System

Abstract

A system is provided.SOLUTION: The system includes a means for collecting employee information and storing the information in a database, a means for collecting business contents and required personnel data and storing the data in the database, a means for analyzing the employee information and the business data by a generation AI and calculating the optimum matching of the employee and the business, a means for transmitting and displaying an appropriate arrangement proposal to a terminal, a means for collecting feedback from the terminal and storing the feedback in the database, and a means for performing re-evaluation based on the feedback and finally determining and notifying the optimum arrangement.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With traditional personnel placement, it was difficult to comprehensively evaluate employees' skills, experience, personality, and motivation to select the most suitable candidates. This led to the subjective opinions and biases of personnel managers, which could result in placements that prevented employees from fully utilizing their abilities. It was also difficult for employees to identify the tasks and departments that best suited them, which could lead to lower employee satisfaction. As a result, problems such as a decline in employee motivation, an increase in turnover, and a decline in corporate efficiency could arise. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides a system that includes the following means: First, it has a means for collecting employee information and storing it in a database, and a means for collecting data on job content and desired personnel and storing it in a database. Next, it provides a means for analyzing employee information and job data using generative AI and calculating the optimal employee-job match. Finally, it has a means for sending and displaying appropriate placement proposals to a terminal, and a means for collecting feedback from the terminal and storing it in a database. It also includes a means for analyzing and reevaluating the collected feedback, and for finalizing and notifying the optimal placement. This enables unbiased and fair personnel placement, providing an environment where employees can demonstrate their full potential.

[0006] "Employee Information" refers to data about individual employees, such as their resumes, qualifications, experience, personality traits, and skill sets.

[0007] "Database" refers to a system for systematically storing and managing collected employee information, job descriptions, and desired personnel data.

[0008] "Work content" refers to data that lists the specific work and project details that each department is responsible for.

[0009] "Desired talent data" refers to data that lists the skills, experience, qualifications, personality traits, and other requirements required by each department or project.

[0010] "Generative AI" refers to artificial intelligence that generates natural language and recognizes patterns based on provided data, and automatically performs various evaluations and matching.

[0011] "Matching" refers to the process of using generative AI to analyze employees' skills, experience, personality traits, etc., and select the most suitable job and department.

[0012] "Placement proposals" refer to recommending the most suitable tasks and departments for employees based on the matching results calculated by generative AI.

[0013] "Terminal" refers to a device, such as a computer or smartphone, used by employees and personnel to receive placement proposals.

[0014] "Feedback" refers to the opinions and requests provided by employees or staff in response to proposals.

[0015] "Evaluation logic" refers to the algorithms and calculation methods used by generative AI, and refers to the criteria for optimal matching based on employee information and job content.

[0016] "Reevaluation" refers to the process of adjusting the evaluation logic based on collected feedback and re-matching.

[0017] "Final decision" refers to the optimal placement result determined after reevaluation, and includes notifying the employee and person in charge of the result. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8]FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0019] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0020] First, the terms used in the following description will be explained.

[0021] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0022] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0023] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0024] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0029] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0031] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0032] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0036] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0037] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0038] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0039] The present invention is a system for assigning optimal tasks and departments to employees based on their skills, experience, and personality traits. This system operates in cooperation with a server, terminals, and users.

[0040] Server Processing

[0041] 1. Data Collection and Storage

[0042] The server first collects employee information and stores it in a database. This information includes resumes, qualifications, experience, personality traits, skill sets, etc. It also collects job descriptions and desired personnel data from each department of the company and stores them in the database as well.

[0043] 2. Data analysis and matching

[0044] The server uses generative AI to analyze the collected employee information and work data, comprehensively evaluating employees' skill sets, experience, personality traits, etc., and scores and matches each employee with the most suitable job and department.

[0045] 3. Generate and submit a proposal

[0046] Based on the analysis results, the server creates specific placement proposals, which indicate which departments and tasks each employee is suited to. The proposals are then sent to the devices of the person in charge and the target employees.

[0047] Terminal handling

[0048] 1. Receiving and Displaying Proposals

[0049] The terminal receives the placement proposal sent from the server and displays it on the screen. Employees and personnel can review the proposal and provide feedback if necessary.

[0050] 2. Gathering feedback

[0051] The terminal provides a UI for employees and staff to input their opinions and requests regarding the proposal. Once the feedback is entered, it is sent to the server.

[0052] User Action

[0053] 1. Confirmation of proposal

[0054] Users (employees and staff) check the proposals from the server through their terminals. The proposals include specific details about the tasks and departments that are best suited to them.

[0055] 2. Providing Feedback

[0056] Users input their opinions and requests regarding the proposals and send them to the server via their terminals. This feedback is used as an important factor in the final decision-making process.

[0057] Specific examples

[0058] For example, consider a new project that requires people with specific skill sets and experience. First, the project manager inputs the project details (skills needed, number of people, deadline, etc.) into the server. The server uses this information to use a generative AI model to select the best candidates from the current employee database.

[0059] The server creates a list of candidates evaluated by the AI ​​model and sends it to the project manager and candidate's devices. The project manager and candidate review the proposals on their respective devices and provide feedback. Based on this feedback, the server reevaluates and makes the final placement decision.

[0060] The final allocation results are communicated to the project manager and employee, allowing them to quickly transition to their new job or role. This system ensures unbiased and fair allocation of personnel, providing an environment where employees can perform to their full potential while improving the efficiency and performance of the entire organization.

[0061] The processing flow will be explained below.

[0062] Program processing steps

[0063] Step 1: Collect and store data

[0064] Server Operation

[0065] The server collects information from employees, such as their resumes, qualifications, experience, personality traits, and skill sets.

[0066] The server stores the collected employee information in a database.

[0067] The server collects data from each department about the work content and the personnel required.

[0068] The server stores the collected job details and desired personnel data in a database.

[0069] Step 2: Analyze and match data

[0070] Server Operation

[0071] The server inputs employee information and business data stored in a database into the generative AI.

[0072] The server uses generative AI to comprehensively evaluate employees' skill sets, experience, personality traits, and more.

[0073] Based on the evaluation results obtained from the AI ​​model, the server calculates scores for the departments and tasks that are most suitable for each employee and creates a matching list.

[0074] Step 3: Generate and submit a proposal

[0075] Server Operation

[0076] The server generates specific placement proposals based on the matching list.

[0077] The server transmits the generated placement proposal to the terminals of the person in charge and employees.

[0078] Step 4: Receive and view proposals

[0079] Device behavior

[0080] The terminal receives the placement proposal sent from the server.

[0081] The terminal displays placement proposals on the screen so that employees and staff can review them.

[0082] Step 5: Provide feedback

[0083] User Actions

[0084] Users (employees and staff) check the placement proposals displayed on their terminals.

[0085] The user inputs their opinions and requests regarding the proposal into the terminal.

[0086] Device behavior

[0087] The terminal transmits the input feedback to the server.

[0088] Step 6: Analyze and store feedback

[0089] Server Operation

[0090] The server analyzes the received feedback and stores it in a database.

[0091] The server adjusts its rating logic based on the feedback.

[0092] Step 7: Reassess and finalize

[0093] Server Operation

[0094] Based on the adjusted evaluation logic, the server calls the generation AI again and performs a re-evaluation.

[0095] The server will make a final decision on the optimal placement based on the results of the re-evaluation.

[0096] The server sends the final decision to the person in charge and to the employee's terminal.

[0097] Step 8: Notification of final placement results

[0098] Device behavior

[0099] The terminal receives the final placement result and displays it on the screen.

[0100] The terminal notifies relevant personnel and employees.

[0101] Example 1

[0102] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0103] The purpose of this invention is to effectively realize the right person in the right position in personnel allocation, maximize employee capabilities, and improve a company's business efficiency and performance. Conventional personnel allocation has had issues with subjective judgment and bias based on experience, making fair allocation difficult. It has also been difficult to quickly assign employees with the appropriate skill sets and experience to the appropriate tasks.

[0104] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0105] In this invention, the server includes means for collecting employee information and storing it in a database, means for collecting job content and desired human resource data and storing it in a database, means for analyzing employee information and job data using generative AI and calculating optimal employee-job matching, means for sending and displaying appropriate placement proposals to a terminal, means for collecting feedback from the terminal and storing it in a database, means for reevaluating based on the feedback and finalizing and notifying the optimal placement, means for evaluating each employee's skill set, experience, and personality traits based on the collected employee information and job data and optimally matching them based thereon, means for users to input feedback via a terminal and send it to the server, means for calculating scores for each employee using a generative AI model and proposing the optimal department and job, means for using the collected feedback to proceed with the reevaluation and finalize the placement, means for displaying the proposals and providing a UI for inputting feedback, and means for notifying the responsible department and employee of the final placement results after the reevaluation. This not only enables fair and objective personnel placement, optimally utilizing employee abilities, but also improves the company's business efficiency and performance.

[0106] "Employee Information" refers to detailed profile data such as an employee's biography, qualifications, experience, personality traits, and skill set.

[0107] "Job content" refers to the specific work and responsibilities performed in each department or project, as well as the required skills and experience.

[0108] "Generative AI" refers to algorithms that use artificial intelligence techniques such as machine learning models to analyze data and generate optimal solutions and proposals.

[0109] A "database" refers to an information management system that systematically stores collected information and makes it easy to search and reference.

[0110] "Terminal" refers to hardware such as a computer or smart device that a user uses to enter information or view suggestions.

[0111] "Feedback" refers to opinions, requests, and evaluation information that users input to the system.

[0112] "Matching" refers to the process of comparing employee information with job requirements to find the optimal combination.

[0113] A "skill set" refers to the specific set of skills, knowledge, and abilities that an employee possesses.

[0114] "Personality traits" refer to characteristics, behavioral patterns, and psychological attributes related to an employee's personality.

[0115] "Evaluation logic" refers to the rules and algorithms used to evaluate the match between employees and work based on collected data.

[0116] "User" refers to a person such as an employee or person in charge who uses the system.

[0117] "Proposal" refers to specific suggestions regarding optimal work and department placement created by generative AI based on the analysis results.

[0118] "UI (User Interface)" refers to the screen and operation method that allows users to input and operate the system.

[0119] The present invention is a system for assigning optimal tasks and departments to employees based on their skills, experience, and personality traits. This system operates in cooperation with a server, terminals, and users.

[0120] First, the server collects employee information. This information includes resumes, qualifications, experience, personality traits, skill sets, etc. It also collects job descriptions and desired personnel data from each department of the company. This information is stored in a database. This information is collected by the HR department or an internal system. For example, information on new employees can be extracted from the HR system and stored in an SQL server.

[0121] The server then inputs the collected data into a generative AI model that comprehensively evaluates employees' skill sets, experience, and personality traits. This AI model scores and matches each employee with the job and department that best suits them. This model applies machine learning algorithms using programming languages ​​such as Python and R.

[0122] ●Example 1:

[0123] The server inputs a prompt into the generative AI model: "For a new marketing project, please suggest employees with experience in digital marketing. In doing so, please prioritize employees with SEO and SEM skills." Based on this prompt, the AI ​​model selects the most suitable employee from its database and generates a recommendation such as "Ichiro Tanaka: Recommend assignment to the digital marketing project."

[0124] The server then sends the generated placement proposal to the device of the person in charge and the target employee. The proposal is sent via API in JSON format or similar.

[0125] The terminal receives the proposal and displays it on the screen. Users (employees and staff) can review the proposal and provide feedback. For example, an employee can write "I think I'm a good fit for this project. I'd like to participate" in the input field.

[0126] The device sends the feedback entered by the user to the server, which receives it and stores it in a database.

[0127] Finally, the server reevaluates the employee based on the collected feedback and determines the final placement. The reevaluation uses a generative AI model again, recalculating the score based on the feedback. The final placement results after the reevaluation are then notified to the relevant department and employee.

[0128] The introduction of this system will not only enable fair and objective personnel allocation, making optimal use of employee capabilities, but is also expected to improve the company's overall operational efficiency and performance.

[0129] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0130] Step 1:

[0131] Collection of employee information

[0132] The server collects employee information from HR systems and self-reported data.

[0133] Input: Data such as resume, qualifications, experience, personality traits, and skill sets taken from HR systems and self-reporting forms.

[0134] Processing: Convert the acquired data into an appropriate format and store it in a database. For example, convert JSON format data into SQL queries and save them in a SQL server.

[0135] Output: Employee information stored in a database.

[0136] Step 2:

[0137] Collection of work content data

[0138] The server collects data on the work content and required personnel from each department.

[0139] Input: Data such as job requirements from each department, required skill sets, experience, characteristics, etc.

[0140] Processing: Convert the acquired data into an appropriate format and store it in a database. For example, analyze business content data provided in a CSV file and import it into an SQL server.

[0141] Output: Business content data stored in a database.

[0142] Step 3:

[0143] Data analysis and matching

[0144] The server inputs the collected employee information and business data into a generative AI model for analysis.

[0145] Input: Employee information and job description data stored in a database.

[0146] Processing: Input data into a generative AI model, which evaluates and scores each employee's skill set, experience, and personality traits. Based on the results, optimal matching of employees with jobs is performed. For example, a machine learning algorithm written in Python is run to calculate a job aptitude score for each employee.

[0147] Output: Generated employee and job matching data.

[0148] Step 4:

[0149] Generate placement proposals

[0150] The server creates specific placement proposals based on the analysis results.

[0151] Input: Matching data generated by the AI ​​model.

[0152] Processing: Summarize the specific tasks and departments suitable for each employee in a proposal. For example, generate an assignment proposal such as "Ichiro Tanaka: Recommend assignment to the digital marketing project."

[0153] Output: Placement proposal.

[0154] Step 5:

[0155] Submitting and Viewing Proposals

[0156] The server then sends the generated placement proposal to the terminals of the person in charge and the target employee. The terminals receive the proposal and display it on their screens.

[0157] Input: Placement proposal.

[0158] Processing: Send the suggestion data to the device via API in JSON format, for example, by using an HTTP request.

[0159] Output: The placement proposal displayed on the terminal.

[0160] Step 6:

[0161] Gathering feedback

[0162] The terminal provides a UI for users (employees and staff) to input their opinions and requests regarding the proposal and collects feedback.

[0163] Input: Feedback entered by the user.

[0164] Processing: Send the entered feedback to the server and store it in a database. For example, collect the input data from the feedback form and send it to the server via API.

[0165] Output: Feedback data stored in a database.

[0166] Step 7:

[0167] Reevaluation and Final Placement Decision

[0168] The server will re-evaluate based on the collected feedback and make a final placement decision.

[0169] Input: Feedback data.

[0170] Processing: The AI ​​model is re-run taking into account the feedback and recalculating the score. The final placement result is then generated after re-evaluation. For example, the AI ​​model parameters are adjusted based on the feedback and recalculated.

[0171] Output: The final placement result.

[0172] Step 8:

[0173] Notification of final placement results

[0174] The server notifies the department and employee in charge of the final placement result.

[0175] Input: Final placement result.

[0176] Processing: A message is generated to notify the department and employee of the final placement result and sent to the device. For example, notification is sent by email or push notification.

[0177] Output: Final placement results notified to the relevant department and employee.

[0178] (Application example 1)

[0179] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0180] Existing human resource allocation systems can assign optimal tasks based on employees' skills, experience, and personality traits, but applying this to factory robots is a similar challenge. An efficient system is also needed to process diverse data, including robot skill sets and performance data, and to notify updates on tasks in real time.

[0181] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0182] In this invention, the server includes means for collecting employee information and storing it in a database, means for collecting data on job content and desired personnel and storing it in a database, means for analyzing employee information and job data using generative AI and calculating the optimal employee-job match, means for sending and displaying appropriate placement proposals to terminals, means for collecting feedback from terminals and storing it in a database, means for reevaluating based on the feedback and finalizing and notifying the optimal placement, means for collecting robot skill sets and performance data and matching optimal jobs, and means for sending matching results to the server and notifying the robots of job updates in real time. This makes it possible to assign optimal jobs not only to human employees but also to robots in the factory, thereby improving overall production efficiency.

[0183] "Employee information" refers to information held by a company about each employee, such as their resume, qualifications, experience, personality traits, and skill set.

[0184] A "database" is a collection of information that is systematically organized and stored so that it can be easily accessed when needed.

[0185] "Generative AI" is an artificial intelligence technology that generates new data and predictions based on existing data.

[0186] "Feedback" refers to the user's opinions and requests regarding suggestions and assignments.

[0187] A "skill set" is a collection of skills and abilities required to perform a specific job.

[0188] "Performance data" is information about the specific performance and capabilities of a robot or device.

