system

Abstract

We provide the system. [Solution] Means of collecting employee information, Means for pre-processing the employee information, A means for analyzing the appropriate allocation of employees from the pre-processed information using a generating AI, A means for generating employee placement proposals based on the aforementioned analysis results, Means for notifying employees or managers of the aforementioned placement proposal, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] It is required to balance ensuring the mobility of human resources in enterprises and improving employee satisfaction. However, conventional personnel allocation tends to rely on the judgment of personnel staff, and it has been difficult to objectively and efficiently perform optimal allocation. As a result, the skills of employees are not utilized to the maximum extent, and the opportunities for career development are limited, which may cause motivation decline and brain drain.

Means for Solving the Problems

[0005] This invention provides a system that collects employee information and uses a generating AI to analyze that information, thereby generating optimal placement suggestions for each employee. Specifically, it collects employee information such as work history, performance evaluations, and compatibility with supervisors or colleagues, preprocesses this data to prepare it, and then the generating AI performs the analysis. Based on the analysis, it proposes the optimal placement for each employee and notifies the employee or manager of this proposal. Furthermore, it collects feedback on the proposal results and continuously improves the analysis model to enhance the accuracy of the proposals. This makes it possible to optimize personnel allocation within the organization and simultaneously meet employee career development and organizational needs.

[0006] "Employee information" refers to data about employees, such as their work history, performance evaluations, and feedback from supervisors and colleagues.

[0007] "Preprocessing" refers to data processing operations performed to prepare collected employee information into a format suitable for analysis.

[0008] "Generative AI" refers to a system or algorithm that uses artificial intelligence technology to infer the appropriate placement of employees through data analysis.

[0009] "Appropriate placement" refers to assigning employees to the most effective and efficient jobs and organizational structures based on their skills and performance evaluations.

[0010] "Placement suggestions" refer to proposed workplace assignments and roles for employees, based on analysis results generated by AI.

[0011] "Feedback" refers to opinions and evaluations from employees and managers regarding proposed assignments.

[0012] An "analysis model" refers to an algorithm or calculation method used to infer the appropriate placement of employees based on employee information. [Brief explanation of the drawing]

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

[0014] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

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

[0016] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0019] In the following embodiments, a labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

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

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention is a system for optimizing personnel allocation within a company, and is implemented using a program that utilizes generative AI. The following describes how this system's program works in natural language.

[0035] The main role of this system is to propose optimal personnel allocation based on employee information, with the server, terminals, and users each fulfilling their respective roles.

[0036] Data Acquisition and Preprocessing

[0037] The server proactively collects employee information from each company within the group. This information includes work history, performance reviews, records of individual interviews, and self-reports.

[0038] The server converts the collected raw data into a format suitable for analysis, performing standardization and data interpolation as needed.

[0039] Analysis by Generative AI

[0040] The server uses a generation AI to analyze pre-processed employee information. The AI ​​model analyzes each employee's unique skill set, career aspirations, compatibility, and other factors from multiple perspectives.

[0041] The analysis results evaluate the most suitable team and position for each employee and calculate an appropriate score.

[0042] Proposal generation and notification

[0043] The server generates optimal placement suggestions based on the analysis results and creates a list of the most effective positions for each employee.

[0044] The terminal displays the proposed layout plan for the user to review. This information is accessible to both employees and administrators.

[0045] Specific example

[0046] The server performs an analysis using data from person A, who belongs to the sales department. As a result, the AI ​​suggests that person A is also suitable to be a project leader in the marketing department.

[0047] The user (in this case, Person A) reviews a proposed assignment to a marketing department project via their device and considers this option to broaden their career horizons.

[0048] This system simultaneously achieves optimal employee mobility and allocation, contributing to increased satisfaction for both companies and employees. By incorporating flexibility in proposals and a feedback loop, the analytical model is continuously updated, guaranteeing optimal results.

[0049] The following describes the processing flow.

[0050] Step 1:

[0051] Data collection

[0052] The server automatically collects employee information from each company's database. This information includes work history, past performance reviews, and self-reported information. It then creates an integrated profile for each employee.

[0053] Step 2:

[0054] Data preprocessing

[0055] The server cleans the collected data. Specifically, it handles missing values ​​and standardizes the data format to prepare it for analysis.

[0056] Step 3:

[0057] Data Analysis

[0058] The server analyzes pre-processed data using a generative AI model. It performs a multi-layered analysis of employee skill sets, job suitability, compatibility, etc., to predict the optimal placement for each employee.

[0059] Step 4:

[0060] Placement proposal generation

[0061] Based on the analysis results, the server creates placement suggestions for each employee. These suggestions include a list of optimal positions and their suitability levels.

[0062] Step 5:

[0063] proposal notification

[0064] The terminal displays placement suggestions sent from the server. The user reviews these and uses them as input for carrier selection.

[0065] Step 6:

[0066] User actions and feedback

[0067] Users (employees or managers) evaluate the suggestions and take action based on the results (such as requesting a transfer). Providing feedback to the server contributes to improving the accuracy of the AI ​​model.

[0068] Step 7:

[0069] Model update

[0070] The server incorporates feedback data into the analysis model and improves the model's accuracy through machine learning. This process continuously enables improvements in the system's proposal accuracy.

[0071] (Example 1)

[0072] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0073] Traditionally, personnel placement in companies has often relied on the subjective judgment of managers, making it difficult to achieve the right person in the right place. Furthermore, insufficient optimization of personnel placement has resulted in employees' abilities not being fully utilized, leading to a decline in overall organizational productivity.

[0074] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0075] In this invention, the server includes functional means for collecting employee information, functional means for preprocessing the information, and functional means for analyzing the appropriate placement of employees from the preprocessed data using a generating AI. This makes it possible to objectively and efficiently optimize personnel allocation within a company.

[0076] "Employee information" refers to data about individual employees working within a company, including their work history, performance evaluations, and impressions from supervisors or colleagues.

[0077] "Preprocessing" refers to the process of converting collected raw data into a format suitable for analysis, and includes tasks such as data standardization and imputation of missing values.

[0078] "Generative AI" refers to artificial intelligence models that learn from large amounts of data and predict the appropriate allocation of employees.

[0079] "Appropriate placement" refers to proposing the most suitable job or position based on the employee's skill set, career aspirations, and business needs.

[0080] An "analysis model" refers to a model that includes the computational methods and processes used by the generating AI to derive appropriate placement based on employee information.

[0081] "Suitability score" refers to an indicator that quantifies each employee's suitability for their job and position.

[0082] A "terminal" refers to a computer device used by employees or administrators to review deployment proposals generated by the system.

[0083] In order to implement this invention, a system is required in which the server, terminal, and user cooperate with each other.

[0084] The server automatically collects employee information from databases located in each department of the company via the network. This information includes employee work history, performance evaluations, and impressions from supervisors and colleagues. The server uses Python to collect this data and the Pandas library for standardization and preprocessing. If there is missing data, algorithms are used to impute the data.

[0085] The pre-processed data is input into a generative AI model using TENSORFLOW® or PyTorch to perform an analysis of appropriate placement, taking into account employee skills and career aspirations. The AI ​​model is pre-trained to support advanced analysis. A specific example of a prompt message is, "Suggest the optimal position based on the employee's skill set and career aspirations."

[0086] Based on the analysis results, the server calculates an appropriate score for each employee and generates placement suggestions based on that score. These suggestions are sent from the server to the terminal. The terminal displays the information on a dashboard via a web application. Users can use this dashboard to review their placement suggestions and provide feedback to the administrator as needed.

[0087] As a concrete example, based on data from employee A, who works in the sales department, the server performs an analysis and suggests that A is suitable to be a project leader in the marketing department. User A can then review this suggestion via their terminal and consider a new career path.

[0088] This system efficiently optimizes the allocation of personnel within a company, thereby improving the overall productivity of the organization.

[0089] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0090] Step 1:

[0091] The server collects employee information from the databases of each company connected to the network. This information includes work history, performance reviews, and impressions from supervisors and colleagues. The data is extracted using SQL queries and stored on the server as a CSV file.

[0092] Step 2:

[0093] The server reads the collected CSV files using the Python Pandas library and standardizes the data. Specifically, it imputes missing values ​​with appropriate mean values ​​and unifies data in different formats. This ensures that the data is output in a consistent format.

[0094] Step 3:

[0095] The server inputs pre-processed data into the generating AI model. First, it loads the AI ​​model using TensorFlow or PyTorch to prepare the model. Next, it inputs a prompt message into the model: "Suggest the optimal position based on the employee's skill set and career aspirations."

[0096] Step 4:

[0097] The generative AI model performs placement analysis based on preprocessed data and prompt messages. The model calculates a set of appropriate scores for each employee. This generates a list of analyzed appropriate scores, which are used to generate optimal placement suggestions.

[0098] Step 5:

[0099] The server generates the most suitable placement proposal based on the analysis results. Based on the suitability score, it generates a list of the most effective jobs for each employee and outputs this data in JSON format.

[0100] Step 6:

[0101] The terminal receives deployment proposal data in JSON format from the server and displays it through the user interface. Users can access the terminal's dashboard to view their deployment proposals, print them, or send feedback to the administrator.

[0102] (Application Example 1)

[0103] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0104] To improve factory work efficiency and reduce the frequency of breakdowns, the proper placement and allocation of roles of work machinery are crucial. However, considering the individual performance and failure history of each machine when determining the placement requires a great deal of time and effort, which is a factor that reduces the overall efficiency of the factory.

[0105] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0106] In this invention, the server includes means for collecting work machine information, means for pre-processing the work machine information, and means for analyzing the appropriate placement of the work machines from the pre-processed information using a generating AI. This enables optimal placement and role that accurately reflects the performance and failure history of each individual work machine.

[0107] "Work machine information" refers to various data on work machines operating in a factory, and specifically includes information such as work performance, failure history, and efficiency.

[0108] "Preprocessing" refers to the data manipulation required to convert collected machine information into a format suitable for analysis. This includes standardization and imputation of missing values.

[0109] "Generative AI" is a type of machine learning technology that refers to algorithms used to extract features from large amounts of data and perform new analyses. Specifically, it is used to determine the optimal placement of machinery.

[0110] "Analysis" refers to the process of evaluating the optimal placement and role of work machines based on pre-processed machine information using generative AI. This generates placement plans that take into account the performance and failure risk of each machine.

[0111] "Notification" refers to the process of communicating placement suggestions based on the analyzed results to factory managers. This allows managers to confirm efficient and effective placement and reflect it in their actions.

[0112] The system implementing this invention mainly consists of three elements: a server, terminals, and an administrator. The server is responsible for collecting information such as performance and failure history from each machine in the factory. Upon receiving this data, the server first performs preprocessing, including standardization and data cleaning, to prepare it for analysis. The preprocessed data is then input into a generative AI model, which analyzes the optimal placement and role of each machine. Through this process, the generative AI model derives an optimal placement plan.

[0113] The layout proposals obtained from the analysis are generated by the server and sent to terminals used by administrators. These terminals visualize the proposed layouts, allowing administrators to efficiently review them. Based on this, administrators can fine-tune the placement and roles of the machinery within the factory.

[0114] As a concrete example, consider a food factory where machine A is responsible for the packaging process. While highly efficient, it experiences frequent breakdowns. In this case, the generative AI model proposes operating machine A intensively for short periods, while simultaneously deploying machine B as a backup, thereby improving overall efficiency and stability.

[0115] An example of a prompt statement to input into the generated AI model is "Performance 85, Failure History 10, Efficiency 95". This prompt statement provides the AI ​​model with specific evaluation metrics and is used to calculate the optimal placement based on them.

[0116] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0117] Step 1:

[0118] The server collects information from each machine in the factory. Specifically, it automatically retrieves data such as work performance, failure history, and efficiency from the machines. The input for this step is raw data from each machine, and the output is the collected, unprocessed data.

[0119] Step 2:

[0120] The server preprocesses the collected machine data. This process includes data standardization and imputation of missing values. For example, it unifies the data format and fills in missing values ​​with estimated values ​​to prepare the data for analysis. The input for this step is raw machine data, and the output is a formatted dataset.

[0121] Step 3:

[0122] The server uses a generative AI model to analyze the optimal placement from pre-processed data. Specifically, it inputs data such as "performance 85, failure history 10, efficiency 95" as prompts into the AI ​​model and generates a suggestion for the optimal placement of each work machine. The input for this step is a formatted dataset, and the output is the suggested optimal placement of the work machines.

