System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting skill data of employees; means for collecting experience data of the employees; means for collecting personality diagnosis data of the employees; means having an AI for analyzing the skill data, the experience data, and the personality diagnosis data; and means for proposing optimal human resource allocation to projects and teams based on the analysis results.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In modern companies, it is extremely difficult to comprehensively evaluate employee skills, experience, and personality and optimally assign personnel. Workplace compatibility issues and a lack of opportunities for individual employees to grow often lead to poor work efficiency and team performance. Furthermore, poor personnel assignments can lead to lower productivity across the company and lower employee motivation. Therefore, there is a need for a system that can accurately evaluate employee characteristics and propose appropriate assignments. [Means for solving the problem]

[0005] The present invention provides a system including: means for collecting employee skill data, means for collecting employee experience data, means for collecting employee personality assessment data, means having an AI for analyzing the skill data, the experience data, and the personality assessment data; and means for proposing optimal staffing for projects and teams based on the analysis results. The system further includes means for displaying optimal staffing proposals, and means for collecting feedback data after implementing staffing proposals based on the proposals and updating the AI ​​using the feedback data. This can resolve team compatibility issues and a lack of career growth opportunities, improving productivity across the company.

[0006] "Employee" refers to an individual working for a company or organization.

[0007] "Skills data" refers to information about the specific skills and abilities that employees possess.

[0008] "Experience data" refers to information about the types of work and projects an employee has done in the past.

[0009] "Personality diagnostic data" refers to information about an employee's personality and behavioral characteristics obtained through psychological tests and assessments.

[0010] "AI" stands for artificial intelligence, and refers to the technology of making decisions and making predictions by analyzing large amounts of data.

[0011] A "project" refers to a set of tasks or activities that involve investing time and resources to achieve a specific goal.

[0012] A "team" refers to a work group made up of multiple employees who come together to achieve a common goal.

[0013] "Staffing" refers to the proper allocation of employees to specific jobs or projects.

[0014] "Proposal" refers to a specific plan for allocation that suggests optimal personnel allocation.

[0015] "Feedback data" refers to evaluations and opinions about employee performance and project outcomes.

[0016] "Updating" refers to improving and adjusting existing information and models to bring them up to date. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0021] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0022] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0023] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, 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), Bluetooth (registered trademark), etc.

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 1, a 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.

[0028] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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.

[0031] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0035] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The following describes the preferred embodiments of the present invention. Although specific examples are shown in the description, the present invention is not limited to these examples.

[0039] This invention is a system that collects employee skill data, experience data, and personality assessment data and uses them to propose optimal personnel placement. The system is comprehensive, covering everything from collecting employee information to proposing optimal placement and updating the model based on feedback.

[0040] 1. Collection of employee information

[0041] User

[0042] Employees use dedicated terminals to input their skills (e.g., Java programming, data analysis), experience (e.g., five years of experience in the IT industry), and personality test results (e.g., high level of cooperation, high level of self-management).

[0043] Terminal

[0044] The terminal collects and temporarily stores the data entered by the employee, after which it is sent to a server.

[0045] server

[0046] The server receives the data sent from the device and stores it in a database, where it undergoes formatting and cleaning processes and is converted into a format suitable for the AI ​​model.

[0047] 2. Data Analysis

[0048] server

[0049] The server extracts employee skill data, experience data, and personality assessment data from the database and performs feature engineering. The features are then input into an AI model to analyze employee aptitude ratings and compatibility between employees. Based on the results of this analysis, the server calculates the optimal personnel allocation for each project and team.

[0050] 3. Proposal for optimal layout

[0051] server

[0052] Based on the analysis results of the AI ​​model, the server creates a list proposing optimal personnel placement, which is then sent to the project manager's device.

[0053] Terminal

[0054] The terminal displays a list of proposals for review by the project manager, who then makes staffing decisions based on the proposals displayed.

[0055] 4. Gather feedback and update the model

[0056] User

[0057] After the project is implemented, project managers and employees provide feedback on the success of the project and the performance of the assigned personnel.

[0058] Terminal

[0059] The terminal collects the feedback data and sends it to the server.

[0060] server

[0061] The server receives the feedback data and stores it in a database, which is then used to retrain the AI ​​model and improve the accuracy of future suggestions.

[0062] As a concrete example, consider the case where an employee, Mr. Sato, is proposed as the leader of a company-wide data analysis project. Mr. Sato has five years of experience in the IT industry and is highly skilled in Java programming and data analysis. Furthermore, a personality assessment reveals that he is highly cooperative and has the ability to lead a team. Based on this information, the AI ​​model analyzes that Mr. Sato is highly suitable as the leader of the data analysis project and recommends that he be appointed as the leader. After the project is completed, the project manager provides feedback on Mr. Sato's leadership and project performance, and this data is used to inform future proposals.

[0063] This will optimize personnel allocation across the company, improving team productivity and promoting employee career growth.

[0064] The processing flow will be explained below.

[0065] Step 1:

[0066] Entering employee data

[0067] User

[0068] Employees use dedicated terminals to enter their skills (e.g., Java programming, data analysis), experience (e.g., 5 years of experience in the IT industry), and personality test results (e.g., high self-management ability, high collaborative ability).

[0069] Step 2:

[0070] Temporary storage and transmission of data

[0071] Terminal

[0072] The entered data is temporarily stored and then sent to the server in batch or real-time.

[0073] Step 3:

[0074] Receiving and storing data

[0075] server

[0076] It receives data sent from devices, stores it in a database, and also formats and cleans the data, converting it into a format suitable for AI models.

[0077] Step 4:

[0078] Preprocessing

[0079] server

[0080] Employee data is extracted from the database and preprocessing is performed, such as filling in missing values ​​and removing outliers.

[0081] Step 5:

[0082] Feature Engineering

[0083] server

[0084] Features are generated from the extracted data, and information such as skills, experience, and personality test results are converted into a format that can be input into an AI model as variables.

[0085] Step 6:

[0086] Inputting data into AI models and analyzing it

[0087] server

[0088] The features are input into an AI model to evaluate employee characteristics and suitability, which also analyzes compatibility between employees and project suitability.

[0089] Step 7:

[0090] Calculating optimal staffing

[0091] server

[0092] Based on the evaluation results calculated by the AI ​​model, the optimal personnel allocation for each project and team is calculated. For example, it determines that Mr. Sato is the best suited person to be the "leader of the data analysis project."

[0093] Step 8:

[0094] Generate and submit a list of placement proposals

[0095] server

[0096] A placement proposal list is generated and its contents are sent to the terminal of the project manager.

[0097] Step 9:

[0098] Displaying the placement proposal list

[0099] Terminal

[0100] The list of submitted placement proposals is displayed to the project manager for review and approval, and the employee is notified as well.

[0101] Step 10:

[0102] Implementing staffing

[0103] User

[0104] The project manager will refer to the proposed deployment list and actually deploy the personnel, and the employees will begin work at their new locations.

[0105] Step 11:

[0106] Entering feedback data

[0107] User

[0108] After the project is completed, feedback on the success of the project and the performance of the assigned employees is entered into a dedicated terminal.

[0109] Step 12:

[0110] Collecting and sending feedback

[0111] Terminal

[0112] The collected feedback data is temporarily stored and retransmitted to the server in batch or real-time.

[0113] Step 13:

[0114] Receiving feedback and updating the model

[0115] server

[0116] The received feedback data is stored in a database, and the feedback information is used to retrain the AI ​​model, improving the accuracy of future suggestions.

[0117] The above are the specific processing steps of this system.

[0118] Example 1

[0119] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0120] Optimal personnel placement based on employee skills, experience, and personality is an important issue for many companies. Traditional methods often rely on subjective judgment and bias, resulting in insufficient optimal placement. Furthermore, there is no system in place to properly utilize feedback after placement, making it difficult to use it for future placements.

[0121] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0122] In this invention, the server includes means for inputting employee skill data, means for inputting employee experience data, means for inputting employee personality diagnosis data, means for collecting the skill data, the experience data, and the personality diagnosis data and storing them in a database, means for performing data cleaning and shaping, means for performing feature engineering, means for having a generative AI model for analyzing the shaped data, means for generating a proposal list of optimal personnel assignments for projects and teams based on the analysis results, and means for transmitting the proposal list to a terminal of a project manager. This enables optimal personnel assignments based on objective and appropriate data.

[0123] "Employee skills data" refers to information about the skills and knowledge that employees possess in specific tasks or jobs.

[0124] "Employee experience data" refers to information that represents the history and performance of the work or duties that an employee has performed to date.

[0125] "Employee personality assessment data" refers to test results used to assess an employee's personality traits and behavioral patterns.

[0126] "Means of storing data in a database" refers to the systems and technologies used to store collected data in an organized manner and make it easy to manage and search.

[0127] "Data cleaning and formatting" refers to operations performed to correct inaccurate or missing data and convert it into a form suitable for analysis and use.

[0128] "Feature engineering" refers to the process of extracting useful information from collected data and converting it into a format suitable for analytical models.

[0129] A "generative AI model" is a program that uses machine learning algorithms to analyze data and make predictions or suggestions based on specific purposes.

[0130] "Proposal list" refers to a list of candidates for optimal personnel placement created based on the analysis results of the generative AI model.

[0131] This invention is a system that collects employee skill data, experience data, and personality assessment data and uses them to propose optimal personnel placement. The system is comprehensive, covering everything from collecting employee information to proposing optimal placement and updating the model based on feedback.

[0132] Collection of employee information

[0133] User

[0134] Employees use dedicated terminals to enter the following information:

[0135] Skill data (e.g. Java programming, data analysis)

[0136] Experience data (e.g., 5 years of experience in the IT industry)

[0137] Personality test results (e.g., high cooperativeness, high self-management ability)

[0138] Specific actions

[0139] Employees access the form, fill in each item using text boxes and selection lists, then confirm the information and click the "Submit" button.

[0140] Data transmission and storage

[0141] Terminal

[0142] The terminal temporarily stores the data entered by the user in memory and transmits it to the server via the API.

[0143] Specific actions

[0144] Once the input has been confirmed, the terminal uses JavaScript (registered trademark) to serialize the data into JSON format and sends an HTTP POST request to the server.

[0145] server

[0146] The server receives the data sent from the terminal and stores it in a PostgreSQL database.

[0147] Specific actions

[0148] The server parses the received JSON data and stores the data in the "employees" table in the database using an INSERT statement.

[0149] Data formatting and preparation

[0150] server

[0151] The server formats the stored data and converts it into a format suitable for AI models, using the Python Pandas library for the cleaning process.

[0152] Specific actions

[0153] The server periodically runs Python scripts to impute missing data, remove outliers, and encode categorical data, such as converting "highly cooperative" into a numerical format.

[0154] Analyzing the data

[0155] server

[0156] The server extracts features from the formatted data and inputs them into a Tensorflow (registered trademark)-based generative AI model, which analyzes employee aptitude and compatibility between employees.

[0157] Specific actions

[0158] The server performs feature engineering and feeds the generated features into an AI model, which scores each employee's aptitude and compatibility and calculates the optimal personnel placement.

[0159] Proposal and display of optimal layout

[0160] server

[0161] Based on the analysis results of the generative AI model, the server generates a list of proposals for optimal personnel placement and sends it to the project manager's device.

[0162] Specific actions

[0163] The server generates a list of suggestions in JSON format based on the model results and sends it to the manager's device using an HTTP POST request.

[0164] Terminal

[0165] The terminal receives the suggestion list and displays it on the user interface.

[0166] Specific actions

[0167] The terminal parses the received JSON data and inserts it into an HTML template for displaying the list, allowing the project manager to view the proposal list on the screen.

[0168] Gathering feedback and updating the model

[0169] User

[0170] After the project is completed, the project manager and employees use a feedback form to enter their feedback on the success of the project and the performance of the assigned personnel.

[0171] Specific actions

[0172] The user accesses the Google® Form or custom feedback form, fills in each item, and submits it.

[0173] Terminal

[0174] The terminal collects the feedback data and sends it to the server.

[0175] Specific actions

[0176] After entering the feedback, the device presses a button, serializes the feedback data into JSON format, and sends it to the server via an HTTP POST request.

[0177] server

[0178] The server receives the feedback data, stores it in a database, and uses it to retrain the generative AI model to improve the accuracy of future suggestions.

[0179] Specific actions

[0180] After storing the received data in a database, a batch processing script is executed to retrain the generative AI model, using reinforcement learning techniques to improve the accuracy of the next staffing proposal.

[0181] Example prompt sentence:

[0182] Send employee skills, experience, and personality data to a server to generate optimized code for analysis by AI models.

[0183] Extract employee skills, experience, and personality data as features and generate code to analyze them with an AI model.

[0184] Generate the code to generate and display the optimal staffing list based on the analysis results of the AI ​​model.

[0185] Collect employee feedback data and generate code to retrain your AI models.

[0186] This system optimizes personnel allocation across the company, improving team productivity and promoting employee career growth.

[0187] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0188] Program processing flow

[0189] Step 1: Gather employee information

[0190] User

[0191] Employees use dedicated terminals to enter the following information:

[0192] Input: Skill data (e.g., Java programming, data analysis), experience data (e.g., 5 years of experience in the IT industry), personality test results (e.g., high cooperativeness, high self-management ability)

[0193] Output: The input data is temporarily saved on the device.

[0194] Specific actions

[0195] Employees access the form, fill in each item using text boxes and selection lists, then confirm the information and click the "Submit" button.

[0196] Step 2: Send and store data

[0197] Terminal

[0198] The terminal temporarily stores the data entered by the user in memory and transmits it to the server via the API.

[0199] Input: Data entered by the user

[0200] Output: JSON formatted request data to be sent to the server

[0201] Specific actions

[0202] Once the input has been confirmed, the device uses JavaScript to serialize the data into JSON format and sends an HTTP POST request to the server.

[0203] server

[0204] The server receives the data sent from the terminal and stores it in a PostgreSQL database.

[0205] Input: JSON data sent from the terminal

[0206] Output: Data inserted into the database

[0207] Specific actions

[0208] The server parses the received JSON data and stores the data in the "employees" table in the database using an INSERT statement.

[0209] Step 3: Data Formatting and Preparation

[0210] server

[0211] The server formats the stored data and converts it into a format suitable for AI models, using the Python Pandas library for the cleaning process.

[0212] Input: Raw data stored in a database

[0213] Output: Reshaped and cleaned data

[0214] Specific actions

[0215] The server periodically runs Python scripts to impute missing data, remove outliers, and encode categorical data, such as converting "highly cooperative" into a numerical format.

[0216] Step 4: Analyze the data

[0217] server

[0218] The server extracts features from the formatted data and feeds them into a TensorFlow-based generative AI model, which then analyzes employee aptitude and compatibility between employees.

[0219] Input: Reshaped and cleaned data

[0220] Output: Aptitude assessment and compatibility scores between employees

[0221] Specific actions

[0222] The server performs feature engineering and feeds the generated features into an AI model, which scores each employee's aptitude and compatibility and calculates the optimal personnel placement.

[0223] Step 5: Propose and display optimal layout

[0224] server

[0225] Based on the analysis results of the generative AI model, the server generates a list of proposals for optimal personnel placement and sends it to the project manager's device.

[0226] Input: Aptitude assessment and compatibility scores between employees

[0227] Output: Suggestion list

[0228] Specific actions

[0229] The server generates a list of suggestions in JSON format based on the model results and sends it to the manager's device using an HTTP POST request.

[0230] Terminal

[0231] The terminal receives the suggestion list and displays it on the user interface.

[0232] Input: JSON data of the proposal list sent from the server

[0233] Output: The list of suggestions displayed in the user interface

[0234] Specific actions

[0235] The terminal parses the received JSON data and inserts it into an HTML template for displaying the list, allowing the project manager to view the proposal list on the screen.

[0236] Step 6: Gather feedback and update the model

[0237] User

[0238] After the project is completed, the project manager and employees use a feedback form to enter their feedback on the success of the project and the performance of the assigned personnel.

[0239] Input: Feedback data (success level, performance evaluation)

[0240] Output: Sending feedback data to the server

[0241] Specific actions

[0242] Users access a Google Form or custom feedback form, fill out each field, and submit it.

[0243] Terminal

[0244] The terminal collects the feedback data and sends it to the server.

[0245] Input: User-entered feedback data

[0246] Output: Feedback data sent to the server in JSON format

[0247] Specific actions

[0248] After entering the feedback, the device presses a button, serializes the feedback data into JSON format, and sends it to the server via an HTTP POST request.

[0249] server

[0250] The server receives the feedback data, stores it in a database, and uses it to retrain the generative AI model to improve the accuracy of future suggestions.

[0251] Input: Received feedback data

[0252] Output: An updated generative AI model

[0253] Specific actions

[0254] After storing the received data in a database, a batch processing script is executed to retrain the generative AI model, using reinforcement learning techniques to improve the accuracy of the next staffing proposal.

[0255] (Application example 1)

[0256] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0257] To achieve optimal allocation and task sharing between human resources and robots in a factory, it is important to make appropriate allocation proposals based on employee ability, history, and personality evaluations. However, conventional systems have difficulty effectively collecting and analyzing this data and implementing optimal allocation proposals as a system. Furthermore, there is a lack of a process for collecting feedback on performance after allocation and reflecting it in future proposals. This makes it difficult to improve production efficiency and maximize employee satisfaction.

[0258] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0259] In this invention, the server includes means for collecting employee ability data, means for collecting employee history data, means for collecting employee personality assessment data, means having AI for analyzing the ability data, the history data, and the personality assessment data, means for proposing optimal personnel allocation for projects and groups based on the analysis results, and means for proposing optimal task sub-areas for robots and human workers in the factory. This enables optimal allocation and task sharing of personnel and robots in the factory, thereby improving productivity and maximizing employee satisfaction.

[0260] "Competence data" is information about an employee's skills, qualifications, and technical abilities.

[0261] "Historical data" refers to information about an employee's past work experience, performance, and employment history.

[0262] "Personality assessment data" is information that indicates the results of an assessment of an employee's personality traits and behavioral patterns.

[0263] "Artificial intelligence" refers to machine learning algorithms and models that analyze employee ability data, history data, and personality assessment data to propose optimal personnel placement and task allocation.

[0264] "Placement proposal" refers to proposing the optimal placement of human resources or robots for a project or group.

[0265] A "factory" is a place where products are manufactured or processed, and refers to a facility where many machines and robots are in operation.

[0266] "Task allocation" refers to the appropriate allocation of individual tasks to human workers or robots in a project or business.

[0267] "Feedback data" is data collected after a project is completed, including information on employee and robot performance, success, etc.

[0268] An embodiment of the present invention will be described below. This embodiment is a system for achieving optimal allocation and task sharing between employees and robots in a factory. The system is comprehensive, including employee information collection, data analysis, optimal allocation proposals, feedback, and model updates.

[0269] 1. Collection of employee and robot information

[0270] The server is equipped with various means to collect employee and robot ability data, history data, and personality evaluation data. Employee and robot information is input into the system using devices such as dedicated smartphones, smart glasses, and head-mounted displays.

[0271] 2. Data transmission and storage

[0272] The device temporarily stores the collected information and transmits it to the server in real time, where the server stores the transmitted data in a MySQL (registered trademark) database.

[0273] 3. Data preparation and analysis

[0274] The server uses Python-based processing to extract data from MySQL, format and clean it, then performs feature engineering and feeds it into an AI model using TensorFlow or PyTorch. The model analyzes the data, including the abilities and personalities of employees and robots, to calculate optimal placement.

[0275] 4. Proposal for optimal layout

[0276] Based on the results of the AI ​​model's analysis, the server generates a list of suggestions for optimal staffing and robot task allocation, which is then sent to the project manager's smart device via an interface and displayed in a React Native application.

[0277] 5. Gather feedback and update the model

[0278] After the project is completed, the project manager and employees enter feedback data using their smart devices. The devices then send the collected feedback data back to the server, which stores it in MySQL. Using TensorFlow or PyTorch, the AI ​​model is retrained based on the feedback to improve the accuracy of future proposals.

[0279] Examples:

[0280] At a large manufacturing factory, veteran worker A is selected as the leader of a new project. A has 15 years of experience and is skilled at machine maintenance. A personality assessment reveals that A has strong leadership skills and is capable of leading a team. Meanwhile, less experienced worker B (with strong robot operation skills) is also assigned to the team. The system selects this combination as the leader and proposes the optimal placement for the project's success. The project's evaluation and feedback are then entered into the system and used to make future proposals.

[0281] Example prompt sentence:

[0282] Design a system to optimize factory staffing based on employee skill data, experience data, and personality assessment data. Input each worker's data and send it to the server in real time. The data is analyzed by an AI model, which then proposes optimal staffing. After the project is completed, collect feedback and update the AI ​​model to help with future staffing proposals.

[0283] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0284] Step 1:

[0285] Collecting information about employees and robots

[0286] Users use smart devices (smartphones, smart glasses, head-mounted displays, etc.) to input ability data, history data, and personality assessment data. The input data is temporarily saved in JSON format on the device. Specifically, users manually input the data through a dedicated application, and the device formats it in a format that can be sent to the server in real time.

[0287] input:

[0288] Ability data, history data, personality assessment data

[0289] output:

[0290] Input data in JSON format

[0291] Step 2:

[0292] Data transmission and storage

[0293] The terminals send the collected data in JSON format to a server using a RESTful API, which stores the data in a MySQL database. Specifically, the terminals send data for each employee and robot to an API endpoint, and the server saves the data in the appropriate table.