[0189] "Matching" is the process of finding the best combination based on specific conditions.

[0190] A "server" is a central control device that processes information and manages data.

[0191] A "terminal" is a device that is connected to a server and can be directly operated by a user.

[0192] "Real time" refers to the immediacy in which state changes and data processing occur almost simultaneously.

[0193] The "best fit" is the work that can be performed most efficiently and effectively based on skill sets, experience, and performance data.

[0194] The present invention is a system that assigns optimal tasks and departments to employees and robots based on their skills, experience, and personality traits. This system operates in cooperation with a server, terminals, and users.

[0195] Server Processing

[0196] 1. Data Collection and Storage

[0197] The server collects employee and robot information and stores it in a database. This information includes resumes, qualifications, experience, personality traits, skill sets, and performance data. It also collects job descriptions and desired personnel data from each department and stores them in the database.

[0198] 2. Data analysis and matching

[0199] The server uses generative AI to analyze the collected employee information and work data. The AI ​​comprehensively evaluates the skill sets, experience, personality traits, and performance data of employees and robots, scoring and matching them with the most suitable jobs and departments.

[0200] 3. Generate and submit a proposal

[0201] Based on the analysis results, the server creates a specific placement proposal, which indicates which department and task each employee and robot is suited to. The proposal is then sent to the terminals of the person in charge and the target employee and robot.

[0202] Terminal handling

[0203] 1. Receiving and Displaying Proposals

[0204] The terminal receives the placement proposal sent from the server and displays it on the screen. Employees and personnel can review the proposal and provide feedback if necessary.

[0205] 2. Gathering feedback

[0206] The terminal provides a UI for employees and staff to input their opinions and requests regarding the proposal. Once the feedback is entered, it is sent to the server.

[0207] User Action

[0208] 1. Confirmation of proposal

[0209] Users (employees, staff, and robot managers) check the proposals from the server via their terminals. The proposals contain specific details about the tasks and departments that are best suited for them or the robots they are responsible for.

[0210] 2. Providing Feedback

[0211] Users input their opinions and requests regarding the proposals and send them to the server via their terminals. This feedback is used as an important factor in the final decision-making process.

[0212] Specific examples

[0213] For example, consider the launch of a new production line. This project requires human resources and robots with specific skill sets and experience. First, the project manager inputs project details (e.g., required skills, number of people, deadline) into the server. Based on this information, the server uses a generative AI model to select the most suitable employees and robots. The server then creates a list of candidates and robots evaluated by the AI ​​model and sends it to the project manager and stakeholders. The project manager and stakeholders review the proposals on their own devices and provide feedback. Based on this feedback, the server reevaluates and makes a final placement decision. The project manager and employees / robots are notified of the final placement results, allowing them to quickly transition to new tasks and roles.

[0214] Example prompts for generative AI models

[0215] "Robot A's skill set: [welding accuracy, speed, energy consumption]. Required tasks: Welding, part assembly, quality inspection. Please assign the most appropriate task to it."

[0216] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0217] Step 1:

[0218] The server collects employee information and robot information. Specifically, it stores employee resumes, qualifications, experience, personality traits, skill sets, and robot performance data in a database. This information is treated as input data. The input data is stored in the database and used in subsequent processes.

[0219] Step 2:

[0220] The server collects job descriptions and desired personnel data from each department and stores it in a database. This information includes the required skills, experience, work content, and specific performance requirements. The input data are job details, which are also stored in the database.

[0221] Step 3:

[0222] The server uses generative AI to analyze the collected employee information and business data as input. This analysis comprehensively evaluates each employee and robot's skill set, experience, personality traits, and performance data. Data calculations are used to calculate and score the optimal employee-job match. The output is a list of optimal matches.

[0223] Step 4:

[0224] The server then creates a specific placement proposal based on the analysis results. This placement proposal specifically indicates which department and task each employee and robot is suited to. The server then creates the placement proposal as output and sends it to the terminal.

[0225] Step 5:

[0226] The terminal receives the placement proposal sent from the server and displays it on the screens of the target employee and robot administrator. This proposal includes specific work content and reasons for the placement. The user receives this information as input data and confirms it.

[0227] Step 6:

[0228] The terminal provides a UI for employees and robot administrators to input their opinions and requests regarding the proposals, and collects feedback. The input feedback is sent from the terminal to the server, and the feedback data is obtained as output.

[0229] Step 7:

[0230] The server performs a re-evaluation based on the collected feedback data, which may result in modifications to the initial placement proposal. The re-evaluation process uses the feedback as input to adjust the evaluation logic and finalize the optimal placement. The output includes the finalized placement proposal.

[0231] Step 8:

[0232] The server notifies the final placement results and sends them to the terminals of the employees and robots. This allows each employee and robot to quickly transition to new tasks and roles. The input is the re-evaluated placement plan, and the output is the notified final placement result.

[0233] As a specific example, a prompt sentence for a generative AI model is used: "Robot A's skill set: [welding accuracy, speed, energy consumption]. Required tasks: Please assign the most appropriate task from among welding, part assembly, and quality inspection."

[0234] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0235] This invention is a system that assigns optimal tasks and departments based on employees' skills, experience, personality traits, and emotions. This system operates in cooperation with a server, terminals, users, and an emotion engine.

[0236] Server Processing

[0237] 1. Data Collection and Storage

[0238] The server first collects employee information and stores it in a database. This information includes resumes, qualifications, experience, personality traits, skill sets, etc. It also collects job descriptions and desired personnel data from each department and stores it in the database.

[0239] 2. Emotional Data Recognition

[0240] The server uses an emotion engine to recognize the emotions of employees and staff in real time, and this emotion data is used as important information when evaluating employees and collecting feedback.

[0241] 3. Data analysis and matching

[0242] The server inputs the collected employee information, work data, and emotional data into the generative AI, which then comprehensively evaluates each employee's skill set, experience, personality traits, emotions, etc., and scores and matches them with the department and job that best suits them.

[0243] 4. Generate and submit proposals

[0244] Based on the analysis results, the server creates specific placement proposals, which indicate which departments and tasks each employee is suited to. These proposals also take into account emotional data. The proposals are then sent to the devices of the person in charge and the target employee.

[0245] Terminal handling

[0246] 1. Receiving and Displaying Proposals

[0247] The terminal receives the placement proposal sent from the server and displays it on the screen. Employees and personnel can review the proposal and provide feedback if necessary.

[0248] 2. Gathering feedback

[0249] The terminal provides a UI for employees and staff to input their opinions and requests regarding the proposal. Once the feedback is entered, the terminal sends it to the server.

[0250] User Action

[0251] 1. Confirmation of proposal

[0252] Users (employees and staff) check the proposals from the server through their terminals. The proposals include specific details about the tasks and departments that are best suited to them.

[0253] 2. Providing Feedback

[0254] Users input their opinions and requests regarding the proposals and send them to the server via their devices. This feedback includes emotional data recognized by the emotion engine.

[0255] Specific examples

[0256] For example, imagine a new project is being launched. This project requires people with a specific skill set and experience. The project manager enters the project details (skills needed, number of people, deadline, etc.) into the server. The server uses this information to select the best candidates using a database of current employees and emotional data from an emotion engine.

[0257] The server creates a list of candidates evaluated by the AI ​​model and emotion engine and sends it to the project manager and candidate's devices. The project manager and candidate review the proposals on their respective devices and provide feedback. Based on this feedback, the server reevaluates and makes the final placement decision.

[0258] The final allocation results are notified to the project manager and employee, allowing them to quickly transition to new tasks and roles. Using an emotion engine, allocation can be made taking into account the emotional state of employees, contributing to improved employee satisfaction and performance. This system ensures unbiased and fair personnel allocation, providing an environment where employees can perform to their full potential, while also improving the efficiency and performance of the entire organization.

[0259] The processing flow will be explained below.

[0260] Program processing steps

[0261] Step 1: Collect and store data

[0262] Server Operation

[0263] The server collects information from employees, such as their resumes, qualifications, experience, personality traits, and skill sets.

[0264] The server stores the collected employee information in a database.

[0265] The server collects data from each department about the work content and the personnel required.

[0266] The server stores the collected job details and desired personnel data in a database.

[0267] Step 2: Recognizing and storing emotion data

[0268] Server Operation

[0269] The server uses an emotion engine to recognize the emotion data of employees and personnel in real time.

[0270] The server organizes the recognized emotion data for each employee and stores it in a database.

[0271] Step 3: Analyze and match data

[0272] Server Operation

[0273] The server inputs employee information, business data, and emotional data stored in a database into the generative AI.

[0274] The server uses generative AI to comprehensively assess an employee's skill set, experience, personality traits, and emotional state.

[0275] Based on the evaluation results obtained from the AI ​​model, the server calculates scores for the departments and tasks that are most suitable for each employee and creates a matching list.

[0276] Step 4: Generate and submit a proposal

[0277] Server Operation

[0278] The server generates specific placement proposals based on the matching list, which also reflect the emotional data.

[0279] The server transmits the generated placement proposal to the terminals of the person in charge and employees.

[0280] Step 5: Receive and view proposals

[0281] Device behavior

[0282] The terminal receives the placement proposal sent from the server.

[0283] The terminal displays placement proposals on the screen so that employees and staff can review them.

[0284] Step 6: Provide feedback

[0285] User Actions

[0286] Users (employees and staff) check the placement proposals displayed on their terminals.

[0287] The user inputs their opinions and requests regarding the proposal into the terminal.

[0288] Device behavior

[0289] The terminal transmits the input feedback to the server.

[0290] Step 7: Collect and analyze feedback

[0291] Server Operation

[0292] The server stores the received feedback and analyzes it, including emotional data.

[0293] Based on the results of the feedback analysis, the server adjusts the evaluation logic and reflects it in the database.

[0294] Step 8: Reassess and finalize

[0295] Server Operation

[0296] Based on the adjusted evaluation logic, the server calls the generation AI again and performs a re-evaluation.

[0297] The server will make a final decision on the optimal placement based on the results of the re-evaluation.

[0298] The server sends the final decision to the person in charge and to the employee's terminal.

[0299] Step 9: Notification and confirmation of final placement results

[0300] Device behavior

[0301] The terminal receives the final placement result and displays it on the screen.

[0302] The terminal notifies relevant personnel and employees.

[0303] Example 2

[0304] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0305] While traditional employee placement systems can take into account employee skills, experience, and personality traits, they have difficulty taking into account employees' emotional state. As a result, employee satisfaction and performance decline, making it difficult to achieve optimal personnel placement. Furthermore, the process of collecting feedback and reevaluating it is inefficient, making it difficult to make quick placement decisions.

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

[0307] In this invention, the server includes means for collecting employee information and storing it in a database, means for collecting job content and desired personnel data and storing it in a database, means for recognizing and evaluating emotional data in real time, means for analyzing employee information, job data, and emotional data using generative AI and calculating the optimal employee-job match, means for generating optimal placement proposals and sending and displaying them to the terminal, means for collecting feedback from the terminal and storing it in a database, and means for reevaluating based on the feedback and emotional data, and for finalizing and notifying the optimal placement. This enables optimal personnel placement that takes employees' emotional states into consideration, thereby improving employee satisfaction and performance. Furthermore, the efficient collection of feedback and reevaluation process enables rapid placement decisions.

[0308] "Employee Information" means key data relating to an employee, such as an employee's biography, qualifications, experience, personality traits, and skill set.

[0309] "Job Description" refers to data that details the specific tasks an employee will be responsible for, as well as the skill set and experience required for those tasks.

[0310] "Desired talent data" refers to data that defines the characteristics of an ideal candidate, such as the skills, experience, and personality traits required for a specific job or department.

[0311] "Emotion data" refers to real-time assessment of the current emotional state of employees and personnel, and is obtained by the emotion engine.

[0312] "Generative AI" is an artificial intelligence that can analyze input data and generate optimal information, and is a model used to match employees with jobs and generate placement proposals.

[0313] A "database" is a system for centrally storing and managing collected employee information, job descriptions, desired personnel data, emotional data, feedback, etc.

[0314] "Terminal" refers to a device that employees and staff use to receive and display proposals from the server and to input and send opinions and requests as feedback.

[0315] "Feedback" refers to opinions and requests provided by employees and staff via terminals, and is information used for reevaluation and optimal placement decisions.

[0316] "Reevaluation" refers to the process of re-analyzing the collected feedback and sentiment data, and is a key step in arriving at a final placement decision.

[0317] "Placement proposals" refer to recommendations made by generative AI based on the analysis results, indicating which department or job each employee would be best suited to.

[0318] This invention is a system that assigns optimal tasks and departments to employees based on their skills, experience, personality traits, and emotions. This system is primarily composed of a server, terminals, and users, and operates through the cooperation of an emotion engine and a generative AI model. A specific embodiment of this system is shown below.

[0319] Server Features

[0320] Data Collection and Storage

[0321] The server periodically collects employee information (resume, qualifications, experience, personality traits, skill sets, etc.) and stores it in a database. To do this, the server implements an API to retrieve data from various sources and centrally manages the information using database technologies such as SQLite or RDS.

[0322] Emotional Data Recognition

[0323] The server uses an emotion engine (e.g., IBM Watson's emotion analysis API) to recognize the emotion data of employees and personnel in real time. The emotion data is sent to the emotion engine via the API when text is entered, and the evaluation results are stored in the database.

[0324] Data analysis and matching

[0325] The server inputs the collected employee information, business data, and emotional data into a generative AI model (e.g., GPT-4). The generative AI model uses this information to optimally match employees with jobs and assign scores. The model is primarily operated in a cloud environment such as Google Colab.

[0326] Generate and submit proposals

[0327] The server generates optimal placement proposals based on the analysis results of the generative AI model. These proposals are created in a format such as JSON and sent to the devices of employees and personnel in charge.

[0328] Device Features

[0329] Receiving and Viewing Suggestions

[0330] The terminal receives the placement proposal sent from the server and displays it on the user interface, allowing employees and personnel in charge to confirm the proposal.

[0331] Gathering feedback

[0332] The device provides a UI for users to input their opinions and requests regarding the proposal. The feedback entered by the user is sent to the server via the device and stored in a database.

[0333] User Roles

[0334] Review the proposal

[0335] Users (employees and staff) can check the proposals from the server through their terminals, which show the tasks and departments that are best suited to the user.

[0336] Providing feedback

[0337] Users input their opinions and requests regarding the suggestions and send them to the server via their devices. The feedback also includes emotional data recognized by the emotion engine.

[0338] Specific examples

[0339] For example, consider a new project that requires a specific skill set and experience, so the project manager inputs the project details (skills needed, number of people, deadline, etc.) into the server. The server uses this information to select the best candidates using the current employee database and sentiment data from the sentiment engine.

[0340] The candidate list, evaluated using the generative AI model and emotion engine, is sent from the server to the project manager's and candidate's devices. The project manager and candidate review the proposals on their respective devices and provide feedback. Based on this feedback, the server reevaluates and determines the final placement. The project manager and employee are notified of the final placement results, enabling them to quickly transition to new tasks and roles.

[0341] Prompt Sentence Examples

[0342] "Select the best candidates for a new project. The project requires the following skills: programming, project management, and communication. The deadline is within 3 months. Use an emotion engine to take the candidate's emotions into account when selecting candidates."

[0343] In this way, the system achieves optimal personnel placement by linking the server, terminals, users, emotion engine, and generative AI model to evaluate employee skills and emotions in a balanced manner.

[0344] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0345] Step 1: Collecting and storing employee information

[0346] Server Processing

[0347] The server collects employee resumes, qualifications, experience, personality traits, and skill sets and stores them in a database. The inputs include information periodically updated by employees and new employee data provided by the HR department. The server organizes this information and stores the data using SQLite or RDS. Specifically, when an employee enters new credentials from their terminal, the data is sent to the server and the database is updated.

[0348] Input: Employee biography, qualifications, experience, personality traits, skill set

[0349] Output: Employee information stored in the database

[0350] Step 2: Collecting and storing business and department information

[0351] Server Processing

[0352] The server collects job descriptions and required personnel data from each department and stores it in a database. It provides an interface where department personnel can enter the required skill sets, number of employees, and job descriptions. Specifically, when a department manager enters details of a new project (for example, "online marketing campaign"), the information is stored in the database.

[0353] Input: Required skill sets, number of people, job description

[0354] Output: Business and department information stored in the database

[0355] Step 3: Obtaining emotion data

[0356] Server Processing

[0357] The server uses an emotion engine to obtain emotional data from employees and staff in real time. Input includes text data such as feedback. The server sends this data to the emotion engine, which evaluates the emotional state. Specifically, if an employee types, "I'm confident about this project," the text data is sent to the emotion engine, which evaluates it as a positive emotion.