[0123] Step 4:

[0124] The server generates a layout proposal as an analysis result and notifies the administrator's terminal. This procedure provides information to visually display the optimal layout plan, making it easy for the administrator to review. The input for this step is the optimal layout plan for the work machines, and the output is a display of the layout proposal that the administrator can view.

[0125] Step 5:

[0126] Administrators using the terminal review the provided deployment proposals and adjust the placement of work equipment and operational policies as needed. This step provides an interface to enable administrators to make quick decisions based on the proposals. The input for this step is the deployment proposal displayed on the terminal, and the output is the deployment policy finalized by the administrator.

[0127] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0128] This invention is a system that combines generative AI and an emotion engine to optimize personnel allocation within a company. This system aims to propose the optimal placement for each individual employee, taking into account not only employee information but also user emotion data.

[0129] Data Acquisition and Preprocessing

[0130] The server acquires employee information and user sentiment data from various input devices and software. Employee information includes work history, performance data, and feedback from supervisors and colleagues, while sentiment data includes emotional states obtained through voice and facial recognition.

[0131] The server cleans and integrates this data, preparing it in a standard format necessary for analysis.

[0132] Analysis and proposal generation

[0133] The server uses generative AI to perform a multi-faceted analysis of pre-processed employee information and emotional data. The generative AI model considers the employee's skill set, character, and emotional tendencies during the analysis.

[0134] As a result, the server determines the most suitable job placement for each employee and makes placement recommendations based on this. These recommendations include detailed information about the optimal team and role, as well as a suitability score for the placement.

[0135] Proposal notification and feedback

[0136] The terminal receives deployment proposals sent from the server and displays them to the user in a visually clear and easy-to-understand manner.

[0137] Users review this and consider it as an option for job placement. They then provide feedback on the proposal and their choices, contributing to further system improvements.

[0138] Specific example

[0139] The server analyzes B's work history, performance evaluations, and self-reported questionnaires from the development department, while also collecting emotional data for B during normal times and during project progress.

[0140] Based on the analysis, the AI ​​determines that Person B performs well under stress and is therefore well-suited to be a project manager for important projects. Stress management suggestions are also provided based on emotional data.

[0141] The user reviews the proposed role and provides feedback on any possible support before accepting the role of project manager.

[0142] Thus, this invention achieves overall optimization that takes into account not only employee job performance but also their emotional well-being. By utilizing the emotional engine, it becomes possible to improve both employee satisfaction and productivity.

[0143] The following describes the processing flow.

[0144] Step 1:

[0145] Data collection

[0146] The server collects employee information and emotional data from each employee's database or device. Employee information includes work history, performance reviews, and feedback, while emotional data deals with the results of voice tone and facial expression analysis collected through the emotion engine.

[0147] Step 2:

[0148] Data preprocessing

[0149] The server performs cleaning operations on the collected data. By removing noise and missing data and standardizing it, it prepares the data for the analysis model to function effectively.

[0150] Step 3:

[0151] Emotion analysis

[0152] The server utilizes an emotion engine to analyze collected emotional data. This allows it to quantify employees' stress levels and motivation, taking into account their personality traits as well.

[0153] Step 4:

[0154] Data Analysis

[0155] The server inputs pre-processed employee information and emotional data into a generating AI model, which then performs a multidimensional analysis to determine the optimal job placement for each employee. The generating AI evaluates both the employee's skills and emotional tendencies to suggest appropriate roles and teams.

[0156] Step 5:

[0157] Proposal generation

[0158] The server generates placement suggestions based on the analysis results. These suggestions include the optimal position for each employee, a suitability score for that position, and stress management suggestions based on emotional data.

[0159] Step 6:

[0160] Distribution of proposals

[0161] The terminal displays placement suggestions received from the server via a user interface. Users can then review this information and use it to consider their job and role choices.

[0162] Step 7:

[0163] User actions and feedback

[0164] Users review the presented placement proposals and make selections that align with their career goals. They then provide feedback to the system, including their choices and opinions, contributing to improvements in the accuracy of future proposals.

[0165] Step 8:

[0166] Model improvements

[0167] The server collects feedback and uses it to update the generative AI model and the emotion engine's analysis algorithms. This allows the system to continuously improve and increase accuracy.

[0168] (Example 2)

[0169] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0170] In today's business environment, optimizing employee job assignments is crucial for improving productivity. However, traditional methods rely solely on employee skills and work experience, failing to consider emotions and preferences. This results in increased employee stress and dissatisfaction, leading to a decline in overall organizational efficiency.

[0171] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0172] In this invention, the server includes means for aggregating employee information and emotional data, means for preprocessing the employee information and emotional data, and means for using a generative AI model to comprehensively analyze the appropriate placement of employees from the preprocessed data. This makes it possible to propose appropriate job placements that also take into account the emotional state of each employee.

[0173] "Employee information" refers to detailed data about individual employees, including their work history, performance data, and workplace feedback.

[0174] "Emotional data" refers to data that indicates the emotional state of employees, collected using voice recognition and facial recognition technologies.

[0175] "Preprocessing" refers to the process of cleaning collected raw data and preparing it into a unified format.

[0176] A "generative AI model" is an artificial intelligence model that analyzes large amounts of data and generates appropriate job placement suggestions.

[0177] "Multifaceted analysis" refers to analyzing data while taking various factors into consideration, and deriving optimal conclusions from multiple perspectives.

[0178] A "prompt statement" is a sentence used to instruct a generative AI model to perform a specific task, and it serves to give instructions to the AI ​​model.

[0179] The "suitability score" is a numerical value that indicates how well-suited an employee is for a particular job, based on the results of analysis by a generative AI model.

[0180] This invention is a system for optimizing employee job assignments within a company, combining a generative AI model and an emotion engine. The system operates as follows:

[0181] Data Acquisition and Preprocessing

[0182] The server acquires employee information through the company's information systems and sensors. This information includes work history, performance data, and feedback. It also collects employee sentiment data using speech recognition and facial recognition technology.

[0183] These data are first cleaned, with duplicates removed and outliers corrected, and then formatted into a format suitable for analysis.

[0184] Data analysis and proposal generation

[0185] The server inputs pre-processed data into a generative AI model and performs multidimensional analysis. Based on the employees' skill sets and emotional tendencies, it calculates the most suitable job placement. Using prompts, the server asks the generative AI model "which employee is suitable for which role."

[0186] Based on this analysis, placement suggestions are generated along with suitability scores. These suggestions include specific job details and team composition.

[0187] Proposal notification and feedback

[0188] The terminal visualizes and notifies the user of the placement proposal. The user interface displays the presented placement proposal and its summary, designed to be easily understood by the user.

[0189] The user reviews this proposal and considers whether to apply it. The feedback is recorded in the system and used to improve the analysis algorithms of the generated AI model.

[0190] Specific example

[0191] For example, in the case of employees in the development department, appropriate placement may be determined based on past work history, performance data, and emotional data from stable project management. In this case, a prompt message such as "Based on B's work history and emotional data, please tell me the optimal role and stress management suggestion for the next project" can be input into the AI ​​model, and placement suggestions can be generated based on the results. In this way, the invention aims to improve the productivity of the entire organization by comprehensively considering the job performance and emotional state of employees.

[0192] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0193] Step 1:

[0194] The server acquires employee information and sentiment data from the company's information systems and sensors. Its inputs include employee work history, performance data, feedback, and sentiment data obtained through voice and facial recognition. Specifically, it collects necessary data from databases and cloud services to form an initial dataset.

[0195] Step 2:

[0196] The server cleans and standardizes the acquired raw data. It uses raw employee information and sentiment data as input. Specifically, it cleans the data (imputing missing values, correcting outliers) and standardizes the format, generating formatted data suitable for analysis as output.

[0197] Step 3:

[0198] The server inputs formatted data into the generating AI model and performs multi-dimensional data analysis. It creates prompt statements asking, "Which employee is best suited for which role?" Specifically, it provides the AI ​​model with data on skill sets and emotional tendencies, and makes appropriate placement decisions for each employee. The output is an analysis result that includes job placement suggestions.

[0199] Step 4:

[0200] The server generates specific placement proposals based on the analysis results. Using the analyzed data as input, it concretizes the generated job placement proposals. Specifically, it calculates team composition, role details, and suitability scores, forming the final proposal as output.

[0201] Step 5:

[0202] The terminal receives job placement proposals sent from the server and visualizes and notifies the user. It takes job placement proposals from the server as input and displays the proposals clearly on the user interface. The notified job placement proposals are presented to the user as output.

[0203] Step 6:

[0204] The user reviews the presented placement proposals and decides whether to accept or modify them. Specifically, they evaluate the proposals and input feedback into the system. This collects feedback data and outputs results that contribute to future analysis and improvement of the accuracy of the proposals.

[0205] (Application Example 2)

[0206] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0207] Current personnel placement methods make it difficult to adequately consider the emotional state and skill sets of individual workers, resulting in inefficient placement. Furthermore, there is a need for a system that utilizes emotional data to gain a more detailed understanding of workers' aptitudes and propose optimal tasks. Solving this challenge is crucial.

[0208] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0209] In this invention, the server includes means for collecting information, means for preprocessing the information, and means for analyzing the appropriate placement from the preprocessed information using a generating AI. This makes it possible to consider worker performance based on emotional data and propose the optimal task placement.

[0210] "Information" refers to all data, including the worker's career history, evaluations, evaluations from others, and emotional state.

[0211] "Preprocessing" refers to the process of preparing collected information into a format necessary for analysis.

[0212] "Generative AI" refers to an artificial intelligence system that analyzes the appropriate placement of workers based on collected information and generates proposals.

[0213] "Analysis" refers to the process of evaluating workers' skills and emotions from multiple perspectives based on pre-processed information, and deriving the optimal placement plan.

[0214] "Assignment proposals" refer to the most efficient task and job assignment plans presented to workers based on the analysis results.

[0215] "Emotional data" refers to data that indicates the emotional state of a worker, and is information acquired using technologies such as speech recognition and facial recognition.

[0216] "Feedback" refers to information used to improve the accuracy of an analysis model, based on opinions and evaluations from workers or managers.

[0217] To implement this invention, a server will take the lead in collecting, preprocessing, analyzing, and generating proposals for information. The hardware used will include a server, a voice sensor, a facial recognition camera, and a wearable device. The software used will include Python, TensorFlow (for implementing the generative AI model), and OpenCV (for facial recognition).

[0218] The server first collects career data, job evaluation data, and emotional data from workers. This emotional data includes audio data from voice sensors and image data from facial recognition cameras. The server cleans and preprocesses this collected data into a format suitable for analysis.

[0219] The generative AI model uses formatted data to comprehensively analyze workers' skill sets and emotional states. Based on this analysis, it proposes the optimal task or job assignment for each worker. These proposals include specific tasks and work content designed to maximize worker performance, and the proposals, along with an suitability score, are notified to the terminal.

[0220] The terminal visually presents the proposals, allowing users to intuitively understand the information. Users can consider the proposals, choose whether to accept them, and provide further feedback to the server, thereby contributing to the improvement of the analysis model.

[0221] For example, if a worker's stress level is determined to be low today, the generating AI may suggest relevant manufacturing tasks. This allows the worker to select tasks that are appropriate for their current state.

[0222] An example of a prompt is: "Prompt to input to the generating AI model: 'Consider the worker's emotional state and work history, and suggest the most suitable factory task.'"

[0223] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0224] Step 1:

[0225] The server collects worker history data, job evaluations, and emotional data. Specifically, it receives audio data obtained from voice sensors and image data captured by facial recognition cameras. This input data is stored in cloud storage.

[0226] Step 2:

[0227] The server cleans the collected data and preprocesses it into a format suitable for analysis. Specifically, it extracts sentiment indicators from audio data and converts facial image data into sentiment analysis data using OpenCV. The output of the preprocessing is a cleansed and standardized dataset.

[0228] Step 3:

[0229] The server inputs pre-processed data into a generating AI model to analyze the workers' skills and emotional states. Using prompts, it instructs the generating AI to "consider the workers' emotional states and work experience and suggest the most suitable factory tasks." The analysis output is the most suitable task suggestion and suitability score for each worker.

[0230] Step 4:

[0231] The server sends the generated suggestions and their suitability scores to the terminal. The specific output includes a list of tasks for the worker and their detailed information.

[0232] Step 5:

[0233] The terminal visually displays suggestions received from the server to the user. The user interface allows workers to intuitively understand the information.

[0234] Step 6:

[0235] The user reviews the suggested task on their device and chooses whether to accept it. They then use the feedback option to enter their reasons for choosing the task and any suggestions they may have, and send this information to the server.