[0294] input:

[0295] Input data in JSON format

[0296] output:

[0297] MySQL database entries

[0298] Step 3:

[0299] Data Shaping and Cleaning

[0300] The server uses Python scripts to extract data from a MySQL database and then performs data conditioning and cleaning, such as imputing missing values, handling outliers, and encoding categorical data. Specifically, the server executes database queries and processes the retrieved data using Pandas.

[0301] input:

[0302] MySQL database entries

[0303] output:

[0304] Formatted and cleaned data

[0305] Step 4:

[0306] Feature Engineering

[0307] The server uses the cleaned and formatted data to generate features. Specifically, it converts the data into a form suitable for machine learning and prepares it for input into an AI model. For example, it standardizes and scales the numerical data and extracts important features.

[0308] input:

[0309] Formatted and cleaned data

[0310] output:

[0311] Feature-engineered data

[0312] Step 5:

[0313] Analysis by AI model

[0314] The server uses TensorFlow or PyTorch to input feature-engineered data into an AI model for analysis, which evaluates the suitability of employees and robots and generates an optimal deployment list. Specifically, the server loads the model, inputs data, and outputs prediction results.

[0315] input:

[0316] Feature-engineered data

[0317] output:

[0318] List of best talent and robot deployments

[0319] Step 6:

[0320] Displaying the optimal placement list

[0321] The server sends the generated optimized placement list to the project manager's smart device via API. The React Native application on the device receives and displays this data. Specifically, the server sends data via API in real time, allowing the list to be visually confirmed on the device.

[0322] input:

[0323] List of best talent and robot deployments

[0324] output:

[0325] A list of proposals displayed on the project manager's smart device

[0326] Step 7:

[0327] Feedback collection

[0328] After completing a project, users enter feedback data using their smart devices. The devices then send the collected feedback data to the server. Specifically, users enter their feedback through the application, and the devices then send it to the server.

[0329] input:

[0330] Feedback Data

[0331] output:

[0332] Feedback data stored on the server

[0333] Step 8:

[0334] Updating a Model

[0335] The server stores the collected feedback data in MySQL and retrains the AI ​​model using TensorFlow or PyTorch, improving the accuracy of the next optimal placement proposal. Specifically, the server updates the AI ​​model using new data and validates the model's performance.

[0336] input:

[0337] Feedback Data

[0338] output:

[0339] Retrained AI model

[0340] Example prompt sentence:

[0341] Design a system to optimize factory staffing based on employee skill data, experience data, and personality assessment data. Input each worker's data and send it to the server in real time. The data is analyzed by an AI model, which then proposes optimal staffing. After the project is completed, collect feedback and update the AI ​​model to help with future staffing proposals.

[0342] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0343] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will be described in detail below with reference to the accompanying drawings, in which: FIG.

[0344] This invention is a system that collects employee skill data, experience data, and personality assessment data and uses them to propose optimal personnel placement. Furthermore, this system is characterized by incorporating an emotion engine that recognizes user emotions, and by utilizing the collected emotion data for analysis. The system is comprehensive, covering everything from collecting employee information to proposing optimal placement and updating the model based on feedback.

[0345] 1. Collect employee information and sentiment data

[0346] User

[0347] Employees use dedicated terminals to enter their skills (e.g., Java programming, data analysis), experience (e.g., 5 years of experience in the IT industry), and personality test results (e.g., high self-management ability, high collaborative ability).

[0348] Terminal

[0349] The device uses an emotion engine to collect employee emotional data (e.g., stress level, motivation), which is generated by analyzing the employee's facial expressions, voice, text input, etc.

[0350] Terminal

[0351] The collected employee data and emotion data are temporarily stored and then sent to the server in batch or real-time.

[0352] server

[0353] It receives data sent from the device and stores it in a database, where it undergoes formatting and cleaning processes and is converted into a format suitable for the AI ​​model.

[0354] 2. Data Analysis

[0355] server

[0356] The server extracts employee skill data, experience data, personality assessment data, and emotional data from the database and performs feature engineering. These features are then input into an AI model, which performs multifaceted analysis, including employee aptitude assessment, compatibility between employees, mental health, and stress levels.

[0357] 3. Proposal for optimal layout

[0358] server

[0359] Based on the analysis results of the AI ​​model, a list of optimal personnel placement suggestions is created and sent to the project manager's device.

[0360] Terminal

[0361] A list of proposals is displayed for review by the project manager, who then makes staffing decisions based on the proposals displayed.

[0362] 4. Gather feedback and update the model

[0363] User

[0364] After the project is completed, project managers and employees provide feedback on the project's success and the performance of the assigned personnel.

[0365] Terminal

[0366] Feedback data is collected and sent to a server.

[0367] server

[0368] Feedback data is received and stored in a database, which is then used to retrain the AI ​​model and improve the accuracy of future suggestions.

[0369] As a concrete example, consider the case where an employee, Mr. Sato, is proposed as the leader of a company-wide data analysis project. Mr. Sato has five years of experience in the IT industry and is highly skilled in Java programming and data analysis. The emotion engine also confirms that his current stress level is low and his motivation is high. Based on this information, the AI ​​model analyzes that Mr. Sato is highly qualified to lead the data analysis project and recommends that he be appointed as the leader. After the project is completed, the project manager provides feedback on Mr. Sato's leadership and project performance, and this data is used to inform future proposals.

[0370] This will optimize the allocation of personnel throughout the company, improving team productivity and promoting employee career growth. The use of the emotion engine also makes it possible to consider the mental health of employees, providing a healthier work environment.

[0371] The processing flow will be explained below.

[0372] Step 1:

[0373] Entering employee data

[0374] User

[0375] Employees use dedicated terminals to enter their skills (e.g., Java programming, data analysis), experience (e.g., 5 years of experience in the IT industry), and personality test results (e.g., high self-management ability, high collaborative ability).

[0376] Step 2:

[0377] Collecting Emotional Data

[0378] Terminal

[0379] The company uses an emotion engine to collect employee emotional data, which analyzes facial expressions, voice, and text inputs in real time while employees are logged in to their devices, to generate emotional indicators such as stress levels and motivation.

[0380] Step 3:

[0381] Temporary storage and transmission of data

[0382] Terminal

[0383] The collected skill data, experience data, personality assessment data, and emotion data are temporarily stored and then transmitted to a server in batch processing or real time.

[0384] Step 4:

[0385] Receiving and storing data

[0386] server

[0387] It receives data sent from the device and stores it in a database, where it undergoes formatting and cleaning processes and is converted into a format suitable for the AI ​​model.

[0388] Step 5:

[0389] Preprocessing

[0390] server

[0391] Employee data and emotion data are extracted from the database, and preprocessing such as filling in missing values ​​and removing outliers is performed.

[0392] Step 6:

[0393] Feature Engineering

[0394] server

[0395] Features are generated from the extracted data, and skills, experience, personality assessment results, and emotional data (stress level, motivation, etc.) are put into a format that can be input into an AI model as variables.

[0396] Step 7:

[0397] Inputting data into AI models and analyzing it

[0398] server

[0399] The features are fed into an AI model to evaluate employee characteristics and suitability, which analyzes not only compatibility between employees and project suitability, but also mental health and stress levels.

[0400] Step 8:

[0401] Calculating optimal staffing

[0402] server

[0403] Based on the evaluation results calculated by the AI ​​model, the optimal personnel allocation for each project and team is calculated. For example, based on emotional data, it may determine that Sato, who has a low stress level and is highly motivated, is the best suited to be the "leader of the data analysis project."

[0404] Step 9:

[0405] Generate and submit a list of placement proposals

[0406] server

[0407] A placement proposal list is generated and its contents are sent to the terminal of the project manager.

[0408] Step 10:

[0409] Displaying the placement proposal list

[0410] Terminal

[0411] The list of submitted placement proposals is displayed to the project manager for review and approval, and the employee is notified as well.

[0412] Step 11:

[0413] Implementing staffing

[0414] User

[0415] The project manager will refer to the proposed deployment list and actually deploy the personnel, and the employees will begin work at their new locations.

[0416] Step 12:

[0417] Entering feedback data

[0418] User

[0419] After the project is completed, feedback on the success of the project and the performance of the assigned employees is entered into a dedicated terminal.

[0420] Step 13:

[0421] Collecting and sending feedback

[0422] Terminal

[0423] The collected feedback data is temporarily stored and retransmitted to the server in batch or real-time.

[0424] Step 14:

[0425] Receiving feedback and updating the model

[0426] server

[0427] The received feedback data is stored in a database, and the feedback information is used to retrain the AI ​​model, improving the accuracy of future suggestions.

[0428] This allows for the optimization of personnel allocation across the company, improving team productivity and promoting employee career growth. Furthermore, the use of an emotion engine can provide a healthy work environment that takes into consideration the mental health of employees. The specific processing steps are as follows:

[0429] Example 2

[0430] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0431] In order to properly assign employees, not only employee skills and experience are important, but also personality and emotional data. However, with conventional systems, it was difficult to centrally collect, integrate, and analyze this data. In particular, it was difficult to grasp employees' emotional states in real time, which resulted in a decrease in the accuracy of personnel assignments and a negative impact on employee motivation and stress levels. This issue needs to be resolved.

[0432] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0433] In this invention, the server includes means for collecting employee skill data, means for collecting employee experience data, means for collecting employee personality assessment data, means for collecting employee emotion data, means having an AI for analyzing the skill data, the experience data, and the personality assessment data, and means for proposing personnel placement based on the analysis results and emotion data. This enables highly accurate personnel placement that takes into account not only employee skills and experience, but also personality and emotion.

[0434] "Skills data" is information about the skills and abilities of employees.

[0435] "Experience data" is information about the work and projects an employee has previously worked on.

[0436] "Personality diagnostic data" is information obtained as a result of evaluating an employee's personality and behavioral characteristics.

[0437] "Emotional data" is information that measures an employee's emotional state, including stress levels and motivation.

[0438] "AI" is a system that uses artificial intelligence technology to analyze data and make predictions.

[0439] "Means" refers to a method or device used to achieve a particular purpose.

[0440] A "server" is a central computer system that collects, processes, stores, and distributes data.

[0441] "Proposal" refers to recommending the optimal personnel placement based on the analysis results.

[0442] "Feedback data" refers to information regarding the results and evaluations after personnel placement has actually been carried out.

[0443] "Model updating" refers to retraining an AI model based on collected feedback data to improve the accuracy of its next predictions and suggestions.

[0444] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will be described in detail below with reference to the accompanying drawings, in which: FIG.

[0445] This invention is a system that collects employee skill data, experience data, and personality assessment data and uses them to propose optimal personnel placement. Furthermore, this system is characterized by incorporating an emotion engine that recognizes user emotions, and by utilizing the collected emotion data for analysis. The system is comprehensive, covering everything from collecting employee information to proposing optimal placement and updating the model based on feedback.

[0446] 1. Collect employee information and sentiment data

[0447] User

[0448] Employees use dedicated terminals to enter their skills (e.g., programming, data analysis), experience (e.g., several years of industry experience), and personality test results (e.g., high self-management ability, high collaborative ability).

[0449] Terminal

[0450] The device uses a built-in emotion engine to collect employee emotional data, which analyzes facial expressions, voice, and text input to generate emotional data such as stress levels and motivation.

[0451] Terminal

[0452] The collected employee data and emotion data are temporarily stored in local storage and then sent to the server in batch or real-time.

[0453] server

[0454] It receives data sent from the device and stores it in a database, where it undergoes formatting and cleaning processes and is converted into a format suitable for the AI ​​model.

[0455] 2. Data Analysis

[0456] server

[0457] The server extracts employee skill data, experience data, personality assessment data, and emotional data from the database and performs feature engineering. These features are then input into an AI model, which performs multifaceted analysis, including employee aptitude assessment, compatibility between employees, mental health, and stress levels.

[0458] 3. Proposal for optimal layout

[0459] server

[0460] Based on the analysis results of the AI ​​model, a list of optimal personnel placement suggestions is created and sent to the project manager's device.

[0461] Terminal

[0462] A list of proposals is displayed for review by the project manager, who then makes staffing decisions based on the proposals displayed.

[0463] 4. Gather feedback and update the model

[0464] User

[0465] After the project is completed, project managers and employees provide feedback on the project's success and the performance of the assigned personnel.

[0466] Terminal

[0467] The terminal collects the feedback data and sends it to the server.

[0468] server

[0469] Feedback data is received and stored in a database, which is then used to retrain the AI ​​model and improve the accuracy of future suggestions.

[0470] As a concrete example, consider the case where an employee is proposed as a leader for a data analytics project. This employee has several years of industry experience and is highly skilled in programming and data analysis. The emotion engine also identifies that the employee has low stress levels and high motivation. Based on this information, the AI ​​model makes a recommendation and assigns the employee to the leadership role. After the project is completed, the manager provides feedback on the employee's performance, and this data is used to inform future proposals.

[0471] Example prompt sentence:

[0472] Please explain the system that collects employee skill data, experience data, personality assessment data, and emotional data to suggest optimal staffing.

[0473] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0474] Step 1:

[0475] Collecting employee information and sentiment data

[0476] User

[0477] Users use dedicated terminals to input their skill data (e.g., programming, data analysis), experience data (e.g., several years of industry experience), and personality assessment data (e.g., high self-management ability, high cooperativeness), which allows detailed information on each user's abilities and characteristics to be collected.

[0478] Terminal

[0479] The device uses an emotion engine to collect user emotion data. The emotion engine analyzes the user's facial expressions, voice, text input, etc. to generate emotion data such as stress level and motivation. For example, a camera captures the user's facial expressions, and a voice input device analyzes the tone of voice.

[0480] input

[0481] User skill data, experience data, personality assessment data, and emotional data.

[0482] output

[0483] Integrated data temporarily stored on the device.

[0484] Step 2:

[0485] Data transmission and storage

[0486] Terminal

[0487] The collected data is temporarily stored in the device's local storage, and then sent to the server in batch or real-time.

[0488] server

[0489] The server receives the data sent from the device and stores it in a database. The received data is then formatted and cleaned to convert it into a format suitable for the AI ​​model. For example, it checks for outliers and removes duplicate data.

[0490] input

[0491] Aggregated data sent from the device.

[0492] output

[0493] Formatted data stored in a database.

[0494] Step 3:

[0495] Analyzing the data

[0496] server

[0497] The server extracts the necessary data (skill data, experience data, personality assessment data, and emotional data) from the database and performs feature engineering. These features are then input into an AI model for multifaceted analysis. For example, employee aptitude assessments, compatibility between employees, mental health, and stress levels are also evaluated.

[0498] input

[0499] Formatted data stored in a database.

[0500] output

[0501] Data after feature engineering and analysis results from an AI model.

[0502] Step 4:

[0503] Proposal for optimal placement

[0504] server

[0505] Based on the analysis results of the AI ​​model, a list of optimal personnel placement suggestions is created. Specifically, a list of placement candidates is generated based on the analysis results.

[0506] Terminal

[0507] The proposal list is sent to the project manager's terminal, where it is displayed, and the project manager makes personnel allocation decisions based on the list.

[0508] input

[0509] Analysis results from the AI ​​model.

[0510] output

[0511] A list that suggests optimal staffing.

[0512] Step 5:

[0513] Gathering feedback and updating the model

[0514] User

[0515] After the project is completed, project managers and employees provide feedback on the project's success and the performance of the assigned personnel.

[0516] Terminal

[0517] Feedback data is collected and sent to the server. Evaluation scores and comments are entered through a dedicated form.

[0518] server

[0519] Feedback data is received and stored in a database, which is then used to retrain the AI ​​model and improve the accuracy of future suggestions.

[0520] input

[0521] Feedback data on project success and talent performance.

[0522] output

[0523] Updated AI model, improved accuracy for next proposal.

[0524] (Application example 2)

[0525] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0526] Conventional factory personnel allocation systems suggest personnel placement based on employee skills, experience, and personality assessments, but do not consider employee emotional data or robot work data. This makes it difficult to manage employee motivation and mental health, and to optimize collaboration with robots. The present invention aims to propose optimal personnel and robot placement in a collaborative environment between humans and robots by considering employee emotional data and robot work data, thereby improving factory productivity and realizing a healthy working environment.

[0527] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0528] In this invention, the server includes means for collecting employee skill data, means for collecting employee experience data, means for collecting employee personality diagnosis data, means for collecting employee emotion data, an AI module for analyzing the skill data, the experience data, the personality diagnosis data, and the emotion data, and means for proposing optimal allocation of human resources and robots for a project and a collaborative work environment based on the analysis results. This makes it possible to optimally allocate human resources and robots taking into account the emotional states of employees.

[0529] "Skill data" is the specialized skills and knowledge possessed by employees expressed as numerical or categorical data.

[0530] "Experience data" refers to historical information such as the type of work an employee has done in the past, the duration of that work, and the results they have achieved.

[0531] "Personality assessment data" is data that evaluates an employee's personality traits and psychological tendencies and records them as numerical or categorical data.

[0532] "Emotional data" is numerical and categorical data that expresses the emotional state of employees and indicates real-time situations and trends.

[0533] An "AI module" is a system component that analyzes collected data and runs artificial intelligence algorithms to make optimal recommendations and predictions.

[0534] "Optimal allocation of human resources and robots" refers to an effective allocation method that maximizes the use of the respective capabilities and conditions of humans and robots to improve production efficiency and work quality.

[0535] "Feedback data" is data that records evaluations, points for reflection, and areas for improvement regarding work or projects that have been carried out.

[0536] A "collaborative work environment" refers to a place or situation where humans and robots work together, complementing each other's roles.

[0537] The following describes in detail an embodiment of the present invention as an optimal human resource allocation system for factory robots. This system collects and analyzes skill data, experience data, personality diagnostic data, and emotional data of employees and robots to propose optimal allocation.

[0538] Program processing and hardware / software used

[0539] Hardware

[0540] Wearable devices: Capture employee vital and emotional data.

[0541] Robot: Collects work data and sends it to the server.

[0542] Terminal: A device where employees enter data and managers review the results.

[0543] software

[0544] AI module: Analyzes collected data and suggests optimal placement. Uses TensorFlow and PyTorch.

[0545] Database: Stores collected data. Uses MySQL or MongoDB.

[0546] Emotion Engine: Analyzes employee emotion data using IBM Watson® and Microsoft® Azure® Emotion APIs.

[0547] Data processing and calculation

[0548] Data Shaping and Cleaning: Shaping and cleaning data using Python's Pandas library.

[0549] Feature engineering: Using Scikit-learn, we extract features and convert them into a format suitable for AI models.

[0550] Clustering algorithms: Clustering algorithms such as KMeans are used to propose optimal placement of employees and robots.

[0551] Specific example of system operation

[0552] 1. Employee data collection

[0553] Employees use smartphones or wearable devices to input their skills, experience and emotional data.

[0554] The robot automatically collects operational data and sends it to a server.

[0555] 2. Data analysis

[0556] The server formats and cleans the employee's skill data, experience data, personality assessment data, and emotional data sent from the terminal, and builds an AI model using neural networks, random forests, etc.

[0557] The emotion engine analyzes employee emotion data and generates a score.

[0558] KMeans clustering is performed to propose optimal placement of personnel and robots.

[0559] 3. Display placement proposals

[0560] The server generates optimal placement proposals based on the analysis results of the AI ​​module, which are then sent to the administrator's device and displayed in real time.

[0561] Based on the displayed proposals, the manager decides on the allocation of human resources and robots for the project and the collaborative work environment.

[0562] 4. Gather feedback and update the model

[0563] After the project is completed, managers and employees enter their project evaluations and feedback through the terminal.

[0564] The feedback data is sent to the server and stored in a database, and the AI ​​module uses this feedback data to retrain itself and improve the accuracy of its next suggestions.

[0565] Prompt Sentence Examples

[0566] "Analyze employee skills, experience, personality test results, and sentiment data to suggest optimal staffing and robot placement for a new product line installation project."

[0567] This allows the present invention to take into account the emotional state of employees and make optimal allocations of personnel and robots.

[0568] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0569] Step 1: Collect employee data

[0570] Users use smartphones or wearable devices to input their skills (e.g., programming skills or machine operation skills), experience (e.g., years of project experience or area of ​​expertise), personality assessment results (e.g., self-management ability or cooperativeness), and emotional data (e.g., stress level or motivation). In addition, the robot's work data (e.g., operating time or error rate) is automatically collected and sent from the device to a server. The input data is temporarily stored and sent to the server in real time or batch processing.

[0571] Step 2: Data Shaping and Cleaning

[0572] The server receives employee and robot data sent from the terminals. The received data is formatted and cleaned using Python's Pandas library, which completes incomplete data and removes invalid data. The skill data, experience data, personality assessment data, and emotion data collected as input data are converted into a unified format and output as clean data.

[0573] Step 3: Feature Engineering

[0574] The server performs feature engineering using the formatted and cleaned data. Using Scikit-learn, it extracts features such as skills, experience, personality assessment results, and emotional data and converts them into a format suitable for machine learning models. This process involves standardizing, normalizing, and encoding categorical data based on the input cleaned data to generate a feature dataset.

[0575] Step 4: Clustering and analysis

[0576] The server inputs the feature dataset into the KMeans clustering algorithm and analyzes the optimal allocation of employees and robots through clustering. Based on the input feature dataset, the server generates grouped clusters, evaluates the characteristics of each group, and outputs optimal employee and robot allocation proposals.

[0577] Step 5: Generate and view placement proposals

[0578] The server generates optimal personnel and robot placement proposals based on the clustering results. The generated proposals are sent to the administrator's terminal in real time and displayed on the screen. The administrator checks the displayed proposals, modifies them as necessary, and reflects them in the actual placement. The input data for the proposals are the clustering results and the administrator's operations, and the output data is the final placement proposal.