[0358] Input: Text data such as feedback

[0359] Output: Emotion data stored in a database

[0360] Step 4: Analyze and match data

[0361] Server Processing

[0362] The server inputs the collected employee information, job data, and emotional data into a generative AI model to match the optimal employee with the job. Inputs include the employee's skill set, experience, personality traits, and emotional data. The generative AI model (e.g., GPT-4) performs scoring and matching based on this data. Specifically, the server extracts employees who have Java programming qualifications and marketing experience, and inputs this into the generative AI model to match them with the optimal job.

[0363] Input: Employee information, business data, emotional data

[0364] Output: Matched optimal employee-job pairs

[0365] Step 5: Generate and submit a proposal

[0366] Server Processing

[0367] The server generates optimal placement proposals based on the analysis results of the generative AI model. The proposals are created in formats such as JSON and sent to the devices of employees and staff. Specifically, the server generates a proposal stating that "Employee A is ideal for the digital marketing campaign project" and sends the proposal data to the devices of Employee A and his manager.

[0368] Input: Analysis results

[0369] Output: Placement proposal sent to the device

[0370] Step 6: Review the proposal and gather feedback

[0371] Terminal handling

[0372] The terminal receives the placement proposal sent from the server and displays it on the user interface. Employees and personnel in charge review the proposal and input their own opinions and requests. Specifically, when Employee A reviews the proposal on the terminal and inputs feedback such as "I'm interested in this job," that information is sent to the server.

[0373] Input: Placement proposal

[0374] Output: Feedback sent to the server

[0375] Step 7: Reassess the feedback and finalize

[0376] Server Processing

[0377] The server inputs the collected feedback data back into the generation AI model and re-evaluates the employee, taking into account emotional data. It then makes a final placement decision and notifies each employee and the person in charge of the results. Specifically, the server receives feedback from employee A, re-evaluates the employee, generates the final placement result, and notifies the employee that "employee A has been officially assigned to the digital marketing campaign project."

[0378] Input: Feedback data, emotion data

[0379] Output: Notification of final placement result

[0380] (Application example 2)

[0381] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0382] While there were systems in place that assigned optimal tasks and departments to employees based on their skills, experience, personality traits, and emotions, there were no systems that proposed optimal placements for robots used in factories based on their skills, operating status, and environmental data. This made it difficult to maximize the robots' work efficiency, and improving the efficiency of the entire factory was a challenge.

[0383] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting employee information and storing it in a database, means for collecting data on job content and desired personnel and storing it in a database, means for analyzing employee information and job data using generative AI and calculating the optimal employee-job match, means for sending and displaying appropriate placement proposals to the terminal, means for collecting feedback from the terminal and storing it in a database, means for reevaluating based on the feedback and finalizing and notifying the optimal placement, means for collecting robot information and storing it in a database, and means for generating optimal placement proposals based on the robot's skills, operating data, and environmental data. This enables optimal placement of robots used in a factory and maximizes work efficiency.

[0384] "Employee Information" is information including an employee's biography, qualifications, experience, personality traits, and skill set.

[0385] A "database" is a system for systematically storing and managing collected information.

[0386] "Means" refers to a method or device used to achieve a particular purpose.

[0387] "Generative AI" refers to artificial intelligence that analyzes data and generates optimal conclusions and suggestions.

[0388] "Matching" refers to finding the optimal combination based on specific conditions.

[0389] "Terminal" refers to the device used by employees and administrators to interface with the system.

[0390] "Feedback" refers to opinions and requests provided regarding a system or proposal.

[0391] "Reassessment" is the process of repeating the initial assessment based on the feedback collected.

[0392] "Robot information" refers to information including the robot's skills, operating status, and environmental data.

[0393] "Operation data" is data that indicates how much work the robot has performed.

[0394] "Environmental data" refers to data including temperature, humidity, and other environmental conditions of the work area.

[0395] This invention is a system that proposes optimal placement of employees and robots based on their skills, experience, environmental information, etc. This system operates in cooperation with a server, terminals, users, and generative AI.

[0396] Server Processing

[0397] The server collects employee and robot information and stores it in a database. Employee information includes resumes, qualifications, experience, personality traits, and skill sets, while robot information includes skills, operating data, and environmental data (temperature, humidity, etc.). The server then uses generative AI to analyze the collected information and calculate the optimal employee-job match and optimal robot placement. These placement proposals are sent to and displayed on the terminal.

[0398] Terminal handling

[0399] The terminal receives the placement proposal sent from the server and displays it on the screen. Employees, personnel, and robot administrators can use the UI to review the proposal and provide feedback as needed. The provided feedback is sent from the terminal to the server.

[0400] User Action

[0401] Users (employees, staff, and robot administrators) review the proposals from the server through their terminals. The proposals specifically describe the tasks and placements that are best suited for them and the robots. Users input their opinions and requests regarding the proposals and send them to the server through their terminals. This feedback includes data that will be used to reassess the evaluation.

[0402] Specific examples

[0403] For example, consider the launch of a new project. This project requires human resources and robots with specific skill sets and experience. The project manager enters project details (required skills, number of people, deadline, etc.) into the server. Using this information, the server selects the most suitable candidates and robots using data from the current employee database and robot database. The server then creates a candidate list and robot placement proposals evaluated using generative AI and evaluation logic, and sends them to the project manager and each employee and robot administrator's device. The project manager, employee, and robot administrator review the proposals on their respective devices and provide feedback. Based on this feedback, the server reevaluates and makes the final placement decision.

[0404] Here are some examples of prompts for generative AI models:

[0405] text

[0406] Develop an application that proposes the optimal placement of robots in a factory by applying a system that assigns employees the most suitable tasks and departments based on their skills, experience, personality traits, and emotions. Please output the program code that generates the proposal using the following specific data:

[0407] Robot ID: 1, Skill Level: 7, Operating Time: 120, Area Temperature: 20, Area Humidity: 50

[0408] Robot ID: 2, Skill Level: 5, Run Time: 150, Area Temperature: 22, Area Humidity: 55

[0409] Robot ID: 3, Skill Level: 9, Operating Hours: 100, Area Temperature: 21, Area Humidity: 52

[0410] Robot ID: 4, Skill Level: 6, Operating Hours: 130, Area Temperature: 19, Area Humidity: 48

[0411] This invention enables optimal allocation of employees and robots and maximizes work efficiency.

[0412] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0413] Step 1:

[0414] The server collects employee information and robot information. Employee information includes resumes, qualifications, experience, personality traits, and skill sets, while robot information includes skills, operating data, and environmental data (temperature, humidity, etc.). This collected information is temporarily stored in a database. The input is employee information and robot information, and the output is the information stored in the database.

[0415] Step 2:

[0416] The server collects job descriptions and required personnel data and stores them in a database. The job descriptions include project details (required skills, number of people, deadline, etc.), and the required personnel data describes the skills and qualifications required for each job. The input is the job description and required personnel data, and the output is the job data stored in the database.

[0417] Step 3:

[0418] The server inputs the collected employee information, robot information, and business data into the generative AI. The generative AI comprehensively evaluates the employee skill sets, experience, personality traits, and emotions, as well as the robot's skills, operating status, and environmental data, to calculate the optimal employee-to-business match and optimal robot placement. The inputs are employee information, robot information, and business data, and the output is the optimal match and placement proposal.

[0419] Step 4:

[0420] The server sends the optimal placement proposal generated by the generative AI to the device. The user receives the proposal through the device and checks the proposal displayed on the screen. The input is the optimal placement proposal, and the output is the placement proposal displayed on the device.

[0421] Step 5:

[0422] The user inputs their opinions and requests about the proposal as feedback using the device's UI. This feedback is sent from the device to the server. The input is the feedback, and the output is the feedback sent to the server.

[0423] Step 6:

[0424] The server analyzes the collected feedback and adjusts the evaluation logic. After adjusting the evaluation logic, the server re-evaluates using generative AI to finally determine the optimal placement. The input is the feedback, and the output is the adjusted evaluation logic and the re-evaluated placement proposal.

[0425] Step 7:

[0426] The server notifies the terminal of the final placement proposal and notifies the user. The user can confirm the final placement proposal through the terminal and quickly transition to the new job or role. The input is the final placement proposal, and the output is the final proposal displayed on the terminal.

[0427] The above are the specific processing steps of the system that realizes this application example. This step-by-step processing makes it possible to achieve optimal allocation of employees and robots and maximize work efficiency.

[0428] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0429] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0430] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0431] [Second embodiment]

[0432] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0433] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0434] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0435] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0436] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0437] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0438] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0439] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0440] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0441] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0442] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0443] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0444] The present invention is a system for assigning optimal tasks and departments to employees based on their skills, experience, and personality traits. This system operates in cooperation with a server, terminals, and users.

[0445] Server Processing

[0446] 1. Data Collection and Storage

[0447] The server first collects employee information and stores it in a database. This information includes resumes, qualifications, experience, personality traits, skill sets, etc. It also collects job descriptions and desired personnel data from each department of the company and stores them in the database as well.

[0448] 2. Data analysis and matching

[0449] The server uses generative AI to analyze the collected employee information and work data, comprehensively evaluating employees' skill sets, experience, personality traits, etc., and scores and matches each employee with the most suitable job and department.

[0450] 3. Generate and submit a proposal

[0451] Based on the analysis results, the server creates specific placement proposals, which indicate which departments and tasks each employee is suited to. The proposals are then sent to the devices of the person in charge and the target employees.

[0452] Terminal handling

[0453] 1. Receiving and Displaying Proposals

[0454] The terminal receives the placement proposal sent from the server and displays it on the screen. Employees and personnel can review the proposal and provide feedback if necessary.

[0455] 2. Gathering feedback

[0456] The terminal provides a UI for employees and staff to input their opinions and requests regarding the proposal. Once the feedback is entered, it is sent to the server.

[0457] User Action

[0458] 1. Confirmation of proposal

[0459] Users (employees and staff) check the proposals from the server through their terminals. The proposals include specific details about the tasks and departments that are best suited to them.

[0460] 2. Providing Feedback

[0461] Users input their opinions and requests regarding the proposals and send them to the server via their terminals. This feedback is used as an important factor in the final decision-making process.

[0462] Specific examples

[0463] For example, consider a new project that requires people with specific skill sets and experience. First, the project manager inputs the project details (skills needed, number of people, deadline, etc.) into the server. The server uses this information to use a generative AI model to select the best candidates from the current employee database.

[0464] The server creates a list of candidates evaluated by the AI ​​model and sends it to the project manager and candidate's devices. The project manager and candidate review the proposals on their respective devices and provide feedback. Based on this feedback, the server reevaluates and makes the final placement decision.

[0465] The final allocation results are communicated to the project manager and employee, allowing them to quickly transition to their new job or role. This system ensures unbiased and fair allocation of personnel, providing an environment where employees can perform to their full potential while improving the efficiency and performance of the entire organization.

[0466] The processing flow will be explained below.

[0467] Program processing steps

[0468] Step 1: Collect and store data

[0469] Server Operation

[0470] The server collects information from employees, such as their resumes, qualifications, experience, personality traits, and skill sets.

[0471] The server stores the collected employee information in a database.

[0472] The server collects data from each department about the work content and the personnel required.

[0473] The server stores the collected job details and desired personnel data in a database.

[0474] Step 2: Analyze and match data

[0475] Server Operation

[0476] The server inputs employee information and business data stored in a database into the generative AI.

[0477] The server uses generative AI to comprehensively evaluate employees' skill sets, experience, personality traits, and more.

[0478] Based on the evaluation results obtained from the AI ​​model, the server calculates scores for the departments and tasks that are most suitable for each employee and creates a matching list.

[0479] Step 3: Generate and submit a proposal

[0480] Server Operation

[0481] The server generates specific placement proposals based on the matching list.

[0482] The server transmits the generated placement proposal to the terminals of the person in charge and employees.

[0483] Step 4: Receive and view proposals

[0484] Device behavior

[0485] The terminal receives the placement proposal sent from the server.

[0486] The terminal displays placement proposals on the screen so that employees and staff can review them.

[0487] Step 5: Provide feedback

[0488] User Actions

[0489] Users (employees and staff) check the placement proposals displayed on their terminals.

[0490] The user inputs their opinions and requests regarding the proposal into the terminal.

[0491] Device behavior

[0492] The terminal transmits the input feedback to the server.

[0493] Step 6: Analyze and store feedback

[0494] Server Operation

[0495] The server analyzes the received feedback and stores it in a database.

[0496] The server adjusts its rating logic based on the feedback.

[0497] Step 7: Reassess and finalize

[0498] Server Operation

[0499] Based on the adjusted evaluation logic, the server calls the generation AI again and performs a re-evaluation.

[0500] The server will make a final decision on the optimal placement based on the results of the re-evaluation.

[0501] The server sends the final decision to the person in charge and to the employee's terminal.

[0502] Step 8: Notification of final placement results

[0503] Device behavior

[0504] The terminal receives the final placement result and displays it on the screen.

[0505] The terminal notifies relevant personnel and employees.

[0506] Example 1

[0507] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0508] The purpose of this invention is to effectively realize the right person in the right position in personnel allocation, maximize employee capabilities, and improve a company's business efficiency and performance. Conventional personnel allocation has had issues with subjective judgment and bias based on experience, making fair allocation difficult. It has also been difficult to quickly assign employees with the appropriate skill sets and experience to the appropriate tasks.

[0509] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0510] In this invention, the server includes means for collecting employee information and storing it in a database, means for collecting job content and desired human resource data and storing it in a database, means for analyzing employee information and job data using generative AI and calculating optimal employee-job matching, means for sending and displaying appropriate placement proposals to a terminal, means for collecting feedback from the terminal and storing it in a database, means for reevaluating based on the feedback and finalizing and notifying the optimal placement, means for evaluating each employee's skill set, experience, and personality traits based on the collected employee information and job data and optimally matching them based thereon, means for users to input feedback via a terminal and send it to the server, means for calculating scores for each employee using a generative AI model and proposing the optimal department and job, means for using the collected feedback to proceed with the reevaluation and finalize the placement, means for displaying the proposals and providing a UI for inputting feedback, and means for notifying the responsible department and employee of the final placement results after the reevaluation. This not only enables fair and objective personnel placement, optimally utilizing employee abilities, but also improves the company's business efficiency and performance.

[0511] "Employee Information" refers to detailed profile data such as an employee's biography, qualifications, experience, personality traits, and skill set.

[0512] "Job content" refers to the specific work and responsibilities performed in each department or project, as well as the required skills and experience.

[0513] "Generative AI" refers to algorithms that use artificial intelligence techniques such as machine learning models to analyze data and generate optimal solutions and proposals.

[0514] A "database" refers to an information management system that systematically stores collected information and makes it easy to search and reference.

[0515] "Terminal" refers to hardware such as a computer or smart device that a user uses to enter information or view suggestions.

[0516] "Feedback" refers to opinions, requests, and evaluation information that users input to the system.

[0517] "Matching" refers to the process of comparing employee information with job requirements to find the optimal combination.

[0518] A "skill set" refers to the specific set of skills, knowledge, and abilities that an employee possesses.

[0519] "Personality traits" refer to characteristics, behavioral patterns, and psychological attributes related to an employee's personality.

[0520] "Evaluation logic" refers to the rules and algorithms used to evaluate the match between employees and work based on collected data.

[0521] "User" refers to a person such as an employee or person in charge who uses the system.

[0522] "Proposal" refers to specific suggestions regarding optimal work and department placement created by generative AI based on the analysis results.

[0523] "UI (User Interface)" refers to the screen and operation method that allows users to input and operate the system.

[0524] The present invention is a system for assigning optimal tasks and departments to employees based on their skills, experience, and personality traits. This system operates in cooperation with a server, terminals, and users.

[0525] First, the server collects employee information. This information includes resumes, qualifications, experience, personality traits, skill sets, etc. It also collects job descriptions and desired personnel data from each department of the company. This information is stored in a database. This information is collected by the HR department or an internal system. For example, information on new employees can be extracted from the HR system and stored in an SQL server.

[0526] The server then inputs the collected data into a generative AI model that comprehensively evaluates employees' skill sets, experience, and personality traits. This AI model scores and matches each employee with the job and department that best suits them. This model applies machine learning algorithms using programming languages ​​such as Python and R.

[0527] ●Example 1:

[0528] The server inputs a prompt into the generative AI model: "For a new marketing project, please suggest employees with experience in digital marketing. In doing so, please prioritize employees with SEO and SEM skills." Based on this prompt, the AI ​​model selects the most suitable employee from its database and generates a recommendation such as "Ichiro Tanaka: Recommend assignment to the digital marketing project."