[0236] Step 7:

[0237] The server receives user feedback and uses it to improve the analysis model of the generated AI. This aims to improve the accuracy of future suggestions. The feedback output is integrated into the training data for the next analysis model.

[0238] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0239] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search)<url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0240] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0241] [Second Embodiment]

[0242] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0243] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0244] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0245] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0246] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0247] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0248] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0249] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0250] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0251] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0252] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0253] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0254] This invention is a system for optimizing personnel allocation within a company, and is implemented using a program that utilizes generative AI. The following describes how this system's program works in natural language.

[0255] The main role of this system is to propose optimal personnel allocation based on employee information, with the server, terminals, and users each fulfilling their respective roles.

[0256] Data Acquisition and Preprocessing

[0257] The server proactively collects employee information from each company within the group. This information includes work history, performance reviews, records of individual interviews, and self-reports.

[0258] The server converts the collected raw data into a format suitable for analysis, performing standardization and data interpolation as needed.

[0259] Analysis by Generative AI

[0260] The server uses a generation AI to analyze pre-processed employee information. The AI ​​model analyzes each employee's unique skill set, career aspirations, compatibility, and other factors from multiple perspectives.

[0261] The analysis results evaluate the most suitable team and position for each employee and calculate an appropriate score.

[0262] Proposal generation and notification

[0263] The server generates optimal placement suggestions based on the analysis results and creates a list of the most effective positions for each employee.

[0264] The terminal displays the proposed layout plan for the user to review. This information is accessible to both employees and administrators.

[0265] Specific example

[0266] The server performs an analysis using data from person A, who belongs to the sales department. As a result, the AI ​​suggests that person A is also suitable to be a project leader in the marketing department.

[0267] The user (in this case, Person A) reviews a proposed assignment to a marketing department project via their device and considers this option to broaden their career horizons.

[0268] This system simultaneously achieves optimal employee mobility and allocation, contributing to increased satisfaction for both companies and employees. By incorporating flexibility in proposals and a feedback loop, the analytical model is continuously updated, guaranteeing optimal results.

[0269] The following describes the processing flow.

[0270] Step 1:

[0271] Data collection

[0272] The server automatically collects employee information from each company's database. This information includes work history, past performance reviews, and self-reported information. It then creates an integrated profile for each employee.

[0273] Step 2:

[0274] Data preprocessing

[0275] The server cleans the collected data. Specifically, it handles missing values ​​and standardizes the data format to prepare it for analysis.

[0276] Step 3:

[0277] Data Analysis

[0278] The server analyzes pre-processed data using a generative AI model. It performs a multi-layered analysis of employee skill sets, job suitability, compatibility, etc., to predict the optimal placement for each employee.

[0279] Step 4:

[0280] Placement proposal generation

[0281] Based on the analysis results, the server creates placement suggestions for each employee. These suggestions include a list of optimal positions and their suitability levels.

[0282] Step 5:

[0283] Proposal Notification

[0284] The terminal displays the placement proposal sent from the server. The user checks this and uses it as input for carrier selection.

[0285] Step 6:

[0286] User Action and Feedback

[0287] The user (employee or administrator) evaluates the proposal and takes actions (such as applying for transfer) based on the results. By providing feedback to the server, it contributes to improving the accuracy of the AI model.

[0288] Step 7:

[0289] Model Update

[0290] The server reflects the feedback data in the analysis model and improves the model's accuracy through machine learning. This process continuously enables the improvement of the system's proposal accuracy.

[0291] (Example 1)

[0292] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0293] In the past, personnel placement in enterprises often relied on the subjective judgment of managers, and there was a problem that it was difficult to achieve the right person in the right place. Also, if the optimization of placement was insufficient, the abilities of personnel could not be fully exerted, and there was also a problem that the productivity of the entire organization decreased.

[0294] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following respective means.

[0295] In this invention, the server includes functional means for collecting employee information, functional means for preprocessing the information, and functional means for analyzing the appropriate placement of employees from the preprocessed data using a generating AI. This makes it possible to objectively and efficiently optimize personnel allocation within a company.

[0296] "Employee information" refers to data about individual employees working within a company, including their work history, performance evaluations, and impressions from supervisors or colleagues.

[0297] "Preprocessing" refers to the process of converting collected raw data into a format suitable for analysis, and includes tasks such as data standardization and imputation of missing values.

[0298] "Generative AI" refers to artificial intelligence models that learn from large amounts of data and predict the appropriate allocation of employees.

[0299] "Appropriate placement" refers to proposing the most suitable job or position based on the employee's skill set, career aspirations, and business needs.

[0300] An "analysis model" refers to a model that includes the computational methods and processes used by the generating AI to derive appropriate placement based on employee information.

[0301] "Suitability score" refers to an indicator that quantifies each employee's suitability for their job and position.

[0302] A "terminal" refers to a computer device used by employees or administrators to review deployment proposals generated by the system.

[0303] In order to implement this invention, a system is required in which the server, terminal, and user cooperate with each other.

[0304] The server automatically collects employee information from databases deployed in each department of the company via the network. This information includes employees' work histories, performance evaluations, and impressions from supervisors and colleagues. The server uses Python to collect this data and performs standardization and preprocessing using the Pandas library. If there is missing data, algorithms are utilized to complement the data.

[0305] The preprocessed data is input into a generative AI model using TensorFlow or PyTorch, and an analysis of proper placement is performed considering employees' skills and career aspirations. The AI model has been pre-trained and supports advanced analysis. Specific examples of prompt texts include things like "Propose the optimal position based on the employee's skill set and career intentions."

[0306] As the analysis result, the server calculates an appropriate score for each employee and generates placement proposals based on it. These proposals are sent from the server to the terminal. The terminal displays the information on a dashboard through a web application. The user can use this dashboard to view their placement proposals and provide feedback to the administrator if necessary.

[0307] As a specific example, based on the data of employee A who works in the sales department, the server conducts an analysis and proposes that A is suitable as a project leader in the marketing department. Employee A, who is the user, can view this proposal through the terminal and consider a new career path.

[0308] This system efficiently performs the optimal placement of human resources within the company, thereby improving the productivity of the entire organization.

[0309] The flow of the specific process in Example 1 will be described using Figure 11.

[0310] Step 1:

[0311] The server collects employee information from the databases of each company connected to the network. This information includes work history, performance reviews, and impressions from supervisors and colleagues. The data is extracted using SQL queries and stored on the server as a CSV file.

[0312] Step 2:

[0313] The server reads the collected CSV files using the Python Pandas library and standardizes the data. Specifically, it imputes missing values ​​with appropriate mean values ​​and unifies data in different formats. This ensures that the data is output in a consistent format.

[0314] Step 3:

[0315] The server inputs pre-processed data into the generating AI model. First, it loads the AI ​​model using TensorFlow or PyTorch to prepare the model. Next, it inputs a prompt message into the model: "Suggest the optimal position based on the employee's skill set and career aspirations."

[0316] Step 4:

[0317] The generative AI model performs placement analysis based on preprocessed data and prompt messages. The model calculates a set of appropriate scores for each employee. This generates a list of analyzed appropriate scores, which are used to generate optimal placement suggestions.

[0318] Step 5:

[0319] The server generates the most suitable placement proposal based on the analysis results. Based on the suitability score, it generates a list of the most effective jobs for each employee and outputs this data in JSON format.

[0320] Step 6:

[0321] The terminal receives deployment proposal data in JSON format from the server and displays it through the user interface. Users can access the terminal's dashboard to view their deployment proposals, print them, or send feedback to the administrator.

[0322] (Application Example 1)

[0323] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0324] To improve factory work efficiency and reduce the frequency of breakdowns, the proper placement and allocation of roles of work machinery are crucial. However, considering the individual performance and failure history of each machine when determining the placement requires a great deal of time and effort, which is a factor that reduces the overall efficiency of the factory.

[0325] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0326] In this invention, the server includes means for collecting work machine information, means for pre-processing the work machine information, and means for analyzing the appropriate placement of the work machines from the pre-processed information using a generating AI. This enables optimal placement and role that accurately reflects the performance and failure history of each individual work machine.

[0327] "Work machine information" refers to various data on work machines operating in a factory, and specifically includes information such as work performance, failure history, and efficiency.

[0328] "Preprocessing" refers to the data manipulation required to convert collected machine information into a format suitable for analysis. This includes standardization and imputation of missing values.

[0329] "Generative AI" is a type of machine learning technology that refers to algorithms used to extract features from large amounts of data and perform new analyses. Specifically, it is used to determine the optimal placement of machinery.

[0330] "Analysis" refers to the process of evaluating the optimal placement and role of work machines based on pre-processed machine information using generative AI. This generates placement plans that take into account the performance and failure risk of each machine.

[0331] "Notification" refers to the process of communicating placement suggestions based on the analyzed results to factory managers. This allows managers to confirm efficient and effective placement and reflect it in their actions.

[0332] The system implementing this invention mainly consists of three elements: a server, terminals, and an administrator. The server is responsible for collecting information such as performance and failure history from each machine in the factory. Upon receiving this data, the server first performs preprocessing, including standardization and data cleaning, to prepare it for analysis. The preprocessed data is then input into a generative AI model, which analyzes the optimal placement and role of each machine. Through this process, the generative AI model derives an optimal placement plan.

[0333] The layout proposals obtained from the analysis are generated by the server and sent to terminals used by administrators. These terminals visualize the proposed layouts, allowing administrators to efficiently review them. Based on this, administrators can fine-tune the placement and roles of the machinery within the factory.

[0334] As a concrete example, consider a food factory where machine A is responsible for the packaging process. While highly efficient, it experiences frequent breakdowns. In this case, the generative AI model proposes operating machine A intensively for short periods, while simultaneously deploying machine B as a backup, thereby improving overall efficiency and stability.

[0335] An example of a prompt statement to input into the generated AI model is "Performance 85, Failure History 10, Efficiency 95". This prompt statement provides the AI ​​model with specific evaluation metrics and is used to calculate the optimal placement based on them.

[0336] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0337] Step 1:

[0338] The server collects information from each machine in the factory. Specifically, it automatically retrieves data such as work performance, failure history, and efficiency from the machines. The input for this step is raw data from each machine, and the output is the collected, unprocessed data.

[0339] Step 2:

[0340] The server preprocesses the collected machine data. This process includes data standardization and imputation of missing values. For example, it unifies the data format and fills in missing values ​​with estimated values ​​to prepare the data for analysis. The input for this step is raw machine data, and the output is a formatted dataset.

[0341] Step 3:

[0342] The server uses a generative AI model to analyze the optimal placement from pre-processed data. Specifically, it inputs data such as "performance 85, failure history 10, efficiency 95" as prompts into the AI ​​model and generates a suggestion for the optimal placement of each work machine. The input for this step is a formatted dataset, and the output is the suggested optimal placement of the work machines.

[0343] Step 4:

[0344] The server generates a layout proposal as an analysis result and notifies the administrator's terminal. This procedure provides information to visually display the optimal layout plan, making it easy for the administrator to review. The input for this step is the optimal layout plan for the work machines, and the output is a display of the layout proposal that the administrator can view.

[0345] Step 5:

[0346] Administrators using the terminal review the provided deployment proposals and adjust the placement of work equipment and operational policies as needed. This step provides an interface to enable administrators to make quick decisions based on the proposals. The input for this step is the deployment proposal displayed on the terminal, and the output is the deployment policy finalized by the administrator.

[0347] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0348] This invention is a system that combines generative AI and an emotion engine to optimize personnel allocation within a company. This system aims to propose the optimal placement for each individual employee, taking into account not only employee information but also user emotion data.

[0349] Data Acquisition and Preprocessing

[0350] The server acquires employee information and user sentiment data from various input devices and software. Employee information includes work history, performance data, and feedback from supervisors and colleagues, while sentiment data includes emotional states obtained through voice and facial recognition.

[0351] The server cleans and integrates this data, preparing it in a standard format necessary for analysis.

[0352] Analysis and proposal generation

[0353] The server uses generative AI to perform a multi-faceted analysis of pre-processed employee information and emotional data. The generative AI model considers the employee's skill set, character, and emotional tendencies during the analysis.

[0354] As a result, the server determines the most suitable job placement for each employee and makes placement recommendations based on this. These recommendations include detailed information about the optimal team and role, as well as a suitability score for the placement.

[0355] Proposal notification and feedback

[0356] The terminal receives deployment proposals sent from the server and displays them to the user in a visually clear and easy-to-understand manner.