[0579] Step 6: Collect and analyze feedback

[0580] After the project is completed, users (managers and employees) input feedback on the success of the project and the performance of the assigned personnel and robots. The terminal collects this feedback data and sends it to the server. The input data here is the feedback content, and the output data is the formatted feedback data.

[0581] Step 7: Update the AI ​​model

[0582] The server receives the collected feedback data and stores it in a database. The stored feedback data is used to retrain the AI ​​model to improve the accuracy of the next proposal. The feedback data is used as input data and an updated AI model is generated as output data.

[0583] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0584] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0585] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0586] [Second embodiment]

[0587] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0588] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0589] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0590] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0591] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0592] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0593] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0594] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0595] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0596] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0597] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0598] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0599] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The following describes the preferred embodiments of the present invention. Although specific examples are shown in the description, the present invention is not limited to these examples.

[0600] This invention is a system that collects employee skill data, experience data, and personality assessment data and uses them to propose optimal personnel placement. The system is comprehensive, covering everything from collecting employee information to proposing optimal placement and updating the model based on feedback.

[0601] 1. Collection of employee information

[0602] User

[0603] Employees use dedicated terminals to input their skills (e.g., Java programming, data analysis), experience (e.g., 5 years of experience in the IT industry), and personality test results (e.g., high level of cooperation, high level of self-management).

[0604] Terminal

[0605] The terminal collects and temporarily stores the data entered by the employee, after which it is sent to a server.

[0606] server

[0607] The server receives the data sent from the device and stores it in a database, where it undergoes formatting and cleaning processes and is converted into a format suitable for the AI ​​model.

[0608] 2. Data Analysis

[0609] server

[0610] The server extracts employee skill data, experience data, and personality assessment data from the database and performs feature engineering. The features are then input into an AI model to analyze employee aptitude ratings and compatibility between employees. Based on the results of this analysis, the server calculates the optimal personnel allocation for each project and team.

[0611] 3. Proposal for optimal layout

[0612] server

[0613] Based on the analysis results of the AI ​​model, the server creates a list proposing optimal personnel placement, which is then sent to the project manager's device.

[0614] Terminal

[0615] The terminal displays a list of proposals for review by the project manager, who then makes staffing decisions based on the proposals displayed.

[0616] 4. Gather feedback and update the model

[0617] User

[0618] After the project is implemented, project managers and employees provide feedback on the success of the project and the performance of the assigned personnel.

[0619] Terminal

[0620] The terminal collects the feedback data and sends it to the server.

[0621] server

[0622] The server receives the feedback data and stores it in a database, which is then used to retrain the AI ​​model and improve the accuracy of future suggestions.

[0623] As a concrete example, consider the case where an employee, Mr. Sato, is proposed as the leader of a company-wide data analysis project. Mr. Sato has five years of experience in the IT industry and is highly skilled in Java programming and data analysis. Furthermore, a personality assessment reveals that he is highly cooperative and has the ability to lead a team. Based on this information, the AI ​​model analyzes that Mr. Sato is highly suitable as the leader of the data analysis project and recommends that he be appointed as the leader. After the project is completed, the project manager provides feedback on Mr. Sato's leadership and project performance, and this data is used to inform future proposals.

[0624] This will optimize personnel allocation across the company, improving team productivity and promoting employee career growth.

[0625] The processing flow will be explained below.

[0626] Step 1:

[0627] Entering employee data

[0628] User

[0629] Employees use dedicated terminals to enter their skills (e.g., Java programming, data analysis), experience (e.g., 5 years of experience in the IT industry), and personality test results (e.g., high self-management ability, high collaborative ability).

[0630] Step 2:

[0631] Temporary storage and transmission of data

[0632] Terminal

[0633] The entered data is temporarily stored and then sent to the server in batch or real-time.

[0634] Step 3:

[0635] Receiving and storing data

[0636] server

[0637] It receives data sent from devices, stores it in a database, and also formats and cleans the data, converting it into a format suitable for AI models.

[0638] Step 4:

[0639] Preprocessing

[0640] server

[0641] Employee data is extracted from the database and preprocessing is performed, such as filling in missing values ​​and removing outliers.

[0642] Step 5:

[0643] Feature Engineering

[0644] server

[0645] Features are generated from the extracted data, and information such as skills, experience, and personality test results are converted into a format that can be input into an AI model as variables.

[0646] Step 6:

[0647] Inputting data into AI models and analyzing it

[0648] server

[0649] The features are input into an AI model to evaluate employee characteristics and suitability, which also analyzes compatibility between employees and project suitability.

[0650] Step 7:

[0651] Calculating optimal staffing

[0652] server

[0653] Based on the evaluation results calculated by the AI ​​model, the optimal personnel allocation for each project and team is calculated. For example, it determines that Mr. Sato is the best suited person to be the "leader of the data analysis project."

[0654] Step 8:

[0655] Generate and submit a list of placement proposals

[0656] server

[0657] A placement proposal list is generated and its contents are sent to the terminal of the project manager.

[0658] Step 9:

[0659] Displaying the placement proposal list

[0660] Terminal

[0661] The list of submitted placement proposals is displayed to the project manager for review and approval, and the employee is notified as well.

[0662] Step 10:

[0663] Implementing staffing

[0664] User

[0665] The project manager will refer to the proposed deployment list and actually deploy the personnel, and the employees will begin work at their new locations.

[0666] Step 11:

[0667] Entering feedback data

[0668] User

[0669] After the project is completed, feedback on the success of the project and the performance of the assigned employees is entered into a dedicated terminal.

[0670] Step 12:

[0671] Collecting and sending feedback

[0672] Terminal

[0673] The collected feedback data is temporarily stored and retransmitted to the server in batch or real-time.

[0674] Step 13:

[0675] Receiving feedback and updating the model

[0676] server

[0677] The received feedback data is stored in a database, and the feedback information is used to retrain the AI ​​model, improving the accuracy of future suggestions.

[0678] The above are the specific processing steps of this system.

[0679] Example 1

[0680] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0681] Optimal personnel placement based on employee skills, experience, and personality is an important issue for many companies. Traditional methods often rely on subjective judgment and bias, resulting in insufficient optimal placement. Furthermore, there is no system in place to properly utilize feedback after placement, making it difficult to use it for future placements.

[0682] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0683] In this invention, the server includes means for inputting employee skill data, means for inputting employee experience data, means for inputting employee personality diagnosis data, means for collecting the skill data, the experience data, and the personality diagnosis data and storing them in a database, means for performing data cleaning and shaping, means for performing feature engineering, means for having a generative AI model for analyzing the shaped data, means for generating a proposal list of optimal personnel assignments for projects and teams based on the analysis results, and means for transmitting the proposal list to a terminal of a project manager. This enables optimal personnel assignments based on objective and appropriate data.

[0684] "Employee skills data" refers to information about the skills and knowledge that employees possess in specific tasks or jobs.

[0685] "Employee experience data" refers to information that represents the history and performance of the work or duties that an employee has performed to date.

[0686] "Employee personality assessment data" refers to test results used to assess an employee's personality traits and behavioral patterns.

[0687] "Means of storing data in a database" refers to the systems and technologies used to store collected data in an organized manner and make it easy to manage and search.

[0688] "Data cleaning and formatting" refers to operations performed to correct inaccurate or missing data and convert it into a form suitable for analysis and use.

[0689] "Feature engineering" refers to the process of extracting useful information from collected data and converting it into a format suitable for analytical models.

[0690] A "generative AI model" is a program that uses machine learning algorithms to analyze data and make predictions or suggestions based on specific purposes.

[0691] "Proposal list" refers to a list of candidates for optimal personnel placement created based on the analysis results of the generative AI model.

[0692] This invention is a system that collects employee skill data, experience data, and personality assessment data and uses them to propose optimal personnel placement. The system is comprehensive, covering everything from collecting employee information to proposing optimal placement and updating the model based on feedback.

[0693] Collection of employee information

[0694] User

[0695] Employees use dedicated terminals to enter the following information:

[0696] Skill data (e.g. Java programming, data analysis)

[0697] Experience data (e.g., 5 years of experience in the IT industry)

[0698] Personality test results (e.g., high cooperativeness, high self-management ability)

[0699] Specific actions

[0700] Employees access the form, fill in each item using text boxes and selection lists, then confirm the information and click the "Submit" button.

[0701] Data transmission and storage

[0702] Terminal

[0703] The terminal temporarily stores the data entered by the user in memory and transmits it to the server via the API.

[0704] Specific actions

[0705] Once the input has been confirmed, the device uses JavaScript to serialize the data into JSON format and sends an HTTP POST request to the server.

[0706] server

[0707] The server receives the data sent from the terminal and stores it in a PostgreSQL database.

[0708] Specific actions

[0709] The server parses the received JSON data and stores the data in the "employees" table in the database using an INSERT statement.

[0710] Data formatting and preparation

[0711] server

[0712] The server formats the stored data and converts it into a format suitable for AI models, using the Python Pandas library for the cleaning process.

[0713] Specific actions

[0714] The server periodically runs Python scripts to impute missing data, remove outliers, and encode categorical data, such as converting "highly cooperative" into a numerical format.

[0715] Analyzing the data

[0716] server

[0717] The server extracts features from the formatted data and feeds them into a TensorFlow-based generative AI model, which then analyzes employee aptitude and compatibility between employees.

[0718] Specific actions

[0719] The server performs feature engineering and feeds the generated features into an AI model, which scores each employee's aptitude and compatibility and calculates the optimal personnel placement.

[0720] Proposal and display of optimal layout

[0721] server

[0722] Based on the analysis results of the generative AI model, the server generates a list of proposals for optimal personnel placement and sends it to the project manager's device.

[0723] Specific actions

[0724] The server generates a list of suggestions in JSON format based on the model results and sends it to the manager's device using an HTTP POST request.

[0725] Terminal

[0726] The terminal receives the suggestion list and displays it on the user interface.

[0727] Specific actions

[0728] The terminal parses the received JSON data and inserts it into an HTML template for displaying the list, allowing the project manager to view the proposal list on the screen.

[0729] Gathering feedback and updating the model

[0730] User

[0731] After the project is completed, the project manager and employees use a feedback form to enter their feedback on the success of the project and the performance of the assigned personnel.

[0732] Specific actions

[0733] Users access a Google Form or custom feedback form, fill out each field, and submit it.

[0734] Terminal

[0735] The terminal collects the feedback data and sends it to the server.

[0736] Specific actions

[0737] After entering the feedback, the device presses a button, serializes the feedback data into JSON format, and sends it to the server via an HTTP POST request.

[0738] server

[0739] The server receives the feedback data, stores it in a database, and uses it to retrain the generative AI model to improve the accuracy of future suggestions.

[0740] Specific actions

[0741] After storing the received data in a database, a batch processing script is executed to retrain the generative AI model, using reinforcement learning techniques to improve the accuracy of the next staffing proposal.

[0742] Example prompt sentence:

[0743] Send employee skills, experience, and personality data to a server to generate optimized code for analysis by AI models.

[0744] Extract employee skills, experience, and personality data as features and generate code to analyze them with an AI model.

[0745] Generate the code to generate and display the optimal staffing list based on the analysis results of the AI ​​model.

[0746] Collect employee feedback data and generate code to retrain your AI models.

[0747] This system optimizes personnel allocation across the company, improving team productivity and promoting employee career growth.

[0748] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0749] Program processing flow

[0750] Step 1: Gather employee information

[0751] User

[0752] Employees use dedicated terminals to enter the following information:

[0753] Input: Skill data (e.g., Java programming, data analysis), experience data (e.g., 5 years of experience in the IT industry), personality test results (e.g., high cooperativeness, high self-management ability)

[0754] Output: The input data is temporarily saved on the device.

[0755] Specific actions

[0756] Employees access the form, fill in each item using text boxes and selection lists, then confirm the information and click the "Submit" button.

[0757] Step 2: Send and store data

[0758] Terminal

[0759] The terminal temporarily stores the data entered by the user in memory and transmits it to the server via the API.

[0760] Input: Data entered by the user

[0761] Output: JSON formatted request data to be sent to the server

[0762] Specific actions

[0763] Once the input has been confirmed, the device uses JavaScript to serialize the data into JSON format and sends an HTTP POST request to the server.

[0764] server

[0765] The server receives the data sent from the terminal and stores it in a PostgreSQL database.

[0766] Input: JSON data sent from the terminal

[0767] Output: Data inserted into the database

[0768] Specific actions

[0769] The server parses the received JSON data and stores the data in the "employees" table in the database using an INSERT statement.

[0770] Step 3: Data Formatting and Preparation

[0771] server

[0772] The server formats the stored data and converts it into a format suitable for AI models, using the Python Pandas library for the cleaning process.

[0773] Input: Raw data stored in a database

[0774] Output: Reshaped and cleaned data

[0775] Specific actions

[0776] The server periodically runs Python scripts to impute missing data, remove outliers, and encode categorical data, such as converting "highly cooperative" into a numerical format.

[0777] Step 4: Analyze the data

[0778] server

[0779] The server extracts features from the formatted data and feeds them into a TensorFlow-based generative AI model, which then analyzes employee aptitude and compatibility between employees.

[0780] Input: Reshaped and cleaned data

[0781] Output: Aptitude assessment and compatibility scores between employees

[0782] Specific actions

[0783] The server performs feature engineering and feeds the generated features into an AI model, which scores each employee's aptitude and compatibility and calculates the optimal personnel placement.

[0784] Step 5: Propose and display optimal layout

[0785] server

[0786] Based on the analysis results of the generative AI model, the server generates a list of proposals for optimal personnel placement and sends it to the project manager's device.

[0787] Input: Aptitude assessment and compatibility scores between employees

[0788] Output: Suggestion list

[0789] Specific actions

[0790] The server generates a list of suggestions in JSON format based on the model results and sends it to the manager's device using an HTTP POST request.

[0791] Terminal

[0792] The terminal receives the suggestion list and displays it on the user interface.

[0793] Input: JSON data of the proposal list sent from the server

[0794] Output: The list of suggestions displayed in the user interface

[0795] Specific actions

[0796] The terminal parses the received JSON data and inserts it into an HTML template for displaying the list, allowing the project manager to view the proposal list on the screen.

[0797] Step 6: Gather feedback and update the model

[0798] User

[0799] After the project is completed, the project manager and employees use a feedback form to enter their feedback on the success of the project and the performance of the assigned personnel.

[0800] Input: Feedback data (success level, performance evaluation)

[0801] Output: Sending feedback data to the server

[0802] Specific actions

[0803] Users access a Google Form or custom feedback form, fill out each field, and submit it.

[0804] Terminal

[0805] The terminal collects the feedback data and sends it to the server.

[0806] Input: User-entered feedback data

[0807] Output: Feedback data sent to the server in JSON format

[0808] Specific actions

[0809] After entering the feedback, the device presses a button, serializes the feedback data into JSON format, and sends it to the server via an HTTP POST request.

[0810] server

[0811] The server receives the feedback data, stores it in a database, and uses it to retrain the generative AI model to improve the accuracy of future suggestions.

[0812] Input: Received feedback data

[0813] Output: An updated generative AI model

[0814] Specific actions

[0815] After storing the received data in a database, a batch processing script is executed to retrain the generative AI model, using reinforcement learning techniques to improve the accuracy of the next staffing proposal.

[0816] (Application example 1)

[0817] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0818] To achieve optimal allocation and task sharing between human resources and robots in a factory, it is important to make appropriate allocation proposals based on employee ability, history, and personality evaluations. However, conventional systems have difficulty effectively collecting and analyzing this data and implementing optimal allocation proposals as a system. Furthermore, there is a lack of a process for collecting feedback on performance after allocation and reflecting it in future proposals. This makes it difficult to improve production efficiency and maximize employee satisfaction.

[0819] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0820] In this invention, the server includes means for collecting employee ability data, means for collecting employee history data, means for collecting employee personality assessment data, means having AI for analyzing the ability data, the history data, and the personality assessment data, means for proposing optimal personnel allocation for projects and groups based on the analysis results, and means for proposing optimal task sub-areas for robots and human workers in the factory. This enables optimal allocation and task sharing of personnel and robots in the factory, thereby improving productivity and maximizing employee satisfaction.

[0821] "Competence data" is information about an employee's skills, qualifications, and technical abilities.

[0822] "Historical data" refers to information about an employee's past work experience, performance, and employment history.

[0823] "Personality assessment data" is information that indicates the results of an assessment of an employee's personality traits and behavioral patterns.

[0824] "Artificial intelligence" refers to machine learning algorithms and models that analyze employee ability data, history data, and personality assessment data to propose optimal personnel placement and task allocation.

[0825] "Placement proposal" refers to proposing the optimal placement of human resources or robots for a project or group.

[0826] A "factory" is a place where products are manufactured or processed, and refers to a facility where many machines and robots are in operation.

[0827] "Task allocation" refers to the appropriate allocation of individual tasks to human workers or robots in a project or business.

[0828] "Feedback data" is data collected after a project is completed, including information on employee and robot performance, success, etc.

[0829] An embodiment of the present invention will be described below. This embodiment is a system for achieving optimal allocation and task sharing between employees and robots in a factory. The system is comprehensive, including employee information collection, data analysis, optimal allocation proposals, feedback, and model updates.

[0830] 1. Collection of employee and robot information

[0831] The server is equipped with various means to collect employee and robot ability data, history data, and personality evaluation data. Employee and robot information is input into the system using devices such as dedicated smartphones, smart glasses, and head-mounted displays.

[0832] 2. Data transmission and storage

[0833] The device temporarily stores the collected information and transmits it in real time to a server, which stores the data in a MySQL database.

[0834] 3. Data preparation and analysis

[0835] The server uses Python-based processing to extract data from MySQL, format and clean it, then performs feature engineering and feeds it into an AI model using TensorFlow or PyTorch. The model analyzes the data, including the abilities and personalities of employees and robots, to calculate optimal placement.

[0836] 4. Proposal for optimal layout

[0837] Based on the results of the AI ​​model's analysis, the server generates a list of suggestions for optimal staffing and robot task allocation, which is then sent to the project manager's smart device via an interface and displayed in a React Native application.

[0838] 5. Gather feedback and update the model

[0839] After the project is completed, the project manager and employees enter feedback data using their smart devices. The devices then send the collected feedback data back to the server, which stores it in MySQL. Using TensorFlow or PyTorch, the AI ​​model is retrained based on the feedback to improve the accuracy of future proposals.

[0840] Examples:

[0841] At a large manufacturing factory, veteran worker A is selected as the leader of a new project. A has 15 years of experience and is skilled at machine maintenance. A personality assessment reveals that A has strong leadership skills and is capable of leading a team. Meanwhile, less experienced worker B (with strong robot operation skills) is also assigned to the team. The system selects this combination as the leader and proposes the optimal placement for the project's success. The project's evaluation and feedback are then entered into the system and used to make future proposals.

[0842] Example prompt sentence:

[0843] Design a system to optimize factory staffing based on employee skill data, experience data, and personality assessment data. Input each worker's data and send it to the server in real time. The data is analyzed by an AI model, which then proposes optimal staffing. After the project is completed, collect feedback and update the AI ​​model to help with future staffing proposals.

[0844] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0845] Step 1:

[0846] Collecting information about employees and robots

[0847] Users use smart devices (smartphones, smart glasses, head-mounted displays, etc.) to input ability data, history data, and personality assessment data. The input data is temporarily saved in JSON format on the device. Specifically, users manually input the data through a dedicated application, and the device formats it in a format that can be sent to the server in real time.

[0848] input:

[0849] Ability data, history data, personality assessment data

[0850] output:

[0851] Input data in JSON format

[0852] Step 2:

[0853] Data transmission and storage

[0854] The terminals send the collected data in JSON format to a server using a RESTful API, which stores the data in a MySQL database. Specifically, the terminals send data for each employee and robot to an API endpoint, and the server saves the data in the appropriate table.

[0855] input:

[0856] Input data in JSON format

[0857] output:

[0858] MySQL database entries

[0859] Step 3:

[0860] Data Shaping and Cleaning

[0861] The server uses Python scripts to extract data from a MySQL database and then performs data conditioning and cleaning, such as imputing missing values, handling outliers, and encoding categorical data. Specifically, the server executes database queries and processes the retrieved data using Pandas.

[0862] input:

[0863] MySQL database entries

[0864] output:

[0865] Formatted and cleaned data

[0866] Step 4:

[0867] Feature Engineering

[0868] The server uses the cleaned and formatted data to generate features. Specifically, it converts the data into a form suitable for machine learning and prepares it for input into an AI model. For example, it standardizes and scales the numerical data and extracts important features.

[0869] input:

[0870] Formatted and cleaned data

[0871] output:

[0872] Feature-engineered data

[0873] Step 5:

[0874] Analysis by AI model

[0875] The server uses TensorFlow or PyTorch to input feature-engineered data into an AI model for analysis, which evaluates the suitability of employees and robots and generates an optimal deployment list. Specifically, the server loads the model, inputs data, and outputs prediction results.

[0876] input:

[0877] Feature-engineered data

[0878] output:

[0879] List of best talent and robot deployments

[0880] Step 6:

[0881] Displaying the optimal placement list

[0882] The server sends the generated optimized placement list to the project manager's smart device via API. The React Native application on the device receives and displays this data. Specifically, the server sends data via API in real time, allowing the list to be visually confirmed on the device.

[0883] input:

[0884] List of best talent and robot deployments

[0885] output:

[0886] A list of proposals displayed on the project manager's smart device

[0887] Step 7:

[0888] Feedback collection

[0889] After completing a project, users enter feedback data using their smart devices. The devices then send the collected feedback data to the server. Specifically, users enter their feedback through the application, and the devices then send it to the server.