[0529] The server then sends the generated placement proposal to the device of the person in charge and the target employee. The proposal is sent via API in JSON format or similar.

[0530] The terminal receives the proposal and displays it on the screen. Users (employees and staff) can review the proposal and provide feedback. For example, an employee can write "I think I'm a good fit for this project. I'd like to participate" in the input field.

[0531] The device sends the feedback entered by the user to the server, which receives it and stores it in a database.

[0532] Finally, the server reevaluates the employee based on the collected feedback and determines the final placement. The reevaluation uses a generative AI model again, recalculating the score based on the feedback. The final placement results after the reevaluation are then notified to the relevant department and employee.

[0533] The introduction of this system will not only enable fair and objective personnel allocation, making optimal use of employee capabilities, but is also expected to improve the company's overall operational efficiency and performance.

[0534] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0535] Step 1:

[0536] Collection of employee information

[0537] The server collects employee information from HR systems and self-reported data.

[0538] Input: Data such as resume, qualifications, experience, personality traits, and skill sets taken from HR systems and self-reporting forms.

[0539] Processing: Convert the acquired data into an appropriate format and store it in a database. For example, convert JSON format data into SQL queries and save them in a SQL server.

[0540] Output: Employee information stored in a database.

[0541] Step 2:

[0542] Collection of work content data

[0543] The server collects data on the work content and required personnel from each department.

[0544] Input: Data such as job requirements from each department, required skill sets, experience, characteristics, etc.

[0545] Processing: Convert the acquired data into an appropriate format and store it in a database. For example, analyze business content data provided in a CSV file and import it into an SQL server.

[0546] Output: Business content data stored in a database.

[0547] Step 3:

[0548] Data analysis and matching

[0549] The server inputs the collected employee information and business data into a generative AI model for analysis.

[0550] Input: Employee information and job description data stored in a database.

[0551] Processing: Input data into a generative AI model, which evaluates and scores each employee's skill set, experience, and personality traits. Based on the results, optimal matching of employees with jobs is performed. For example, a machine learning algorithm written in Python is run to calculate a job aptitude score for each employee.

[0552] Output: Generated employee and job matching data.

[0553] Step 4:

[0554] Generate placement proposals

[0555] The server creates specific placement proposals based on the analysis results.

[0556] Input: Matching data generated by the AI ​​model.

[0557] Processing: Summarize the specific tasks and departments suitable for each employee in a proposal. For example, generate an assignment proposal such as "Ichiro Tanaka: Recommend assignment to the digital marketing project."

[0558] Output: Placement proposal.

[0559] Step 5:

[0560] Submitting and Viewing Proposals

[0561] The server then sends the generated placement proposal to the terminals of the person in charge and the target employee. The terminals receive the proposal and display it on their screens.

[0562] Input: Placement proposal.

[0563] Processing: Send the suggestion data to the device via API in JSON format, for example, by using an HTTP request.

[0564] Output: The placement proposal displayed on the terminal.

[0565] Step 6:

[0566] Gathering feedback

[0567] The terminal provides a UI for users (employees and staff) to input their opinions and requests regarding the proposal and collects feedback.

[0568] Input: Feedback entered by the user.

[0569] Processing: Send the entered feedback to the server and store it in a database. For example, collect the input data from the feedback form and send it to the server via API.

[0570] Output: Feedback data stored in a database.

[0571] Step 7:

[0572] Reevaluation and Final Placement Decision

[0573] The server will re-evaluate based on the collected feedback and make a final placement decision.

[0574] Input: Feedback data.

[0575] Processing: The AI ​​model is re-run taking into account the feedback and recalculating the score. The final placement result is then generated after re-evaluation. For example, the AI ​​model parameters are adjusted based on the feedback and recalculated.

[0576] Output: The final placement result.

[0577] Step 8:

[0578] Notification of final placement results

[0579] The server notifies the department and employee in charge of the final placement result.

[0580] Input: Final placement result.

[0581] Processing: A message is generated to notify the department and employee of the final placement result and sent to the device. For example, notification is sent by email or push notification.

[0582] Output: Final placement results notified to the relevant department and employee.

[0583] (Application example 1)

[0584] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0585] Existing human resource allocation systems can assign optimal tasks based on employees' skills, experience, and personality traits, but applying this to factory robots is a similar challenge. An efficient system is also needed to process diverse data, including robot skill sets and performance data, and to notify updates on tasks in real time.

[0586] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0587] In this invention, the server includes means for collecting employee information and storing it in a database, means for collecting data on job content and desired personnel and storing it in a database, means for analyzing employee information and job data using generative AI and calculating the optimal employee-job match, means for sending and displaying appropriate placement proposals to terminals, means for collecting feedback from terminals and storing it in a database, means for reevaluating based on the feedback and finalizing and notifying the optimal placement, means for collecting robot skill sets and performance data and matching optimal jobs, and means for sending matching results to the server and notifying the robots of job updates in real time. This makes it possible to assign optimal jobs not only to human employees but also to robots in the factory, thereby improving overall production efficiency.

[0588] "Employee information" refers to information held by a company about each employee, such as their resume, qualifications, experience, personality traits, and skill set.

[0589] A "database" is a collection of information that is systematically organized and stored so that it can be easily accessed when needed.

[0590] "Generative AI" is an artificial intelligence technology that generates new data and predictions based on existing data.

[0591] "Feedback" refers to the user's opinions and requests regarding suggestions and assignments.

[0592] A "skill set" is a collection of skills and abilities required to perform a specific job.

[0593] "Performance data" is information about the specific performance and capabilities of a robot or device.

[0594] "Matching" is the process of finding the best combination based on specific conditions.

[0595] A "server" is a central control device that processes information and manages data.

[0596] A "terminal" is a device that is connected to a server and can be directly operated by a user.

[0597] "Real time" refers to the immediacy in which state changes and data processing occur almost simultaneously.

[0598] The "best fit" is the work that can be performed most efficiently and effectively based on skill sets, experience, and performance data.

[0599] The present invention is a system that assigns optimal tasks and departments to employees and robots based on their skills, experience, and personality traits. This system operates in cooperation with a server, terminals, and users.

[0600] Server Processing

[0601] 1. Data Collection and Storage

[0602] The server collects employee and robot information and stores it in a database. This information includes resumes, qualifications, experience, personality traits, skill sets, and performance data. It also collects job descriptions and desired personnel data from each department and stores them in the database.

[0603] 2. Data analysis and matching

[0604] The server uses generative AI to analyze the collected employee information and work data. The AI ​​comprehensively evaluates the skill sets, experience, personality traits, and performance data of employees and robots, scoring and matching them with the most suitable jobs and departments.

[0605] 3. Generate and submit a proposal

[0606] Based on the analysis results, the server creates a specific placement proposal, which indicates which department and task each employee and robot is suited to. The proposal is then sent to the terminals of the person in charge and the target employee and robot.

[0607] Terminal handling

[0608] 1. Receiving and Displaying Proposals

[0609] The terminal receives the placement proposal sent from the server and displays it on the screen. Employees and personnel can review the proposal and provide feedback if necessary.

[0610] 2. Gathering feedback

[0611] The terminal provides a UI for employees and staff to input their opinions and requests regarding the proposal. Once the feedback is entered, it is sent to the server.

[0612] User Action

[0613] 1. Confirmation of proposal

[0614] Users (employees, staff, and robot managers) check the proposals from the server via their terminals. The proposals contain specific details about the tasks and departments that are best suited for them or the robots they are responsible for.

[0615] 2. Providing Feedback

[0616] Users input their opinions and requests regarding the proposals and send them to the server via their terminals. This feedback is used as an important factor in the final decision-making process.

[0617] Specific examples

[0618] For example, consider the launch of a new production line. This project requires human resources and robots with specific skill sets and experience. First, the project manager inputs project details (e.g., required skills, number of people, deadline) into the server. Based on this information, the server uses a generative AI model to select the most suitable employees and robots. The server then creates a list of candidates and robots evaluated by the AI ​​model and sends it to the project manager and stakeholders. The project manager and stakeholders review the proposals on their own devices and provide feedback. Based on this feedback, the server reevaluates and makes a final placement decision. The project manager and employees / robots are notified of the final placement results, allowing them to quickly transition to new tasks and roles.

[0619] Example prompts for generative AI models

[0620] "Robot A's skill set: [welding accuracy, speed, energy consumption]. Required tasks: Welding, part assembly, quality inspection. Please assign the most appropriate task to it."

[0621] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0622] Step 1:

[0623] The server collects employee information and robot information. Specifically, it stores employee resumes, qualifications, experience, personality traits, skill sets, and robot performance data in a database. This information is treated as input data. The input data is stored in the database and used in subsequent processes.

[0624] Step 2:

[0625] The server collects job descriptions and desired personnel data from each department and stores it in a database. This information includes the required skills, experience, work content, and specific performance requirements. The input data are job details, which are also stored in the database.

[0626] Step 3:

[0627] The server uses generative AI to analyze the collected employee information and business data as input. This analysis comprehensively evaluates each employee and robot's skill set, experience, personality traits, and performance data. Data calculations are used to calculate and score the optimal employee-job match. The output is a list of optimal matches.

[0628] Step 4:

[0629] The server then creates a specific placement proposal based on the analysis results. This placement proposal specifically indicates which department and task each employee and robot is suited to. The server then creates the placement proposal as output and sends it to the terminal.

[0630] Step 5:

[0631] The terminal receives the placement proposal sent from the server and displays it on the screens of the target employee and robot administrator. This proposal includes specific work content and reasons for the placement. The user receives this information as input data and confirms it.

[0632] Step 6:

[0633] The terminal provides a UI for employees and robot administrators to input their opinions and requests regarding the proposals, and collects feedback. The input feedback is sent from the terminal to the server, and the feedback data is obtained as output.

[0634] Step 7:

[0635] The server performs a re-evaluation based on the collected feedback data, which may result in modifications to the initial placement proposal. The re-evaluation process uses the feedback as input to adjust the evaluation logic and finalize the optimal placement. The output includes the finalized placement proposal.

[0636] Step 8:

[0637] The server notifies the final placement results and sends them to the terminals of the employees and robots. This allows each employee and robot to quickly transition to new tasks and roles. The input is the re-evaluated placement plan, and the output is the notified final placement result.

[0638] As a specific example, a prompt sentence for a generative AI model is used: "Robot A's skill set: [welding accuracy, speed, energy consumption]. Required tasks: Please assign the most appropriate task from among welding, part assembly, and quality inspection."

[0639] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0640] This invention is a system that assigns optimal tasks and departments based on employees' skills, experience, personality traits, and emotions. This system operates in cooperation with a server, terminals, users, and an emotion engine.

[0641] Server Processing

[0642] 1. Data Collection and Storage

[0643] The server first collects employee information and stores it in a database. This information includes resumes, qualifications, experience, personality traits, skill sets, etc. It also collects job descriptions and desired personnel data from each department and stores it in the database.

[0644] 2. Emotional Data Recognition

[0645] The server uses an emotion engine to recognize the emotions of employees and staff in real time, and this emotion data is used as important information when evaluating employees and collecting feedback.

[0646] 3. Data analysis and matching

[0647] The server inputs the collected employee information, work data, and emotional data into the generative AI, which then comprehensively evaluates each employee's skill set, experience, personality traits, emotions, etc., and scores and matches them with the department and job that best suits them.

[0648] 4. Generate and submit proposals

[0649] Based on the analysis results, the server creates specific placement proposals, which indicate which departments and tasks each employee is suited to. These proposals also take into account emotional data. The proposals are then sent to the devices of the person in charge and the target employee.

[0650] Terminal handling

[0651] 1. Receiving and Displaying Proposals

[0652] The terminal receives the placement proposal sent from the server and displays it on the screen. Employees and personnel can review the proposal and provide feedback if necessary.

[0653] 2. Gathering feedback

[0654] The terminal provides a UI for employees and staff to input their opinions and requests regarding the proposal. Once the feedback is entered, the terminal sends it to the server.

[0655] User Action

[0656] 1. Confirmation of proposal

[0657] Users (employees and staff) check the proposals from the server through their terminals. The proposals include specific details about the tasks and departments that are best suited to them.

[0658] 2. Providing Feedback

[0659] Users input their opinions and requests regarding the proposals and send them to the server via their devices. This feedback includes emotional data recognized by the emotion engine.

[0660] Specific examples

[0661] For example, imagine a new project is being launched. This project requires people with a specific skill set and experience. The project manager enters the project details (skills needed, number of people, deadline, etc.) into the server. The server uses this information to select the best candidates using a database of current employees and emotional data from an emotion engine.

[0662] The server creates a list of candidates evaluated by the AI ​​model and emotion engine and sends it to the project manager and candidate's devices. The project manager and candidate review the proposals on their respective devices and provide feedback. Based on this feedback, the server reevaluates and makes the final placement decision.

[0663] The final allocation results are notified to the project manager and employee, allowing them to quickly transition to new tasks and roles. Using an emotion engine, allocation can be made taking into account the emotional state of employees, contributing to improved employee satisfaction and performance. This system ensures unbiased and fair personnel allocation, providing an environment where employees can perform to their full potential, while also improving the efficiency and performance of the entire organization.

[0664] The processing flow will be explained below.

[0665] Program processing steps

[0666] Step 1: Collect and store data

[0667] Server Operation

[0668] The server collects information from employees, such as their resumes, qualifications, experience, personality traits, and skill sets.

[0669] The server stores the collected employee information in a database.

[0670] The server collects data from each department about the work content and the personnel required.

[0671] The server stores the collected job details and desired personnel data in a database.

[0672] Step 2: Recognizing and storing emotion data

[0673] Server Operation

[0674] The server uses an emotion engine to recognize the emotion data of employees and personnel in real time.

[0675] The server organizes the recognized emotion data for each employee and stores it in a database.

[0676] Step 3: Analyze and match data

[0677] Server Operation

[0678] The server inputs employee information, business data, and emotional data stored in a database into the generative AI.

[0679] The server uses generative AI to comprehensively assess an employee's skill set, experience, personality traits, and emotional state.

[0680] Based on the evaluation results obtained from the AI ​​model, the server calculates scores for the departments and tasks that are most suitable for each employee and creates a matching list.

[0681] Step 4: Generate and submit a proposal

[0682] Server Operation

[0683] The server generates specific placement proposals based on the matching list, which also reflect the emotional data.

[0684] The server transmits the generated placement proposal to the terminals of the person in charge and employees.

[0685] Step 5: Receive and view proposals

[0686] Device behavior

[0687] The terminal receives the placement proposal sent from the server.

[0688] The terminal displays placement proposals on the screen so that employees and staff can review them.

[0689] Step 6: Provide feedback

[0690] User Actions

[0691] Users (employees and staff) check the placement proposals displayed on their terminals.

[0692] The user inputs their opinions and requests regarding the proposal into the terminal.

[0693] Device behavior

[0694] The terminal transmits the input feedback to the server.

[0695] Step 7: Collect and analyze feedback

[0696] Server Operation

[0697] The server stores the received feedback and analyzes it, including emotional data.

[0698] Based on the results of the feedback analysis, the server adjusts the evaluation logic and reflects it in the database.

[0699] Step 8: Reassess and finalize

[0700] Server Operation

[0701] Based on the adjusted evaluation logic, the server calls the generation AI again and performs a re-evaluation.

[0702] The server will make a final decision on the optimal placement based on the results of the re-evaluation.

[0703] The server sends the final decision to the person in charge and to the employee's terminal.

[0704] Step 9: Notification and confirmation of final placement results

[0705] Device behavior

[0706] The terminal receives the final placement result and displays it on the screen.

[0707] The terminal notifies relevant personnel and employees.

[0708] Example 2

[0709] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0710] While traditional employee placement systems can take into account employee skills, experience, and personality traits, they have difficulty taking into account employees' emotional state. As a result, employee satisfaction and performance decline, making it difficult to achieve optimal personnel placement. Furthermore, the process of collecting feedback and reevaluating it is inefficient, making it difficult to make quick placement decisions.

[0711] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0712] In this invention, the server includes means for collecting employee information and storing it in a database, means for collecting job content and desired personnel data and storing it in a database, means for recognizing and evaluating emotional data in real time, means for analyzing employee information, job data, and emotional data using generative AI and calculating the optimal employee-job match, means for generating optimal placement proposals and sending and displaying them to the terminal, means for collecting feedback from the terminal and storing it in a database, and means for reevaluating based on the feedback and emotional data, and for finalizing and notifying the optimal placement. This enables optimal personnel placement that takes employees' emotional states into consideration, thereby improving employee satisfaction and performance. Furthermore, the efficient collection of feedback and reevaluation process enables rapid placement decisions.