[0357] Users review this and consider it as an option for job placement. They then provide feedback on the proposal and their choices, contributing to further system improvements.

[0358] Specific example

[0359] The server analyzes B's work history, performance evaluations, and self-reported questionnaires from the development department, while also collecting emotional data for B during normal times and during project progress.

[0360] Based on the analysis, the AI ​​determines that Person B performs well under stress and is therefore well-suited to be a project manager for important projects. Stress management suggestions are also provided based on emotional data.

[0361] The user reviews the proposed role and provides feedback on any possible support before accepting the role of project manager.

[0362] Thus, this invention achieves overall optimization that takes into account not only employee job performance but also their emotional well-being. By utilizing the emotional engine, it becomes possible to improve both employee satisfaction and productivity.

[0363] The following describes the processing flow.

[0364] Step 1:

[0365] Data collection

[0366] The server collects employee information and emotional data from each employee's database or device. Employee information includes work history, performance reviews, and feedback, while emotional data deals with the results of voice tone and facial expression analysis collected through the emotion engine.

[0367] Step 2:

[0368] Data preprocessing

[0369] The server performs cleaning operations on the collected data. By removing noise and missing data and standardizing it, it prepares the data for the analysis model to function effectively.

[0370] Step 3:

[0371] Emotion analysis

[0372] The server utilizes an emotion engine to analyze collected emotional data. This allows it to quantify employees' stress levels and motivation, taking into account their personality traits as well.

[0373] Step 4:

[0374] Data Analysis

[0375] The server inputs pre-processed employee information and emotional data into a generating AI model, which then performs a multidimensional analysis to determine the optimal job placement for each employee. The generating AI evaluates both the employee's skills and emotional tendencies to suggest appropriate roles and teams.

[0376] Step 5:

[0377] Proposal generation

[0378] The server generates placement suggestions based on the analysis results. These suggestions include the optimal position for each employee, a suitability score for that position, and stress management suggestions based on emotional data.

[0379] Step 6:

[0380] Distribution of proposals

[0381] The terminal displays placement suggestions received from the server via a user interface. Users can then review this information and use it to consider their job and role choices.

[0382] Step 7:

[0383] User actions and feedback

[0384] Users review the presented placement proposals and make selections that align with their career goals. They then provide feedback to the system, including their choices and opinions, contributing to improvements in the accuracy of future proposals.

[0385] Step 8:

[0386] Model improvements

[0387] The server collects feedback and uses it to update the generative AI model and the emotion engine's analysis algorithms. This allows the system to continuously improve and increase accuracy.

[0388] (Example 2)

[0389] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0390] In today's business environment, optimizing employee job assignments is crucial for improving productivity. However, traditional methods rely solely on employee skills and work experience, failing to consider emotions and preferences. This results in increased employee stress and dissatisfaction, leading to a decline in overall organizational efficiency.

[0391] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0392] In this invention, the server includes means for aggregating employee information and emotional data, means for preprocessing the employee information and emotional data, and means for using a generative AI model to comprehensively analyze the appropriate placement of employees from the preprocessed data. This makes it possible to propose appropriate job placements that also take into account the emotional state of each employee.

[0393] "Employee information" refers to detailed data about individual employees, including their work history, performance data, and workplace feedback.

[0394] "Emotional data" refers to data that indicates the emotional state of employees, collected using voice recognition and facial recognition technologies.

[0395] "Preprocessing" refers to the process of cleaning collected raw data and preparing it into a unified format.

[0396] A "generative AI model" is an artificial intelligence model that analyzes large amounts of data and generates appropriate job placement suggestions.

[0397] "Multifaceted analysis" refers to analyzing data while taking various factors into consideration, and deriving optimal conclusions from multiple perspectives.

[0398] A "prompt statement" is a sentence used to instruct a generative AI model to perform a specific task, and it serves to give instructions to the AI ​​model.

[0399] The "suitability score" is a numerical value that indicates how well-suited an employee is for a particular job, based on the results of analysis by a generative AI model.

[0400] This invention is a system for optimizing employee job assignments within a company, combining a generative AI model and an emotion engine. The system operates as follows:

[0401] Data Acquisition and Preprocessing

[0402] The server acquires employee information through the company's information systems and sensors. This information includes work history, performance data, and feedback. It also collects employee sentiment data using speech recognition and facial recognition technology.

[0403] These data are first cleaned, with duplicates removed and outliers corrected, and then formatted into a format suitable for analysis.

[0404] Data analysis and proposal generation

[0405] The server inputs pre-processed data into a generative AI model and performs multidimensional analysis. Based on the employees' skill sets and emotional tendencies, it calculates the most suitable job placement. Using prompts, the server asks the generative AI model "which employee is suitable for which role."

[0406] Based on this analysis, placement suggestions are generated along with suitability scores. These suggestions include specific job details and team composition.

[0407] Proposal notification and feedback

[0408] The terminal visualizes and notifies the user of the placement proposal. The user interface displays the presented placement proposal and its summary, designed to be easily understood by the user.

[0409] The user reviews this proposal and considers whether to apply it. The feedback is recorded in the system and used to improve the analysis algorithms of the generated AI model.

[0410] Specific example

[0411] For example, in the case of employees in the development department, appropriate placement may be determined based on past work history, performance data, and emotional data from stable project management. In this case, a prompt message such as "Based on B's work history and emotional data, please tell me the optimal role and stress management suggestion for the next project" can be input into the AI ​​model, and placement suggestions can be generated based on the results. In this way, the invention aims to improve the productivity of the entire organization by comprehensively considering the job performance and emotional state of employees.

[0412] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0413] Step 1:

[0414] The server acquires employee information and sentiment data from the company's information systems and sensors. Its inputs include employee work history, performance data, feedback, and sentiment data obtained through voice and facial recognition. Specifically, it collects necessary data from databases and cloud services to form an initial dataset.

[0415] Step 2:

[0416] The server cleans and standardizes the acquired raw data. It uses raw employee information and sentiment data as input. Specifically, it cleans the data (imputing missing values, correcting outliers) and standardizes the format, generating formatted data suitable for analysis as output.

[0417] Step 3:

[0418] The server inputs formatted data into the generating AI model and performs multi-dimensional data analysis. It creates prompt statements asking, "Which employee is best suited for which role?" Specifically, it provides the AI ​​model with data on skill sets and emotional tendencies, and makes appropriate placement decisions for each employee. The output is an analysis result that includes job placement suggestions.

[0419] Step 4:

[0420] The server generates specific placement proposals based on the analysis results. Using the analyzed data as input, it concretizes the generated job placement proposals. Specifically, it calculates team composition, role details, and suitability scores, forming the final proposal as output.

[0421] Step 5:

[0422] The terminal receives job placement proposals sent from the server and visualizes and notifies the user. It takes job placement proposals from the server as input and displays the proposals clearly on the user interface. The notified job placement proposals are presented to the user as output.

[0423] Step 6:

[0424] The user reviews the presented placement proposals and decides whether to accept or modify them. Specifically, they evaluate the proposals and input feedback into the system. This collects feedback data and outputs results that contribute to future analysis and improvement of the accuracy of the proposals.

[0425] (Application Example 2)

[0426] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0427] Current personnel placement methods make it difficult to adequately consider the emotional state and skill sets of individual workers, resulting in inefficient placement. Furthermore, there is a need for a system that utilizes emotional data to gain a more detailed understanding of workers' aptitudes and propose optimal tasks. Solving this challenge is crucial.

[0428] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0429] In this invention, the server includes means for collecting information, means for preprocessing the information, and means for analyzing the appropriate placement from the preprocessed information using a generating AI. This makes it possible to consider worker performance based on emotional data and propose the optimal task placement.

[0430] "Information" refers to all data, including the worker's career history, evaluations, evaluations from others, and emotional state.

[0431] "Preprocessing" refers to the process of preparing collected information into a format necessary for analysis.

[0432] "Generative AI" refers to an artificial intelligence system that analyzes the appropriate placement of workers based on collected information and generates proposals.

[0433] "Analysis" refers to the process of evaluating workers' skills and emotions from multiple perspectives based on pre-processed information, and deriving the optimal placement plan.

[0434] "Assignment proposals" refer to the most efficient task and job assignment plans presented to workers based on the analysis results.

[0435] "Emotional data" refers to data that indicates the emotional state of a worker, and is information acquired using technologies such as speech recognition and facial recognition.

[0436] "Feedback" refers to information used to improve the accuracy of an analysis model, based on opinions and evaluations from workers or managers.

[0437] To implement this invention, a server will take the lead in collecting, preprocessing, analyzing, and generating proposals for information. The hardware used will include a server, a voice sensor, a facial recognition camera, and a wearable device. The software used will include Python, TensorFlow (for implementing the generative AI model), and OpenCV (for facial recognition).

[0438] The server first collects career data, job evaluation data, and emotional data from workers. This emotional data includes audio data from voice sensors and image data from facial recognition cameras. The server cleans and preprocesses this collected data into a format suitable for analysis.

[0439] The generative AI model uses formatted data to comprehensively analyze workers' skill sets and emotional states. Based on this analysis, it proposes the optimal task or job assignment for each worker. These proposals include specific tasks and work content designed to maximize worker performance, and the proposals, along with an suitability score, are notified to the terminal.

[0440] The terminal visually presents the proposals, allowing users to intuitively understand the information. Users can consider the proposals, choose whether to accept them, and provide further feedback to the server, thereby contributing to the improvement of the analysis model.

[0441] For example, if a worker's stress level is determined to be low today, the generating AI may suggest relevant manufacturing tasks. This allows the worker to select tasks that are appropriate for their current state.

[0442] An example of a prompt is: "Prompt to input to the generating AI model: 'Consider the worker's emotional state and work history, and suggest the most suitable factory task.'"

[0443] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0444] Step 1:

[0445] The server collects worker history data, job evaluations, and emotional data. Specifically, it receives audio data obtained from voice sensors and image data captured by facial recognition cameras. This input data is stored in cloud storage.

[0446] Step 2:

[0447] The server cleans the collected data and preprocesses it into a format suitable for analysis. Specifically, it extracts sentiment indicators from audio data and converts facial image data into sentiment analysis data using OpenCV. The output of the preprocessing is a cleansed and standardized dataset.

[0448] Step 3:

[0449] The server inputs pre-processed data into a generating AI model to analyze the workers' skills and emotional states. Using prompts, it instructs the generating AI to "consider the workers' emotional states and work experience and suggest the most suitable factory tasks." The analysis output is the most suitable task suggestion and suitability score for each worker.

[0450] Step 4:

[0451] The server sends the generated suggestions and their suitability scores to the terminal. The specific output includes a list of tasks for the worker and their detailed information.

[0452] Step 5:

[0453] The terminal visually displays suggestions received from the server to the user. The user interface allows workers to intuitively understand the information.

[0454] Step 6:

[0455] The user reviews the suggested task on their device and chooses whether to accept it. They then use the feedback option to enter their reasons for choosing the task and any suggestions they may have, and send this information to the server.

[0456] Step 7:

[0457] The server receives user feedback and uses it to improve the analysis model of the generated AI. This aims to improve the accuracy of future suggestions. The feedback output is integrated into the training data for the next analysis model.

[0458] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0459] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0460] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0461] [Third Embodiment]

[0462] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0463] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0464] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0465] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0466] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0467] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0468] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0469] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0470] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0471] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0472] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0473] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0474] This invention is a system for optimizing personnel allocation within a company, and is implemented using a program that utilizes generative AI. The following describes how this system's program works in natural language.

[0475] The main role of this system is to propose optimal personnel allocation based on employee information, with the server, terminals, and users each fulfilling their respective roles.

[0476] Data Acquisition and Preprocessing

[0477] The server proactively collects employee information from each company within the group. This information includes work history, performance reviews, records of individual interviews, and self-reports.

[0478] The server converts the collected raw data into a format suitable for analysis, performing standardization and data interpolation as needed.

[0479] Analysis by Generative AI

[0480] The server uses a generation AI to analyze pre-processed employee information. The AI ​​model analyzes each employee's unique skill set, career aspirations, compatibility, and other factors from multiple perspectives.

[0481] The analysis results evaluate the most suitable team and position for each employee and calculate an appropriate score.

[0482] Proposal generation and notification

[0483] The server generates optimal placement suggestions based on the analysis results and creates a list of the most effective positions for each employee.

[0484] The terminal displays the proposed layout plan for the user to review. This information is accessible to both employees and administrators.

[0485] Specific example

[0486] The server performs an analysis using data from person A, who belongs to the sales department. As a result, the AI ​​suggests that person A is also suitable to be a project leader in the marketing department.