[0890] input:

[0891] Feedback Data

[0892] output:

[0893] Feedback data stored on the server

[0894] Step 8:

[0895] Updating a Model

[0896] The server stores the collected feedback data in MySQL and retrains the AI ​​model using TensorFlow or PyTorch, improving the accuracy of the next optimal placement proposal. Specifically, the server updates the AI ​​model using new data and validates the model's performance.

[0897] input:

[0898] Feedback Data

[0899] output:

[0900] Retrained AI model

[0901] Example prompt sentence:

[0902] Design a system to optimize factory staffing based on employee skill data, experience data, and personality assessment data. Input each worker's data and send it to the server in real time. The data is analyzed by an AI model, which then proposes optimal staffing. After the project is completed, collect feedback and update the AI ​​model to help with future staffing proposals.

[0903] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0904] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will be described in detail below with reference to the accompanying drawings, in which: FIG.

[0905] This invention is a system that collects employee skill data, experience data, and personality assessment data and uses them to propose optimal personnel placement. Furthermore, this system is characterized by incorporating an emotion engine that recognizes user emotions, and by utilizing the collected emotion data for analysis. The system is comprehensive, covering everything from collecting employee information to proposing optimal placement and updating the model based on feedback.

[0906] 1. Collect employee information and sentiment data

[0907] User

[0908] Employees use dedicated terminals to enter their skills (e.g., Java programming, data analysis), experience (e.g., 5 years of experience in the IT industry), and personality test results (e.g., high self-management ability, high collaborative ability).

[0909] Terminal

[0910] The device uses an emotion engine to collect employee emotional data (e.g., stress level, motivation), which is generated by analyzing the employee's facial expressions, voice, text input, etc.

[0911] Terminal

[0912] The collected employee data and emotion data are temporarily stored and then sent to the server in batch or real-time.

[0913] server

[0914] It receives data sent from the device and stores it in a database, where it undergoes formatting and cleaning processes and is converted into a format suitable for the AI ​​model.

[0915] 2. Data Analysis

[0916] server

[0917] The server extracts employee skill data, experience data, personality assessment data, and emotional data from the database and performs feature engineering. These features are then input into an AI model, which performs multifaceted analysis, including employee aptitude assessment, compatibility between employees, mental health, and stress levels.

[0918] 3. Proposal for optimal layout

[0919] server

[0920] Based on the analysis results of the AI ​​model, a list of optimal personnel placement suggestions is created and sent to the project manager's device.

[0921] Terminal

[0922] A list of proposals is displayed for review by the project manager, who then makes staffing decisions based on the proposals displayed.

[0923] 4. Gather feedback and update the model

[0924] User

[0925] After the project is completed, project managers and employees provide feedback on the project's success and the performance of the assigned personnel.

[0926] Terminal

[0927] Feedback data is collected and sent to a server.

[0928] server

[0929] Feedback data is received and stored in a database, which is then used to retrain the AI ​​model and improve the accuracy of future suggestions.

[0930] As a concrete example, consider the case where an employee, Mr. Sato, is proposed as the leader of a company-wide data analysis project. Mr. Sato has five years of experience in the IT industry and is highly skilled in Java programming and data analysis. The emotion engine also confirms that his current stress level is low and his motivation is high. Based on this information, the AI ​​model analyzes that Mr. Sato is highly qualified to lead the data analysis project and recommends that he be appointed as the leader. After the project is completed, the project manager provides feedback on Mr. Sato's leadership and project performance, and this data is used to inform future proposals.

[0931] This will optimize the allocation of personnel throughout the company, improving team productivity and promoting employee career growth. The use of the emotion engine also makes it possible to consider the mental health of employees, providing a healthier work environment.

[0932] The processing flow will be explained below.

[0933] Step 1:

[0934] Entering employee data

[0935] User

[0936] Employees use dedicated terminals to enter their skills (e.g., Java programming, data analysis), experience (e.g., 5 years of experience in the IT industry), and personality test results (e.g., high self-management ability, high collaborative ability).

[0937] Step 2:

[0938] Collecting Emotional Data

[0939] Terminal

[0940] The company uses an emotion engine to collect employee emotional data, which analyzes facial expressions, voice, and text inputs in real time while employees are logged in to their devices, to generate emotional indicators such as stress levels and motivation.

[0941] Step 3:

[0942] Temporary storage and transmission of data

[0943] Terminal

[0944] The collected skill data, experience data, personality assessment data, and emotion data are temporarily stored and then transmitted to a server in batch processing or real time.

[0945] Step 4:

[0946] Receiving and storing data

[0947] server

[0948] It receives data sent from the device and stores it in a database, where it undergoes formatting and cleaning processes and is converted into a format suitable for the AI ​​model.

[0949] Step 5:

[0950] Preprocessing

[0951] server

[0952] Employee data and emotion data are extracted from the database, and preprocessing such as filling in missing values ​​and removing outliers is performed.

[0953] Step 6:

[0954] Feature Engineering

[0955] server

[0956] Features are generated from the extracted data, and skills, experience, personality assessment results, and emotional data (stress level, motivation, etc.) are put into a format that can be input into an AI model as variables.

[0957] Step 7:

[0958] Inputting data into AI models and analyzing it

[0959] server

[0960] The features are fed into an AI model to evaluate employee characteristics and suitability, which analyzes not only compatibility between employees and project suitability, but also mental health and stress levels.

[0961] Step 8:

[0962] Calculating optimal staffing

[0963] server

[0964] Based on the evaluation results calculated by the AI ​​model, the optimal personnel allocation for each project and team is calculated. For example, based on emotional data, it may determine that Sato, who has a low stress level and is highly motivated, is the best suited to be the "leader of the data analysis project."

[0965] Step 9:

[0966] Generate and submit a list of placement proposals

[0967] server

[0968] A placement proposal list is generated and its contents are sent to the terminal of the project manager.

[0969] Step 10:

[0970] Displaying the placement proposal list

[0971] Terminal

[0972] The list of submitted placement proposals is displayed to the project manager for review and approval, and the employee is notified as well.

[0973] Step 11:

[0974] Implementing staffing

[0975] User

[0976] The project manager will refer to the proposed deployment list and actually deploy the personnel, and the employees will begin work at their new locations.

[0977] Step 12:

[0978] Entering feedback data

[0979] User

[0980] After the project is completed, feedback on the success of the project and the performance of the assigned employees is entered into a dedicated terminal.

[0981] Step 13:

[0982] Collecting and sending feedback

[0983] Terminal

[0984] The collected feedback data is temporarily stored and retransmitted to the server in batch or real-time.

[0985] Step 14:

[0986] Receiving feedback and updating the model

[0987] server

[0988] The received feedback data is stored in a database, and the feedback information is used to retrain the AI ​​model, improving the accuracy of future suggestions.

[0989] This allows for the optimization of personnel allocation across the company, improving team productivity and promoting employee career growth. Furthermore, the use of an emotion engine can provide a healthy work environment that takes into consideration the mental health of employees. The specific processing steps are as follows:

[0990] Example 2

[0991] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0992] In order to properly assign employees, not only employee skills and experience are important, but also personality and emotional data. However, with conventional systems, it was difficult to centrally collect, integrate, and analyze this data. In particular, it was difficult to grasp employees' emotional states in real time, which resulted in a decrease in the accuracy of personnel assignments and a negative impact on employee motivation and stress levels. This issue needs to be resolved.

[0993] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0994] In this invention, the server includes means for collecting employee skill data, means for collecting employee experience data, means for collecting employee personality assessment data, means for collecting employee emotion data, means having an AI for analyzing the skill data, the experience data, and the personality assessment data, and means for proposing personnel placement based on the analysis results and emotion data. This enables highly accurate personnel placement that takes into account not only employee skills and experience, but also personality and emotion.

[0995] "Skills data" is information about the skills and abilities of employees.

[0996] "Experience data" is information about the work and projects an employee has previously worked on.

[0997] "Personality diagnostic data" is information obtained as a result of evaluating an employee's personality and behavioral characteristics.

[0998] "Emotional data" is information that measures an employee's emotional state, including stress levels and motivation.

[0999] "AI" is a system that uses artificial intelligence technology to analyze data and make predictions.

[1000] "Means" refers to a method or device used to achieve a particular purpose.

[1001] A "server" is a central computer system that collects, processes, stores, and distributes data.

[1002] "Proposal" refers to recommending the optimal personnel placement based on the analysis results.

[1003] "Feedback data" refers to information regarding the results and evaluations after personnel placement has actually been carried out.

[1004] "Model updating" refers to retraining an AI model based on collected feedback data to improve the accuracy of its next predictions and suggestions.

[1005] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will be described in detail below with reference to the accompanying drawings, in which: FIG.

[1006] This invention is a system that collects employee skill data, experience data, and personality assessment data and uses them to propose optimal personnel placement. Furthermore, this system is characterized by incorporating an emotion engine that recognizes user emotions, and by utilizing the collected emotion data for analysis. The system is comprehensive, covering everything from collecting employee information to proposing optimal placement and updating the model based on feedback.

[1007] 1. Collect employee information and sentiment data

[1008] User

[1009] Employees use dedicated terminals to enter their skills (e.g., programming, data analysis), experience (e.g., several years of industry experience), and personality test results (e.g., high self-management ability, high collaborative ability).

[1010] Terminal

[1011] The device uses a built-in emotion engine to collect employee emotional data, which analyzes facial expressions, voice, and text input to generate emotional data such as stress levels and motivation.

[1012] Terminal

[1013] The collected employee data and emotion data are temporarily stored in local storage and then sent to the server in batch or real-time.

[1014] server

[1015] It receives data sent from the device and stores it in a database, where it undergoes formatting and cleaning processes and is converted into a format suitable for the AI ​​model.

[1016] 2. Data Analysis

[1017] server

[1018] The server extracts employee skill data, experience data, personality assessment data, and emotional data from the database and performs feature engineering. These features are then input into an AI model, which performs multifaceted analysis, including employee aptitude assessment, compatibility between employees, mental health, and stress levels.

[1019] 3. Proposal for optimal layout

[1020] server

[1021] Based on the analysis results of the AI ​​model, a list of optimal personnel placement suggestions is created and sent to the project manager's device.

[1022] Terminal

[1023] A list of proposals is displayed for review by the project manager, who then makes staffing decisions based on the proposals displayed.

[1024] 4. Gather feedback and update the model

[1025] User

[1026] After the project is completed, project managers and employees provide feedback on the project's success and the performance of the assigned personnel.

[1027] Terminal

[1028] The terminal collects the feedback data and sends it to the server.

[1029] server

[1030] Feedback data is received and stored in a database, which is then used to retrain the AI ​​model and improve the accuracy of future suggestions.

[1031] As a concrete example, consider the case where an employee is proposed as a leader for a data analytics project. This employee has several years of industry experience and is highly skilled in programming and data analysis. The emotion engine also identifies that the employee has low stress levels and high motivation. Based on this information, the AI ​​model makes a recommendation and assigns the employee to the leadership role. After the project is completed, the manager provides feedback on the employee's performance, and this data is used to inform future proposals.

[1032] Example prompt sentence:

[1033] Please explain the system that collects employee skill data, experience data, personality assessment data, and emotional data to suggest optimal staffing.

[1034] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1035] Step 1:

[1036] Collecting employee information and sentiment data

[1037] User

[1038] Users use dedicated terminals to input their skill data (e.g., programming, data analysis), experience data (e.g., several years of industry experience), and personality assessment data (e.g., high self-management ability, high cooperativeness), which allows detailed information on each user's abilities and characteristics to be collected.

[1039] Terminal

[1040] The device uses an emotion engine to collect user emotion data. The emotion engine analyzes the user's facial expressions, voice, text input, etc. to generate emotion data such as stress level and motivation. For example, a camera captures the user's facial expressions, and a voice input device analyzes the tone of voice.

[1041] input

[1042] User skill data, experience data, personality assessment data, and emotional data.

[1043] output

[1044] Integrated data temporarily stored on the device.

[1045] Step 2:

[1046] Data transmission and storage

[1047] Terminal

[1048] The collected data is temporarily stored in the device's local storage, and then sent to the server in batch or real-time.

[1049] server

[1050] The server receives the data sent from the device and stores it in a database. The received data is then formatted and cleaned to convert it into a format suitable for the AI ​​model. For example, it checks for outliers and removes duplicate data.

[1051] input

[1052] Aggregated data sent from the device.

[1053] output

[1054] Formatted data stored in a database.

[1055] Step 3:

[1056] Analyzing the data

[1057] server

[1058] The server extracts the necessary data (skill data, experience data, personality assessment data, and emotional data) from the database and performs feature engineering. These features are then input into an AI model for multifaceted analysis. For example, employee aptitude assessments, compatibility between employees, mental health, and stress levels are also evaluated.

[1059] input

[1060] Formatted data stored in a database.

[1061] output

[1062] Data after feature engineering and analysis results from an AI model.

[1063] Step 4:

[1064] Proposal for optimal placement

[1065] server

[1066] Based on the analysis results of the AI ​​model, a list of optimal personnel placement suggestions is created. Specifically, a list of placement candidates is generated based on the analysis results.

[1067] Terminal

[1068] The proposal list is sent to the project manager's terminal, where it is displayed, and the project manager makes personnel allocation decisions based on the list.

[1069] input

[1070] Analysis results from the AI ​​model.

[1071] output

[1072] A list that suggests optimal staffing.

[1073] Step 5:

[1074] Gathering feedback and updating the model

[1075] User

[1076] After the project is completed, project managers and employees provide feedback on the project's success and the performance of the assigned personnel.

[1077] Terminal

[1078] Feedback data is collected and sent to the server. Evaluation scores and comments are entered through a dedicated form.

[1079] server

[1080] Feedback data is received and stored in a database, which is then used to retrain the AI ​​model and improve the accuracy of future suggestions.

[1081] input

[1082] Feedback data on project success and talent performance.

[1083] output

[1084] Updated AI model, improved accuracy for next proposal.

[1085] (Application example 2)

[1086] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1087] Conventional factory personnel allocation systems suggest personnel placement based on employee skills, experience, and personality assessments, but do not consider employee emotional data or robot work data. This makes it difficult to manage employee motivation and mental health, and to optimize collaboration with robots. The present invention aims to propose optimal personnel and robot placement in a collaborative environment between humans and robots by considering employee emotional data and robot work data, thereby improving factory productivity and realizing a healthy working environment.

[1088] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1089] In this invention, the server includes means for collecting employee skill data, means for collecting employee experience data, means for collecting employee personality diagnosis data, means for collecting employee emotion data, an AI module for analyzing the skill data, the experience data, the personality diagnosis data, and the emotion data, and means for proposing optimal allocation of human resources and robots for a project and a collaborative work environment based on the analysis results. This makes it possible to optimally allocate human resources and robots taking into account the emotional states of employees.

[1090] "Skill data" is the specialized skills and knowledge possessed by employees expressed as numerical or categorical data.

[1091] "Experience data" refers to historical information such as the type of work an employee has done in the past, the duration of that work, and the results they have achieved.

[1092] "Personality assessment data" is data that evaluates an employee's personality traits and psychological tendencies and records them as numerical or categorical data.

[1093] "Emotional data" is numerical and categorical data that expresses the emotional state of employees and indicates real-time situations and trends.

[1094] An "AI module" is a system component that analyzes collected data and runs artificial intelligence algorithms to make optimal recommendations and predictions.

[1095] "Optimal allocation of human resources and robots" refers to an effective allocation method that maximizes the use of the respective capabilities and conditions of humans and robots to improve production efficiency and work quality.

[1096] "Feedback data" is data that records evaluations, points for reflection, and areas for improvement regarding work or projects that have been carried out.

[1097] A "collaborative work environment" refers to a place or situation where humans and robots work together, complementing each other's roles.

[1098] The following describes in detail an embodiment of the present invention as an optimal human resource allocation system for factory robots. This system collects and analyzes skill data, experience data, personality diagnostic data, and emotional data of employees and robots to propose optimal allocation.

[1099] Program processing and hardware / software used

[1100] Hardware

[1101] Wearable devices: Capture employee vital and emotional data.

[1102] Robot: Collects work data and sends it to the server.

[1103] Terminal: A device where employees enter data and managers review the results.

[1104] software

[1105] AI module: Analyzes collected data and suggests optimal placement. Uses TensorFlow and PyTorch.

[1106] Database: Stores collected data. Uses MySQL or MongoDB.

[1107] Sentiment Engine: Analyzes employee sentiment data using IBM Watson and Microsoft Azure Emotion APIs.

[1108] Data processing and calculation

[1109] Data Shaping and Cleaning: Shaping and cleaning data using Python's Pandas library.

[1110] Feature engineering: Using Scikit-learn, we extract features and convert them into a format suitable for AI models.

[1111] Clustering algorithms: Clustering algorithms such as KMeans are used to propose optimal placement of employees and robots.

[1112] Specific example of system operation

[1113] 1. Employee data collection

[1114] Employees use smartphones or wearable devices to input their skills, experience and emotional data.

[1115] The robot automatically collects operational data and sends it to a server.

[1116] 2. Data analysis

[1117] The server formats and cleans the employee's skill data, experience data, personality assessment data, and emotional data sent from the terminal, and builds an AI model using neural networks, random forests, etc.

[1118] The emotion engine analyzes employee emotion data and generates a score.

[1119] KMeans clustering is performed to propose optimal placement of personnel and robots.

[1120] 3. Display placement proposals

[1121] The server generates optimal placement proposals based on the analysis results of the AI ​​module, which are then sent to the administrator's device and displayed in real time.

[1122] Based on the displayed proposals, the manager decides on the allocation of human resources and robots for the project and the collaborative work environment.

[1123] 4. Gather feedback and update the model

[1124] After the project is completed, managers and employees enter their project evaluations and feedback through the terminal.

[1125] The feedback data is sent to the server and stored in a database, and the AI ​​module uses this feedback data to retrain itself and improve the accuracy of its next suggestions.

[1126] Prompt Sentence Examples

[1127] "Analyze employee skills, experience, personality test results, and sentiment data to suggest optimal staffing and robot placement for a new product line installation project."

[1128] This allows the present invention to take into account the emotional state of employees and make optimal allocations of personnel and robots.

[1129] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1130] Step 1: Collect employee data

[1131] Users use smartphones or wearable devices to input their skills (e.g., programming skills or machine operation skills), experience (e.g., years of project experience or area of ​​expertise), personality assessment results (e.g., self-management ability or cooperativeness), and emotional data (e.g., stress level or motivation). In addition, the robot's work data (e.g., operating time or error rate) is automatically collected and sent from the device to a server. The input data is temporarily stored and sent to the server in real time or batch processing.

[1132] Step 2: Data Shaping and Cleaning

[1133] The server receives employee and robot data sent from the terminals. The received data is formatted and cleaned using Python's Pandas library, which completes incomplete data and removes invalid data. The skill data, experience data, personality assessment data, and emotion data collected as input data are converted into a unified format and output as clean data.

[1134] Step 3: Feature Engineering

[1135] The server performs feature engineering using the formatted and cleaned data. Using Scikit-learn, it extracts features such as skills, experience, personality assessment results, and emotional data and converts them into a format suitable for machine learning models. This process involves standardizing, normalizing, and encoding categorical data based on the input cleaned data to generate a feature dataset.

[1136] Step 4: Clustering and analysis

[1137] The server inputs the feature dataset into the KMeans clustering algorithm and analyzes the optimal allocation of employees and robots through clustering. Based on the input feature dataset, the server generates grouped clusters, evaluates the characteristics of each group, and outputs optimal employee and robot allocation proposals.

[1138] Step 5: Generate and view placement proposals

[1139] The server generates optimal personnel and robot placement proposals based on the clustering results. The generated proposals are sent to the administrator's terminal in real time and displayed on the screen. The administrator checks the displayed proposals, modifies them as necessary, and reflects them in the actual placement. The input data for the proposals are the clustering results and the administrator's operations, and the output data is the final placement proposal.

[1140] Step 6: Collect and analyze feedback

[1141] After the project is completed, users (managers and employees) input feedback on the success of the project and the performance of the assigned personnel and robots. The terminal collects this feedback data and sends it to the server. The input data here is the feedback content, and the output data is the formatted feedback data.

[1142] Step 7: Update the AI ​​model

[1143] The server receives the collected feedback data and stores it in a database. The stored feedback data is used to retrain the AI ​​model to improve the accuracy of the next proposal. The feedback data is used as input data and an updated AI model is generated as output data.

[1144] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1145] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1146] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1147] [Third embodiment]

[1148] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1149] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1150] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1151] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1152] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1154] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1155] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1156] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1157] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1158] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1159] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[1160] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The following describes the preferred embodiments of the present invention. Although specific examples are shown in the description, the present invention is not limited to these examples.

[1161] This invention is a system that collects employee skill data, experience data, and personality assessment data and uses them to propose optimal personnel placement. The system is comprehensive, covering everything from collecting employee information to proposing optimal placement and updating the model based on feedback.

[1162] 1. Collection of employee information

[1163] User

[1164] Employees use dedicated terminals to input their skills (e.g., Java programming, data analysis), experience (e.g., 5 years of experience in the IT industry), and personality test results (e.g., high level of cooperation, high level of self-management).

[1165] Terminal

[1166] The terminal collects and temporarily stores the data entered by the employee, after which it is sent to a server.

[1167] server

[1168] The server receives the data sent from the device and stores it in a database, where it undergoes formatting and cleaning processes and is converted into a format suitable for the AI ​​model.

[1169] 2. Data Analysis

[1170] server

[1171] The server extracts employee skill data, experience data, and personality assessment data from the database and performs feature engineering. The features are then input into an AI model to analyze employee aptitude ratings and compatibility between employees. Based on the results of this analysis, the server calculates the optimal personnel allocation for each project and team.