[0713] "Employee Information" means key data relating to an employee, such as an employee's biography, qualifications, experience, personality traits, and skill set.

[0714] "Job Description" refers to data that details the specific tasks an employee will be responsible for, as well as the skill set and experience required for those tasks.

[0715] "Desired talent data" refers to data that defines the characteristics of an ideal candidate, such as the skills, experience, and personality traits required for a specific job or department.

[0716] "Emotion data" refers to real-time assessment of the current emotional state of employees and personnel, and is obtained by the emotion engine.

[0717] "Generative AI" is an artificial intelligence that can analyze input data and generate optimal information, and is a model used to match employees with jobs and generate placement proposals.

[0718] A "database" is a system for centrally storing and managing collected employee information, job descriptions, desired personnel data, emotional data, feedback, etc.

[0719] "Terminal" refers to a device that employees and staff use to receive and display proposals from the server and to input and send opinions and requests as feedback.

[0720] "Feedback" refers to opinions and requests provided by employees and staff via terminals, and is information used for reevaluation and optimal placement decisions.

[0721] "Reevaluation" refers to the process of re-analyzing the collected feedback and sentiment data, and is a key step in arriving at a final placement decision.

[0722] "Placement proposals" refer to recommendations made by generative AI based on the analysis results, indicating which department or job each employee would be best suited to.

[0723] This invention is a system that assigns optimal tasks and departments to employees based on their skills, experience, personality traits, and emotions. This system is primarily composed of a server, terminals, and users, and operates through the cooperation of an emotion engine and a generative AI model. A specific embodiment of this system is shown below.

[0724] Server Features

[0725] Data Collection and Storage

[0726] The server periodically collects employee information (resume, qualifications, experience, personality traits, skill sets, etc.) and stores it in a database. To do this, the server implements an API to retrieve data from various sources and centrally manages the information using database technologies such as SQLite or RDS.

[0727] Emotional Data Recognition

[0728] The server uses an emotion engine (e.g., IBM Watson's emotion analysis API) to recognize the emotion data of employees and personnel in real time. The emotion data is sent to the emotion engine via the API when text is entered, and the evaluation results are stored in the database.

[0729] Data analysis and matching

[0730] The server inputs the collected employee information, business data, and emotional data into a generative AI model (e.g., GPT-4). The generative AI model uses this information to optimally match employees with jobs and assign scores. The model is primarily operated in a cloud environment such as Google Colab.

[0731] Generate and submit proposals

[0732] The server generates optimal placement proposals based on the analysis results of the generative AI model. These proposals are created in a format such as JSON and sent to the devices of employees and personnel in charge.

[0733] Device Features

[0734] Receiving and Viewing Suggestions

[0735] The terminal receives the placement proposal sent from the server and displays it on the user interface, allowing employees and personnel in charge to confirm the proposal.

[0736] Gathering feedback

[0737] The device provides a UI for users to input their opinions and requests regarding the proposal. The feedback entered by the user is sent to the server via the device and stored in a database.

[0738] User Roles

[0739] Review the proposal

[0740] Users (employees and staff) can check the proposals from the server through their terminals, which show the tasks and departments that are best suited to the user.

[0741] Providing feedback

[0742] Users input their opinions and requests regarding the suggestions and send them to the server via their devices. The feedback also includes emotional data recognized by the emotion engine.

[0743] Specific examples

[0744] For example, consider a new project that requires a specific skill set and experience, so the project manager inputs the project details (skills needed, number of people, deadline, etc.) into the server. The server uses this information to select the best candidates using the current employee database and sentiment data from the sentiment engine.

[0745] The candidate list, evaluated using the generative AI model and emotion engine, is sent from the server to the project manager's and candidate's devices. The project manager and candidate review the proposals on their respective devices and provide feedback. Based on this feedback, the server reevaluates and determines the final placement. The project manager and employee are notified of the final placement results, enabling them to quickly transition to new tasks and roles.

[0746] Prompt Sentence Examples

[0747] "Select the best candidates for a new project. The project requires the following skills: programming, project management, and communication. The deadline is within 3 months. Use an emotion engine to take the candidate's emotions into account when selecting candidates."

[0748] In this way, the system achieves optimal personnel placement by linking the server, terminals, users, emotion engine, and generative AI model to evaluate employee skills and emotions in a balanced manner.

[0749] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0750] Step 1: Collecting and storing employee information

[0751] Server Processing

[0752] The server collects employee resumes, qualifications, experience, personality traits, and skill sets and stores them in a database. The inputs include information periodically updated by employees and new employee data provided by the HR department. The server organizes this information and stores the data using SQLite or RDS. Specifically, when an employee enters new credentials from their terminal, the data is sent to the server and the database is updated.

[0753] Input: Employee biography, qualifications, experience, personality traits, skill set

[0754] Output: Employee information stored in the database

[0755] Step 2: Collecting and storing business and department information

[0756] Server Processing

[0757] The server collects job descriptions and required personnel data from each department and stores it in a database. It provides an interface where department personnel can enter the required skill sets, number of employees, and job descriptions. Specifically, when a department manager enters details of a new project (for example, "online marketing campaign"), the information is stored in the database.

[0758] Input: Required skill sets, number of people, job description

[0759] Output: Business and department information stored in the database

[0760] Step 3: Obtaining emotion data

[0761] Server Processing

[0762] The server uses an emotion engine to obtain emotional data from employees and staff in real time. Input includes text data such as feedback. The server sends this data to the emotion engine, which evaluates the emotional state. Specifically, if an employee types, "I'm confident about this project," the text data is sent to the emotion engine, which evaluates it as a positive emotion.

[0763] Input: Text data such as feedback

[0764] Output: Emotion data stored in a database

[0765] Step 4: Analyze and match data

[0766] Server Processing

[0767] The server inputs the collected employee information, job data, and emotional data into a generative AI model to match the optimal employee with the job. Inputs include the employee's skill set, experience, personality traits, and emotional data. The generative AI model (e.g., GPT-4) performs scoring and matching based on this data. Specifically, the server extracts employees who have Java programming qualifications and marketing experience, and inputs this into the generative AI model to match them with the optimal job.

[0768] Input: Employee information, business data, emotional data

[0769] Output: Matched optimal employee-job pairs

[0770] Step 5: Generate and submit a proposal

[0771] Server Processing

[0772] The server generates optimal placement proposals based on the analysis results of the generative AI model. The proposals are created in formats such as JSON and sent to the devices of employees and staff. Specifically, the server generates a proposal stating that "Employee A is ideal for the digital marketing campaign project" and sends the proposal data to the devices of Employee A and his manager.

[0773] Input: Analysis results

[0774] Output: Placement proposal sent to the device

[0775] Step 6: Review the proposal and gather feedback

[0776] Terminal handling

[0777] The terminal receives the placement proposal sent from the server and displays it on the user interface. Employees and personnel in charge review the proposal and input their own opinions and requests. Specifically, when Employee A reviews the proposal on the terminal and inputs feedback such as "I'm interested in this job," that information is sent to the server.

[0778] Input: Placement proposal

[0779] Output: Feedback sent to the server

[0780] Step 7: Reassess the feedback and finalize

[0781] Server Processing

[0782] The server inputs the collected feedback data back into the generation AI model and re-evaluates the employee, taking into account emotional data. It then makes a final placement decision and notifies each employee and the person in charge of the results. Specifically, the server receives feedback from employee A, re-evaluates the employee, generates the final placement result, and notifies the employee that "employee A has been officially assigned to the digital marketing campaign project."

[0783] Input: Feedback data, emotion data

[0784] Output: Notification of final placement result

[0785] (Application example 2)

[0786] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0787] While there were systems in place that assigned optimal tasks and departments to employees based on their skills, experience, personality traits, and emotions, there were no systems that proposed optimal placements for robots used in factories based on their skills, operating status, and environmental data. This made it difficult to maximize the robots' work efficiency, and improving the efficiency of the entire factory was a challenge.

[0788] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting employee information and storing it in a database, means for collecting data on job content and desired personnel and storing it in a database, means for analyzing employee information and job data using generative AI and calculating the optimal employee-job match, means for sending and displaying appropriate placement proposals to the terminal, means for collecting feedback from the terminal and storing it in a database, means for reevaluating based on the feedback and finalizing and notifying the optimal placement, means for collecting robot information and storing it in a database, and means for generating optimal placement proposals based on the robot's skills, operating data, and environmental data. This enables optimal placement of robots used in a factory and maximizes work efficiency.

[0789] "Employee Information" is information including an employee's biography, qualifications, experience, personality traits, and skill set.

[0790] A "database" is a system for systematically storing and managing collected information.

[0791] "Means" refers to a method or device used to achieve a particular purpose.

[0792] "Generative AI" refers to artificial intelligence that analyzes data and generates optimal conclusions and suggestions.

[0793] "Matching" refers to finding the optimal combination based on specific conditions.

[0794] "Terminal" refers to the device used by employees and administrators to interface with the system.

[0795] "Feedback" refers to opinions and requests provided regarding a system or proposal.

[0796] "Reassessment" is the process of repeating the initial assessment based on the feedback collected.

[0797] "Robot information" refers to information including the robot's skills, operating status, and environmental data.

[0798] "Operation data" is data that indicates how much work the robot has performed.

[0799] "Environmental data" refers to data including temperature, humidity, and other environmental conditions of the work area.

[0800] This invention is a system that proposes optimal placement of employees and robots based on their skills, experience, environmental information, etc. This system operates in cooperation with a server, terminals, users, and generative AI.

[0801] Server Processing

[0802] The server collects employee and robot information and stores it in a database. Employee information includes resumes, qualifications, experience, personality traits, and skill sets, while robot information includes skills, operating data, and environmental data (temperature, humidity, etc.). The server then uses generative AI to analyze the collected information and calculate the optimal employee-job match and optimal robot placement. These placement proposals are sent to and displayed on the terminal.

[0803] Terminal handling

[0804] The terminal receives the placement proposal sent from the server and displays it on the screen. Employees, personnel, and robot administrators can use the UI to review the proposal and provide feedback as needed. The provided feedback is sent from the terminal to the server.

[0805] User Action

[0806] Users (employees, staff, and robot administrators) review the proposals from the server through their terminals. The proposals specifically describe the tasks and placements that are best suited for them and the robots. Users input their opinions and requests regarding the proposals and send them to the server through their terminals. This feedback includes data that will be used to reassess the evaluation.

[0807] Specific examples

[0808] For example, consider the launch of a new project. This project requires human resources and robots with specific skill sets and experience. The project manager enters project details (required skills, number of people, deadline, etc.) into the server. Using this information, the server selects the most suitable candidates and robots using data from the current employee database and robot database. The server then creates a candidate list and robot placement proposals evaluated using generative AI and evaluation logic, and sends them to the project manager and each employee and robot administrator's device. The project manager, employee, and robot administrator review the proposals on their respective devices and provide feedback. Based on this feedback, the server reevaluates and makes the final placement decision.

[0809] Here are some examples of prompts for generative AI models:

[0810] text

[0811] Develop an application that proposes the optimal placement of robots in a factory by applying a system that assigns employees the most suitable tasks and departments based on their skills, experience, personality traits, and emotions. Please output the program code that generates the proposal using the following specific data:

[0812] Robot ID: 1, Skill Level: 7, Operating Time: 120, Area Temperature: 20, Area Humidity: 50

[0813] Robot ID: 2, Skill Level: 5, Run Time: 150, Area Temperature: 22, Area Humidity: 55

[0814] Robot ID: 3, Skill Level: 9, Operating Hours: 100, Area Temperature: 21, Area Humidity: 52

[0815] Robot ID: 4, Skill Level: 6, Operating Hours: 130, Area Temperature: 19, Area Humidity: 48

[0816] This invention enables optimal allocation of employees and robots and maximizes work efficiency.

[0817] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0818] Step 1:

[0819] The server collects employee information and robot information. Employee information includes resumes, qualifications, experience, personality traits, and skill sets, while robot information includes skills, operating data, and environmental data (temperature, humidity, etc.). This collected information is temporarily stored in a database. The input is employee information and robot information, and the output is the information stored in the database.

[0820] Step 2:

[0821] The server collects job descriptions and required personnel data and stores them in a database. The job descriptions include project details (required skills, number of people, deadline, etc.), and the required personnel data describes the skills and qualifications required for each job. The input is the job description and required personnel data, and the output is the job data stored in the database.

[0822] Step 3:

[0823] The server inputs the collected employee information, robot information, and business data into the generative AI. The generative AI comprehensively evaluates the employee skill sets, experience, personality traits, and emotions, as well as the robot's skills, operating status, and environmental data, to calculate the optimal employee-to-business match and optimal robot placement. The inputs are employee information, robot information, and business data, and the output is the optimal match and placement proposal.

[0824] Step 4:

[0825] The server sends the optimal placement proposal generated by the generative AI to the device. The user receives the proposal through the device and checks the proposal displayed on the screen. The input is the optimal placement proposal, and the output is the placement proposal displayed on the device.

[0826] Step 5:

[0827] The user inputs their opinions and requests about the proposal as feedback using the device's UI. This feedback is sent from the device to the server. The input is the feedback, and the output is the feedback sent to the server.

[0828] Step 6:

[0829] The server analyzes the collected feedback and adjusts the evaluation logic. After adjusting the evaluation logic, the server re-evaluates using generative AI to finally determine the optimal placement. The input is the feedback, and the output is the adjusted evaluation logic and the re-evaluated placement proposal.

[0830] Step 7:

[0831] The server notifies the terminal of the final placement proposal and notifies the user. The user can confirm the final placement proposal through the terminal and quickly transition to the new job or role. The input is the final placement proposal, and the output is the final proposal displayed on the terminal.

[0832] The above are the specific processing steps of the system that realizes this application example. This step-by-step processing makes it possible to achieve optimal allocation of employees and robots and maximize work efficiency.

[0833] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0834] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0835] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0836] [Third embodiment]

[0837] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0838] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0839] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0840] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0841] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0842] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0843] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0844] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0845] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0846] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0847] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0848] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0849] The present invention is a system for assigning optimal tasks and departments to employees based on their skills, experience, and personality traits. This system operates in cooperation with a server, terminals, and users.

[0850] Server Processing

[0851] 1. Data Collection and Storage

[0852] The server first collects employee information and stores it in a database. This information includes resumes, qualifications, experience, personality traits, skill sets, etc. It also collects job descriptions and desired personnel data from each department of the company and stores them in the database as well.

[0853] 2. Data analysis and matching

[0854] The server uses generative AI to analyze the collected employee information and work data, comprehensively evaluating employees' skill sets, experience, personality traits, etc., and scores and matches each employee with the most suitable job and department.

[0855] 3. Generate and submit a proposal

[0856] Based on the analysis results, the server creates specific placement proposals, which indicate which departments and tasks each employee is suited to. The proposals are then sent to the devices of the person in charge and the target employees.

[0857] Terminal handling

[0858] 1. Receiving and Displaying Proposals

[0859] The terminal receives the placement proposal sent from the server and displays it on the screen. Employees and personnel can review the proposal and provide feedback if necessary.

[0860] 2. Gathering feedback

[0861] The terminal provides a UI for employees and staff to input their opinions and requests regarding the proposal. Once the feedback is entered, it is sent to the server.

[0862] User Action

[0863] 1. Confirmation of proposal

[0864] Users (employees and staff) check the proposals from the server through their terminals. The proposals include specific details about the tasks and departments that are best suited to them.

[0865] 2. Providing Feedback

[0866] Users input their opinions and requests regarding the proposals and send them to the server via their terminals. This feedback is used as an important factor in the final decision-making process.

[0867] Specific examples

[0868] For example, consider a new project that requires people with specific skill sets and experience. First, the project manager inputs the project details (skills needed, number of people, deadline, etc.) into the server. The server uses this information to use a generative AI model to select the best candidates from the current employee database.

[0869] The server creates a list of candidates evaluated by the AI ​​model and sends it to the project manager and candidate's devices. The project manager and candidate review the proposals on their respective devices and provide feedback. Based on this feedback, the server reevaluates and makes the final placement decision.

[0870] The final allocation results are communicated to the project manager and employee, allowing them to quickly transition to their new job or role. This system ensures unbiased and fair allocation of personnel, providing an environment where employees can perform to their full potential while improving the efficiency and performance of the entire organization.

[0871] The processing flow will be explained below.

[0872] Program processing steps

[0873] Step 1: Collect and store data

[0874] Server Operation

[0875] The server collects information from employees, such as their resumes, qualifications, experience, personality traits, and skill sets.