[0487] The user (in this case, Person A) reviews a proposed assignment to a marketing department project via their device and considers this option to broaden their career horizons.

[0488] This system simultaneously achieves optimal employee mobility and allocation, contributing to increased satisfaction for both companies and employees. By incorporating flexibility in proposals and a feedback loop, the analytical model is continuously updated, guaranteeing optimal results.

[0489] The following describes the processing flow.

[0490] Step 1:

[0491] Data collection

[0492] The server automatically collects employee information from each company's database. This information includes work history, past performance reviews, and self-reported information. It then creates an integrated profile for each employee.

[0493] Step 2:

[0494] Data preprocessing

[0495] The server cleans the collected data. Specifically, it handles missing values ​​and standardizes the data format to prepare it for analysis.

[0496] Step 3:

[0497] Data Analysis

[0498] The server analyzes pre-processed data using a generative AI model. It performs a multi-layered analysis of employee skill sets, job suitability, compatibility, etc., to predict the optimal placement for each employee.

[0499] Step 4:

[0500] Placement proposal generation

[0501] Based on the analysis results, the server creates placement suggestions for each employee. These suggestions include a list of optimal positions and their suitability levels.

[0502] Step 5:

[0503] proposal notification

[0504] The terminal displays placement suggestions sent from the server. The user reviews these and uses them as input for carrier selection.

[0505] Step 6:

[0506] User actions and feedback

[0507] Users (employees or managers) evaluate the suggestions and take action based on the results (such as requesting a transfer). Providing feedback to the server contributes to improving the accuracy of the AI ​​model.

[0508] Step 7:

[0509] Model update

[0510] The server incorporates feedback data into the analysis model and improves the model's accuracy through machine learning. This process continuously enables improvements in the system's proposal accuracy.

[0511] (Example 1)

[0512] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0513] Traditionally, personnel placement in companies has often relied on the subjective judgment of managers, making it difficult to achieve the right person in the right place. Furthermore, insufficient optimization of personnel placement has resulted in employees' abilities not being fully utilized, leading to a decline in overall organizational productivity.

[0514] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0515] In this invention, the server includes functional means for collecting employee information, functional means for preprocessing the information, and functional means for analyzing the appropriate placement of employees from the preprocessed data using a generating AI. This makes it possible to objectively and efficiently optimize personnel allocation within a company.

[0516] "Employee information" refers to data about individual employees working within a company, including their work history, performance evaluations, and impressions from supervisors or colleagues.

[0517] "Preprocessing" refers to the process of converting collected raw data into a format suitable for analysis, and includes tasks such as data standardization and imputation of missing values.

[0518] "Generative AI" refers to artificial intelligence models that learn from large amounts of data and predict the appropriate allocation of employees.

[0519] "Appropriate placement" refers to proposing the most suitable job or position based on the employee's skill set, career aspirations, and business needs.

[0520] An "analysis model" refers to a model that includes the computational methods and processes used by the generating AI to derive appropriate placement based on employee information.

[0521] "Suitability score" refers to an indicator that quantifies each employee's suitability for their job and position.

[0522] A "terminal" refers to a computer device used by employees or administrators to review deployment proposals generated by the system.

[0523] In order to implement this invention, a system is required in which the server, terminal, and user cooperate with each other.

[0524] The server automatically collects employee information from databases located in each department of the company via the network. This information includes employee work history, performance evaluations, and impressions from supervisors and colleagues. The server uses Python to collect this data and the Pandas library for standardization and preprocessing. If there is missing data, algorithms are used to impute the data.

[0525] The pre-processed data is input into a generative AI model using TensorFlow or PyTorch, which performs an analysis of appropriate placement, taking into account employee skills and career aspirations. The AI ​​model is pre-trained to support advanced analysis. A specific example of a prompt message is, "Suggest the optimal position based on the employee's skill set and career aspirations."

[0526] Based on the analysis results, the server calculates an appropriate score for each employee and generates placement suggestions based on that score. These suggestions are sent from the server to the terminal. The terminal displays the information on a dashboard via a web application. Users can use this dashboard to review their placement suggestions and provide feedback to the administrator as needed.

[0527] As a concrete example, based on data from employee A, who works in the sales department, the server performs an analysis and suggests that A is suitable to be a project leader in the marketing department. User A can then review this suggestion via their terminal and consider a new career path.

[0528] This system efficiently optimizes the allocation of personnel within a company, thereby improving the overall productivity of the organization.

[0529] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0530] Step 1:

[0531] The server collects employee information from the databases of each company connected to the network. This information includes work history, performance reviews, and impressions from supervisors and colleagues. The data is extracted using SQL queries and stored on the server as a CSV file.

[0532] Step 2:

[0533] The server reads the collected CSV files using the Python Pandas library and standardizes the data. Specifically, it imputes missing values ​​with appropriate mean values ​​and unifies data in different formats. This ensures that the data is output in a consistent format.

[0534] Step 3:

[0535] The server inputs pre-processed data into the generating AI model. First, it loads the AI ​​model using TensorFlow or PyTorch to prepare the model. Next, it inputs a prompt message into the model: "Suggest the optimal position based on the employee's skill set and career aspirations."

[0536] Step 4:

[0537] The generative AI model performs placement analysis based on preprocessed data and prompt messages. The model calculates a set of appropriate scores for each employee. This generates a list of analyzed appropriate scores, which are used to generate optimal placement suggestions.

[0538] Step 5:

[0539] The server generates the most suitable placement proposal based on the analysis results. Based on the suitability score, it generates a list of the most effective jobs for each employee and outputs this data in JSON format.

[0540] Step 6:

[0541] The terminal receives deployment proposal data in JSON format from the server and displays it through the user interface. Users can access the terminal's dashboard to view their deployment proposals, print them, or send feedback to the administrator.

[0542] (Application Example 1)

[0543] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0544] To improve factory work efficiency and reduce the frequency of breakdowns, the proper placement and allocation of roles of work machinery are crucial. However, considering the individual performance and failure history of each machine when determining the placement requires a great deal of time and effort, which is a factor that reduces the overall efficiency of the factory.

[0545] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0546] In this invention, the server includes means for collecting work machine information, means for pre-processing the work machine information, and means for analyzing the appropriate placement of the work machines from the pre-processed information using a generating AI. This enables optimal placement and role that accurately reflects the performance and failure history of each individual work machine.

[0547] "Work machine information" refers to various data on work machines operating in a factory, and specifically includes information such as work performance, failure history, and efficiency.

[0548] "Preprocessing" refers to the data manipulation required to convert collected machine information into a format suitable for analysis. This includes standardization and imputation of missing values.

[0549] "Generative AI" is a type of machine learning technology that refers to algorithms used to extract features from large amounts of data and perform new analyses. Specifically, it is used to determine the optimal placement of machinery.

[0550] "Analysis" refers to the process of evaluating the optimal placement and role of work machines based on pre-processed machine information using generative AI. This generates placement plans that take into account the performance and failure risk of each machine.

[0551] "Notification" refers to the process of communicating placement suggestions based on the analyzed results to factory managers. This allows managers to confirm efficient and effective placement and reflect it in their actions.

[0552] The system implementing this invention mainly consists of three elements: a server, terminals, and an administrator. The server is responsible for collecting information such as performance and failure history from each machine in the factory. Upon receiving this data, the server first performs preprocessing, including standardization and data cleaning, to prepare it for analysis. The preprocessed data is then input into a generative AI model, which analyzes the optimal placement and role of each machine. Through this process, the generative AI model derives an optimal placement plan.

[0553] The layout proposals obtained from the analysis are generated by the server and sent to terminals used by administrators. These terminals visualize the proposed layouts, allowing administrators to efficiently review them. Based on this, administrators can fine-tune the placement and roles of the machinery within the factory.

[0554] As a concrete example, consider a food factory where machine A is responsible for the packaging process. While highly efficient, it experiences frequent breakdowns. In this case, the generative AI model proposes operating machine A intensively for short periods, while simultaneously deploying machine B as a backup, thereby improving overall efficiency and stability.

[0555] An example of a prompt statement to input into the generated AI model is "Performance 85, Failure History 10, Efficiency 95". This prompt statement provides the AI ​​model with specific evaluation metrics and is used to calculate the optimal placement based on them.

[0556] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0557] Step 1:

[0558] The server collects information from each machine in the factory. Specifically, it automatically retrieves data such as work performance, failure history, and efficiency from the machines. The input for this step is raw data from each machine, and the output is the collected, unprocessed data.

[0559] Step 2:

[0560] The server preprocesses the collected machine data. This process includes data standardization and imputation of missing values. For example, it unifies the data format and fills in missing values ​​with estimated values ​​to prepare the data for analysis. The input for this step is raw machine data, and the output is a formatted dataset.

[0561] Step 3:

[0562] The server uses a generative AI model to analyze the optimal placement from pre-processed data. Specifically, it inputs data such as "performance 85, failure history 10, efficiency 95" as prompts into the AI ​​model and generates a suggestion for the optimal placement of each work machine. The input for this step is a formatted dataset, and the output is the suggested optimal placement of the work machines.

[0563] Step 4:

[0564] The server generates a layout proposal as an analysis result and notifies the administrator's terminal. This procedure provides information to visually display the optimal layout plan, making it easy for the administrator to review. The input for this step is the optimal layout plan for the work machines, and the output is a display of the layout proposal that the administrator can view.

[0565] Step 5:

[0566] Administrators using the terminal review the provided deployment proposals and adjust the placement of work equipment and operational policies as needed. This step provides an interface to enable administrators to make quick decisions based on the proposals. The input for this step is the deployment proposal displayed on the terminal, and the output is the deployment policy finalized by the administrator.

[0567] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0568] This invention is a system that combines generative AI and an emotion engine to optimize personnel allocation within a company. This system aims to propose the optimal placement for each individual employee, taking into account not only employee information but also user emotion data.

[0569] Data Acquisition and Preprocessing

[0570] The server acquires employee information and user sentiment data from various input devices and software. Employee information includes work history, performance data, and feedback from supervisors and colleagues, while sentiment data includes emotional states obtained through voice and facial recognition.

[0571] The server cleans and integrates this data, preparing it in a standard format necessary for analysis.

[0572] Analysis and proposal generation

[0573] The server uses generative AI to perform a multi-faceted analysis of pre-processed employee information and emotional data. The generative AI model considers the employee's skill set, character, and emotional tendencies during the analysis.

[0574] As a result, the server determines the most suitable job placement for each employee and makes placement recommendations based on this. These recommendations include detailed information about the optimal team and role, as well as a suitability score for the placement.

[0575] Proposal notification and feedback

[0576] The terminal receives deployment proposals sent from the server and displays them to the user in a visually clear and easy-to-understand manner.

[0577] Users review this and consider it as an option for job placement. They then provide feedback on the proposal and their choices, contributing to further system improvements.

[0578] Specific example

[0579] The server analyzes B's work history, performance evaluations, and self-reported questionnaires from the development department, while also collecting emotional data for B during normal times and during project progress.

[0580] Based on the analysis, the AI ​​determines that Person B performs well under stress and is therefore well-suited to be a project manager for important projects. Stress management suggestions are also provided based on emotional data.

[0581] The user reviews the proposed role and provides feedback on any possible support before accepting the role of project manager.

[0582] Thus, this invention achieves overall optimization that takes into account not only employee job performance but also their emotional well-being. By utilizing the emotional engine, it becomes possible to improve both employee satisfaction and productivity.

[0583] The following describes the processing flow.

[0584] Step 1:

[0585] Data collection

[0586] The server collects employee information and emotional data from each employee's database or device. Employee information includes work history, performance reviews, and feedback, while emotional data deals with the results of voice tone and facial expression analysis collected through the emotion engine.

[0587] Step 2:

[0588] Data preprocessing

[0589] The server performs cleaning operations on the collected data. By removing noise and missing data and standardizing it, it prepares the data for the analysis model to function effectively.

[0590] Step 3:

[0591] Emotion analysis

[0592] The server utilizes an emotion engine to analyze collected emotional data. This allows it to quantify employees' stress levels and motivation, taking into account their personality traits as well.

[0593] Step 4:

[0594] Data Analysis

[0595] The server inputs pre-processed employee information and emotional data into a generating AI model, which then performs a multidimensional analysis to determine the optimal job placement for each employee. The generating AI evaluates both the employee's skills and emotional tendencies to suggest appropriate roles and teams.

[0596] Step 5:

[0597] Proposal generation

[0598] The server generates placement suggestions based on the analysis results. These suggestions include the optimal position for each employee, a suitability score for that position, and stress management suggestions based on emotional data.

