Data analysis and talent recommendation

[0743] The server analyzes collected data and emotional data to evaluate employees' skill sets and emotional states. It selects the most suitable employees that match the project requirements and presents a recommendation list to the user. The list may be adjusted to take emotional states into account.

[0744] Provision and optimization of educational information

[0745] The server selects appropriate educational information for the chosen employees and provides it to the users through their terminals. The emotion engine optimizes the content and timing of the educational information delivery according to the user's emotional state, supporting effective reskilling.

[0746] Performance monitoring and emotional feedback

[0747] During and after a project, the server monitors employee performance data and evaluates it in conjunction with emotional data. The evaluation results are fed back to the user, prompting them to take new actions based on their emotional state. For example, if an employee is experiencing stress during a project, additional support or training may be suggested.

[0748] This system enables flexible and accurate personnel placement and training that takes emotions into consideration, simultaneously improving the efficiency of human resource utilization within companies and increasing employee satisfaction. Specifically, for example, in a data analysis project, if sentiment analysis determines that a user lacks motivation for a task, the server can suggest relevant training or actions that will lead to increased motivation.

[0749] The following describes the processing flow.

[0750] Step 1:

[0751] The user uses a terminal to input their skills, past project experience, and desired training areas. During this process, the terminal utilizes an emotion engine to recognize the user's emotional state from their facial expressions and tone of voice, generating emotional data. This generated data is then sent to a server.

[0752] Step 2:

[0753] The server stores skill and sentiment data sent from terminals in a database. Furthermore, it continuously updates the database by retrieving employee evaluation data from other internal systems.

[0754] Step 3:

[0755] The server analyzes the collected data using generative AI. It analyzes the user's skill set and emotional data, and evaluates the appropriate technical abilities for each field. It also determines the user's current mental state based on the emotional data.

[0756] Step 4:

[0757] The user enters project requirements information using a terminal. The terminal sends this information to a server, which provides background information related to the required skills and aptitudes.

[0758] Step 5:

[0759] The server selects the most suitable employee based on project requirements information and analyzed skills and sentiment data. In doing so, it considers not only technical abilities but also the employee's emotional state to recommend the most suitable person for the project. The results are presented to the user via a terminal.

[0760] Step 6:

[0761] The server selects and provides necessary educational information to the chosen employees. Based on an emotion engine, the content and timing of the educational information are adjusted according to the user's emotional state. Users can view the educational information and obtain details on their devices.

[0762] Step 7:

[0763] During and after the project, the server monitors and evaluates employee performance and emotional data. Based on the evaluation results, it updates educational information as needed and provides feedback to users through their terminals. This feedback allows users to decide on their next actions based on their emotional state.

[0764] (Example 2)

[0765] 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".

[0766] In modern project management, appropriate personnel placement and training that consider not only employees' skills and experience but also their emotional state are crucial. However, traditional systems have not adequately collected and analyzed emotional information, which can lead to decreased employee motivation and stress negatively impacting project outcomes. Furthermore, the provision of training information has not been optimized according to individual emotional states, making it difficult to effectively utilize human resources.

[0767] 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.

[0768] In this invention, the server includes means for collecting and managing employee identification information and emotional information; means for analyzing the identification information and emotional information to determine the employee's technical ability and emotional state; and means for selecting appropriate employees with the aforementioned technical ability and emotional state based on project requirements information, and adjusting the recommendation list according to the emotional state. This enables flexible and accurate personnel placement and training that also takes emotional factors into consideration.

[0769] "Specific information" refers to individual data such as an employee's skills and experience, and is necessary to identify individual employees and manage their respective abilities and past performance.

[0770] "Emotional information" refers to data about the emotional state of employees, including stress, excitement, and feelings of security, and is used to evaluate the psychological impact on a project.

[0771] "Analysis" refers to the process of processing collected data to determine the abilities and emotional state of employees, and is the act of analyzing and evaluating data.

[0772] "Technical competence" refers to the specialized knowledge and skills that an employee needs to meet the requirements of a specific project.

[0773] "Emotional state" refers to the psychological and emotional conditions that employees experience while performing their duties, and is a factor that can potentially affect project performance.

[0774] A "recommendation list" refers to a list created to suggest the most suitable employee based on project requirements and employee suitability.

[0775] "Educational information" refers to learning materials and training programs provided to support employees in improving their abilities and acquiring skills.

[0776] "Performance" refers to the results and achievements that employees have accomplished in a project, and is used as an indicator or standard.