[1172] 3. Proposal for optimal layout

[1173] server

[1174] Based on the analysis results of the AI ​​model, the server creates a list proposing optimal personnel placement, which is then sent to the project manager's device.

[1175] Terminal

[1176] The terminal displays a list of proposals for review by the project manager, who then makes staffing decisions based on the proposals displayed.

[1177] 4. Gather feedback and update the model

[1178] User

[1179] After the project is implemented, project managers and employees provide feedback on the success of the project and the performance of the assigned personnel.

[1180] Terminal

[1181] The terminal collects the feedback data and sends it to the server.

[1182] server

[1183] The server receives the feedback data and stores it in a database, which is then used to retrain the AI ​​model and improve the accuracy of future suggestions.

[1184] As a concrete example, consider the case where an employee, Mr. Sato, is proposed as the leader of a company-wide data analysis project. Mr. Sato has five years of experience in the IT industry and is highly skilled in Java programming and data analysis. Furthermore, a personality assessment reveals that he is highly cooperative and has the ability to lead a team. Based on this information, the AI ​​model analyzes that Mr. Sato is highly suitable as the leader of the data analysis project and recommends that he be appointed as the leader. After the project is completed, the project manager provides feedback on Mr. Sato's leadership and project performance, and this data is used to inform future proposals.

[1185] This will optimize personnel allocation across the company, improving team productivity and promoting employee career growth.

[1186] The processing flow will be explained below.

[1187] Step 1:

[1188] Entering employee data

[1189] User

[1190] Employees use dedicated terminals to enter their skills (e.g., Java programming, data analysis), experience (e.g., 5 years of experience in the IT industry), and personality test results (e.g., high self-management ability, high collaborative ability).

[1191] Step 2:

[1192] Temporary storage and transmission of data

[1193] Terminal

[1194] The entered data is temporarily stored and then sent to the server in batch or real-time.

[1195] Step 3:

[1196] Receiving and storing data

[1197] server

[1198] It receives data sent from devices, stores it in a database, and also formats and cleans the data, converting it into a format suitable for AI models.

[1199] Step 4:

[1200] Preprocessing

[1201] server

[1202] Employee data is extracted from the database and preprocessing is performed, such as filling in missing values ​​and removing outliers.

[1203] Step 5:

[1204] Feature Engineering

[1205] server

[1206] Features are generated from the extracted data, and information such as skills, experience, and personality test results are converted into a format that can be input into an AI model as variables.

[1207] Step 6:

[1208] Inputting data into AI models and analyzing it

[1209] server

[1210] The features are input into an AI model to evaluate employee characteristics and suitability, which also analyzes compatibility between employees and project suitability.

[1211] Step 7:

[1212] Calculating optimal staffing

[1213] server

[1214] Based on the evaluation results calculated by the AI ​​model, the optimal personnel allocation for each project and team is calculated. For example, it determines that Mr. Sato is the best suited person to be the "leader of the data analysis project."

[1215] Step 8:

[1216] Generate and submit a list of placement proposals

[1217] server

[1218] A placement proposal list is generated and its contents are sent to the terminal of the project manager.

[1219] Step 9:

[1220] Displaying the placement proposal list

[1221] Terminal

[1222] The list of submitted placement proposals is displayed to the project manager for review and approval, and the employee is notified as well.

[1223] Step 10:

[1224] Implementing staffing

[1225] User

[1226] The project manager will refer to the proposed deployment list and actually deploy the personnel, and the employees will begin work at their new locations.

[1227] Step 11:

[1228] Entering feedback data

[1229] User

[1230] After the project is completed, feedback on the success of the project and the performance of the assigned employees is entered into a dedicated terminal.

[1231] Step 12:

[1232] Collecting and sending feedback

[1233] Terminal

[1234] The collected feedback data is temporarily stored and retransmitted to the server in batch or real-time.

[1235] Step 13:

[1236] Receiving feedback and updating the model

[1237] server

[1238] The received feedback data is stored in a database, and the feedback information is used to retrain the AI ​​model, improving the accuracy of future suggestions.

[1239] The above are the specific processing steps of this system.

[1240] Example 1

[1241] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1242] Optimal personnel placement based on employee skills, experience, and personality is an important issue for many companies. Traditional methods often rely on subjective judgment and bias, resulting in insufficient optimal placement. Furthermore, there is no system in place to properly utilize feedback after placement, making it difficult to use it for future placements.

[1243] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1244] In this invention, the server includes means for inputting employee skill data, means for inputting employee experience data, means for inputting employee personality diagnosis data, means for collecting the skill data, the experience data, and the personality diagnosis data and storing them in a database, means for performing data cleaning and shaping, means for performing feature engineering, means for having a generative AI model for analyzing the shaped data, means for generating a proposal list of optimal personnel assignments for projects and teams based on the analysis results, and means for transmitting the proposal list to a terminal of a project manager. This enables optimal personnel assignments based on objective and appropriate data.

[1245] "Employee skills data" refers to information about the skills and knowledge that employees possess in specific tasks or jobs.

[1246] "Employee experience data" refers to information that represents the history and performance of the work or duties that an employee has performed to date.

[1247] "Employee personality assessment data" refers to test results used to assess an employee's personality traits and behavioral patterns.

[1248] "Means of storing data in a database" refers to the systems and technologies used to store collected data in an organized manner and make it easy to manage and search.

[1249] "Data cleaning and formatting" refers to operations performed to correct inaccurate or missing data and convert it into a form suitable for analysis and use.

[1250] "Feature engineering" refers to the process of extracting useful information from collected data and converting it into a format suitable for analytical models.

[1251] A "generative AI model" is a program that uses machine learning algorithms to analyze data and make predictions or suggestions based on specific purposes.

[1252] "Proposal list" refers to a list of candidates for optimal personnel placement created based on the analysis results of the generative AI model.

[1253] This invention is a system that collects employee skill data, experience data, and personality assessment data and uses them to propose optimal personnel placement. The system is comprehensive, covering everything from collecting employee information to proposing optimal placement and updating the model based on feedback.

[1254] Collection of employee information

[1255] User

[1256] Employees use dedicated terminals to enter the following information:

[1257] Skill data (e.g. Java programming, data analysis)

[1258] Experience data (e.g., 5 years of experience in the IT industry)

[1259] Personality test results (e.g., high cooperativeness, high self-management ability)

[1260] Specific actions

[1261] Employees access the form, fill in each item using text boxes and selection lists, then confirm the information and click the "Submit" button.

[1262] Data transmission and storage

[1263] Terminal

[1264] The terminal temporarily stores the data entered by the user in memory and transmits it to the server via the API.

[1265] Specific actions

[1266] Once the input has been confirmed, the device uses JavaScript to serialize the data into JSON format and sends an HTTP POST request to the server.

[1267] server

[1268] The server receives the data sent from the terminal and stores it in a PostgreSQL database.

[1269] Specific actions

[1270] The server parses the received JSON data and stores the data in the "employees" table in the database using an INSERT statement.

[1271] Data formatting and preparation

[1272] server

[1273] The server formats the stored data and converts it into a format suitable for AI models, using the Python Pandas library for the cleaning process.

[1274] Specific actions

[1275] The server periodically runs Python scripts to impute missing data, remove outliers, and encode categorical data, such as converting "highly cooperative" into a numerical format.

[1276] Analyzing the data

[1277] server

[1278] The server extracts features from the formatted data and feeds them into a TensorFlow-based generative AI model, which then analyzes employee aptitude and compatibility between employees.

[1279] Specific actions

[1280] The server performs feature engineering and feeds the generated features into an AI model, which scores each employee's aptitude and compatibility and calculates the optimal personnel placement.

[1281] Proposal and display of optimal layout

[1282] server

[1283] Based on the analysis results of the generative AI model, the server generates a list of proposals for optimal personnel placement and sends it to the project manager's device.

[1284] Specific actions

[1285] The server generates a list of suggestions in JSON format based on the model results and sends it to the manager's device using an HTTP POST request.

[1286] Terminal

[1287] The terminal receives the suggestion list and displays it on the user interface.

[1288] Specific actions

[1289] The terminal parses the received JSON data and inserts it into an HTML template for displaying the list, allowing the project manager to view the proposal list on the screen.

[1290] Gathering feedback and updating the model

[1291] User

[1292] After the project is completed, the project manager and employees use a feedback form to enter their feedback on the success of the project and the performance of the assigned personnel.

[1293] Specific actions

[1294] Users access a Google Form or custom feedback form, fill out each field, and submit it.

[1295] Terminal

[1296] The terminal collects the feedback data and sends it to the server.

[1297] Specific actions

[1298] After entering the feedback, the device presses a button, serializes the feedback data into JSON format, and sends it to the server via an HTTP POST request.

[1299] server

[1300] The server receives the feedback data, stores it in a database, and uses it to retrain the generative AI model to improve the accuracy of future suggestions.

[1301] Specific actions

[1302] After storing the received data in a database, a batch processing script is executed to retrain the generative AI model, using reinforcement learning techniques to improve the accuracy of the next staffing proposal.

[1303] Example prompt sentence:

[1304] Send employee skills, experience, and personality data to a server to generate optimized code for analysis by AI models.

[1305] Extract employee skills, experience, and personality data as features and generate code to analyze them with an AI model.

[1306] Generate the code to generate and display the optimal staffing list based on the analysis results of the AI ​​model.

[1307] Collect employee feedback data and generate code to retrain your AI models.

[1308] This system optimizes personnel allocation across the company, improving team productivity and promoting employee career growth.

[1309] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1310] Program processing flow

[1311] Step 1: Gather employee information

[1312] User

[1313] Employees use dedicated terminals to enter the following information:

[1314] Input: Skill data (e.g., Java programming, data analysis), experience data (e.g., 5 years of experience in the IT industry), personality test results (e.g., high cooperativeness, high self-management ability)

[1315] Output: The input data is temporarily saved on the device.

[1316] Specific actions

[1317] Employees access the form, fill in each item using text boxes and selection lists, then confirm the information and click the "Submit" button.

[1318] Step 2: Send and store data

[1319] Terminal

[1320] The terminal temporarily stores the data entered by the user in memory and transmits it to the server via the API.

[1321] Input: Data entered by the user

[1322] Output: JSON formatted request data to be sent to the server

[1323] Specific actions

[1324] Once the input has been confirmed, the device uses JavaScript to serialize the data into JSON format and sends an HTTP POST request to the server.

[1325] server

[1326] The server receives the data sent from the terminal and stores it in a PostgreSQL database.

[1327] Input: JSON data sent from the terminal

[1328] Output: Data inserted into the database

[1329] Specific actions

[1330] The server parses the received JSON data and stores the data in the "employees" table in the database using an INSERT statement.

[1331] Step 3: Data Formatting and Preparation

[1332] server

[1333] The server formats the stored data and converts it into a format suitable for AI models, using the Python Pandas library for the cleaning process.

[1334] Input: Raw data stored in a database

[1335] Output: Reshaped and cleaned data

[1336] Specific actions

[1337] The server periodically runs Python scripts to impute missing data, remove outliers, and encode categorical data, such as converting "highly cooperative" into a numerical format.

[1338] Step 4: Analyze the data

[1339] server

[1340] The server extracts features from the formatted data and feeds them into a TensorFlow-based generative AI model, which then analyzes employee aptitude and compatibility between employees.

[1341] Input: Reshaped and cleaned data

[1342] Output: Aptitude assessment and compatibility scores between employees

[1343] Specific actions

[1344] The server performs feature engineering and feeds the generated features into an AI model, which scores each employee's aptitude and compatibility and calculates the optimal personnel placement.

[1345] Step 5: Propose and display optimal layout

[1346] server

[1347] Based on the analysis results of the generative AI model, the server generates a list of proposals for optimal personnel placement and sends it to the project manager's device.

[1348] Input: Aptitude assessment and compatibility scores between employees

[1349] Output: Suggestion list

[1350] Specific actions

[1351] The server generates a list of suggestions in JSON format based on the model results and sends it to the manager's device using an HTTP POST request.

[1352] Terminal

[1353] The terminal receives the suggestion list and displays it on the user interface.

[1354] Input: JSON data of the proposal list sent from the server

[1355] Output: The list of suggestions displayed in the user interface

[1356] Specific actions

[1357] The terminal parses the received JSON data and inserts it into an HTML template for displaying the list, allowing the project manager to view the proposal list on the screen.

[1358] Step 6: Gather feedback and update the model

[1359] User

[1360] After the project is completed, the project manager and employees use a feedback form to enter their feedback on the success of the project and the performance of the assigned personnel.

[1361] Input: Feedback data (success level, performance evaluation)

[1362] Output: Sending feedback data to the server

[1363] Specific actions

[1364] Users access a Google Form or custom feedback form, fill out each field, and submit it.

[1365] Terminal

[1366] The terminal collects the feedback data and sends it to the server.

[1367] Input: User-entered feedback data

[1368] Output: Feedback data sent to the server in JSON format

[1369] Specific actions

[1370] After entering the feedback, the device presses a button, serializes the feedback data into JSON format, and sends it to the server via an HTTP POST request.

[1371] server

[1372] The server receives the feedback data, stores it in a database, and uses it to retrain the generative AI model to improve the accuracy of future suggestions.

[1373] Input: Received feedback data

[1374] Output: An updated generative AI model

[1375] Specific actions

[1376] After storing the received data in a database, a batch processing script is executed to retrain the generative AI model, using reinforcement learning techniques to improve the accuracy of the next staffing proposal.

[1377] (Application example 1)

[1378] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1379] To achieve optimal allocation and task sharing between human resources and robots in a factory, it is important to make appropriate allocation proposals based on employee ability, history, and personality evaluations. However, conventional systems have difficulty effectively collecting and analyzing this data and implementing optimal allocation proposals as a system. Furthermore, there is a lack of a process for collecting feedback on performance after allocation and reflecting it in future proposals. This makes it difficult to improve production efficiency and maximize employee satisfaction.

[1380] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1381] In this invention, the server includes means for collecting employee ability data, means for collecting employee history data, means for collecting employee personality assessment data, means having AI for analyzing the ability data, the history data, and the personality assessment data, means for proposing optimal personnel allocation for projects and groups based on the analysis results, and means for proposing optimal task sub-areas for robots and human workers in the factory. This enables optimal allocation and task sharing of personnel and robots in the factory, thereby improving productivity and maximizing employee satisfaction.

[1382] "Competence data" is information about an employee's skills, qualifications, and technical abilities.

[1383] "Historical data" refers to information about an employee's past work experience, performance, and employment history.

[1384] "Personality assessment data" is information that indicates the results of an assessment of an employee's personality traits and behavioral patterns.

[1385] "Artificial intelligence" refers to machine learning algorithms and models that analyze employee ability data, history data, and personality assessment data to propose optimal personnel placement and task allocation.

[1386] "Placement proposal" refers to proposing the optimal placement of human resources or robots for a project or group.

[1387] A "factory" is a place where products are manufactured or processed, and refers to a facility where many machines and robots are in operation.

[1388] "Task allocation" refers to the appropriate allocation of individual tasks to human workers or robots in a project or business.

[1389] "Feedback data" is data collected after a project is completed, including information on employee and robot performance, success, etc.

[1390] An embodiment of the present invention will be described below. This embodiment is a system for achieving optimal allocation and task sharing between employees and robots in a factory. The system is comprehensive, including employee information collection, data analysis, optimal allocation proposals, feedback, and model updates.

[1391] 1. Collection of employee and robot information

[1392] The server is equipped with various means to collect employee and robot ability data, history data, and personality evaluation data. Employee and robot information is input into the system using devices such as dedicated smartphones, smart glasses, and head-mounted displays.

[1393] 2. Data transmission and storage

[1394] The device temporarily stores the collected information and transmits it in real time to a server, which stores the data in a MySQL database.

[1395] 3. Data preparation and analysis

[1396] The server uses Python-based processing to extract data from MySQL, format and clean it, then performs feature engineering and feeds it into an AI model using TensorFlow or PyTorch. The model analyzes the data, including the abilities and personalities of employees and robots, to calculate optimal placement.

[1397] 4. Proposal for optimal layout

[1398] Based on the results of the AI ​​model's analysis, the server generates a list of suggestions for optimal staffing and robot task allocation, which is then sent to the project manager's smart device via an interface and displayed in a React Native application.

[1399] 5. Gather feedback and update the model

[1400] After the project is completed, the project manager and employees enter feedback data using their smart devices. The devices then send the collected feedback data back to the server, which stores it in MySQL. Using TensorFlow or PyTorch, the AI ​​model is retrained based on the feedback to improve the accuracy of future proposals.

[1401] Examples:

[1402] At a large manufacturing factory, veteran worker A is selected as the leader of a new project. A has 15 years of experience and is skilled at machine maintenance. A personality assessment reveals that A has strong leadership skills and is capable of leading a team. Meanwhile, less experienced worker B (with strong robot operation skills) is also assigned to the team. The system selects this combination as the leader and proposes the optimal placement for the project's success. The project's evaluation and feedback are then entered into the system and used to make future proposals.

[1403] Example prompt sentence:

[1404] Design a system to optimize factory staffing based on employee skill data, experience data, and personality assessment data. Input each worker's data and send it to the server in real time. The data is analyzed by an AI model, which then proposes optimal staffing. After the project is completed, collect feedback and update the AI ​​model to help with future staffing proposals.

[1405] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1406] Step 1:

[1407] Collecting information about employees and robots

[1408] Users use smart devices (smartphones, smart glasses, head-mounted displays, etc.) to input ability data, history data, and personality assessment data. The input data is temporarily saved in JSON format on the device. Specifically, users manually input the data through a dedicated application, and the device formats it in a format that can be sent to the server in real time.

[1409] input:

[1410] Ability data, history data, personality assessment data

[1411] output:

[1412] Input data in JSON format

[1413] Step 2:

[1414] Data transmission and storage

[1415] The terminals send the collected data in JSON format to a server using a RESTful API, which stores the data in a MySQL database. Specifically, the terminals send data for each employee and robot to an API endpoint, and the server saves the data in the appropriate table.

[1416] input:

[1417] Input data in JSON format

[1418] output:

[1419] MySQL database entries

[1420] Step 3:

[1421] Data Shaping and Cleaning

[1422] The server uses Python scripts to extract data from a MySQL database and then performs data conditioning and cleaning, such as imputing missing values, handling outliers, and encoding categorical data. Specifically, the server executes database queries and processes the retrieved data using Pandas.

[1423] input:

[1424] MySQL database entries

[1425] output:

[1426] Formatted and cleaned data

[1427] Step 4:

[1428] Feature Engineering

[1429] The server uses the cleaned and formatted data to generate features. Specifically, it converts the data into a form suitable for machine learning and prepares it for input into an AI model. For example, it standardizes and scales the numerical data and extracts important features.

[1430] input:

[1431] Formatted and cleaned data

[1432] output:

[1433] Feature-engineered data

[1434] Step 5:

[1435] Analysis by AI model

[1436] The server uses TensorFlow or PyTorch to input feature-engineered data into an AI model for analysis, which evaluates the suitability of employees and robots and generates an optimal deployment list. Specifically, the server loads the model, inputs data, and outputs prediction results.

[1437] input:

[1438] Feature-engineered data

[1439] output:

[1440] List of best talent and robot deployments

[1441] Step 6:

[1442] Displaying the optimal placement list

[1443] The server sends the generated optimized placement list to the project manager's smart device via API. The React Native application on the device receives and displays this data. Specifically, the server sends data via API in real time, allowing the list to be visually confirmed on the device.

[1444] input:

[1445] List of best talent and robot deployments

[1446] output:

[1447] A list of proposals displayed on the project manager's smart device

[1448] Step 7:

[1449] Feedback collection

[1450] After completing a project, users enter feedback data using their smart devices. The devices then send the collected feedback data to the server. Specifically, users enter their feedback through the application, and the devices then send it to the server.

[1451] input:

[1452] Feedback Data

[1453] output:

[1454] Feedback data stored on the server

[1455] Step 8:

[1456] Updating a Model

[1457] The server stores the collected feedback data in MySQL and retrains the AI ​​model using TensorFlow or PyTorch, improving the accuracy of the next optimal placement proposal. Specifically, the server updates the AI ​​model using new data and validates the model's performance.

[1458] input:

[1459] Feedback Data

[1460] output:

[1461] Retrained AI model

[1462] Example prompt sentence:

[1463] Design a system to optimize factory staffing based on employee skill data, experience data, and personality assessment data. Input each worker's data and send it to the server in real time. The data is analyzed by an AI model, which then proposes optimal staffing. After the project is completed, collect feedback and update the AI ​​model to help with future staffing proposals.

[1464] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1465] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will be described in detail below with reference to the accompanying drawings, in which: FIG.

[1466] This invention is a system that collects employee skill data, experience data, and personality assessment data and uses them to propose optimal personnel placement. Furthermore, this system is characterized by incorporating an emotion engine that recognizes user emotions, and by utilizing the collected emotion data for analysis. The system is comprehensive, covering everything from collecting employee information to proposing optimal placement and updating the model based on feedback.

[1467] 1. Collect employee information and sentiment data

[1468] User

[1469] Employees use dedicated terminals to enter their skills (e.g., Java programming, data analysis), experience (e.g., 5 years of experience in the IT industry), and personality test results (e.g., high self-management ability, high collaborative ability).

[1470] Terminal

[1471] The device uses an emotion engine to collect employee emotional data (e.g., stress level, motivation), which is generated by analyzing the employee's facial expressions, voice, text input, etc.

[1472] Terminal

[1473] The collected employee data and emotion data are temporarily stored and then sent to the server in batch or real-time.

[1474] server

[1475] It receives data sent from the device and stores it in a database, where it undergoes formatting and cleaning processes and is converted into a format suitable for the AI ​​model.