[0599] Step 6:

[0600] Distribution of proposals

[0601] The terminal displays placement suggestions received from the server via a user interface. Users can then review this information and use it to consider their job and role choices.

[0602] Step 7:

[0603] User actions and feedback

[0604] Users review the presented placement proposals and make selections that align with their career goals. They then provide feedback to the system, including their choices and opinions, contributing to improvements in the accuracy of future proposals.

[0605] Step 8:

[0606] Model improvements

[0607] The server collects feedback and uses it to update the generative AI model and the emotion engine's analysis algorithms. This allows the system to continuously improve and increase accuracy.

[0608] (Example 2)

[0609] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0610] In today's business environment, optimizing employee job assignments is crucial for improving productivity. However, traditional methods rely solely on employee skills and work experience, failing to consider emotions and preferences. This results in increased employee stress and dissatisfaction, leading to a decline in overall organizational efficiency.

[0611] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0612] In this invention, the server includes means for aggregating employee information and emotional data, means for preprocessing the employee information and emotional data, and means for using a generative AI model to comprehensively analyze the appropriate placement of employees from the preprocessed data. This makes it possible to propose appropriate job placements that also take into account the emotional state of each employee.

[0613] "Employee information" refers to detailed data about individual employees, including their work history, performance data, and workplace feedback.

[0614] "Emotional data" refers to data that indicates the emotional state of employees, collected using voice recognition and facial recognition technologies.

[0615] "Preprocessing" refers to the process of cleaning collected raw data and preparing it into a unified format.

[0616] A "generative AI model" is an artificial intelligence model that analyzes large amounts of data and generates appropriate job placement suggestions.

[0617] "Multifaceted analysis" refers to analyzing data while taking various factors into consideration, and deriving optimal conclusions from multiple perspectives.

[0618] A "prompt statement" is a sentence used to instruct a generative AI model to perform a specific task, and it serves to give instructions to the AI ​​model.

[0619] The "suitability score" is a numerical value that indicates how well-suited an employee is for a particular job, based on the results of analysis by a generative AI model.

[0620] This invention is a system for optimizing employee job assignments within a company, combining a generative AI model and an emotion engine. The system operates as follows:

[0621] Data Acquisition and Preprocessing

[0622] The server acquires employee information through the company's information systems and sensors. This information includes work history, performance data, and feedback. It also collects employee sentiment data using speech recognition and facial recognition technology.

[0623] These data are first cleaned, with duplicates removed and outliers corrected, and then formatted into a format suitable for analysis.

[0624] Data analysis and proposal generation

[0625] The server inputs pre-processed data into a generative AI model and performs multidimensional analysis. Based on the employees' skill sets and emotional tendencies, it calculates the most suitable job placement. Using prompts, the server asks the generative AI model "which employee is suitable for which role."

[0626] Based on this analysis, placement suggestions are generated along with suitability scores. These suggestions include specific job details and team composition.

[0627] Proposal notification and feedback

[0628] The terminal visualizes and notifies the user of the placement proposal. The user interface displays the presented placement proposal and its summary, designed to be easily understood by the user.

[0629] The user reviews this proposal and considers whether to apply it. The feedback is recorded in the system and used to improve the analysis algorithms of the generated AI model.

[0630] Specific example

[0631] For example, in the case of employees in the development department, appropriate placement may be determined based on past work history, performance data, and emotional data from stable project management. In this case, a prompt message such as "Based on B's work history and emotional data, please tell me the optimal role and stress management suggestion for the next project" can be input into the AI ​​model, and placement suggestions can be generated based on the results. In this way, the invention aims to improve the productivity of the entire organization by comprehensively considering the job performance and emotional state of employees.

[0632] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0633] Step 1:

[0634] The server acquires employee information and sentiment data from the company's information systems and sensors. Its inputs include employee work history, performance data, feedback, and sentiment data obtained through voice and facial recognition. Specifically, it collects necessary data from databases and cloud services to form an initial dataset.

[0635] Step 2:

[0636] The server cleans and standardizes the acquired raw data. It uses raw employee information and sentiment data as input. Specifically, it cleans the data (imputing missing values, correcting outliers) and standardizes the format, generating formatted data suitable for analysis as output.

[0637] Step 3:

[0638] The server inputs formatted data into the generating AI model and performs multi-dimensional data analysis. It creates prompt statements asking, "Which employee is best suited for which role?" Specifically, it provides the AI ​​model with data on skill sets and emotional tendencies, and makes appropriate placement decisions for each employee. The output is an analysis result that includes job placement suggestions.

[0639] Step 4:

[0640] The server generates specific placement proposals based on the analysis results. Using the analyzed data as input, it concretizes the generated job placement proposals. Specifically, it calculates team composition, role details, and suitability scores, forming the final proposal as output.

[0641] Step 5:

[0642] The terminal receives job placement proposals sent from the server and visualizes and notifies the user. It takes job placement proposals from the server as input and displays the proposals clearly on the user interface. The notified job placement proposals are presented to the user as output.

[0643] Step 6:

[0644] The user reviews the presented placement proposals and decides whether to accept or modify them. Specifically, they evaluate the proposals and input feedback into the system. This collects feedback data and outputs results that contribute to future analysis and improvement of the accuracy of the proposals.

[0645] (Application Example 2)

[0646] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0647] Current personnel placement methods make it difficult to adequately consider the emotional state and skill sets of individual workers, resulting in inefficient placement. Furthermore, there is a need for a system that utilizes emotional data to gain a more detailed understanding of workers' aptitudes and propose optimal tasks. Solving this challenge is crucial.

[0648] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0649] In this invention, the server includes means for collecting information, means for preprocessing the information, and means for analyzing the appropriate placement from the preprocessed information using a generating AI. This makes it possible to consider worker performance based on emotional data and propose the optimal task placement.

[0650] "Information" refers to all data, including the worker's career history, evaluations, evaluations from others, and emotional state.

[0651] "Preprocessing" refers to the process of preparing collected information into a format necessary for analysis.

[0652] "Generative AI" refers to an artificial intelligence system that analyzes the appropriate placement of workers based on collected information and generates proposals.

[0653] "Analysis" refers to the process of evaluating workers' skills and emotions from multiple perspectives based on pre-processed information, and deriving the optimal placement plan.

[0654] "Assignment proposals" refer to the most efficient task and job assignment plans presented to workers based on the analysis results.

[0655] "Emotional data" refers to data that indicates the emotional state of a worker, and is information acquired using technologies such as speech recognition and facial recognition.

[0656] "Feedback" refers to information used to improve the accuracy of an analysis model, based on opinions and evaluations from workers or managers.

[0657] To implement this invention, a server will take the lead in collecting, preprocessing, analyzing, and generating proposals for information. The hardware used will include a server, a voice sensor, a facial recognition camera, and a wearable device. The software used will include Python, TensorFlow (for implementing the generative AI model), and OpenCV (for facial recognition).

[0658] The server first collects career data, job evaluation data, and emotional data from workers. This emotional data includes audio data from voice sensors and image data from facial recognition cameras. The server cleans and preprocesses this collected data into a format suitable for analysis.

[0659] The generative AI model uses formatted data to comprehensively analyze workers' skill sets and emotional states. Based on this analysis, it proposes the optimal task or job assignment for each worker. These proposals include specific tasks and work content designed to maximize worker performance, and the proposals, along with an suitability score, are notified to the terminal.

[0660] The terminal visually presents the proposals, allowing users to intuitively understand the information. Users can consider the proposals, choose whether to accept them, and provide further feedback to the server, thereby contributing to the improvement of the analysis model.

[0661] For example, if a worker's stress level is determined to be low today, the generating AI may suggest relevant manufacturing tasks. This allows the worker to select tasks that are appropriate for their current state.

[0662] An example of a prompt is: "Prompt to input to the generating AI model: 'Consider the worker's emotional state and work history, and suggest the most suitable factory task.'"

[0663] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0664] Step 1:

[0665] The server collects worker history data, job evaluations, and emotional data. Specifically, it receives audio data obtained from voice sensors and image data captured by facial recognition cameras. This input data is stored in cloud storage.

[0666] Step 2:

[0667] The server cleans the collected data and preprocesses it into a format suitable for analysis. Specifically, it extracts sentiment indicators from audio data and converts facial image data into sentiment analysis data using OpenCV. The output of the preprocessing is a cleansed and standardized dataset.

[0668] Step 3:

[0669] The server inputs pre-processed data into a generating AI model to analyze the workers' skills and emotional states. Using prompts, it instructs the generating AI to "consider the workers' emotional states and work experience and suggest the most suitable factory tasks." The analysis output is the most suitable task suggestion and suitability score for each worker.

[0670] Step 4:

[0671] The server sends the generated suggestions and their suitability scores to the terminal. The specific output includes a list of tasks for the worker and their detailed information.

[0672] Step 5:

[0673] The terminal visually displays suggestions received from the server to the user. The user interface allows workers to intuitively understand the information.

[0674] Step 6:

[0675] The user reviews the suggested task on their device and chooses whether to accept it. They then use the feedback option to enter their reasons for choosing the task and any suggestions they may have, and send this information to the server.

[0676] Step 7:

[0677] The server receives user feedback and uses it to improve the analysis model of the generated AI. This aims to improve the accuracy of future suggestions. The feedback output is integrated into the training data for the next analysis model.

[0678] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0679] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0680] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0681] [Fourth Embodiment]

[0682] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0683] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0684] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0685] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0686] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0687] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0688] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0689] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0690] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0691] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0692] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0693] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0694] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0695] This invention is a system for optimizing personnel allocation within a company, and is implemented using a program that utilizes generative AI. The following describes how this system's program works in natural language.

[0696] The main role of this system is to propose optimal personnel allocation based on employee information, with the server, terminals, and users each fulfilling their respective roles.

[0697] Data Acquisition and Preprocessing

[0698] The server proactively collects employee information from each company within the group. This information includes work history, performance reviews, records of individual interviews, and self-reports.

[0699] The server converts the collected raw data into a format suitable for analysis, performing standardization and data interpolation as needed.

[0700] Analysis by Generative AI

[0701] The server uses a generation AI to analyze pre-processed employee information. The AI ​​model analyzes each employee's unique skill set, career aspirations, compatibility, and other factors from multiple perspectives.

[0702] The analysis results evaluate the most suitable team and position for each employee and calculate an appropriate score.

[0703] Proposal generation and notification

[0704] The server generates optimal placement suggestions based on the analysis results and creates a list of the most effective positions for each employee.

[0705] The terminal displays the proposed layout plan for the user to review. This information is accessible to both employees and administrators.

[0706] Specific example

[0707] The server performs an analysis using data from person A, who belongs to the sales department. As a result, the AI ​​suggests that person A is also suitable to be a project leader in the marketing department.

[0708] The user (in this case, Person A) reviews a proposed assignment to a marketing department project via their device and considers this option to broaden their career horizons.

[0709] This system simultaneously achieves optimal employee mobility and allocation, contributing to increased satisfaction for both companies and employees. By incorporating flexibility in proposals and a feedback loop, the analytical model is continuously updated, guaranteeing optimal results.

[0710] The following describes the processing flow.

[0711] Step 1:

[0712] Data collection

[0713] The server automatically collects employee information from each company's database. This information includes work history, past performance reviews, and self-reported information. It then creates an integrated profile for each employee.

[0714] Step 2:

[0715] Data preprocessing

[0716] The server cleans the collected data. Specifically, it handles missing values ​​and standardizes the data format to prepare it for analysis.

[0717] Step 3:

[0718] Data Analysis

[0719] The server analyzes pre-processed data using a generative AI model. It performs a multi-layered analysis of employee skill sets, job suitability, compatibility, etc., to predict the optimal placement for each employee.

[0720] Step 4:

[0721] Placement proposal generation

[0722] Based on the analysis results, the server creates placement suggestions for each employee. These suggestions include a list of optimal positions and their suitability levels.

[0723] Step 5:

[0724] proposal notification

[0725] The terminal displays placement suggestions sent from the server. The user reviews these and uses them as input for carrier selection.

[0726] Step 6:

[0727] User actions and feedback

[0728] Users (employees or managers) evaluate the suggestions and take action based on the results (such as requesting a transfer). Providing feedback to the server contributes to improving the accuracy of the AI ​​model.

[0729] Step 7:

[0730] Model update

[0731] The server incorporates feedback data into the analysis model and improves the model's accuracy through machine learning. This process continuously enables improvements in the system's proposal accuracy.