[0777] "Monitoring" refers to the process of continuously tracking employee performance and emotional state to detect changes or anomalies.

[0778] This invention is a system that collects and manages employee identification and emotional information, selects the most suitable personnel according to the requirements of a specific project, and provides educational support. This system functions through the coordinated operation of servers, terminals, and users.

[0779] The server is responsible for central data management, storing specific and emotional information in the database. This includes employee skill sets, experience, and emotional information. The server uses a generative AI model to analyze this information. The AI ​​model processes prompt statements as input and performs data analysis. This analysis evaluates the employee's technical skills and emotional state.

[0780] The terminal provides an interface for users to input information. Through an emotion engine, the terminal acquires emotional information in real time as the user inputs information and collects it as data. For example, if the system analyzes that a user is experiencing stress while inputting skills for a specific project, that information is also collected simultaneously.

[0781] Users can input project requirements information via their terminal, including necessary skills, experience, and goals. Emotional states are taken into consideration, and the server recommends the most suitable candidates. When providing training information, the timing and content are optimized based on the employee's emotional state.

[0782] For example, in a data analysis project, the AI ​​model might be prompted with the message, "Select the most suitable employee to meet the project requirements and create feedback based on sentiment analysis." The AI ​​then generates a list of recommended employees and provides sentiment-based feedback.

[0783] In this way, the system enables flexible and highly efficient personnel allocation and training support that even takes emotional information into account, achieving both employee motivation maintenance and the success of corporate projects.

[0784] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0785] Step 1:

[0786] Users input information about their skills, experience, and projects through a terminal. The input data is collected by the terminal, while an emotion engine analyzes the user's emotional state in real time. Input includes skill information and project requirements, and output generates skill data and emotion data. The terminal packages this data and sends it to the server.

[0787] Step 2:

[0788] The server stores skill and sentiment data received from the terminals in a database. Here, the received data is not simply stored as is, but is indexed to enable efficient access and analysis. The inputs are skill and sentiment data, and the output is organized database entries.

[0789] Step 3:

[0790] The server uses information from the database to input prompts into the generative AI model. For example, a prompt such as "Create an optimal recommendation list based on the employee's specific skills and emotional state" might be used. The generative AI model analyzes the prompt and evaluates the employee's technical abilities and emotional state. The input here is the prompt and related information from the database, and the output is the evaluation result.

[0791] Step 4:

[0792] The server generates a list of recommended employees suitable for the project based on the evaluation results. This list may be prioritized based on emotional state. The input is the evaluation results, and the output is the adjusted recommendation list. The server sends this list to the terminal and presents it to the user.

[0793] Step 5:

[0794] The server selects appropriate educational information for the chosen employee, taking their emotional state into account, and sends it to the terminal at an optimized timing. For example, if an employee is determined to be highly stressed, educational materials on relaxation techniques will be provided. The input is a database of emotional states and educational materials, and the output is optimized educational information. The user receives this information through the terminal interface.

[0795] Step 6:

[0796] During and after project execution, the server collects employee performance data and combines it with previously collected sentiment data to provide an overall performance evaluation. Based on the evaluation, additional support and training are automatically suggested as needed. The inputs are performance data and sentiment data, and the output is feedback and support suggestions. The terminal notifies the user of this feedback and prompts them to take the next steps.

[0797] (Application Example 2)

[0798] 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".

[0799] In technology projects, including those involving autonomous vehicles, optimizing employee selection and training is a critical challenge. Traditional methods focused solely on employees' technical skills, neglecting their emotional states, which could negatively impact productivity and workplace efficiency. Furthermore, the lack of means to provide appropriate rest and support based on emotional states led to decreased employee satisfaction and performance.

[0800] 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.

[0801] In this invention, the server includes means for collecting and managing employee-specific information, means for analyzing employee technical skills and emotional state, means for selecting the most suitable employee considering their emotional state, and means for providing educational information optimized based on their emotional state. This enables flexible and accurate personnel placement that considers not only technical skills but also emotional aspects, thereby improving employee performance and optimizing the work environment.

[0802] "Employee identification information" refers to individual information about employees, such as their skills, experience, and emotional state, and is data used to ensure appropriate personnel placement within a project.

[0803] "Analysis methods" refer to processes and tools used to analyze collected data and evaluate employees' technical skills and emotional states.

[0804] "Emotional state" refers to information that indicates an employee's current emotions and psychological state, and is a factor that influences the progress of a project.