[1476] 2. Data Analysis

[1477] server

[1478] The server extracts employee skill data, experience data, personality assessment data, and emotional data from the database and performs feature engineering. These features are then input into an AI model, which performs multifaceted analysis, including employee aptitude assessment, compatibility between employees, mental health, and stress levels.

[1479] 3. Proposal for optimal layout

[1480] server

[1481] Based on the analysis results of the AI ​​model, a list of optimal personnel placement suggestions is created and sent to the project manager's device.

[1482] Terminal

[1483] A list of proposals is displayed for review by the project manager, who then makes staffing decisions based on the proposals displayed.

[1484] 4. Gather feedback and update the model

[1485] User

[1486] After the project is completed, project managers and employees provide feedback on the project's success and the performance of the assigned personnel.

[1487] Terminal

[1488] Feedback data is collected and sent to a server.

[1489] server

[1490] Feedback data is received and stored in a database, which is then used to retrain the AI ​​model and improve the accuracy of future suggestions.

[1491] As a concrete example, consider the case where an employee, Mr. Sato, is proposed as the leader of a company-wide data analysis project. Mr. Sato has five years of experience in the IT industry and is highly skilled in Java programming and data analysis. The emotion engine also confirms that his current stress level is low and his motivation is high. Based on this information, the AI ​​model analyzes that Mr. Sato is highly qualified to lead the data analysis project and recommends that he be appointed as the leader. After the project is completed, the project manager provides feedback on Mr. Sato's leadership and project performance, and this data is used to inform future proposals.

[1492] This will optimize the allocation of personnel throughout the company, improving team productivity and promoting employee career growth. The use of the emotion engine also makes it possible to consider the mental health of employees, providing a healthier work environment.

[1493] The processing flow will be explained below.

[1494] Step 1:

[1495] Entering employee data

[1496] User

[1497] Employees use dedicated terminals to enter their skills (e.g., Java programming, data analysis), experience (e.g., 5 years of experience in the IT industry), and personality test results (e.g., high self-management ability, high collaborative ability).

[1498] Step 2:

[1499] Collecting Emotional Data

[1500] Terminal

[1501] The company uses an emotion engine to collect employee emotional data, which analyzes facial expressions, voice, and text inputs in real time while employees are logged in to their devices, to generate emotional indicators such as stress levels and motivation.

[1502] Step 3:

[1503] Temporary storage and transmission of data

[1504] Terminal

[1505] The collected skill data, experience data, personality assessment data, and emotion data are temporarily stored and then transmitted to a server in batch processing or real time.

[1506] Step 4:

[1507] Receiving and storing data

[1508] server

[1509] It receives data sent from the device and stores it in a database, where it undergoes formatting and cleaning processes and is converted into a format suitable for the AI ​​model.

[1510] Step 5:

[1511] Preprocessing

[1512] server

[1513] Employee data and emotion data are extracted from the database, and preprocessing such as filling in missing values ​​and removing outliers is performed.

[1514] Step 6:

[1515] Feature Engineering

[1516] server

[1517] Features are generated from the extracted data, and skills, experience, personality assessment results, and emotional data (stress level, motivation, etc.) are put into a format that can be input into an AI model as variables.

[1518] Step 7:

[1519] Inputting data into AI models and analyzing it

[1520] server

[1521] The features are fed into an AI model to evaluate employee characteristics and suitability, which analyzes not only compatibility between employees and project suitability, but also mental health and stress levels.

[1522] Step 8:

[1523] Calculating optimal staffing

[1524] server

[1525] Based on the evaluation results calculated by the AI ​​model, the optimal personnel allocation for each project and team is calculated. For example, based on emotional data, it may determine that Sato, who has a low stress level and is highly motivated, is the best suited to be the "leader of the data analysis project."

[1526] Step 9:

[1527] Generate and submit a list of placement proposals

[1528] server

[1529] A placement proposal list is generated and its contents are sent to the terminal of the project manager.

[1530] Step 10:

[1531] Displaying the placement proposal list

[1532] Terminal

[1533] The list of submitted placement proposals is displayed to the project manager for review and approval, and the employee is notified as well.

[1534] Step 11:

[1535] Implementing staffing

[1536] User

[1537] The project manager will refer to the proposed deployment list and actually deploy the personnel, and the employees will begin work at their new locations.

[1538] Step 12:

[1539] Entering feedback data

[1540] User

[1541] After the project is completed, feedback on the success of the project and the performance of the assigned employees is entered into a dedicated terminal.

[1542] Step 13:

[1543] Collecting and sending feedback

[1544] Terminal

[1545] The collected feedback data is temporarily stored and retransmitted to the server in batch or real-time.

[1546] Step 14:

[1547] Receiving feedback and updating the model

[1548] server

[1549] The received feedback data is stored in a database, and the feedback information is used to retrain the AI ​​model, improving the accuracy of future suggestions.

[1550] This allows for the optimization of personnel allocation across the company, improving team productivity and promoting employee career growth. Furthermore, the use of an emotion engine can provide a healthy work environment that takes into consideration the mental health of employees. The specific processing steps are as follows:

[1551] Example 2

[1552] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1553] In order to properly assign employees, not only employee skills and experience are important, but also personality and emotional data. However, with conventional systems, it was difficult to centrally collect, integrate, and analyze this data. In particular, it was difficult to grasp employees' emotional states in real time, which resulted in a decrease in the accuracy of personnel assignments and a negative impact on employee motivation and stress levels. This issue needs to be resolved.

[1554] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1555] In this invention, the server includes means for collecting employee skill data, means for collecting employee experience data, means for collecting employee personality assessment data, means for collecting employee emotion data, means having an AI for analyzing the skill data, the experience data, and the personality assessment data, and means for proposing personnel placement based on the analysis results and emotion data. This enables highly accurate personnel placement that takes into account not only employee skills and experience, but also personality and emotion.

[1556] "Skills data" is information about the skills and abilities of employees.

[1557] "Experience data" is information about the work and projects an employee has previously worked on.

[1558] "Personality diagnostic data" is information obtained as a result of evaluating an employee's personality and behavioral characteristics.

[1559] "Emotional data" is information that measures an employee's emotional state, including stress levels and motivation.

[1560] "AI" is a system that uses artificial intelligence technology to analyze data and make predictions.

[1561] "Means" refers to a method or device used to achieve a particular purpose.

[1562] A "server" is a central computer system that collects, processes, stores, and distributes data.

[1563] "Proposal" refers to recommending the optimal personnel placement based on the analysis results.

[1564] "Feedback data" refers to information regarding the results and evaluations after personnel placement has actually been carried out.

[1565] "Model updating" refers to retraining an AI model based on collected feedback data to improve the accuracy of its next predictions and suggestions.

[1566] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will be described in detail below with reference to the accompanying drawings, in which: FIG.

[1567] This invention is a system that collects employee skill data, experience data, and personality assessment data and uses them to propose optimal personnel placement. Furthermore, this system is characterized by incorporating an emotion engine that recognizes user emotions, and by utilizing the collected emotion data for analysis. The system is comprehensive, covering everything from collecting employee information to proposing optimal placement and updating the model based on feedback.

[1568] 1. Collect employee information and sentiment data

[1569] User

[1570] Employees use dedicated terminals to enter their skills (e.g., programming, data analysis), experience (e.g., several years of industry experience), and personality test results (e.g., high self-management ability, high collaborative ability).

[1571] Terminal

[1572] The device uses a built-in emotion engine to collect employee emotional data, which analyzes facial expressions, voice, and text input to generate emotional data such as stress levels and motivation.

[1573] Terminal

[1574] The collected employee data and emotion data are temporarily stored in local storage and then sent to the server in batch or real-time.

[1575] server

[1576] It receives data sent from the device and stores it in a database, where it undergoes formatting and cleaning processes and is converted into a format suitable for the AI ​​model.

[1577] 2. Data Analysis

[1578] server

[1579] The server extracts employee skill data, experience data, personality assessment data, and emotional data from the database and performs feature engineering. These features are then input into an AI model, which performs multifaceted analysis, including employee aptitude assessment, compatibility between employees, mental health, and stress levels.

[1580] 3. Proposal for optimal layout

[1581] server

[1582] Based on the analysis results of the AI ​​model, a list of optimal personnel placement suggestions is created and sent to the project manager's device.

[1583] Terminal

[1584] A list of proposals is displayed for review by the project manager, who then makes staffing decisions based on the proposals displayed.

[1585] 4. Gather feedback and update the model

[1586] User

[1587] After the project is completed, project managers and employees provide feedback on the project's success and the performance of the assigned personnel.

[1588] Terminal

[1589] The terminal collects the feedback data and sends it to the server.

[1590] server

[1591] Feedback data is received and stored in a database, which is then used to retrain the AI ​​model and improve the accuracy of future suggestions.

[1592] As a concrete example, consider the case where an employee is proposed as a leader for a data analytics project. This employee has several years of industry experience and is highly skilled in programming and data analysis. The emotion engine also identifies that the employee has low stress levels and high motivation. Based on this information, the AI ​​model makes a recommendation and assigns the employee to the leadership role. After the project is completed, the manager provides feedback on the employee's performance, and this data is used to inform future proposals.

[1593] Example prompt sentence:

[1594] Please explain the system that collects employee skill data, experience data, personality assessment data, and emotional data to suggest optimal staffing.

[1595] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1596] Step 1:

[1597] Collecting employee information and sentiment data

[1598] User

[1599] Users use dedicated terminals to input their skill data (e.g., programming, data analysis), experience data (e.g., several years of industry experience), and personality assessment data (e.g., high self-management ability, high cooperativeness), which allows detailed information on each user's abilities and characteristics to be collected.

[1600] Terminal

[1601] The device uses an emotion engine to collect user emotion data. The emotion engine analyzes the user's facial expressions, voice, text input, etc. to generate emotion data such as stress level and motivation. For example, a camera captures the user's facial expressions, and a voice input device analyzes the tone of voice.

[1602] input

[1603] User skill data, experience data, personality assessment data, and emotional data.

[1604] output

[1605] Integrated data temporarily stored on the device.

[1606] Step 2:

[1607] Data transmission and storage

[1608] Terminal

[1609] The collected data is temporarily stored in the device's local storage, and then sent to the server in batch or real-time.

[1610] server

[1611] The server receives the data sent from the device and stores it in a database. The received data is then formatted and cleaned to convert it into a format suitable for the AI ​​model. For example, it checks for outliers and removes duplicate data.

[1612] input

[1613] Aggregated data sent from the device.

[1614] output

[1615] Formatted data stored in a database.

[1616] Step 3:

[1617] Analyzing the data

[1618] server

[1619] The server extracts the necessary data (skill data, experience data, personality assessment data, and emotional data) from the database and performs feature engineering. These features are then input into an AI model for multifaceted analysis. For example, employee aptitude assessments, compatibility between employees, mental health, and stress levels are also evaluated.

[1620] input

[1621] Formatted data stored in a database.

[1622] output

[1623] Data after feature engineering and analysis results from an AI model.

[1624] Step 4:

[1625] Proposal for optimal placement

[1626] server

[1627] Based on the analysis results of the AI ​​model, a list of optimal personnel placement suggestions is created. Specifically, a list of placement candidates is generated based on the analysis results.

[1628] Terminal

[1629] The proposal list is sent to the project manager's terminal, where it is displayed, and the project manager makes personnel allocation decisions based on the list.

[1630] input

[1631] Analysis results from the AI ​​model.

[1632] output

[1633] A list that suggests optimal staffing.

[1634] Step 5:

[1635] Gathering feedback and updating the model

[1636] User

[1637] After the project is completed, project managers and employees provide feedback on the project's success and the performance of the assigned personnel.

[1638] Terminal

[1639] Feedback data is collected and sent to the server. Evaluation scores and comments are entered through a dedicated form.

[1640] server

[1641] Feedback data is received and stored in a database, which is then used to retrain the AI ​​model and improve the accuracy of future suggestions.

[1642] input

[1643] Feedback data on project success and talent performance.

[1644] output

[1645] Updated AI model, improved accuracy for next proposal.

[1646] (Application example 2)

[1647] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1648] Conventional factory personnel allocation systems suggest personnel placement based on employee skills, experience, and personality assessments, but do not consider employee emotional data or robot work data. This makes it difficult to manage employee motivation and mental health, and to optimize collaboration with robots. The present invention aims to propose optimal personnel and robot placement in a collaborative environment between humans and robots by considering employee emotional data and robot work data, thereby improving factory productivity and realizing a healthy working environment.

[1649] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1650] In this invention, the server includes means for collecting employee skill data, means for collecting employee experience data, means for collecting employee personality diagnosis data, means for collecting employee emotion data, an AI module for analyzing the skill data, the experience data, the personality diagnosis data, and the emotion data, and means for proposing optimal allocation of human resources and robots for a project and a collaborative work environment based on the analysis results. This makes it possible to optimally allocate human resources and robots taking into account the emotional states of employees.

[1651] "Skill data" is the specialized skills and knowledge possessed by employees expressed as numerical or categorical data.

[1652] "Experience data" refers to historical information such as the type of work an employee has done in the past, the duration of that work, and the results they have achieved.

[1653] "Personality assessment data" is data that evaluates an employee's personality traits and psychological tendencies and records them as numerical or categorical data.

[1654] "Emotional data" is numerical and categorical data that expresses the emotional state of employees and indicates real-time situations and trends.

[1655] An "AI module" is a system component that analyzes collected data and runs artificial intelligence algorithms to make optimal recommendations and predictions.

[1656] "Optimal allocation of human resources and robots" refers to an effective allocation method that maximizes the use of the respective capabilities and conditions of humans and robots to improve production efficiency and work quality.

[1657] "Feedback data" is data that records evaluations, points for reflection, and areas for improvement regarding work or projects that have been carried out.

[1658] A "collaborative work environment" refers to a place or situation where humans and robots work together, complementing each other's roles.

[1659] The following describes in detail an embodiment of the present invention as an optimal human resource allocation system for factory robots. This system collects and analyzes skill data, experience data, personality diagnostic data, and emotional data of employees and robots to propose optimal allocation.

[1660] Program processing and hardware / software used

[1661] Hardware

[1662] Wearable devices: Capture employee vital and emotional data.

[1663] Robot: Collects work data and sends it to the server.

[1664] Terminal: A device where employees enter data and managers review the results.

[1665] software

[1666] AI module: Analyzes collected data and suggests optimal placement. Uses TensorFlow and PyTorch.

[1667] Database: Stores collected data. Uses MySQL or MongoDB.

[1668] Sentiment Engine: Analyzes employee sentiment data using IBM Watson and Microsoft Azure Emotion APIs.

[1669] Data processing and calculation

[1670] Data Shaping and Cleaning: Shaping and cleaning data using Python's Pandas library.

[1671] Feature engineering: Using Scikit-learn, we extract features and convert them into a format suitable for AI models.

[1672] Clustering algorithms: Clustering algorithms such as KMeans are used to propose optimal placement of employees and robots.

[1673] Specific example of system operation

[1674] 1. Employee data collection

[1675] Employees use smartphones or wearable devices to input their skills, experience and emotional data.

[1676] The robot automatically collects operational data and sends it to a server.

[1677] 2. Data analysis

[1678] The server formats and cleans the employee's skill data, experience data, personality assessment data, and emotional data sent from the terminal, and builds an AI model using neural networks, random forests, etc.

[1679] The emotion engine analyzes employee emotion data and generates a score.

[1680] KMeans clustering is performed to propose optimal placement of personnel and robots.

[1681] 3. Display placement proposals

[1682] The server generates optimal placement proposals based on the analysis results of the AI ​​module, which are then sent to the administrator's device and displayed in real time.

[1683] Based on the displayed proposals, the manager decides on the allocation of human resources and robots for the project and the collaborative work environment.

[1684] 4. Gather feedback and update the model

[1685] After the project is completed, managers and employees enter their project evaluations and feedback through the terminal.

[1686] The feedback data is sent to the server and stored in a database, and the AI ​​module uses this feedback data to retrain itself and improve the accuracy of its next suggestions.

[1687] Prompt Sentence Examples

[1688] "Analyze employee skills, experience, personality test results, and sentiment data to suggest optimal staffing and robot placement for a new product line installation project."

[1689] This allows the present invention to take into account the emotional state of employees and make optimal allocations of personnel and robots.

[1690] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1691] Step 1: Collect employee data

[1692] Users use smartphones or wearable devices to input their skills (e.g., programming skills or machine operation skills), experience (e.g., years of project experience or area of ​​expertise), personality assessment results (e.g., self-management ability or cooperativeness), and emotional data (e.g., stress level or motivation). In addition, the robot's work data (e.g., operating time or error rate) is automatically collected and sent from the device to a server. The input data is temporarily stored and sent to the server in real time or batch processing.

[1693] Step 2: Data Shaping and Cleaning

[1694] The server receives employee and robot data sent from the terminals. The received data is formatted and cleaned using Python's Pandas library, which completes incomplete data and removes invalid data. The skill data, experience data, personality assessment data, and emotion data collected as input data are converted into a unified format and output as clean data.

[1695] Step 3: Feature Engineering

[1696] The server performs feature engineering using the formatted and cleaned data. Using Scikit-learn, it extracts features such as skills, experience, personality assessment results, and emotional data and converts them into a format suitable for machine learning models. This process involves standardizing, normalizing, and encoding categorical data based on the input cleaned data to generate a feature dataset.

[1697] Step 4: Clustering and analysis

[1698] The server inputs the feature dataset into the KMeans clustering algorithm and analyzes the optimal allocation of employees and robots through clustering. Based on the input feature dataset, the server generates grouped clusters, evaluates the characteristics of each group, and outputs optimal employee and robot allocation proposals.

[1699] Step 5: Generate and view placement proposals

[1700] The server generates optimal personnel and robot placement proposals based on the clustering results. The generated proposals are sent to the administrator's terminal in real time and displayed on the screen. The administrator checks the displayed proposals, modifies them as necessary, and reflects them in the actual placement. The input data for the proposals are the clustering results and the administrator's operations, and the output data is the final placement proposal.

[1701] Step 6: Collect and analyze feedback

[1702] After the project is completed, users (managers and employees) input feedback on the success of the project and the performance of the assigned personnel and robots. The terminal collects this feedback data and sends it to the server. The input data here is the feedback content, and the output data is the formatted feedback data.

[1703] Step 7: Update the AI ​​model

[1704] The server receives the collected feedback data and stores it in a database. The stored feedback data is used to retrain the AI ​​model to improve the accuracy of the next proposal. The feedback data is used as input data and an updated AI model is generated as output data.

[1705] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1706] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1707] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1708] [Fourth embodiment]

[1709] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1710] 7, a 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.

[1711] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1712] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1713] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1714] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1715] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1716] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1717] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1718] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1719] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1720] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1721] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1722] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The following describes the preferred embodiments of the present invention. Although specific examples are shown in the description, the present invention is not limited to these examples.

[1723] This invention is a system that collects employee skill data, experience data, and personality assessment data and uses them to propose optimal personnel placement. The system is comprehensive, covering everything from collecting employee information to proposing optimal placement and updating the model based on feedback.

[1724] 1. Collection of employee information

[1725] User

[1726] Employees use dedicated terminals to input their skills (e.g., Java programming, data analysis), experience (e.g., 5 years of experience in the IT industry), and personality test results (e.g., high level of cooperation, high level of self-management).

[1727] Terminal

[1728] The terminal collects and temporarily stores the data entered by the employee, after which it is sent to a server.

[1729] server

[1730] The server receives the data sent from the device and stores it in a database, where it undergoes formatting and cleaning processes and is converted into a format suitable for the AI ​​model.

[1731] 2. Data Analysis

[1732] server

[1733] The server extracts employee skill data, experience data, and personality assessment data from the database and performs feature engineering. The features are then input into an AI model to analyze employee aptitude ratings and compatibility between employees. Based on the results of this analysis, the server calculates the optimal personnel allocation for each project and team.

[1734] 3. Proposal for optimal layout

[1735] server

[1736] Based on the analysis results of the AI ​​model, the server creates a list proposing optimal personnel placement, which is then sent to the project manager's device.

[1737] Terminal

[1738] The terminal displays a list of proposals for review by the project manager, who then makes staffing decisions based on the proposals displayed.

[1739] 4. Gather feedback and update the model

[1740] User

[1741] After the project is implemented, project managers and employees provide feedback on the success of the project and the performance of the assigned personnel.

[1742] Terminal

[1743] The terminal collects the feedback data and sends it to the server.

[1744] server

[1745] The server receives the feedback data and stores it in a database, which is then used to retrain the AI ​​model and improve the accuracy of future suggestions.

[1746] As a concrete example, consider the case where an employee, Mr. Sato, is proposed as the leader of a company-wide data analysis project. Mr. Sato has five years of experience in the IT industry and is highly skilled in Java programming and data analysis. Furthermore, a personality assessment reveals that he is highly cooperative and has the ability to lead a team. Based on this information, the AI ​​model analyzes that Mr. Sato is highly suitable as the leader of the data analysis project and recommends that he be appointed as the leader. After the project is completed, the project manager provides feedback on Mr. Sato's leadership and project performance, and this data is used to inform future proposals.

[1747] This will optimize personnel allocation across the company, improving team productivity and promoting employee career growth.

[1748] The processing flow will be explained below.

[1749] Step 1:

[1750] Entering employee data

[1751] User

[1752] Employees use dedicated terminals to enter their skills (e.g., Java programming, data analysis), experience (e.g., 5 years of experience in the IT industry), and personality test results (e.g., high self-management ability, high collaborative ability).

[1753] Step 2:

[1754] Temporary storage and transmission of data

[1755] Terminal

[1756] The entered data is temporarily stored and then sent to the server in batch or real-time.