[0732] (Example 1)

[0733] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0734] Traditionally, personnel placement in companies has often relied on the subjective judgment of managers, making it difficult to achieve the right person in the right place. Furthermore, insufficient optimization of personnel placement has resulted in employees' abilities not being fully utilized, leading to a decline in overall organizational productivity.

[0735] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0736] In this invention, the server includes functional means for collecting employee information, functional means for preprocessing the information, and functional means for analyzing the appropriate placement of employees from the preprocessed data using a generating AI. This makes it possible to objectively and efficiently optimize personnel allocation within a company.

[0737] "Employee information" refers to data about individual employees working within a company, including their work history, performance evaluations, and impressions from supervisors or colleagues.

[0738] "Preprocessing" refers to the process of converting collected raw data into a format suitable for analysis, and includes tasks such as data standardization and imputation of missing values.

[0739] "Generative AI" refers to artificial intelligence models that learn from large amounts of data and predict the appropriate allocation of employees.

[0740] "Appropriate placement" refers to proposing the most suitable job or position based on the employee's skill set, career aspirations, and business needs.

[0741] An "analysis model" refers to a model that includes the computational methods and processes used by the generating AI to derive appropriate placement based on employee information.

[0742] "Suitability score" refers to an indicator that quantifies each employee's suitability for their job and position.

[0743] A "terminal" refers to a computer device used by employees or administrators to review deployment proposals generated by the system.

[0744] In order to implement this invention, a system is required in which the server, terminal, and user cooperate with each other.

[0745] The server automatically collects employee information from databases located in each department of the company via the network. This information includes employee work history, performance evaluations, and impressions from supervisors and colleagues. The server uses Python to collect this data and the Pandas library for standardization and preprocessing. If there is missing data, algorithms are used to impute the data.

[0746] The pre-processed data is input into a generative AI model using TensorFlow or PyTorch, which performs an analysis of appropriate placement, taking into account employee skills and career aspirations. The AI ​​model is pre-trained to support advanced analysis. A specific example of a prompt message is, "Suggest the optimal position based on the employee's skill set and career aspirations."

[0747] Based on the analysis results, the server calculates an appropriate score for each employee and generates placement suggestions based on that score. These suggestions are sent from the server to the terminal. The terminal displays the information on a dashboard via a web application. Users can use this dashboard to review their placement suggestions and provide feedback to the administrator as needed.

[0748] As a concrete example, based on data from employee A, who works in the sales department, the server performs an analysis and suggests that A is suitable to be a project leader in the marketing department. User A can then review this suggestion via their terminal and consider a new career path.

[0749] This system efficiently optimizes the allocation of personnel within a company, thereby improving the overall productivity of the organization.

[0750] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0751] Step 1:

[0752] The server collects employee information from the databases of each company connected to the network. This information includes work history, performance reviews, and impressions from supervisors and colleagues. The data is extracted using SQL queries and stored on the server as a CSV file.

[0753] Step 2:

[0754] The server reads the collected CSV files using the Python Pandas library and standardizes the data. Specifically, it imputes missing values ​​with appropriate mean values ​​and unifies data in different formats. This ensures that the data is output in a consistent format.

[0755] Step 3:

[0756] The server inputs pre-processed data into the generating AI model. First, it loads the AI ​​model using TensorFlow or PyTorch to prepare the model. Next, it inputs a prompt message into the model: "Suggest the optimal position based on the employee's skill set and career aspirations."

[0757] Step 4:

[0758] The generative AI model performs placement analysis based on preprocessed data and prompt messages. The model calculates a set of appropriate scores for each employee. This generates a list of analyzed appropriate scores, which are used to generate optimal placement suggestions.

[0759] Step 5:

[0760] The server generates the most suitable placement proposal based on the analysis results. Based on the suitability score, it generates a list of the most effective jobs for each employee and outputs this data in JSON format.

[0761] Step 6:

[0762] The terminal receives deployment proposal data in JSON format from the server and displays it through the user interface. Users can access the terminal's dashboard to view their deployment proposals, print them, or send feedback to the administrator.

[0763] (Application Example 1)

[0764] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0765] To improve factory work efficiency and reduce the frequency of breakdowns, the proper placement and allocation of roles of work machinery are crucial. However, considering the individual performance and failure history of each machine when determining the placement requires a great deal of time and effort, which is a factor that reduces the overall efficiency of the factory.

[0766] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0767] In this invention, the server includes means for collecting work machine information, means for pre-processing the work machine information, and means for analyzing the appropriate placement of the work machines from the pre-processed information using a generating AI. This enables optimal placement and role that accurately reflects the performance and failure history of each individual work machine.

[0768] "Work machine information" refers to various data on work machines operating in a factory, and specifically includes information such as work performance, failure history, and efficiency.

[0769] "Preprocessing" refers to the data manipulation required to convert collected machine information into a format suitable for analysis. This includes standardization and imputation of missing values.

[0770] "Generative AI" is a type of machine learning technology that refers to algorithms used to extract features from large amounts of data and perform new analyses. Specifically, it is used to determine the optimal placement of machinery.

[0771] "Analysis" refers to the process of evaluating the optimal placement and role of work machines based on pre-processed machine information using generative AI. This generates placement plans that take into account the performance and failure risk of each machine.

[0772] "Notification" refers to the process of communicating placement suggestions based on the analyzed results to factory managers. This allows managers to confirm efficient and effective placement and reflect it in their actions.

[0773] The system implementing this invention mainly consists of three elements: a server, terminals, and an administrator. The server is responsible for collecting information such as performance and failure history from each machine in the factory. Upon receiving this data, the server first performs preprocessing, including standardization and data cleaning, to prepare it for analysis. The preprocessed data is then input into a generative AI model, which analyzes the optimal placement and role of each machine. Through this process, the generative AI model derives an optimal placement plan.

[0774] The layout proposals obtained from the analysis are generated by the server and sent to terminals used by administrators. These terminals visualize the proposed layouts, allowing administrators to efficiently review them. Based on this, administrators can fine-tune the placement and roles of the machinery within the factory.

[0775] As a concrete example, consider a food factory where machine A is responsible for the packaging process. While highly efficient, it experiences frequent breakdowns. In this case, the generative AI model proposes operating machine A intensively for short periods, while simultaneously deploying machine B as a backup, thereby improving overall efficiency and stability.

[0776] An example of a prompt statement to input into the generated AI model is "Performance 85, Failure History 10, Efficiency 95". This prompt statement provides the AI ​​model with specific evaluation metrics and is used to calculate the optimal placement based on them.

[0777] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0778] Step 1:

[0779] The server collects information from each machine in the factory. Specifically, it automatically retrieves data such as work performance, failure history, and efficiency from the machines. The input for this step is raw data from each machine, and the output is the collected, unprocessed data.

[0780] Step 2:

[0781] The server preprocesses the collected machine data. This process includes data standardization and imputation of missing values. For example, it unifies the data format and fills in missing values ​​with estimated values ​​to prepare the data for analysis. The input for this step is raw machine data, and the output is a formatted dataset.

[0782] Step 3:

[0783] The server uses a generative AI model to analyze the optimal placement from pre-processed data. Specifically, it inputs data such as "performance 85, failure history 10, efficiency 95" as prompts into the AI ​​model and generates a suggestion for the optimal placement of each work machine. The input for this step is a formatted dataset, and the output is the suggested optimal placement of the work machines.

[0784] Step 4:

[0785] The server generates a layout proposal as an analysis result and notifies the administrator's terminal. This procedure provides information to visually display the optimal layout plan, making it easy for the administrator to review. The input for this step is the optimal layout plan for the work machines, and the output is a display of the layout proposal that the administrator can view.

[0786] Step 5:

[0787] Administrators using the terminal review the provided deployment proposals and adjust the placement of work equipment and operational policies as needed. This step provides an interface to enable administrators to make quick decisions based on the proposals. The input for this step is the deployment proposal displayed on the terminal, and the output is the deployment policy finalized by the administrator.

[0788] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0789] This invention is a system that combines generative AI and an emotion engine to optimize personnel allocation within a company. This system aims to propose the optimal placement for each individual employee, taking into account not only employee information but also user emotion data.

[0790] Data Acquisition and Preprocessing

[0791] The server acquires employee information and user sentiment data from various input devices and software. Employee information includes work history, performance data, and feedback from supervisors and colleagues, while sentiment data includes emotional states obtained through voice and facial recognition.

[0792] The server cleans and integrates this data, preparing it in a standard format necessary for analysis.

[0793] Analysis and proposal generation

[0794] The server uses generative AI to perform a multi-faceted analysis of pre-processed employee information and emotional data. The generative AI model considers the employee's skill set, character, and emotional tendencies during the analysis.

[0795] As a result, the server determines the most suitable job placement for each employee and makes placement recommendations based on this. These recommendations include detailed information about the optimal team and role, as well as a suitability score for the placement.

[0796] Proposal notification and feedback

[0797] The terminal receives deployment proposals sent from the server and displays them to the user in a visually clear and easy-to-understand manner.

[0798] Users review this and consider it as an option for job placement. They then provide feedback on the proposal and their choices, contributing to further system improvements.

[0799] Specific example

[0800] The server analyzes B's work history, performance evaluations, and self-reported questionnaires from the development department, while also collecting emotional data for B during normal times and during project progress.

[0801] Based on the analysis, the AI ​​determines that Person B performs well under stress and is therefore well-suited to be a project manager for important projects. Stress management suggestions are also provided based on emotional data.

[0802] The user reviews the proposed role and provides feedback on any possible support before accepting the role of project manager.

[0803] Thus, this invention achieves overall optimization that takes into account not only employee job performance but also their emotional well-being. By utilizing the emotional engine, it becomes possible to improve both employee satisfaction and productivity.

[0804] The following describes the processing flow.

[0805] Step 1:

[0806] Data collection

[0807] The server collects employee information and emotional data from each employee's database or device. Employee information includes work history, performance reviews, and feedback, while emotional data deals with the results of voice tone and facial expression analysis collected through the emotion engine.

[0808] Step 2:

[0809] Data preprocessing

[0810] The server performs cleaning operations on the collected data. By removing noise and missing data and standardizing it, it prepares the data for the analysis model to function effectively.

[0811] Step 3:

[0812] Emotion analysis

[0813] The server utilizes an emotion engine to analyze collected emotional data. This allows it to quantify employees' stress levels and motivation, taking into account their personality traits as well.

[0814] Step 4:

[0815] Data Analysis

[0816] The server inputs pre-processed employee information and emotional data into a generating AI model, which then performs a multidimensional analysis to determine the optimal job placement for each employee. The generating AI evaluates both the employee's skills and emotional tendencies to suggest appropriate roles and teams.

[0817] Step 5:

[0818] Proposal generation

[0819] The server generates placement suggestions based on the analysis results. These suggestions include the optimal position for each employee, a suitability score for that position, and stress management suggestions based on emotional data.

[0820] Step 6:

[0821] Distribution of proposals

[0822] The terminal displays placement suggestions received from the server via a user interface. Users can then review this information and use it to consider their job and role choices.

[0823] Step 7:

[0824] User actions and feedback

[0825] Users review the presented placement proposals and make selections that align with their career goals. They then provide feedback to the system, including their choices and opinions, contributing to improvements in the accuracy of future proposals.

[0826] Step 8:

[0827] Model improvements

[0828] The server collects feedback and uses it to update the generative AI model and the emotion engine's analysis algorithms. This allows the system to continuously improve and increase accuracy.

[0829] (Example 2)

[0830] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0831] In today's business environment, optimizing employee job assignments is crucial for improving productivity. However, traditional methods rely solely on employee skills and work experience, failing to consider emotions and preferences. This results in increased employee stress and dissatisfaction, leading to a decline in overall organizational efficiency.

[0832] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0833] In this invention, the server includes means for aggregating employee information and emotional data, means for preprocessing the employee information and emotional data, and means for using a generative AI model to comprehensively analyze the appropriate placement of employees from the preprocessed data. This makes it possible to propose appropriate job placements that also take into account the emotional state of each employee.

[0834] "Employee information" refers to detailed data about individual employees, including their work history, performance data, and workplace feedback.

[0835] "Emotional data" refers to data that indicates the emotional state of employees, collected using voice recognition and facial recognition technologies.

[0836] "Preprocessing" refers to the process of cleaning collected raw data and preparing it into a unified format.

[0837] A "generative AI model" is an artificial intelligence model that analyzes large amounts of data and generates appropriate job placement suggestions.