[0876] The server stores the collected employee information in a database.

[0877] The server collects data from each department about the work content and the personnel required.

[0878] The server stores the collected job details and desired personnel data in a database.

[0879] Step 2: Analyze and match data

[0880] Server Operation

[0881] The server inputs employee information and business data stored in a database into the generative AI.

[0882] The server uses generative AI to comprehensively evaluate employees' skill sets, experience, personality traits, and more.

[0883] Based on the evaluation results obtained from the AI ​​model, the server calculates scores for the departments and tasks that are most suitable for each employee and creates a matching list.

[0884] Step 3: Generate and submit a proposal

[0885] Server Operation

[0886] The server generates specific placement proposals based on the matching list.

[0887] The server transmits the generated placement proposal to the terminals of the person in charge and employees.

[0888] Step 4: Receive and view proposals

[0889] Device behavior

[0890] The terminal receives the placement proposal sent from the server.

[0891] The terminal displays placement proposals on the screen so that employees and staff can review them.

[0892] Step 5: Provide feedback

[0893] User Actions

[0894] Users (employees and staff) check the placement proposals displayed on their terminals.

[0895] The user inputs their opinions and requests regarding the proposal into the terminal.

[0896] Device behavior

[0897] The terminal transmits the input feedback to the server.

[0898] Step 6: Analyze and store feedback

[0899] Server Operation

[0900] The server analyzes the received feedback and stores it in a database.

[0901] The server adjusts its rating logic based on the feedback.

[0902] Step 7: Reassess and finalize

[0903] Server Operation

[0904] Based on the adjusted evaluation logic, the server calls the generation AI again and performs a re-evaluation.

[0905] The server will make a final decision on the optimal placement based on the results of the re-evaluation.

[0906] The server sends the final decision to the person in charge and to the employee's terminal.

[0907] Step 8: Notification of final placement results

[0908] Device behavior

[0909] The terminal receives the final placement result and displays it on the screen.

[0910] The terminal notifies relevant personnel and employees.

[0911] Example 1

[0912] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0913] The purpose of this invention is to effectively realize the right person in the right position in personnel allocation, maximize employee capabilities, and improve a company's business efficiency and performance. Conventional personnel allocation has had issues with subjective judgment and bias based on experience, making fair allocation difficult. It has also been difficult to quickly assign employees with the appropriate skill sets and experience to the appropriate tasks.

[0914] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0915] In this invention, the server includes means for collecting employee information and storing it in a database, means for collecting job content and desired human resource data and storing it in a database, means for analyzing employee information and job data using generative AI and calculating optimal employee-job matching, means for sending and displaying appropriate placement proposals to a terminal, means for collecting feedback from the terminal and storing it in a database, means for reevaluating based on the feedback and finalizing and notifying the optimal placement, means for evaluating each employee's skill set, experience, and personality traits based on the collected employee information and job data and optimally matching them based thereon, means for users to input feedback via a terminal and send it to the server, means for calculating scores for each employee using a generative AI model and proposing the optimal department and job, means for using the collected feedback to proceed with the reevaluation and finalize the placement, means for displaying the proposals and providing a UI for inputting feedback, and means for notifying the responsible department and employee of the final placement results after the reevaluation. This not only enables fair and objective personnel placement, optimally utilizing employee abilities, but also improves the company's business efficiency and performance.

[0916] "Employee Information" refers to detailed profile data such as an employee's biography, qualifications, experience, personality traits, and skill set.

[0917] "Job content" refers to the specific work and responsibilities performed in each department or project, as well as the required skills and experience.

[0918] "Generative AI" refers to algorithms that use artificial intelligence techniques such as machine learning models to analyze data and generate optimal solutions and proposals.

[0919] A "database" refers to an information management system that systematically stores collected information and makes it easy to search and reference.

[0920] "Terminal" refers to hardware such as a computer or smart device that a user uses to enter information or view suggestions.

[0921] "Feedback" refers to opinions, requests, and evaluation information that users input to the system.

[0922] "Matching" refers to the process of comparing employee information with job requirements to find the optimal combination.

[0923] A "skill set" refers to the specific set of skills, knowledge, and abilities that an employee possesses.

[0924] "Personality traits" refer to characteristics, behavioral patterns, and psychological attributes related to an employee's personality.

[0925] "Evaluation logic" refers to the rules and algorithms used to evaluate the match between employees and work based on collected data.

[0926] "User" refers to a person such as an employee or person in charge who uses the system.

[0927] "Proposal" refers to specific suggestions regarding optimal work and department placement created by generative AI based on the analysis results.

[0928] "UI (User Interface)" refers to the screen and operation method that allows users to input and operate the system.

[0929] The present invention is a system for assigning optimal tasks and departments to employees based on their skills, experience, and personality traits. This system operates in cooperation with a server, terminals, and users.

[0930] First, the server collects employee information. This information includes resumes, qualifications, experience, personality traits, skill sets, etc. It also collects job descriptions and desired personnel data from each department of the company. This information is stored in a database. This information is collected by the HR department or an internal system. For example, information on new employees can be extracted from the HR system and stored in an SQL server.

[0931] The server then inputs the collected data into a generative AI model that comprehensively evaluates employees' skill sets, experience, and personality traits. This AI model scores and matches each employee with the job and department that best suits them. This model applies machine learning algorithms using programming languages ​​such as Python and R.

[0932] ●Example 1:

[0933] The server inputs a prompt into the generative AI model: "For a new marketing project, please suggest employees with experience in digital marketing. In doing so, please prioritize employees with SEO and SEM skills." Based on this prompt, the AI ​​model selects the most suitable employee from its database and generates a recommendation such as "Ichiro Tanaka: Recommend assignment to the digital marketing project."

[0934] The server then sends the generated placement proposal to the device of the person in charge and the target employee. The proposal is sent via API in JSON format or similar.

[0935] The terminal receives the proposal and displays it on the screen. Users (employees and staff) can review the proposal and provide feedback. For example, an employee can write "I think I'm a good fit for this project. I'd like to participate" in the input field.

[0936] The device sends the feedback entered by the user to the server, which receives it and stores it in a database.

[0937] Finally, the server reevaluates the employee based on the collected feedback and determines the final placement. The reevaluation uses a generative AI model again, recalculating the score based on the feedback. The final placement results after the reevaluation are then notified to the relevant department and employee.

[0938] The introduction of this system will not only enable fair and objective personnel allocation, making optimal use of employee capabilities, but is also expected to improve the company's overall operational efficiency and performance.

[0939] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0940] Step 1:

[0941] Collection of employee information

[0942] The server collects employee information from HR systems and self-reported data.

[0943] Input: Data such as resume, qualifications, experience, personality traits, and skill sets taken from HR systems and self-reporting forms.

[0944] Processing: Convert the acquired data into an appropriate format and store it in a database. For example, convert JSON format data into SQL queries and save them in a SQL server.

[0945] Output: Employee information stored in a database.

[0946] Step 2:

[0947] Collection of work content data

[0948] The server collects data on the work content and required personnel from each department.

[0949] Input: Data such as job requirements from each department, required skill sets, experience, characteristics, etc.

[0950] Processing: Convert the acquired data into an appropriate format and store it in a database. For example, analyze business content data provided in a CSV file and import it into an SQL server.

[0951] Output: Business content data stored in a database.

[0952] Step 3:

[0953] Data analysis and matching

[0954] The server inputs the collected employee information and business data into a generative AI model for analysis.

[0955] Input: Employee information and job description data stored in a database.

[0956] Processing: Input data into a generative AI model, which evaluates and scores each employee's skill set, experience, and personality traits. Based on the results, optimal matching of employees with jobs is performed. For example, a machine learning algorithm written in Python is run to calculate a job aptitude score for each employee.

[0957] Output: Generated employee and job matching data.

[0958] Step 4:

[0959] Generate placement proposals

[0960] The server creates specific placement proposals based on the analysis results.

[0961] Input: Matching data generated by the AI ​​model.

[0962] Processing: Summarize the specific tasks and departments suitable for each employee in a proposal. For example, generate an assignment proposal such as "Ichiro Tanaka: Recommend assignment to the digital marketing project."

[0963] Output: Placement proposal.

[0964] Step 5:

[0965] Submitting and Viewing Proposals

[0966] The server then sends the generated placement proposal to the terminals of the person in charge and the target employee. The terminals receive the proposal and display it on their screens.

[0967] Input: Placement proposal.

[0968] Processing: Send the suggestion data to the device via API in JSON format, for example, by using an HTTP request.

[0969] Output: The placement proposal displayed on the terminal.

[0970] Step 6:

[0971] Gathering feedback

[0972] The terminal provides a UI for users (employees and staff) to input their opinions and requests regarding the proposal and collects feedback.

[0973] Input: Feedback entered by the user.

[0974] Processing: Send the entered feedback to the server and store it in a database. For example, collect the input data from the feedback form and send it to the server via API.

[0975] Output: Feedback data stored in a database.

[0976] Step 7:

[0977] Reevaluation and Final Placement Decision

[0978] The server will re-evaluate based on the collected feedback and make a final placement decision.

[0979] Input: Feedback data.

[0980] Processing: The AI ​​model is re-run taking into account the feedback and recalculating the score. The final placement result is then generated after re-evaluation. For example, the AI ​​model parameters are adjusted based on the feedback and recalculated.

[0981] Output: The final placement result.

[0982] Step 8:

[0983] Notification of final placement results

[0984] The server notifies the department and employee in charge of the final placement result.

[0985] Input: Final placement result.

[0986] Processing: A message is generated to notify the department and employee of the final placement result and sent to the device. For example, notification is sent by email or push notification.

[0987] Output: Final placement results notified to the relevant department and employee.

[0988] (Application example 1)

[0989] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0990] Existing human resource allocation systems can assign optimal tasks based on employees' skills, experience, and personality traits, but applying this to factory robots is a similar challenge. An efficient system is also needed to process diverse data, including robot skill sets and performance data, and to notify updates on tasks in real time.

[0991] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0992] In this invention, the server includes means for collecting employee information and storing it in a database, means for collecting data on job content and desired personnel and storing it in a database, means for analyzing employee information and job data using generative AI and calculating the optimal employee-job match, means for sending and displaying appropriate placement proposals to terminals, means for collecting feedback from terminals and storing it in a database, means for reevaluating based on the feedback and finalizing and notifying the optimal placement, means for collecting robot skill sets and performance data and matching optimal jobs, and means for sending matching results to the server and notifying the robots of job updates in real time. This makes it possible to assign optimal jobs not only to human employees but also to robots in the factory, thereby improving overall production efficiency.

[0993] "Employee information" refers to information held by a company about each employee, such as their resume, qualifications, experience, personality traits, and skill set.

[0994] A "database" is a collection of information that is systematically organized and stored so that it can be easily accessed when needed.

[0995] "Generative AI" is an artificial intelligence technology that generates new data and predictions based on existing data.

[0996] "Feedback" refers to the user's opinions and requests regarding suggestions and assignments.

[0997] A "skill set" is a collection of skills and abilities required to perform a specific job.

[0998] "Performance data" is information about the specific performance and capabilities of a robot or device.

[0999] "Matching" is the process of finding the best combination based on specific conditions.

[1000] A "server" is a central control device that processes information and manages data.

[1001] A "terminal" is a device that is connected to a server and can be directly operated by a user.

[1002] "Real time" refers to the immediacy in which state changes and data processing occur almost simultaneously.

[1003] The "best fit" is the work that can be performed most efficiently and effectively based on skill sets, experience, and performance data.

[1004] The present invention is a system that assigns optimal tasks and departments to employees and robots based on their skills, experience, and personality traits. This system operates in cooperation with a server, terminals, and users.

[1005] Server Processing

[1006] 1. Data Collection and Storage

[1007] The server collects employee and robot information and stores it in a database. This information includes resumes, qualifications, experience, personality traits, skill sets, and performance data. It also collects job descriptions and desired personnel data from each department and stores them in the database.

[1008] 2. Data analysis and matching

[1009] The server uses generative AI to analyze the collected employee information and work data. The AI ​​comprehensively evaluates the skill sets, experience, personality traits, and performance data of employees and robots, scoring and matching them with the most suitable jobs and departments.

[1010] 3. Generate and submit a proposal

[1011] Based on the analysis results, the server creates a specific placement proposal, which indicates which department and task each employee and robot is suited to. The proposal is then sent to the terminals of the person in charge and the target employee and robot.

[1012] Terminal handling

[1013] 1. Receiving and Displaying Proposals

[1014] The terminal receives the placement proposal sent from the server and displays it on the screen. Employees and personnel can review the proposal and provide feedback if necessary.

[1015] 2. Gathering feedback

[1016] The terminal provides a UI for employees and staff to input their opinions and requests regarding the proposal. Once the feedback is entered, it is sent to the server.

[1017] User Action

[1018] 1. Confirmation of proposal

[1019] Users (employees, staff, and robot managers) check the proposals from the server via their terminals. The proposals contain specific details about the tasks and departments that are best suited for them or the robots they are responsible for.

[1020] 2. Providing Feedback

[1021] Users input their opinions and requests regarding the proposals and send them to the server via their terminals. This feedback is used as an important factor in the final decision-making process.

[1022] Specific examples

[1023] For example, consider the launch of a new production line. This project requires human resources and robots with specific skill sets and experience. First, the project manager inputs project details (e.g., required skills, number of people, deadline) into the server. Based on this information, the server uses a generative AI model to select the most suitable employees and robots. The server then creates a list of candidates and robots evaluated by the AI ​​model and sends it to the project manager and stakeholders. The project manager and stakeholders review the proposals on their own devices and provide feedback. Based on this feedback, the server reevaluates and makes a final placement decision. The project manager and employees / robots are notified of the final placement results, allowing them to quickly transition to new tasks and roles.

[1024] Example prompts for generative AI models

[1025] "Robot A's skill set: [welding accuracy, speed, energy consumption]. Required tasks: Welding, part assembly, quality inspection. Please assign the most appropriate task to it."

[1026] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1027] Step 1:

[1028] The server collects employee information and robot information. Specifically, it stores employee resumes, qualifications, experience, personality traits, skill sets, and robot performance data in a database. This information is treated as input data. The input data is stored in the database and used in subsequent processes.

[1029] Step 2:

[1030] The server collects job descriptions and desired personnel data from each department and stores it in a database. This information includes the required skills, experience, work content, and specific performance requirements. The input data are job details, which are also stored in the database.

[1031] Step 3:

[1032] The server uses generative AI to analyze the collected employee information and business data as input. This analysis comprehensively evaluates each employee and robot's skill set, experience, personality traits, and performance data. Data calculations are used to calculate and score the optimal employee-job match. The output is a list of optimal matches.

[1033] Step 4:

[1034] The server then creates a specific placement proposal based on the analysis results. This placement proposal specifically indicates which department and task each employee and robot is suited to. The server then creates the placement proposal as output and sends it to the terminal.

[1035] Step 5:

[1036] The terminal receives the placement proposal sent from the server and displays it on the screens of the target employee and robot administrator. This proposal includes specific work content and reasons for the placement. The user receives this information as input data and confirms it.

[1037] Step 6:

[1038] The terminal provides a UI for employees and robot administrators to input their opinions and requests regarding the proposals, and collects feedback. The input feedback is sent from the terminal to the server, and the feedback data is obtained as output.

[1039] Step 7:

[1040] The server performs a re-evaluation based on the collected feedback data, which may result in modifications to the initial placement proposal. The re-evaluation process uses the feedback as input to adjust the evaluation logic and finalize the optimal placement. The output includes the finalized placement proposal.

[1041] Step 8:

[1042] The server notifies the final placement results and sends them to the terminals of the employees and robots. This allows each employee and robot to quickly transition to new tasks and roles. The input is the re-evaluated placement plan, and the output is the notified final placement result.

[1043] As a specific example, a prompt sentence for a generative AI model is used: "Robot A's skill set: [welding accuracy, speed, energy consumption]. Required tasks: Please assign the most appropriate task from among welding, part assembly, and quality inspection."

[1044] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1045] This invention is a system that assigns optimal tasks and departments based on employees' skills, experience, personality traits, and emotions. This system operates in cooperation with a server, terminals, users, and an emotion engine.

[1046] Server Processing

[1047] 1. Data Collection and Storage

[1048] The server first collects employee information and stores it in a database. This information includes resumes, qualifications, experience, personality traits, skill sets, etc. It also collects job descriptions and desired personnel data from each department and stores it in the database.

[1049] 2. Emotional Data Recognition

[1050] The server uses an emotion engine to recognize the emotions of employees and staff in real time, and this emotion data is used as important information when evaluating employees and collecting feedback.