[1757] Step 3:

[1758] Receiving and storing data

[1759] server

[1760] It receives data sent from devices, stores it in a database, and also formats and cleans the data, converting it into a format suitable for AI models.

[1761] Step 4:

[1762] Preprocessing

[1763] server

[1764] Employee data is extracted from the database and preprocessing is performed, such as filling in missing values ​​and removing outliers.

[1765] Step 5:

[1766] Feature Engineering

[1767] server

[1768] Features are generated from the extracted data, and information such as skills, experience, and personality test results are converted into a format that can be input into an AI model as variables.

[1769] Step 6:

[1770] Inputting data into AI models and analyzing it

[1771] server

[1772] The features are input into an AI model to evaluate employee characteristics and suitability, which also analyzes compatibility between employees and project suitability.

[1773] Step 7:

[1774] Calculating optimal staffing

[1775] server

[1776] Based on the evaluation results calculated by the AI ​​model, the optimal personnel allocation for each project and team is calculated. For example, it determines that Mr. Sato is the best suited person to be the "leader of the data analysis project."

[1777] Step 8:

[1778] Generate and submit a list of placement proposals

[1779] server

[1780] A placement proposal list is generated and its contents are sent to the terminal of the project manager.

[1781] Step 9:

[1782] Displaying the placement proposal list

[1783] Terminal

[1784] The list of submitted placement proposals is displayed to the project manager for review and approval, and the employee is notified as well.

[1785] Step 10:

[1786] Implementing staffing

[1787] User

[1788] The project manager will refer to the proposed deployment list and actually deploy the personnel, and the employees will begin work at their new locations.

[1789] Step 11:

[1790] Entering feedback data

[1791] User

[1792] After the project is completed, feedback on the success of the project and the performance of the assigned employees is entered into a dedicated terminal.

[1793] Step 12:

[1794] Collecting and sending feedback

[1795] Terminal

[1796] The collected feedback data is temporarily stored and retransmitted to the server in batch or real-time.

[1797] Step 13:

[1798] Receiving feedback and updating the model

[1799] server

[1800] The received feedback data is stored in a database, and the feedback information is used to retrain the AI ​​model, improving the accuracy of future suggestions.

[1801] The above are the specific processing steps of this system.

[1802] Example 1

[1803] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1804] Optimal personnel placement based on employee skills, experience, and personality is an important issue for many companies. Traditional methods often rely on subjective judgment and bias, resulting in insufficient optimal placement. Furthermore, there is no system in place to properly utilize feedback after placement, making it difficult to use it for future placements.

[1805] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1806] In this invention, the server includes means for inputting employee skill data, means for inputting employee experience data, means for inputting employee personality diagnosis data, means for collecting the skill data, the experience data, and the personality diagnosis data and storing them in a database, means for performing data cleaning and shaping, means for performing feature engineering, means for having a generative AI model for analyzing the shaped data, means for generating a proposal list of optimal personnel assignments for projects and teams based on the analysis results, and means for transmitting the proposal list to a terminal of a project manager. This enables optimal personnel assignments based on objective and appropriate data.

[1807] "Employee skills data" refers to information about the skills and knowledge that employees possess in specific tasks or jobs.

[1808] "Employee experience data" refers to information that represents the history and performance of the work or duties that an employee has performed to date.

[1809] "Employee personality assessment data" refers to test results used to assess an employee's personality traits and behavioral patterns.

[1810] "Means of storing data in a database" refers to the systems and technologies used to store collected data in an organized manner and make it easy to manage and search.

[1811] "Data cleaning and formatting" refers to operations performed to correct inaccurate or missing data and convert it into a form suitable for analysis and use.

[1812] "Feature engineering" refers to the process of extracting useful information from collected data and converting it into a format suitable for analytical models.

[1813] A "generative AI model" is a program that uses machine learning algorithms to analyze data and make predictions or suggestions based on specific purposes.

[1814] "Proposal list" refers to a list of candidates for optimal personnel placement created based on the analysis results of the generative AI model.

[1815] This invention is a system that collects employee skill data, experience data, and personality assessment data and uses them to propose optimal personnel placement. The system is comprehensive, covering everything from collecting employee information to proposing optimal placement and updating the model based on feedback.

[1816] Collection of employee information

[1817] User

[1818] Employees use dedicated terminals to enter the following information:

[1819] Skill data (e.g. Java programming, data analysis)

[1820] Experience data (e.g., 5 years of experience in the IT industry)

[1821] Personality test results (e.g., high cooperativeness, high self-management ability)

[1822] Specific actions

[1823] Employees access the form, fill in each item using text boxes and selection lists, then confirm the information and click the "Submit" button.

[1824] Data transmission and storage

[1825] Terminal

[1826] The terminal temporarily stores the data entered by the user in memory and transmits it to the server via the API.

[1827] Specific actions

[1828] Once the input has been confirmed, the device uses JavaScript to serialize the data into JSON format and sends an HTTP POST request to the server.

[1829] server

[1830] The server receives the data sent from the terminal and stores it in a PostgreSQL database.

[1831] Specific actions

[1832] The server parses the received JSON data and stores the data in the "employees" table in the database using an INSERT statement.

[1833] Data formatting and preparation

[1834] server

[1835] The server formats the stored data and converts it into a format suitable for AI models, using the Python Pandas library for the cleaning process.

[1836] Specific actions

[1837] The server periodically runs Python scripts to impute missing data, remove outliers, and encode categorical data, such as converting "highly cooperative" into a numerical format.

[1838] Analyzing the data

[1839] server

[1840] The server extracts features from the formatted data and feeds them into a TensorFlow-based generative AI model, which then analyzes employee aptitude and compatibility between employees.

[1841] Specific actions

[1842] The server performs feature engineering and feeds the generated features into an AI model, which scores each employee's aptitude and compatibility and calculates the optimal personnel placement.

[1843] Proposal and display of optimal layout

[1844] server

[1845] Based on the analysis results of the generative AI model, the server generates a list of proposals for optimal personnel placement and sends it to the project manager's device.

[1846] Specific actions

[1847] The server generates a list of suggestions in JSON format based on the model results and sends it to the manager's device using an HTTP POST request.

[1848] Terminal

[1849] The terminal receives the suggestion list and displays it on the user interface.

[1850] Specific actions

[1851] The terminal parses the received JSON data and inserts it into an HTML template for displaying the list, allowing the project manager to view the proposal list on the screen.

[1852] Gathering feedback and updating the model

[1853] User

[1854] After the project is completed, the project manager and employees use a feedback form to enter their feedback on the success of the project and the performance of the assigned personnel.

[1855] Specific actions

[1856] Users access a Google Form or custom feedback form, fill out each field, and submit it.

[1857] Terminal

[1858] The terminal collects the feedback data and sends it to the server.

[1859] Specific actions

[1860] After entering the feedback, the device presses a button, serializes the feedback data into JSON format, and sends it to the server via an HTTP POST request.

[1861] server

[1862] The server receives the feedback data, stores it in a database, and uses it to retrain the generative AI model to improve the accuracy of future suggestions.

[1863] Specific actions

[1864] After storing the received data in a database, a batch processing script is executed to retrain the generative AI model, using reinforcement learning techniques to improve the accuracy of the next staffing proposal.

[1865] Example prompt sentence:

[1866] Send employee skills, experience, and personality data to a server to generate optimized code for analysis by AI models.

[1867] Extract employee skills, experience, and personality data as features and generate code to analyze them with an AI model.

[1868] Generate the code to generate and display the optimal staffing list based on the analysis results of the AI ​​model.

[1869] Collect employee feedback data and generate code to retrain your AI models.

[1870] This system optimizes personnel allocation across the company, improving team productivity and promoting employee career growth.

[1871] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1872] Program processing flow

[1873] Step 1: Gather employee information

[1874] User

[1875] Employees use dedicated terminals to enter the following information:

[1876] Input: Skill data (e.g., Java programming, data analysis), experience data (e.g., 5 years of experience in the IT industry), personality test results (e.g., high cooperativeness, high self-management ability)

[1877] Output: The input data is temporarily saved on the device.

[1878] Specific actions

[1879] Employees access the form, fill in each item using text boxes and selection lists, then confirm the information and click the "Submit" button.

[1880] Step 2: Send and store data

[1881] Terminal

[1882] The terminal temporarily stores the data entered by the user in memory and transmits it to the server via the API.

[1883] Input: Data entered by the user

[1884] Output: JSON formatted request data to be sent to the server

[1885] Specific actions

[1886] Once the input has been confirmed, the device uses JavaScript to serialize the data into JSON format and sends an HTTP POST request to the server.

[1887] server

[1888] The server receives the data sent from the terminal and stores it in a PostgreSQL database.

[1889] Input: JSON data sent from the terminal

[1890] Output: Data inserted into the database

[1891] Specific actions

[1892] The server parses the received JSON data and stores the data in the "employees" table in the database using an INSERT statement.

[1893] Step 3: Data Formatting and Preparation

[1894] server

[1895] The server formats the stored data and converts it into a format suitable for AI models, using the Python Pandas library for the cleaning process.

[1896] Input: Raw data stored in a database

[1897] Output: Reshaped and cleaned data

[1898] Specific actions

[1899] The server periodically runs Python scripts to impute missing data, remove outliers, and encode categorical data, such as converting "highly cooperative" into a numerical format.

[1900] Step 4: Analyze the data

[1901] server

[1902] The server extracts features from the formatted data and feeds them into a TensorFlow-based generative AI model, which then analyzes employee aptitude and compatibility between employees.

[1903] Input: Reshaped and cleaned data

[1904] Output: Aptitude assessment and compatibility scores between employees

[1905] Specific actions

[1906] The server performs feature engineering and feeds the generated features into an AI model, which scores each employee's aptitude and compatibility and calculates the optimal personnel placement.

[1907] Step 5: Propose and display optimal layout

[1908] server

[1909] Based on the analysis results of the generative AI model, the server generates a list of proposals for optimal personnel placement and sends it to the project manager's device.

[1910] Input: Aptitude assessment and compatibility scores between employees

[1911] Output: Suggestion list

[1912] Specific actions

[1913] The server generates a list of suggestions in JSON format based on the model results and sends it to the manager's device using an HTTP POST request.

[1914] Terminal

[1915] The terminal receives the suggestion list and displays it on the user interface.

[1916] Input: JSON data of the proposal list sent from the server

[1917] Output: The list of suggestions displayed in the user interface

[1918] Specific actions

[1919] The terminal parses the received JSON data and inserts it into an HTML template for displaying the list, allowing the project manager to view the proposal list on the screen.

[1920] Step 6: Gather feedback and update the model

[1921] User

[1922] After the project is completed, the project manager and employees use a feedback form to enter their feedback on the success of the project and the performance of the assigned personnel.

[1923] Input: Feedback data (success level, performance evaluation)

[1924] Output: Sending feedback data to the server

[1925] Specific actions

[1926] Users access a Google Form or custom feedback form, fill out each field, and submit it.

[1927] Terminal

[1928] The terminal collects the feedback data and sends it to the server.

[1929] Input: User-entered feedback data

[1930] Output: Feedback data sent to the server in JSON format

[1931] Specific actions

[1932] After entering the feedback, the device presses a button, serializes the feedback data into JSON format, and sends it to the server via an HTTP POST request.

[1933] server

[1934] The server receives the feedback data, stores it in a database, and uses it to retrain the generative AI model to improve the accuracy of future suggestions.

[1935] Input: Received feedback data

[1936] Output: An updated generative AI model

[1937] Specific actions

[1938] After storing the received data in a database, a batch processing script is executed to retrain the generative AI model, using reinforcement learning techniques to improve the accuracy of the next staffing proposal.

[1939] (Application example 1)

[1940] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1941] To achieve optimal allocation and task sharing between human resources and robots in a factory, it is important to make appropriate allocation proposals based on employee ability, history, and personality evaluations. However, conventional systems have difficulty effectively collecting and analyzing this data and implementing optimal allocation proposals as a system. Furthermore, there is a lack of a process for collecting feedback on performance after allocation and reflecting it in future proposals. This makes it difficult to improve production efficiency and maximize employee satisfaction.

[1942] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1943] In this invention, the server includes means for collecting employee ability data, means for collecting employee history data, means for collecting employee personality assessment data, means having AI for analyzing the ability data, the history data, and the personality assessment data, means for proposing optimal personnel allocation for projects and groups based on the analysis results, and means for proposing optimal task sub-areas for robots and human workers in the factory. This enables optimal allocation and task sharing of personnel and robots in the factory, thereby improving productivity and maximizing employee satisfaction.

[1944] "Competence data" is information about an employee's skills, qualifications, and technical abilities.

[1945] "Historical data" refers to information about an employee's past work experience, performance, and employment history.

[1946] "Personality assessment data" is information that indicates the results of an assessment of an employee's personality traits and behavioral patterns.

[1947] "Artificial intelligence" refers to machine learning algorithms and models that analyze employee ability data, history data, and personality assessment data to propose optimal personnel placement and task allocation.

[1948] "Placement proposal" refers to proposing the optimal placement of human resources or robots for a project or group.

[1949] A "factory" is a place where products are manufactured or processed, and refers to a facility where many machines and robots are in operation.

[1950] "Task allocation" refers to the appropriate allocation of individual tasks to human workers or robots in a project or business.

[1951] "Feedback data" is data collected after a project is completed, including information on employee and robot performance, success, etc.

[1952] An embodiment of the present invention will be described below. This embodiment is a system for achieving optimal allocation and task sharing between employees and robots in a factory. The system is comprehensive, including employee information collection, data analysis, optimal allocation proposals, feedback, and model updates.

[1953] 1. Collection of employee and robot information

[1954] The server is equipped with various means to collect employee and robot ability data, history data, and personality evaluation data. Employee and robot information is input into the system using devices such as dedicated smartphones, smart glasses, and head-mounted displays.

[1955] 2. Data transmission and storage

[1956] The device temporarily stores the collected information and transmits it in real time to a server, which stores the data in a MySQL database.

[1957] 3. Data preparation and analysis

[1958] The server uses Python-based processing to extract data from MySQL, format and clean it, then performs feature engineering and feeds it into an AI model using TensorFlow or PyTorch. The model analyzes the data, including the abilities and personalities of employees and robots, to calculate optimal placement.

[1959] 4. Proposal for optimal layout

[1960] Based on the results of the AI ​​model's analysis, the server generates a list of suggestions for optimal staffing and robot task allocation, which is then sent to the project manager's smart device via an interface and displayed in a React Native application.

[1961] 5. Gather feedback and update the model

[1962] After the project is completed, the project manager and employees enter feedback data using their smart devices. The devices then send the collected feedback data back to the server, which stores it in MySQL. Using TensorFlow or PyTorch, the AI ​​model is retrained based on the feedback to improve the accuracy of future proposals.

[1963] Examples:

[1964] At a large manufacturing factory, veteran worker A is selected as the leader of a new project. A has 15 years of experience and is skilled at machine maintenance. A personality assessment reveals that A has strong leadership skills and is capable of leading a team. Meanwhile, less experienced worker B (with strong robot operation skills) is also assigned to the team. The system selects this combination as the leader and proposes the optimal placement for the project's success. The project's evaluation and feedback are then entered into the system and used to make future proposals.

[1965] Example prompt sentence:

[1966] Design a system to optimize factory staffing based on employee skill data, experience data, and personality assessment data. Input each worker's data and send it to the server in real time. The data is analyzed by an AI model, which then proposes optimal staffing. After the project is completed, collect feedback and update the AI ​​model to help with future staffing proposals.

[1967] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1968] Step 1:

[1969] Collecting information about employees and robots

[1970] Users use smart devices (smartphones, smart glasses, head-mounted displays, etc.) to input ability data, history data, and personality assessment data. The input data is temporarily saved in JSON format on the device. Specifically, users manually input the data through a dedicated application, and the device formats it in a format that can be sent to the server in real time.

[1971] input:

[1972] Ability data, history data, personality assessment data

[1973] output:

[1974] Input data in JSON format

[1975] Step 2:

[1976] Data transmission and storage

[1977] The terminals send the collected data in JSON format to a server using a RESTful API, which stores the data in a MySQL database. Specifically, the terminals send data for each employee and robot to an API endpoint, and the server saves the data in the appropriate table.

[1978] input:

[1979] Input data in JSON format

[1980] output:

[1981] MySQL database entries

[1982] Step 3:

[1983] Data Shaping and Cleaning

[1984] The server uses Python scripts to extract data from a MySQL database and then performs data conditioning and cleaning, such as imputing missing values, handling outliers, and encoding categorical data. Specifically, the server executes database queries and processes the retrieved data using Pandas.

[1985] input:

[1986] MySQL database entries

[1987] output:

[1988] Formatted and cleaned data

[1989] Step 4:

[1990] Feature Engineering

[1991] The server uses the cleaned and formatted data to generate features. Specifically, it converts the data into a form suitable for machine learning and prepares it for input into an AI model. For example, it standardizes and scales the numerical data and extracts important features.

[1992] input:

[1993] Formatted and cleaned data

[1994] output:

[1995] Feature-engineered data

[1996] Step 5:

[1997] Analysis by AI model

[1998] The server uses TensorFlow or PyTorch to input feature-engineered data into an AI model for analysis, which evaluates the suitability of employees and robots and generates an optimal deployment list. Specifically, the server loads the model, inputs data, and outputs prediction results.

[1999] input:

[2000] Feature-engineered data

[2001] output:

[2002] List of best talent and robot deployments

[2003] Step 6:

[2004] Displaying the optimal placement list

[2005] The server sends the generated optimized placement list to the project manager's smart device via API. The React Native application on the device receives and displays this data. Specifically, the server sends data via API in real time, allowing the list to be visually confirmed on the device.

[2006] input:

[2007] List of best talent and robot deployments

[2008] output:

[2009] A list of proposals displayed on the project manager's smart device

[2010] Step 7:

[2011] Feedback collection

[2012] After completing a project, users enter feedback data using their smart devices. The devices then send the collected feedback data to the server. Specifically, users enter their feedback through the application, and the devices then send it to the server.

[2013] input:

[2014] Feedback Data

[2015] output:

[2016] Feedback data stored on the server

[2017] Step 8:

[2018] Updating a Model

[2019] The server stores the collected feedback data in MySQL and retrains the AI ​​model using TensorFlow or PyTorch, improving the accuracy of the next optimal placement proposal. Specifically, the server updates the AI ​​model using new data and validates the model's performance.

[2020] input:

[2021] Feedback Data

[2022] output:

[2023] Retrained AI model

[2024] Example prompt sentence:

[2025] Design a system to optimize factory staffing based on employee skill data, experience data, and personality assessment data. Input each worker's data and send it to the server in real time. The data is analyzed by an AI model, which then proposes optimal staffing. After the project is completed, collect feedback and update the AI ​​model to help with future staffing proposals.

[2026] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[2027] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will be described in detail below with reference to the accompanying drawings, in which: FIG.

[2028] This invention is a system that collects employee skill data, experience data, and personality assessment data and uses them to propose optimal personnel placement. Furthermore, this system is characterized by incorporating an emotion engine that recognizes user emotions, and by utilizing the collected emotion data for analysis. The system is comprehensive, covering everything from collecting employee information to proposing optimal placement and updating the model based on feedback.

[2029] 1. Collect employee information and sentiment data

[2030] User

[2031] Employees use dedicated terminals to enter their skills (e.g., Java programming, data analysis), experience (e.g., 5 years of experience in the IT industry), and personality test results (e.g., high self-management ability, high collaborative ability).

[2032] Terminal

[2033] The device uses an emotion engine to collect employee emotional data (e.g., stress level, motivation), which is generated by analyzing the employee's facial expressions, voice, text input, etc.

[2034] Terminal

[2035] The collected employee data and emotion data are temporarily stored and then sent to the server in batch or real-time.

[2036] server

[2037] It receives data sent from the device and stores it in a database, where it undergoes formatting and cleaning processes and is converted into a format suitable for the AI ​​model.

[2038] 2. Data Analysis

[2039] server

[2040] The server extracts employee skill data, experience data, personality assessment data, and emotional data from the database and performs feature engineering. These features are then input into an AI model, which performs multifaceted analysis, including employee aptitude assessment, compatibility between employees, mental health, and stress levels.

[2041] 3. Proposal for optimal layout

[2042] server

[2043] Based on the analysis results of the AI ​​model, a list of optimal personnel placement suggestions is created and sent to the project manager's device.

[2044] Terminal

[2045] A list of proposals is displayed for review by the project manager, who then makes staffing decisions based on the proposals displayed.

[2046] 4. Gather feedback and update the model

[2047] User

[2048] After the project is completed, project managers and employees provide feedback on the project's success and the performance of the assigned personnel.

[2049] Terminal

[2050] Feedback data is collected and sent to a server.

[2051] server

[2052] Feedback data is received and stored in a database, which is then used to retrain the AI ​​model and improve the accuracy of future suggestions.

[2053] As a concrete example, consider the case where an employee, Mr. Sato, is proposed as the leader of a company-wide data analysis project. Mr. Sato has five years of experience in the IT industry and is highly skilled in Java programming and data analysis. The emotion engine also confirms that his current stress level is low and his motivation is high. Based on this information, the AI ​​model analyzes that Mr. Sato is highly qualified to lead the data analysis project and recommends that he be appointed as the leader. After the project is completed, the project manager provides feedback on Mr. Sato's leadership and project performance, and this data is used to inform future proposals.

[2054] This will optimize the allocation of personnel throughout the company, improving team productivity and promoting employee career growth. The use of the emotion engine also makes it possible to consider the mental health of employees, providing a healthier work environment.

[2055] The processing flow will be explained below.