[0838] "Multifaceted analysis" refers to analyzing data while taking various factors into consideration, and deriving optimal conclusions from multiple perspectives.

[0839] A "prompt statement" is a sentence used to instruct a generative AI model to perform a specific task, and it serves to give instructions to the AI ​​model.

[0840] The "suitability score" is a numerical value that indicates how well-suited an employee is for a particular job, based on the results of analysis by a generative AI model.

[0841] This invention is a system for optimizing employee job assignments within a company, combining a generative AI model and an emotion engine. The system operates as follows:

[0842] Data Acquisition and Preprocessing

[0843] The server acquires employee information through the company's information systems and sensors. This information includes work history, performance data, and feedback. It also collects employee sentiment data using speech recognition and facial recognition technology.

[0844] These data are first cleaned, with duplicates removed and outliers corrected, and then formatted into a format suitable for analysis.

[0845] Data analysis and proposal generation

[0846] The server inputs pre-processed data into a generative AI model and performs multidimensional analysis. Based on the employees' skill sets and emotional tendencies, it calculates the most suitable job placement. Using prompts, the server asks the generative AI model "which employee is suitable for which role."

[0847] Based on this analysis, placement suggestions are generated along with suitability scores. These suggestions include specific job details and team composition.

[0848] Proposal notification and feedback

[0849] The terminal visualizes and notifies the user of the placement proposal. The user interface displays the presented placement proposal and its summary, designed to be easily understood by the user.

[0850] The user reviews this proposal and considers whether to apply it. The feedback is recorded in the system and used to improve the analysis algorithms of the generated AI model.

[0851] Specific example

[0852] For example, in the case of employees in the development department, appropriate placement may be determined based on past work history, performance data, and emotional data from stable project management. In this case, a prompt message such as "Based on B's work history and emotional data, please tell me the optimal role and stress management suggestion for the next project" can be input into the AI ​​model, and placement suggestions can be generated based on the results. In this way, the invention aims to improve the productivity of the entire organization by comprehensively considering the job performance and emotional state of employees.

[0853] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0854] Step 1:

[0855] The server acquires employee information and sentiment data from the company's information systems and sensors. Its inputs include employee work history, performance data, feedback, and sentiment data obtained through voice and facial recognition. Specifically, it collects necessary data from databases and cloud services to form an initial dataset.

[0856] Step 2:

[0857] The server cleans and standardizes the acquired raw data. It uses raw employee information and sentiment data as input. Specifically, it cleans the data (imputing missing values, correcting outliers) and standardizes the format, generating formatted data suitable for analysis as output.

[0858] Step 3:

[0859] The server inputs formatted data into the generating AI model and performs multi-dimensional data analysis. It creates prompt statements asking, "Which employee is best suited for which role?" Specifically, it provides the AI ​​model with data on skill sets and emotional tendencies, and makes appropriate placement decisions for each employee. The output is an analysis result that includes job placement suggestions.

[0860] Step 4:

[0861] The server generates specific placement proposals based on the analysis results. Using the analyzed data as input, it concretizes the generated job placement proposals. Specifically, it calculates team composition, role details, and suitability scores, forming the final proposal as output.

[0862] Step 5:

[0863] The terminal receives job placement proposals sent from the server and visualizes and notifies the user. It takes job placement proposals from the server as input and displays the proposals clearly on the user interface. The notified job placement proposals are presented to the user as output.

[0864] Step 6:

[0865] The user reviews the presented placement proposals and decides whether to accept or modify them. Specifically, they evaluate the proposals and input feedback into the system. This collects feedback data and outputs results that contribute to future analysis and improvement of the accuracy of the proposals.

[0866] (Application Example 2)

[0867] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0868] Current personnel placement methods make it difficult to adequately consider the emotional state and skill sets of individual workers, resulting in inefficient placement. Furthermore, there is a need for a system that utilizes emotional data to gain a more detailed understanding of workers' aptitudes and propose optimal tasks. Solving this challenge is crucial.

[0869] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0870] In this invention, the server includes means for collecting information, means for preprocessing the information, and means for analyzing the appropriate placement from the preprocessed information using a generating AI. This makes it possible to consider worker performance based on emotional data and propose the optimal task placement.

[0871] "Information" refers to all data, including the worker's career history, evaluations, evaluations from others, and emotional state.

[0872] "Preprocessing" refers to the process of preparing collected information into a format necessary for analysis.

[0873] "Generative AI" refers to an artificial intelligence system that analyzes the appropriate placement of workers based on collected information and generates proposals.

[0874] "Analysis" refers to the process of evaluating workers' skills and emotions from multiple perspectives based on pre-processed information, and deriving the optimal placement plan.

[0875] "Assignment proposals" refer to the most efficient task and job assignment plans presented to workers based on the analysis results.

[0876] "Emotional data" refers to data that indicates the emotional state of a worker, and is information acquired using technologies such as speech recognition and facial recognition.

[0877] "Feedback" refers to information used to improve the accuracy of an analysis model, based on opinions and evaluations from workers or managers.

[0878] To implement this invention, a server will take the lead in collecting, preprocessing, analyzing, and generating proposals for information. The hardware used will include a server, a voice sensor, a facial recognition camera, and a wearable device. The software used will include Python, TensorFlow (for implementing the generative AI model), and OpenCV (for facial recognition).

[0879] The server first collects career data, job evaluation data, and emotional data from workers. This emotional data includes audio data from voice sensors and image data from facial recognition cameras. The server cleans and preprocesses this collected data into a format suitable for analysis.

[0880] The generative AI model uses formatted data to comprehensively analyze workers' skill sets and emotional states. Based on this analysis, it proposes the optimal task or job assignment for each worker. These proposals include specific tasks and work content designed to maximize worker performance, and the proposals, along with an suitability score, are notified to the terminal.

[0881] The terminal visually presents the proposals, allowing users to intuitively understand the information. Users can consider the proposals, choose whether to accept them, and provide further feedback to the server, thereby contributing to the improvement of the analysis model.

[0882] For example, if a worker's stress level is determined to be low today, the generating AI may suggest relevant manufacturing tasks. This allows the worker to select tasks that are appropriate for their current state.

[0883] An example of a prompt is: "Prompt to input to the generating AI model: 'Consider the worker's emotional state and work history, and suggest the most suitable factory task.'"

[0884] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0885] Step 1:

[0886] The server collects worker history data, job evaluations, and emotional data. Specifically, it receives audio data obtained from voice sensors and image data captured by facial recognition cameras. This input data is stored in cloud storage.

[0887] Step 2:

[0888] The server cleans the collected data and preprocesses it into a format suitable for analysis. Specifically, it extracts sentiment indicators from audio data and converts facial image data into sentiment analysis data using OpenCV. The output of the preprocessing is a cleansed and standardized dataset.

[0889] Step 3:

[0890] The server inputs pre-processed data into a generating AI model to analyze the workers' skills and emotional states. Using prompts, it instructs the generating AI to "consider the workers' emotional states and work experience and suggest the most suitable factory tasks." The analysis output is the most suitable task suggestion and suitability score for each worker.

[0891] Step 4:

[0892] The server sends the generated suggestions and their suitability scores to the terminal. The specific output includes a list of tasks for the worker and their detailed information.

[0893] Step 5:

[0894] The terminal visually displays suggestions received from the server to the user. The user interface allows workers to intuitively understand the information.

[0895] Step 6:

[0896] The user reviews the suggested task on their device and chooses whether to accept it. They then use the feedback option to enter their reasons for choosing the task and any suggestions they may have, and send this information to the server.

[0897] Step 7:

[0898] The server receives user feedback and uses it to improve the analysis model of the generated AI. This aims to improve the accuracy of future suggestions. The feedback output is integrated into the training data for the next analysis model.

[0899] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0900] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0901] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0902] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0903] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0904] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0905] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0906] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0907] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0908] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0909] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0910] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0911] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0912] 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.

[0913] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0914] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0915] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0916] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0917] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0918] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0919] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0920] The following is further disclosed regarding the embodiments described above.

[0921] (Claim 1)

[0922] Means of collecting employee information,

[0923] Means for pre-processing the employee information,

[0924] A means for analyzing the appropriate allocation of employees from the pre-processed information using a generating AI,

[0925] A means for generating employee placement proposals based on the aforementioned analysis results,

[0926] Means for notifying employees or managers of the aforementioned placement proposal,

[0927] A system that includes this.

[0928] (Claim 2)

[0929] The system according to claim 1, wherein the employee information includes work history, performance evaluations, and evaluations from supervisors or colleagues.

[0930] (Claim 3)

[0931] The system according to claim 1, wherein the system includes means for collecting feedback from the employee or manager and improving the analysis model of the generated AI.

[0932] "Example 1"

[0933] (Claim 1)

[0934] A functional means for collecting employee information,

[0935] A functional means for pre-processing the employee information,

[0936] A functional means for analyzing the appropriate allocation of employees from the pre-processed data using a generation AI,

[0937] A functional means for generating employee placement proposals based on the aforementioned analysis results,

[0938] A functional means for notifying employees or managers of the aforementioned placement proposal,

[0939] A functional means for linking the aforementioned employee information with prompt statements for the generated AI model,

[0940] A functional means for calculating an appropriate score based on the analysis results of an AI model,

[0941] A functional means for displaying the generated proposal via a terminal,

[0942] A placement optimization system including...

[0943] (Claim 2)

[0944] The placement optimization system according to claim 1, wherein the employee information includes career history, performance evaluation, and impressions from supervisors or colleagues.

[0945] (Claim 3)

[0946] The placement optimization system according to claim 1, wherein the system includes functional means for collecting opinions from the employee or manager and improving the analysis model of the generated AI.

[0947] "Application Example 1"

[0948] (Claim 1)

[0949] Means for collecting information on work machinery,

[0950] Means for pre-processing the aforementioned work machine information,

[0951] A means for analyzing the appropriate placement of work machines from the pre-processed information using a generating AI,

[0952] A means for generating a proposed layout of the work machinery based on the aforementioned analysis results,

[0953] A means of notifying the administrator of the aforementioned placement proposal,

[0954] A system that includes this.

[0955] (Claim 2)

[0956] The system according to claim 1, wherein the aforementioned work machine information includes work performance, failure history, and efficiency.

[0957] (Claim 3)

[0958] The system according to claim 1, wherein the system includes means for collecting feedback from the administrator and improving the analysis model of the generated AI.

[0959] "Example 2 of combining an emotion engine"

[0960] (Claim 1)

[0961] A means of aggregating employee information and sentiment data,

[0962] Means for preprocessing the employee information and sentiment data,

[0963] A means for analyzing the appropriate allocation of employees from multiple perspectives using the pre-processed data with a generative AI model,

[0964] A means for generating employee placement proposals based on the aforementioned analysis results and prompt messages,

[0965] A means of visually notifying employees or managers of the aforementioned placement proposal,

[0966] A system that includes this.

[0967] (Claim 2)

[0968] The system according to claim 1, wherein the employee information includes work history, performance data, and workplace feedback, and the emotional data is acquired using speech recognition and facial recognition technology.

[0969] (Claim 3)

[0970] The system according to claim 1, wherein the system includes means for collecting feedback from the employee or manager and improving the analysis algorithm of the generated AI model.

[0971] "Application example 2 when combining with an emotional engine"

[0972] (Claim 1)

[0973] Means of collecting information,

[0974] Means for preprocessing the aforementioned information,

[0975] A means for analyzing the appropriate placement from the pre-processed information using a generating AI,

[0976] A means for generating a layout proposal based on the aforementioned analysis results,

[0977] Means for notifying the aforementioned arrangement proposal,

[0978] Means for collecting and processing emotional data,

[0979] A means for analyzing the appropriate placement considering the aforementioned emotional data,

[0980] A system that includes this.

[0981] (Claim 2)

[0982] The system according to claim 1, wherein the aforementioned information includes career history, evaluation, and assessment.

[0983] (Claim 3)

[0984] The system according to claim 1, wherein the system includes means for collecting feedback from the information provider and improving the analysis model of the generated AI. [Explanation of symbols]

[0985] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means of collecting employee information, Means for pre-processing the employee information, A means for analyzing the appropriate allocation of employees from the pre-processed information using a generating AI, A means for generating employee placement proposals based on the aforementioned analysis results, Means for notifying employees or managers of the aforementioned placement proposal, A system that includes this.

2. The system according to claim 1, wherein the employee information includes work history, performance evaluations, and evaluations from supervisors or colleagues.

3. The system according to claim 1, wherein the system includes means for collecting feedback from the employee or manager and improving the analysis model of the generated AI.

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