[1051] 3. Data analysis and matching

[1052] The server inputs the collected employee information, work data, and emotional data into the generative AI, which then comprehensively evaluates each employee's skill set, experience, personality traits, emotions, etc., and scores and matches them with the department and job that best suits them.

[1053] 4. Generate and submit proposals

[1054] Based on the analysis results, the server creates specific placement proposals, which indicate which departments and tasks each employee is suited to. These proposals also take into account emotional data. The proposals are then sent to the devices of the person in charge and the target employee.

[1055] Terminal handling

[1056] 1. Receiving and Displaying Proposals

[1057] The terminal receives the placement proposal sent from the server and displays it on the screen. Employees and personnel can review the proposal and provide feedback if necessary.

[1058] 2. Gathering feedback

[1059] The terminal provides a UI for employees and staff to input their opinions and requests regarding the proposal. Once the feedback is entered, the terminal sends it to the server.

[1060] User Action

[1061] 1. Confirmation of proposal

[1062] Users (employees and staff) check the proposals from the server through their terminals. The proposals include specific details about the tasks and departments that are best suited to them.

[1063] 2. Providing Feedback

[1064] Users input their opinions and requests regarding the proposals and send them to the server via their devices. This feedback includes emotional data recognized by the emotion engine.

[1065] Specific examples

[1066] For example, imagine a new project is being launched. This project requires people with a specific skill set and experience. The project manager enters the project details (skills needed, number of people, deadline, etc.) into the server. The server uses this information to select the best candidates using a database of current employees and emotional data from an emotion engine.

[1067] The server creates a list of candidates evaluated by the AI ​​model and emotion engine and sends it to the project manager and candidate's devices. The project manager and candidate review the proposals on their respective devices and provide feedback. Based on this feedback, the server reevaluates and makes the final placement decision.

[1068] The final allocation results are notified to the project manager and employee, allowing them to quickly transition to new tasks and roles. Using an emotion engine, allocation can be made taking into account the emotional state of employees, contributing to improved employee satisfaction and performance. This system ensures unbiased and fair personnel allocation, providing an environment where employees can perform to their full potential, while also improving the efficiency and performance of the entire organization.

[1069] The processing flow will be explained below.

[1070] Program processing steps

[1071] Step 1: Collect and store data

[1072] Server Operation

[1073] The server collects information from employees, such as their resumes, qualifications, experience, personality traits, and skill sets.

[1074] The server stores the collected employee information in a database.

[1075] The server collects data from each department about the work content and the personnel required.

[1076] The server stores the collected job details and desired personnel data in a database.

[1077] Step 2: Recognizing and storing emotion data

[1078] Server Operation

[1079] The server uses an emotion engine to recognize the emotion data of employees and personnel in real time.

[1080] The server organizes the recognized emotion data for each employee and stores it in a database.

[1081] Step 3: Analyze and match data

[1082] Server Operation

[1083] The server inputs employee information, business data, and emotional data stored in a database into the generative AI.

[1084] The server uses generative AI to comprehensively assess an employee's skill set, experience, personality traits, and emotional state.

[1085] Based on the evaluation results obtained from the AI ​​model, the server calculates scores for the departments and tasks that are most suitable for each employee and creates a matching list.

[1086] Step 4: Generate and submit a proposal

[1087] Server Operation

[1088] The server generates specific placement proposals based on the matching list, which also reflect the emotional data.

[1089] The server transmits the generated placement proposal to the terminals of the person in charge and employees.

[1090] Step 5: Receive and view proposals

[1091] Device behavior

[1092] The terminal receives the placement proposal sent from the server.

[1093] The terminal displays placement proposals on the screen so that employees and staff can review them.

[1094] Step 6: Provide feedback

[1095] User Actions

[1096] Users (employees and staff) check the placement proposals displayed on their terminals.

[1097] The user inputs their opinions and requests regarding the proposal into the terminal.

[1098] Device behavior

[1099] The terminal transmits the input feedback to the server.

[1100] Step 7: Collect and analyze feedback

[1101] Server Operation

[1102] The server stores the received feedback and analyzes it, including emotional data.

[1103] Based on the results of the feedback analysis, the server adjusts the evaluation logic and reflects it in the database.

[1104] Step 8: Reassess and finalize

[1105] Server Operation

[1106] Based on the adjusted evaluation logic, the server calls the generation AI again and performs a re-evaluation.

[1107] The server will make a final decision on the optimal placement based on the results of the re-evaluation.

[1108] The server sends the final decision to the person in charge and to the employee's terminal.

[1109] Step 9: Notification and confirmation of final placement results

[1110] Device behavior

[1111] The terminal receives the final placement result and displays it on the screen.

[1112] The terminal notifies relevant personnel and employees.

[1113] Example 2

[1114] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1115] While traditional employee placement systems can take into account employee skills, experience, and personality traits, they have difficulty taking into account employees' emotional state. As a result, employee satisfaction and performance decline, making it difficult to achieve optimal personnel placement. Furthermore, the process of collecting feedback and reevaluating it is inefficient, making it difficult to make quick placement decisions.

[1116] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1117] In this invention, the server includes means for collecting employee information and storing it in a database, means for collecting job content and desired personnel data and storing it in a database, means for recognizing and evaluating emotional data in real time, means for analyzing employee information, job data, and emotional data using generative AI and calculating the optimal employee-job match, means for generating optimal placement proposals and sending and displaying them to the terminal, means for collecting feedback from the terminal and storing it in a database, and means for reevaluating based on the feedback and emotional data, and for finalizing and notifying the optimal placement. This enables optimal personnel placement that takes employees' emotional states into consideration, thereby improving employee satisfaction and performance. Furthermore, the efficient collection of feedback and reevaluation process enables rapid placement decisions.

[1118] "Employee Information" means key data relating to an employee, such as an employee's biography, qualifications, experience, personality traits, and skill set.

[1119] "Job Description" refers to data that details the specific tasks an employee will be responsible for, as well as the skill set and experience required for those tasks.

[1120] "Desired talent data" refers to data that defines the characteristics of an ideal candidate, such as the skills, experience, and personality traits required for a specific job or department.

[1121] "Emotion data" refers to real-time assessment of the current emotional state of employees and personnel, and is obtained by the emotion engine.

[1122] "Generative AI" is an artificial intelligence that can analyze input data and generate optimal information, and is a model used to match employees with jobs and generate placement proposals.

[1123] A "database" is a system for centrally storing and managing collected employee information, job descriptions, desired personnel data, emotional data, feedback, etc.

[1124] "Terminal" refers to a device that employees and staff use to receive and display proposals from the server and to input and send opinions and requests as feedback.

[1125] "Feedback" refers to opinions and requests provided by employees and staff via terminals, and is information used for reevaluation and optimal placement decisions.

[1126] "Reevaluation" refers to the process of re-analyzing the collected feedback and sentiment data, and is a key step in arriving at a final placement decision.

[1127] "Placement proposals" refer to recommendations made by generative AI based on the analysis results, indicating which department or job each employee would be best suited to.

[1128] This invention is a system that assigns optimal tasks and departments to employees based on their skills, experience, personality traits, and emotions. This system is primarily composed of a server, terminals, and users, and operates through the cooperation of an emotion engine and a generative AI model. A specific embodiment of this system is shown below.

[1129] Server Features

[1130] Data Collection and Storage

[1131] The server periodically collects employee information (resume, qualifications, experience, personality traits, skill sets, etc.) and stores it in a database. To do this, the server implements an API to retrieve data from various sources and centrally manages the information using database technologies such as SQLite or RDS.

[1132] Emotional Data Recognition

[1133] The server uses an emotion engine (e.g., IBM Watson's emotion analysis API) to recognize the emotion data of employees and personnel in real time. The emotion data is sent to the emotion engine via the API when text is entered, and the evaluation results are stored in the database.

[1134] Data analysis and matching

[1135] The server inputs the collected employee information, business data, and emotional data into a generative AI model (e.g., GPT-4). The generative AI model uses this information to optimally match employees with jobs and assign scores. The model is primarily operated in a cloud environment such as Google Colab.

[1136] Generate and submit proposals

[1137] The server generates optimal placement proposals based on the analysis results of the generative AI model. These proposals are created in a format such as JSON and sent to the devices of employees and personnel in charge.

[1138] Device Features

[1139] Receiving and Viewing Suggestions

[1140] The terminal receives the placement proposal sent from the server and displays it on the user interface, allowing employees and personnel in charge to confirm the proposal.

[1141] Gathering feedback

[1142] The device provides a UI for users to input their opinions and requests regarding the proposal. The feedback entered by the user is sent to the server via the device and stored in a database.

[1143] User Roles

[1144] Review the proposal

[1145] Users (employees and staff) can check the proposals from the server through their terminals, which show the tasks and departments that are best suited to the user.

[1146] Providing feedback

[1147] Users input their opinions and requests regarding the suggestions and send them to the server via their devices. The feedback also includes emotional data recognized by the emotion engine.

[1148] Specific examples

[1149] For example, consider a new project that requires a specific skill set and experience, so the project manager inputs the project details (skills needed, number of people, deadline, etc.) into the server. The server uses this information to select the best candidates using the current employee database and sentiment data from the sentiment engine.

[1150] The candidate list, evaluated using the generative AI model and emotion engine, is sent from the server to the project manager's and candidate's devices. The project manager and candidate review the proposals on their respective devices and provide feedback. Based on this feedback, the server reevaluates and determines the final placement. The project manager and employee are notified of the final placement results, enabling them to quickly transition to new tasks and roles.

[1151] Prompt Sentence Examples

[1152] "Select the best candidates for a new project. The project requires the following skills: programming, project management, and communication. The deadline is within 3 months. Use an emotion engine to take the candidate's emotions into account when selecting candidates."

[1153] In this way, the system achieves optimal personnel placement by linking the server, terminals, users, emotion engine, and generative AI model to evaluate employee skills and emotions in a balanced manner.

[1154] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1155] Step 1: Collecting and storing employee information

[1156] Server Processing

[1157] The server collects employee resumes, qualifications, experience, personality traits, and skill sets and stores them in a database. The inputs include information periodically updated by employees and new employee data provided by the HR department. The server organizes this information and stores the data using SQLite or RDS. Specifically, when an employee enters new credentials from their terminal, the data is sent to the server and the database is updated.

[1158] Input: Employee biography, qualifications, experience, personality traits, skill set

[1159] Output: Employee information stored in the database

[1160] Step 2: Collecting and storing business and department information

[1161] Server Processing

[1162] The server collects job descriptions and required personnel data from each department and stores it in a database. It provides an interface where department personnel can enter the required skill sets, number of employees, and job descriptions. Specifically, when a department manager enters details of a new project (for example, "online marketing campaign"), the information is stored in the database.

[1163] Input: Required skill sets, number of people, job description

[1164] Output: Business and department information stored in the database

[1165] Step 3: Obtaining emotion data

[1166] Server Processing

[1167] The server uses an emotion engine to obtain emotional data from employees and staff in real time. Input includes text data such as feedback. The server sends this data to the emotion engine, which evaluates the emotional state. Specifically, if an employee types, "I'm confident about this project," the text data is sent to the emotion engine, which evaluates it as a positive emotion.

[1168] Input: Text data such as feedback

[1169] Output: Emotion data stored in a database

[1170] Step 4: Analyze and match data

[1171] Server Processing

[1172] The server inputs the collected employee information, job data, and emotional data into a generative AI model to match the optimal employee with the job. Inputs include the employee's skill set, experience, personality traits, and emotional data. The generative AI model (e.g., GPT-4) performs scoring and matching based on this data. Specifically, the server extracts employees who have Java programming qualifications and marketing experience, and inputs this into the generative AI model to match them with the optimal job.

[1173] Input: Employee information, business data, emotional data

[1174] Output: Matched optimal employee-job pairs

[1175] Step 5: Generate and submit a proposal

[1176] Server Processing

[1177] The server generates optimal placement proposals based on the analysis results of the generative AI model. The proposals are created in formats such as JSON and sent to the devices of employees and staff. Specifically, the server generates a proposal stating that "Employee A is ideal for the digital marketing campaign project" and sends the proposal data to the devices of Employee A and his manager.

[1178] Input: Analysis results

[1179] Output: Placement proposal sent to the device

[1180] Step 6: Review the proposal and gather feedback

[1181] Terminal handling

[1182] The terminal receives the placement proposal sent from the server and displays it on the user interface. Employees and personnel in charge review the proposal and input their own opinions and requests. Specifically, when Employee A reviews the proposal on the terminal and inputs feedback such as "I'm interested in this job," that information is sent to the server.

[1183] Input: Placement proposal

[1184] Output: Feedback sent to the server

[1185] Step 7: Reassess the feedback and finalize

[1186] Server Processing

[1187] The server inputs the collected feedback data back into the generation AI model and re-evaluates the employee, taking into account emotional data. It then makes a final placement decision and notifies each employee and the person in charge of the results. Specifically, the server receives feedback from employee A, re-evaluates the employee, generates the final placement result, and notifies the employee that "employee A has been officially assigned to the digital marketing campaign project."

[1188] Input: Feedback data, emotion data

[1189] Output: Notification of final placement result

[1190] (Application example 2)

[1191] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1192] While there were systems in place that assigned optimal tasks and departments to employees based on their skills, experience, personality traits, and emotions, there were no systems that proposed optimal placements for robots used in factories based on their skills, operating status, and environmental data. This made it difficult to maximize the robots' work efficiency, and improving the efficiency of the entire factory was a challenge.

[1193] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting employee information and storing it in a database, means for collecting data on job content and desired personnel and storing it in a database, means for analyzing employee information and job data using generative AI and calculating the optimal employee-job match, means for sending and displaying appropriate placement proposals to the terminal, means for collecting feedback from the terminal and storing it in a database, means for reevaluating based on the feedback and finalizing and notifying the optimal placement, means for collecting robot information and storing it in a database, and means for generating optimal placement proposals based on the robot's skills, operating data, and environmental data. This enables optimal placement of robots used in a factory and maximizes work efficiency.

[1194] "Employee Information" is information including an employee's biography, qualifications, experience, personality traits, and skill set.

[1195] A "database" is a system for systematically storing and managing collected information.

[1196] "Means" refers to a method or device used to achieve a particular purpose.

[1197] "Generative AI" refers to artificial intelligence that analyzes data and generates optimal conclusions and suggestions.

[1198] "Matching" refers to finding the optimal combination based on specific conditions.

[1199] "Terminal" refers to the device used by employees and administrators to interface with the system.

[1200] "Feedback" refers to opinions and requests provided regarding a system or proposal.

[1201] "Reassessment" is the process of repeating the initial assessment based on the feedback collected.

[1202] "Robot information" refers to information including the robot's skills, operating status, and environmental data.

[1203] "Operation data" is data that indicates how much work the robot has performed.

[1204] "Environmental data" refers to data including temperature, humidity, and other environmental conditions of the work area.

[1205] This invention is a system that proposes optimal placement of employees and robots based on their skills, experience, environmental information, etc. This system operates in cooperation with a server, terminals, users, and generative AI.

[1206] Server Processing

[1207] The server collects employee and robot information and stores it in a database. Employee information includes resumes, qualifications, experience, personality traits, and skill sets, while robot information includes skills, operating data, and environmental data (temperature, humidity, etc.). The server then uses generative AI to analyze the collected information and calculate the optimal employee-job match and optimal robot placement. These placement proposals are sent to and displayed on the terminal.

[1208] Terminal handling

[1209] The terminal receives the placement proposal sent from the server and displays it on the screen. Employees, personnel, and robot administrators can use the UI to review the proposal and provide feedback as needed. The provided feedback is sent from the terminal to the server.

[1210] User Action

[1211] Users (employees, staff, and robot administrators) review the proposals from the server through their terminals. The proposals specifically describe the tasks and placements that are best suited for them and the robots. Users input their opinions and requests regarding the proposals and send them to the server through their terminals. This feedback includes data that will be used to reassess the evaluation.

[1212] Specific examples

[1213] For example, consider the launch of a new project. This project requires human resources and robots with specific skill sets and experience. The project manager enters project details (required skills, number of people, deadline, etc.) into the server. Using this information, the server selects the most suitable candidates and robots using data from the current employee database and robot database. The server then creates a candidate list and robot placement proposals evaluated using generative AI and evaluation logic, and sends them to the project manager and each employee and robot administrator's device. The project manager, employee, and robot administrator review the proposals on their respective devices and provide feedback. Based on this feedback, the server reevaluates and makes the final placement decision.