[2056] Step 1:

[2057] Entering employee data

[2058] User

[2059] Employees use dedicated terminals to enter their skills (e.g., Java programming, data analysis), experience (e.g., 5 years of experience in the IT industry), and personality test results (e.g., high self-management ability, high collaborative ability).

[2060] Step 2:

[2061] Collecting Emotional Data

[2062] Terminal

[2063] The company uses an emotion engine to collect employee emotional data, which analyzes facial expressions, voice, and text inputs in real time while employees are logged in to their devices, to generate emotional indicators such as stress levels and motivation.

[2064] Step 3:

[2065] Temporary storage and transmission of data

[2066] Terminal

[2067] The collected skill data, experience data, personality assessment data, and emotion data are temporarily stored and then transmitted to a server in batch processing or real time.

[2068] Step 4:

[2069] Receiving and storing data

[2070] server

[2071] It receives data sent from the device and stores it in a database, where it undergoes formatting and cleaning processes and is converted into a format suitable for the AI ​​model.

[2072] Step 5:

[2073] Preprocessing

[2074] server

[2075] Employee data and emotion data are extracted from the database, and preprocessing such as filling in missing values ​​and removing outliers is performed.

[2076] Step 6:

[2077] Feature Engineering

[2078] server

[2079] Features are generated from the extracted data, and skills, experience, personality assessment results, and emotional data (stress level, motivation, etc.) are put into a format that can be input into an AI model as variables.

[2080] Step 7:

[2081] Inputting data into AI models and analyzing it

[2082] server

[2083] The features are fed into an AI model to evaluate employee characteristics and suitability, which analyzes not only compatibility between employees and project suitability, but also mental health and stress levels.

[2084] Step 8:

[2085] Calculating optimal staffing

[2086] server

[2087] Based on the evaluation results calculated by the AI ​​model, the optimal personnel allocation for each project and team is calculated. For example, based on emotional data, it may determine that Sato, who has a low stress level and is highly motivated, is the best suited to be the "leader of the data analysis project."

[2088] Step 9:

[2089] Generate and submit a list of placement proposals

[2090] server

[2091] A placement proposal list is generated and its contents are sent to the terminal of the project manager.

[2092] Step 10:

[2093] Displaying the placement proposal list

[2094] Terminal

[2095] The list of submitted placement proposals is displayed to the project manager for review and approval, and the employee is notified as well.

[2096] Step 11:

[2097] Implementing staffing

[2098] User

[2099] The project manager will refer to the proposed deployment list and actually deploy the personnel, and the employees will begin work at their new locations.

[2100] Step 12:

[2101] Entering feedback data

[2102] User

[2103] After the project is completed, feedback on the success of the project and the performance of the assigned employees is entered into a dedicated terminal.

[2104] Step 13:

[2105] Collecting and sending feedback

[2106] Terminal

[2107] The collected feedback data is temporarily stored and retransmitted to the server in batch or real-time.

[2108] Step 14:

[2109] Receiving feedback and updating the model

[2110] server

[2111] The received feedback data is stored in a database, and the feedback information is used to retrain the AI ​​model, improving the accuracy of future suggestions.

[2112] This allows for the optimization of personnel allocation across the company, improving team productivity and promoting employee career growth. Furthermore, the use of an emotion engine can provide a healthy work environment that takes into consideration the mental health of employees. The specific processing steps are as follows:

[2113] Example 2

[2114] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2115] In order to properly assign employees, not only employee skills and experience are important, but also personality and emotional data. However, with conventional systems, it was difficult to centrally collect, integrate, and analyze this data. In particular, it was difficult to grasp employees' emotional states in real time, which resulted in a decrease in the accuracy of personnel assignments and a negative impact on employee motivation and stress levels. This issue needs to be resolved.

[2116] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[2117] In this invention, the server includes means for collecting employee skill data, means for collecting employee experience data, means for collecting employee personality assessment data, means for collecting employee emotion data, means having an AI for analyzing the skill data, the experience data, and the personality assessment data, and means for proposing personnel placement based on the analysis results and emotion data. This enables highly accurate personnel placement that takes into account not only employee skills and experience, but also personality and emotion.

[2118] "Skills data" is information about the skills and abilities of employees.

[2119] "Experience data" is information about the work and projects an employee has previously worked on.

[2120] "Personality diagnostic data" is information obtained as a result of evaluating an employee's personality and behavioral characteristics.

[2121] "Emotional data" is information that measures an employee's emotional state, including stress levels and motivation.

[2122] "AI" is a system that uses artificial intelligence technology to analyze data and make predictions.

[2123] "Means" refers to a method or device used to achieve a particular purpose.

[2124] A "server" is a central computer system that collects, processes, stores, and distributes data.

[2125] "Proposal" refers to recommending the optimal personnel placement based on the analysis results.

[2126] "Feedback data" refers to information regarding the results and evaluations after personnel placement has actually been carried out.

[2127] "Model updating" refers to retraining an AI model based on collected feedback data to improve the accuracy of its next predictions and suggestions.

[2128] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will be described in detail below with reference to the accompanying drawings, in which: FIG.

[2129] This invention is a system that collects employee skill data, experience data, and personality assessment data and uses them to propose optimal personnel placement. Furthermore, this system is characterized by incorporating an emotion engine that recognizes user emotions, and by utilizing the collected emotion data for analysis. The system is comprehensive, covering everything from collecting employee information to proposing optimal placement and updating the model based on feedback.

[2130] 1. Collect employee information and sentiment data

[2131] User

[2132] Employees use dedicated terminals to enter their skills (e.g., programming, data analysis), experience (e.g., several years of industry experience), and personality test results (e.g., high self-management ability, high collaborative ability).

[2133] Terminal

[2134] The device uses a built-in emotion engine to collect employee emotional data, which analyzes facial expressions, voice, and text input to generate emotional data such as stress levels and motivation.

[2135] Terminal

[2136] The collected employee data and emotion data are temporarily stored in local storage and then sent to the server in batch or real-time.

[2137] server

[2138] It receives data sent from the device and stores it in a database, where it undergoes formatting and cleaning processes and is converted into a format suitable for the AI ​​model.

[2139] 2. Data Analysis

[2140] server

[2141] The server extracts employee skill data, experience data, personality assessment data, and emotional data from the database and performs feature engineering. These features are then input into an AI model, which performs multifaceted analysis, including employee aptitude assessment, compatibility between employees, mental health, and stress levels.

[2142] 3. Proposal for optimal layout

[2143] server

[2144] Based on the analysis results of the AI ​​model, a list of optimal personnel placement suggestions is created and sent to the project manager's device.

[2145] Terminal

[2146] A list of proposals is displayed for review by the project manager, who then makes staffing decisions based on the proposals displayed.

[2147] 4. Gather feedback and update the model

[2148] User

[2149] After the project is completed, project managers and employees provide feedback on the project's success and the performance of the assigned personnel.

[2150] Terminal

[2151] The terminal collects the feedback data and sends it to the server.

[2152] server

[2153] Feedback data is received and stored in a database, which is then used to retrain the AI ​​model and improve the accuracy of future suggestions.

[2154] As a concrete example, consider the case where an employee is proposed as a leader for a data analytics project. This employee has several years of industry experience and is highly skilled in programming and data analysis. The emotion engine also identifies that the employee has low stress levels and high motivation. Based on this information, the AI ​​model makes a recommendation and assigns the employee to the leadership role. After the project is completed, the manager provides feedback on the employee's performance, and this data is used to inform future proposals.

[2155] Example prompt sentence:

[2156] Please explain the system that collects employee skill data, experience data, personality assessment data, and emotional data to suggest optimal staffing.

[2157] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2158] Step 1:

[2159] Collecting employee information and sentiment data

[2160] User

[2161] Users use dedicated terminals to input their skill data (e.g., programming, data analysis), experience data (e.g., several years of industry experience), and personality assessment data (e.g., high self-management ability, high cooperativeness), which allows detailed information on each user's abilities and characteristics to be collected.

[2162] Terminal

[2163] The device uses an emotion engine to collect user emotion data. The emotion engine analyzes the user's facial expressions, voice, text input, etc. to generate emotion data such as stress level and motivation. For example, a camera captures the user's facial expressions, and a voice input device analyzes the tone of voice.

[2164] input

[2165] User skill data, experience data, personality assessment data, and emotional data.

[2166] output

[2167] Integrated data temporarily stored on the device.

[2168] Step 2:

[2169] Data transmission and storage

[2170] Terminal

[2171] The collected data is temporarily stored in the device's local storage, and then sent to the server in batch or real-time.

[2172] server

[2173] The server receives the data sent from the device and stores it in a database. The received data is then formatted and cleaned to convert it into a format suitable for the AI ​​model. For example, it checks for outliers and removes duplicate data.

[2174] input

[2175] Aggregated data sent from the device.

[2176] output

[2177] Formatted data stored in a database.

[2178] Step 3:

[2179] Analyzing the data

[2180] server

[2181] The server extracts the necessary data (skill data, experience data, personality assessment data, and emotional data) from the database and performs feature engineering. These features are then input into an AI model for multifaceted analysis. For example, employee aptitude assessments, compatibility between employees, mental health, and stress levels are also evaluated.

[2182] input

[2183] Formatted data stored in a database.

[2184] output

[2185] Data after feature engineering and analysis results from an AI model.

[2186] Step 4:

[2187] Proposal for optimal placement

[2188] server

[2189] Based on the analysis results of the AI ​​model, a list of optimal personnel placement suggestions is created. Specifically, a list of placement candidates is generated based on the analysis results.

[2190] Terminal

[2191] The proposal list is sent to the project manager's terminal, where it is displayed, and the project manager makes personnel allocation decisions based on the list.

[2192] input

[2193] Analysis results from the AI ​​model.

[2194] output

[2195] A list that suggests optimal staffing.

[2196] Step 5:

[2197] Gathering feedback and updating the model

[2198] User

[2199] After the project is completed, project managers and employees provide feedback on the project's success and the performance of the assigned personnel.

[2200] Terminal

[2201] Feedback data is collected and sent to the server. Evaluation scores and comments are entered through a dedicated form.

[2202] server

[2203] Feedback data is received and stored in a database, which is then used to retrain the AI ​​model and improve the accuracy of future suggestions.

[2204] input

[2205] Feedback data on project success and talent performance.

[2206] output

[2207] Updated AI model, improved accuracy for next proposal.

[2208] (Application example 2)

[2209] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2210] Conventional factory personnel allocation systems suggest personnel placement based on employee skills, experience, and personality assessments, but do not consider employee emotional data or robot work data. This makes it difficult to manage employee motivation and mental health, and to optimize collaboration with robots. The present invention aims to propose optimal personnel and robot placement in a collaborative environment between humans and robots by considering employee emotional data and robot work data, thereby improving factory productivity and realizing a healthy working environment.

[2211] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2212] In this invention, the server includes means for collecting employee skill data, means for collecting employee experience data, means for collecting employee personality diagnosis data, means for collecting employee emotion data, an AI module for analyzing the skill data, the experience data, the personality diagnosis data, and the emotion data, and means for proposing optimal allocation of human resources and robots for a project and a collaborative work environment based on the analysis results. This makes it possible to optimally allocate human resources and robots taking into account the emotional states of employees.

[2213] "Skill data" is the specialized skills and knowledge possessed by employees expressed as numerical or categorical data.

[2214] "Experience data" refers to historical information such as the type of work an employee has done in the past, the duration of that work, and the results they have achieved.

[2215] "Personality assessment data" is data that evaluates an employee's personality traits and psychological tendencies and records them as numerical or categorical data.

[2216] "Emotional data" is numerical and categorical data that expresses the emotional state of employees and indicates real-time situations and trends.

[2217] An "AI module" is a system component that analyzes collected data and runs artificial intelligence algorithms to make optimal recommendations and predictions.

[2218] "Optimal allocation of human resources and robots" refers to an effective allocation method that maximizes the use of the respective capabilities and conditions of humans and robots to improve production efficiency and work quality.

[2219] "Feedback data" is data that records evaluations, points for reflection, and areas for improvement regarding work or projects that have been carried out.

[2220] A "collaborative work environment" refers to a place or situation where humans and robots work together, complementing each other's roles.

[2221] The following describes in detail an embodiment of the present invention as an optimal human resource allocation system for factory robots. This system collects and analyzes skill data, experience data, personality diagnostic data, and emotional data of employees and robots to propose optimal allocation.

[2222] Program processing and hardware / software used

[2223] Hardware

[2224] Wearable devices: Capture employee vital and emotional data.

[2225] Robot: Collects work data and sends it to the server.

[2226] Terminal: A device where employees enter data and managers review the results.

[2227] software

[2228] AI module: Analyzes collected data and suggests optimal placement. Uses TensorFlow and PyTorch.

[2229] Database: Stores collected data. Uses MySQL or MongoDB.

[2230] Sentiment Engine: Analyzes employee sentiment data using IBM Watson and Microsoft Azure Emotion APIs.

[2231] Data processing and calculation

[2232] Data Shaping and Cleaning: Shaping and cleaning data using Python's Pandas library.

[2233] Feature engineering: Using Scikit-learn, we extract features and convert them into a format suitable for AI models.

[2234] Clustering algorithms: Clustering algorithms such as KMeans are used to propose optimal placement of employees and robots.

[2235] Specific example of system operation

[2236] 1. Employee data collection

[2237] Employees use smartphones or wearable devices to input their skills, experience and emotional data.

[2238] The robot automatically collects operational data and sends it to a server.

[2239] 2. Data analysis

[2240] The server formats and cleans the employee's skill data, experience data, personality assessment data, and emotional data sent from the terminal, and builds an AI model using neural networks, random forests, etc.

[2241] The emotion engine analyzes employee emotion data and generates a score.

[2242] KMeans clustering is performed to propose optimal placement of personnel and robots.

[2243] 3. Display placement proposals

[2244] The server generates optimal placement proposals based on the analysis results of the AI ​​module, which are then sent to the administrator's device and displayed in real time.

[2245] Based on the displayed proposals, the manager decides on the allocation of human resources and robots for the project and the collaborative work environment.

[2246] 4. Gather feedback and update the model

[2247] After the project is completed, managers and employees enter their project evaluations and feedback through the terminal.

[2248] The feedback data is sent to the server and stored in a database, and the AI ​​module uses this feedback data to retrain itself and improve the accuracy of its next suggestions.

[2249] Prompt Sentence Examples

[2250] "Analyze employee skills, experience, personality test results, and sentiment data to suggest optimal staffing and robot placement for a new product line installation project."

[2251] This allows the present invention to take into account the emotional state of employees and make optimal allocations of personnel and robots.

[2252] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2253] Step 1: Collect employee data

[2254] Users use smartphones or wearable devices to input their skills (e.g., programming skills or machine operation skills), experience (e.g., years of project experience or area of ​​expertise), personality assessment results (e.g., self-management ability or cooperativeness), and emotional data (e.g., stress level or motivation). In addition, the robot's work data (e.g., operating time or error rate) is automatically collected and sent from the device to a server. The input data is temporarily stored and sent to the server in real time or batch processing.

[2255] Step 2: Data Shaping and Cleaning

[2256] The server receives employee and robot data sent from the terminals. The received data is formatted and cleaned using Python's Pandas library, which completes incomplete data and removes invalid data. The skill data, experience data, personality assessment data, and emotion data collected as input data are converted into a unified format and output as clean data.

[2257] Step 3: Feature Engineering

[2258] The server performs feature engineering using the formatted and cleaned data. Using Scikit-learn, it extracts features such as skills, experience, personality assessment results, and emotional data and converts them into a format suitable for machine learning models. This process involves standardizing, normalizing, and encoding categorical data based on the input cleaned data to generate a feature dataset.

[2259] Step 4: Clustering and analysis

[2260] The server inputs the feature dataset into the KMeans clustering algorithm and analyzes the optimal allocation of employees and robots through clustering. Based on the input feature dataset, the server generates grouped clusters, evaluates the characteristics of each group, and outputs optimal employee and robot allocation proposals.

[2261] Step 5: Generate and view placement proposals

[2262] The server generates optimal personnel and robot placement proposals based on the clustering results. The generated proposals are sent to the administrator's terminal in real time and displayed on the screen. The administrator checks the displayed proposals, modifies them as necessary, and reflects them in the actual placement. The input data for the proposals are the clustering results and the administrator's operations, and the output data is the final placement proposal.

[2263] Step 6: Collect and analyze feedback

[2264] After the project is completed, users (managers and employees) input feedback on the success of the project and the performance of the assigned personnel and robots. The terminal collects this feedback data and sends it to the server. The input data here is the feedback content, and the output data is the formatted feedback data.

[2265] Step 7: Update the AI ​​model

[2266] The server receives the collected feedback data and stores it in a database. The stored feedback data is used to retrain the AI ​​model to improve the accuracy of the next proposal. The feedback data is used as input data and an updated AI model is generated as output data.

[2267] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2268] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2269] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2270] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2271] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2272] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2273] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2274] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2275] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2276] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2277] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2278] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2279] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2280] 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.

[2281] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2282] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2283] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[2284] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[2285] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[2286] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[2287] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[2288] The following is further disclosed regarding the above embodiment.

[2289] (Claim 1)

[2290] A means of collecting employee skills data;

[2291] a means of collecting employee experience data;

[2292] A means of collecting personality assessment data from employees;

[2293] A means having an AI for analyzing the skill data, the experience data, and the personality diagnostic data;

[2294] A means for proposing optimal personnel allocation for projects and teams based on the analysis results;

[2295] A system including:

[2296] (Claim 2)

[2297] 10. The system of claim 1, further comprising means for displaying optimal staffing recommendations.

[2298] (Claim 3)

[2299] A means for collecting feedback data after implementing personnel allocation based on the content of the proposal;

[2300] means for updating an AI using the feedback data;

[2301] The system of claim 1 further comprising:

[2302] "Example 1"

[2303] (Claim 1)

[2304] a means for inputting employee skill data;

[2305] a means for inputting employee experience data;

[2306] a means for inputting employee personality assessment data;

[2307] means for collecting and storing said skill data, said experience data, and said personality diagnostic data in a database;

[2308] a means for performing data cleaning and formatting processes;

[2309] a means for performing feature engineering;

[2310] means for analyzing the formatted data and having a generative AI model;

[2311] A means for generating a list of proposals for optimal personnel allocation for projects and teams based on the analysis results;

[2312] means for transmitting the proposal list to a terminal of a project manager;

[2313] A system including:

[2314] (Claim 2)

[2315] The system of claim 1 further comprising: means for displaying the suggestion list.

[2316] (Claim 3)

[2317] A means for collecting feedback data on the performance of personnel assigned based on said proposal after the project is completed;

[2318] A means for retraining the generative AI model using the feedback data to improve the accuracy of subsequent suggestions;

[2319] The system of claim 1 further comprising:

[2320] "Application Example 1"

[2321] (Claim 1)

[2322] a means of collecting employee performance data;

[2323] a means of collecting employee historical data;

[2324] a means of collecting employee personality assessment data;

[2325] means having an AI for analyzing the ability data, the history data, and the personality assessment data;

[2326] A means for proposing optimal personnel allocation for projects and groups based on the analysis results;

[2327] A means for proposing optimal sub-areas of tasks for robots and human workers in factories;

[2328] A system including:

[2329] (Claim 2)

[2330] 10. The system of claim 1, further comprising means for displaying optimal staffing and task suggestions for robots and human workers.

[2331] (Claim 3)

[2332] A means for collecting feedback data after implementing the allocation of personnel and robots based on the proposal content;

[2333] means for updating an AI using the feedback data;

[2334] The system of claim 1 further comprising:

[2335] "Example 2: Combining Emotion Engines"

[2336] (Claim 1)

[2337] A means of collecting employee skills data;

[2338] a means of collecting employee experience data;

[2339] A means of collecting personality assessment data from employees;

[2340] A means having an AI for analyzing the skill data, the experience data, and the personality diagnostic data;

[2341] A means of collecting employee sentiment data;

[2342] a means for proposing personnel placement based on the analysis results and emotion data;

[2343] A system including:

[2344] (Claim 2)

[2345] The system of claim 1 further comprising means for displaying the suggestion.

[2346] (Claim 3)

[2347] A means for collecting feedback data after implementing personnel allocation based on the content of the proposal;

[2348] means for updating an AI using the feedback data;

[2349] The system of claim 1 further comprising:

[2350] "Application example 2 when combining emotion engines"

[2351] (Claim 1)

[2352] A means of collecting employee skills data;

[2353] a means of collecting employee experience data;

[2354] A means of collecting personality assessment data from employees;

[2355] A means of collecting employee sentiment data;

[2356] an AI module that analyzes the skill data, the experience data, the personality diagnosis data, and the emotion data;

[2357] A means for proposing optimal allocation of human resources and robots for a project and a collaborative work environment based on the analysis results;

[2358] A system including:

[2359] (Claim 2)

[2360] 10. The system of claim 1, further comprising means for displaying a proposal for optimal human resource and robot deployment.

[2361] (Claim 3)

[2362] A means for collecting feedback data after implementing the allocation of personnel and robots based on the proposal content;

[2363] means for updating an AI module using said feedback data;

[2364] The system of claim 1 further comprising: [Explanation of symbols]

[2365] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting employee skills data; a means of collecting employee experience data; A means of collecting personality assessment data from employees; A means having an AI for analyzing the skill data, the experience data, and the personality diagnostic data; A means for proposing optimal personnel allocation for projects and teams based on the analysis results; A system including:

2. The system of claim 1 further comprising means for displaying optimal staffing recommendations.

3. A means for collecting feedback data after implementing personnel allocation based on the content of the proposal; means for updating an AI using the feedback data; The system of claim 1 further comprising:

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