[0805] "Educational information" refers to knowledge and training content provided to employees with the aim of improving their skills and optimizing their performance.

[0806] "Optimized educational information" refers to educational information that is tailored to the emotional state and technical skills of employees and is provided in a way that best suits their needs.

[0807] "Rest or relaxation activities" refer to activities aimed at reducing employee stress and refreshing them, and are proposed based on the employee's emotional state.

[0808] "Clustering" is a term that describes the process of grouping employees based on specific information and emotional data, and combining those with similar characteristics.

[0809] The system for realizing this invention aims to optimize personnel allocation and training by collecting specific information on employees and analyzing their technical skills and emotional state.

[0810] The core of the system is a server that manages a series of processes including data collection, analysis, evaluation, and education delivery. The server receives information transmitted from each employee's smartphone or tablet and stores it in a database. In terms of specific hardware, it uses general server equipment that provides cloud services. In addition, it leverages Google Cloud Platform's natural language processing API to analyze sentiment data in detail.

[0811] Users input their skill information and project data into the device. The device incorporates voice input and text input interfaces, and the entered information is transferred to the server in real time. The device's operating system and applications utilize a general-purpose smartphone OS and application framework.

[0812] The server uses data analysis tools such as Python and TensorFlow to perform employee skill assessments and sentiment analysis based on the collected information. Based on the results, it automatically selects employees suitable for project requirements and prepares to provide optimized training information.

[0813] For example, in an autonomous vehicle maintenance project, if there is an employee who is highly skilled but experiencing stress, the server will notify the terminal with specific actions in the format of "Suggest additional technical training and a 5-minute break."

[0814] An example of a prompt sentence for a generated AI model is, "Suggest a simple relaxation exercise to help staff members who are feeling stressed about vehicle maintenance reset their mood." In this way, the system can provide personalized feedback to each employee.

[0815] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0816] Step 1:

[0817] Users input information about their skills, experience, and projects using smartphones or tablets. This includes text and voice input. The input information is formatted by the device and sent to the server via the internet. During this process, the device uses an emotion engine to analyze the emotional state from the voice data and sends the results along with the data to the server.

[0818] Step 2:

[0819] The server stores data received from terminals in a database. The data is organized by user and includes both skill information and emotional status. The database is managed using query languages ​​such as SQL, enabling efficient data storage and retrieval.

[0820] Step 3:

[0821] The server uses Python and TensorFlow to analyze data and evaluate each user's technical skills and emotional state. This analysis quantifies skill sets and applies a clustering algorithm based on emotional data. The analysis results are output as evaluation profiles generated for each user.

[0822] Step 4:

[0823] The server selects the most suitable employee based on the project requirements. Using an AI model within the server, the selection process considers a balance of skills and emotional state to create a list of optimal users. The selection results are sent to the terminal as a recommendation list.

[0824] Step 5:

[0825] The user's device provides appropriate educational information based on the received recommendation list. The educational content and timing are optimized according to the user's emotional state. For example, if the user is experiencing high stress, a relaxation video might be suggested. This information is displayed on the device screen, prompting the user to take specific action.

[0826] Step 6:

[0827] The server continuously collects employee performance and sentiment data from the database throughout the project, providing situation-based feedback. Based on each user's performance data, it generates new educational information and continuously improves to provide even more effective education and support.

[0828] Step 7:

[0829] The device generates reports as needed, providing users with regular feedback. These reports include a history of performance and emotional state, as well as suggestions for future improvements and next steps. The generated reports serve as valuable resources for users to refer to and decide on their next actions.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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."

[0839] 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.

[0840] 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.

[0841] 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.

[0842] 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.

[0843] 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.

[0844] 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.

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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 as being incorporated by reference.

[0851] The following is further disclosed regarding the embodiments described above.

[0852] (Claim 1)

[0853] Means for collecting and managing employee identification information,

[0854] A means for analyzing the aforementioned specific information and determining the technical capabilities of the employee,

[0855] A means for selecting an appropriate employee with the aforementioned technical capabilities based on project requirements information,

[0856] Means for providing appropriate educational information to the selected employees,

[0857] Means for monitoring the performance of the aforementioned employees and updating the aforementioned educational information,

[0858] A system that includes this.

[0859] (Claim 2)

[0860] The system according to claim 1, further comprising a data analysis means for clustering employees based on the specified information.

[0861] (Claim 3)

[0862] The system according to claim 1, further comprising a user interface means for inputting project requirements information.

[0863] "Example 1"

[0864] (Claim 1)

[0865] A device for collecting and managing employee identification information,

[0866] A device that analyzes the aforementioned specific information and uses a generated AI model to determine the technical capabilities of employees,

[0867] A device that selects and recommends appropriate employees with the aforementioned technical capabilities based on project requirements information,

[0868] A device that provides appropriate educational information to selected employees,

[0869] A device that monitors employee performance, updates the aforementioned training information, and provides feedback,

[0870] A system that includes this.

[0871] (Claim 2)

[0872] The system according to claim 1, wherein the data analysis device has the function of clustering employees from specific information and generating prompt messages.

[0873] (Claim 3)

[0874] The system according to claim 1, comprising a display device for inputting project requirements information and providing information interactively.

[0875] "Application Example 1"

[0876] (Claim 1)

[0877] Means for collecting and managing employee attribute information,

[0878] A means for analyzing the aforementioned attribute information and determining the technical capabilities of the employee,

[0879] A means for selecting an appropriate employee with the aforementioned technical capabilities based on project requirements information,

[0880] Means for providing appropriate educational information to the selected employees,

[0881] Means for monitoring the performance of the aforementioned employees and updating the aforementioned educational information,

[0882] A means of collecting and managing operational information of work machinery,

[0883] A means for positioning the work machine for the optimal task based on the aforementioned operational information,

[0884] Means for providing the operational skills required for the aforementioned work machine,

[0885] A system that includes this.

[0886] (Claim 2)

[0887] The system according to claim 1, comprising a data analysis means for classifying employees based on attribute information.

[0888] (Claim 3)

[0889] The system according to claim 1, further comprising a user interface means for inputting project requirements information.

[0890] "Example 2 of combining an emotion engine"

[0891] (Claim 1)

[0892] Means for collecting and managing employee identification and emotional information,

[0893] A means for analyzing the aforementioned specific information and emotional information to determine the technical ability and emotional state of an employee,

[0894] A means for selecting appropriate employees with the aforementioned technical capabilities and emotional states based on project requirements information, and for adjusting the recommendation list according to the emotional state,

[0895] A means for providing appropriate educational information to the selected employees and optimizing the content and timing of its provision according to their emotional state,

[0896] A means for monitoring the performance and emotional information of the aforementioned employees, updating the aforementioned educational information, and encouraging new behaviors based on emotions,

[0897] A system that includes this.

[0898] (Claim 2)

[0899] The system according to claim 1, comprising a data analysis means that clusters employees from the specified information and emotional information, and has a function to create feedback based on project requirements and emotional analysis.

[0900] (Claim 3)

[0901] The system according to claim 1, comprising a user interface means for inputting project requirements information and emotional information.

[0902] "Application example 2 when combining with an emotional engine"

[0903] (Claim 1)

[0904] Means for collecting and managing employee identification information,

[0905] A means for analyzing the aforementioned specific information and determining the technical ability and emotional state of the employee,

[0906] A means for selecting the most suitable employee based on project requirements information, taking into account the aforementioned technical capabilities and emotional state,

[0907] A means for providing the selected employees with educational information optimized based on their emotional state,

[0908] Means for monitoring the performance and emotional state of the aforementioned employees and updating the aforementioned educational information,

[0909] A means of suggesting rest or relaxation activities to users through emotional analysis,

[0910] A system that includes this.

[0911] (Claim 2)

[0912] The system according to claim 1, further comprising a data analysis means for clustering employees based on the specified information and emotional data.

[0913] (Claim 3)

[0914] The system according to claim 1, comprising a user interface means for inputting project requirements information and emotional feedback. [Explanation of Symbols]

[0915] 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. Means for collecting and managing employee attribute information, A means for analyzing the aforementioned attribute information and determining the technical capabilities of the employee, A means for selecting an appropriate employee with the aforementioned technical capabilities based on project requirements information, Means for providing appropriate educational information to the selected employees, Means for monitoring the performance of the aforementioned employees and updating the aforementioned educational information, A means of collecting and managing operational information of work machinery, A means for positioning the work machine for the optimal task based on the aforementioned operational information, Means for providing the operational skills required for the aforementioned work machine, A system that includes this.

2. The system according to claim 1, comprising a data analysis means for classifying employees based on attribute information.

3. The system according to claim 1, further comprising a user interface means for inputting project requirements information.

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