[1214] Here are some examples of prompts for generative AI models:

[1215] text

[1216] Develop an application that proposes the optimal placement of robots in a factory by applying a system that assigns employees the most suitable tasks and departments based on their skills, experience, personality traits, and emotions. Please output the program code that generates the proposal using the following specific data:

[1217] Robot ID: 1, Skill Level: 7, Operating Time: 120, Area Temperature: 20, Area Humidity: 50

[1218] Robot ID: 2, Skill Level: 5, Run Time: 150, Area Temperature: 22, Area Humidity: 55

[1219] Robot ID: 3, Skill Level: 9, Operating Hours: 100, Area Temperature: 21, Area Humidity: 52

[1220] Robot ID: 4, Skill Level: 6, Operating Hours: 130, Area Temperature: 19, Area Humidity: 48

[1221] This invention enables optimal allocation of employees and robots and maximizes work efficiency.

[1222] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1223] Step 1:

[1224] The server collects employee information and robot information. Employee information includes resumes, qualifications, experience, personality traits, and skill sets, while robot information includes skills, operating data, and environmental data (temperature, humidity, etc.). This collected information is temporarily stored in a database. The input is employee information and robot information, and the output is the information stored in the database.

[1225] Step 2:

[1226] The server collects job descriptions and required personnel data and stores them in a database. The job descriptions include project details (required skills, number of people, deadline, etc.), and the required personnel data describes the skills and qualifications required for each job. The input is the job description and required personnel data, and the output is the job data stored in the database.

[1227] Step 3:

[1228] The server inputs the collected employee information, robot information, and business data into the generative AI. The generative AI comprehensively evaluates the employee skill sets, experience, personality traits, and emotions, as well as the robot's skills, operating status, and environmental data, to calculate the optimal employee-to-business match and optimal robot placement. The inputs are employee information, robot information, and business data, and the output is the optimal match and placement proposal.

[1229] Step 4:

[1230] The server sends the optimal placement proposal generated by the generative AI to the device. The user receives the proposal through the device and checks the proposal displayed on the screen. The input is the optimal placement proposal, and the output is the placement proposal displayed on the device.

[1231] Step 5:

[1232] The user inputs their opinions and requests about the proposal as feedback using the device's UI. This feedback is sent from the device to the server. The input is the feedback, and the output is the feedback sent to the server.

[1233] Step 6:

[1234] The server analyzes the collected feedback and adjusts the evaluation logic. After adjusting the evaluation logic, the server re-evaluates using generative AI to finally determine the optimal placement. The input is the feedback, and the output is the adjusted evaluation logic and the re-evaluated placement proposal.

[1235] Step 7:

[1236] The server notifies the terminal of the final placement proposal and notifies the user. The user can confirm the final placement proposal through the terminal and quickly transition to the new job or role. The input is the final placement proposal, and the output is the final proposal displayed on the terminal.

[1237] The above are the specific processing steps of the system that realizes this application example. This step-by-step processing makes it possible to achieve optimal allocation of employees and robots and maximize work efficiency.

[1238] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1239] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1240] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1241] [Fourth embodiment]

[1242] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1243] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1244] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1245] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1246] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1247] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1248] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1249] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1250] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1251] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1252] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1253] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1254] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1255] The present invention is a system for assigning optimal tasks and departments to employees based on their skills, experience, and personality traits. This system operates in cooperation with a server, terminals, and users.

[1256] Server Processing

[1257] 1. Data Collection and Storage

[1258] The server first collects employee information and stores it in a database. This information includes resumes, qualifications, experience, personality traits, skill sets, etc. It also collects job descriptions and desired personnel data from each department of the company and stores them in the database as well.

[1259] 2. Data analysis and matching

[1260] The server uses generative AI to analyze the collected employee information and work data, comprehensively evaluating employees' skill sets, experience, personality traits, etc., and scores and matches each employee with the most suitable job and department.

[1261] 3. Generate and submit a proposal

[1262] Based on the analysis results, the server creates specific placement proposals, which indicate which departments and tasks each employee is suited to. The proposals are then sent to the devices of the person in charge and the target employees.

[1263] Terminal handling

[1264] 1. Receiving and Displaying Proposals

[1265] The terminal receives the placement proposal sent from the server and displays it on the screen. Employees and personnel can review the proposal and provide feedback if necessary.

[1266] 2. Gathering feedback

[1267] The terminal provides a UI for employees and staff to input their opinions and requests regarding the proposal. Once the feedback is entered, it is sent to the server.

[1268] User Action

[1269] 1. Confirmation of proposal

[1270] Users (employees and staff) check the proposals from the server through their terminals. The proposals include specific details about the tasks and departments that are best suited to them.

[1271] 2. Providing Feedback

[1272] Users input their opinions and requests regarding the proposals and send them to the server via their terminals. This feedback is used as an important factor in the final decision-making process.

[1273] Specific examples

[1274] For example, consider a new project that requires people with specific skill sets and experience. First, the project manager inputs the project details (skills needed, number of people, deadline, etc.) into the server. The server uses this information to use a generative AI model to select the best candidates from the current employee database.

[1275] The server creates a list of candidates evaluated by the AI ​​model and sends it to the project manager and candidate's devices. The project manager and candidate review the proposals on their respective devices and provide feedback. Based on this feedback, the server reevaluates and makes the final placement decision.

[1276] The final allocation results are communicated to the project manager and employee, allowing them to quickly transition to their new job or role. This system ensures unbiased and fair allocation of personnel, providing an environment where employees can perform to their full potential while improving the efficiency and performance of the entire organization.

[1277] The processing flow will be explained below.

[1278] Program processing steps

[1279] Step 1: Collect and store data

[1280] Server Operation

[1281] The server collects information from employees, such as their resumes, qualifications, experience, personality traits, and skill sets.

[1282] The server stores the collected employee information in a database.

[1283] The server collects data from each department about the work content and the personnel required.

[1284] The server stores the collected job details and desired personnel data in a database.

[1285] Step 2: Analyze and match data

[1286] Server Operation

[1287] The server inputs employee information and business data stored in a database into the generative AI.

[1288] The server uses generative AI to comprehensively evaluate employees' skill sets, experience, personality traits, and more.

[1289] Based on the evaluation results obtained from the AI ​​model, the server calculates scores for the departments and tasks that are most suitable for each employee and creates a matching list.

[1290] Step 3: Generate and submit a proposal

[1291] Server Operation

[1292] The server generates specific placement proposals based on the matching list.

[1293] The server transmits the generated placement proposal to the terminals of the person in charge and employees.

[1294] Step 4: Receive and view proposals

[1295] Device behavior

[1296] The terminal receives the placement proposal sent from the server.

[1297] The terminal displays placement proposals on the screen so that employees and staff can review them.

[1298] Step 5: Provide feedback

[1299] User Actions

[1300] Users (employees and staff) check the placement proposals displayed on their terminals.

[1301] The user inputs their opinions and requests regarding the proposal into the terminal.

[1302] Device behavior

[1303] The terminal transmits the input feedback to the server.

[1304] Step 6: Analyze and store feedback

[1305] Server Operation

[1306] The server analyzes the received feedback and stores it in a database.

[1307] The server adjusts its rating logic based on the feedback.

[1308] Step 7: Reassess and finalize

[1309] Server Operation

[1310] Based on the adjusted evaluation logic, the server calls the generation AI again and performs a re-evaluation.

[1311] The server will make a final decision on the optimal placement based on the results of the re-evaluation.

[1312] The server sends the final decision to the person in charge and to the employee's terminal.

[1313] Step 8: Notification of final placement results

[1314] Device behavior

[1315] The terminal receives the final placement result and displays it on the screen.

[1316] The terminal notifies relevant personnel and employees.

[1317] Example 1

[1318] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1319] The purpose of this invention is to effectively realize the right person in the right position in personnel allocation, maximize employee capabilities, and improve a company's business efficiency and performance. Conventional personnel allocation has had issues with subjective judgment and bias based on experience, making fair allocation difficult. It has also been difficult to quickly assign employees with the appropriate skill sets and experience to the appropriate tasks.

[1320] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1321] In this invention, the server includes means for collecting employee information and storing it in a database, means for collecting job content and desired human resource data and storing it in a database, means for analyzing employee information and job data using generative AI and calculating optimal employee-job matching, means for sending and displaying appropriate placement proposals to a terminal, means for collecting feedback from the terminal and storing it in a database, means for reevaluating based on the feedback and finalizing and notifying the optimal placement, means for evaluating each employee's skill set, experience, and personality traits based on the collected employee information and job data and optimally matching them based thereon, means for users to input feedback via a terminal and send it to the server, means for calculating scores for each employee using a generative AI model and proposing the optimal department and job, means for using the collected feedback to proceed with the reevaluation and finalize the placement, means for displaying the proposals and providing a UI for inputting feedback, and means for notifying the responsible department and employee of the final placement results after the reevaluation. This not only enables fair and objective personnel placement, optimally utilizing employee abilities, but also improves the company's business efficiency and performance.

[1322] "Employee Information" refers to detailed profile data such as an employee's biography, qualifications, experience, personality traits, and skill set.

[1323] "Job content" refers to the specific work and responsibilities performed in each department or project, as well as the required skills and experience.

[1324] "Generative AI" refers to algorithms that use artificial intelligence techniques such as machine learning models to analyze data and generate optimal solutions and proposals.

[1325] A "database" refers to an information management system that systematically stores collected information and makes it easy to search and reference.

[1326] "Terminal" refers to hardware such as a computer or smart device that a user uses to enter information or view suggestions.

[1327] "Feedback" refers to opinions, requests, and evaluation information that users input to the system.

[1328] "Matching" refers to the process of comparing employee information with job requirements to find the optimal combination.

[1329] A "skill set" refers to the specific set of skills, knowledge, and abilities that an employee possesses.

[1330] "Personality traits" refer to characteristics, behavioral patterns, and psychological attributes related to an employee's personality.

[1331] "Evaluation logic" refers to the rules and algorithms used to evaluate the match between employees and work based on collected data.

[1332] "User" refers to a person such as an employee or person in charge who uses the system.

[1333] "Proposal" refers to specific suggestions regarding optimal work and department placement created by generative AI based on the analysis results.

[1334] "UI (User Interface)" refers to the screen and operation method that allows users to input and operate the system.

[1335] The present invention is a system for assigning optimal tasks and departments to employees based on their skills, experience, and personality traits. This system operates in cooperation with a server, terminals, and users.

[1336] First, the server collects employee information. This information includes resumes, qualifications, experience, personality traits, skill sets, etc. It also collects job descriptions and desired personnel data from each department of the company. This information is stored in a database. This information is collected by the HR department or an internal system. For example, information on new employees can be extracted from the HR system and stored in an SQL server.

[1337] The server then inputs the collected data into a generative AI model that comprehensively evaluates employees' skill sets, experience, and personality traits. This AI model scores and matches each employee with the job and department that best suits them. This model applies machine learning algorithms using programming languages ​​such as Python and R.

[1338] ●Example 1:

[1339] The server inputs a prompt into the generative AI model: "For a new marketing project, please suggest employees with experience in digital marketing. In doing so, please prioritize employees with SEO and SEM skills." Based on this prompt, the AI ​​model selects the most suitable employee from its database and generates a recommendation such as "Ichiro Tanaka: Recommend assignment to the digital marketing project."

[1340] The server then sends the generated placement proposal to the device of the person in charge and the target employee. The proposal is sent via API in JSON format or similar.

[1341] The terminal receives the proposal and displays it on the screen. Users (employees and staff) can review the proposal and provide feedback. For example, an employee can write "I think I'm a good fit for this project. I'd like to participate" in the input field.

[1342] The device sends the feedback entered by the user to the server, which receives it and stores it in a database.

[1343] Finally, the server reevaluates the employee based on the collected feedback and determines the final placement. The reevaluation uses a generative AI model again, recalculating the score based on the feedback. The final placement results after the reevaluation are then notified to the relevant department and employee.

[1344] The introduction of this system will not only enable fair and objective personnel allocation, making optimal use of employee capabilities, but is also expected to improve the company's overall operational efficiency and performance.

[1345] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1346] Step 1:

[1347] Collection of employee information

[1348] The server collects employee information from HR systems and self-reported data.

[1349] Input: Data such as resume, qualifications, experience, personality traits, and skill sets taken from HR systems and self-reporting forms.

[1350] Processing: Convert the acquired data into an appropriate format and store it in a database. For example, convert JSON format data into SQL queries and save them in a SQL server.

[1351] Output: Employee information stored in a database.

[1352] Step 2:

[1353] Collection of work content data

[1354] The server collects data on the work content and required personnel from each department.

[1355] Input: Data such as job requirements from each department, required skill sets, experience, characteristics, etc.

[1356] Processing: Convert the acquired data into an appropriate format and store it in a database. For example, analyze business content data provided in a CSV file and import it into an SQL server.

[1357] Output: Business content data stored in a database.

[1358] Step 3:

[1359] Data analysis and matching

[1360] The server inputs the collected employee information and business data into a generative AI model for analysis.

[1361] Input: Employee information and job description data stored in a database.

[1362] Processing: Input data into a generative AI model, which evaluates and scores each employee's skill set, experience, and personality traits. Based on the results, optimal matching of employees with jobs is performed. For example, a machine learning algorithm written in Python is run to calculate a job aptitude score for each employee.

[1363] Output: Generated employee and job matching data.

[1364] Step 4:

[1365] Generate placement proposals

[1366] The server creates specific placement proposals based on the analysis results.

[1367] Input: Matching data generated by the AI ​​model.

[1368] Processing: Summarize the specific tasks and departments suitable for each employee in a proposal. For example, generate an assignment proposal such as "Ichiro Tanaka: Recommend assignment to the digital marketing project."

[1369] Output: Placement proposal.

[1370] Step 5:

[1371] Submitting and Viewing Proposals

[1372] The server then sends the generated placement proposal to the terminals of the person in charge and the target employee. The terminals receive the proposal and display it on their screens.

[1373] Input: Placement proposal.

[1374] Processing: Send the suggestion data to the device via API in JSON format, for example, by using an HTTP request.

[1375] Output: The placement proposal displayed on the terminal.

[1376] Step 6:

[1377] Gathering feedback

[1378] The terminal provides a UI for users (employees and staff) to input their opinions and requests regarding the proposal and collects feedback.

[1379] Input: Feedback entered by the user.

[1380] Processing: Send the entered feedback to the server and store it in a database. For example, collect the input data from the feedback form and send it to the server via API.

[1381] Output: Feedback data stored in a database.

[1382] Step 7:

[1383] Reevaluation and Final Placement Decision

[1384] The server will re-evaluate based on the collected feedback and make a final placement decision.

[1385] Input: Feedback data.

[1386] Processing: The AI ​​model is re-run taking into account the feedback and recalculating the score. The final placement result is then generated after re-evaluation. For example, the AI ​​model parameters are adjusted based on the feedback and recalculated.

[1387] Output: The final placement result.

[1388] Step 8:

[1389] Notification of final placement results

[1390] The server notifies the department and employee in charge of the final placement result.

[1391] Input: Final placement result.

[1392] Processing: A message is generated to notify the department and employee of the final placement result and sent to the device. For example, notification is sent by email or push notification.

[1393] Output: Final placement results notified to the relevant department and employee.

[1394] (Application example 1)

[1395] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1396] Existing human resource allocation systems can assign optimal tasks based on employees' skills, experience, and personality traits, but applying this to factory robots is a similar challenge. An efficient system is also needed to process diverse data, including robot skill sets and performance data, and to notify updates on tasks in real time.

[1397] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1398] In this invention, the server includes means for collecting employee information and storing it in a database, means for collecting data on job content and desired personnel and storing it in a database, means for analyzing employee information and job data using generative AI and calculating the optimal employee-job match, means for sending and displaying appropriate placement proposals to terminals, means for collecting feedback from terminals and storing it in a database, means for reevaluating based on the feedback and finalizing and notifying the optimal placement, means for collecting robot skill sets and performance data and matching optimal jobs, and means for sending matching results to the server and notifying the robots of job updates in real time. This makes it possible to assign optimal jobs not only to human employees but also to robots in the factory, thereby improving overall production efficiency.

[1399] "Employee information" refers to information held by a company about each employee, such as their resume, qualifications, experience, personality traits, and skill set.

[1400] A "database" is a collection of information that is systematically organized and stored so that it can be easily accessed when needed.

[1401] "Generative AI" is an artificial intelligence technology that generates new data and predictions based on existing data.

[1402] "Feedback" refers to the user's opinions and requests regarding suggestions and assignments.

[1403] A "skill set" is a collection of skills and abilities required to perform a specific job.

[1404] "Performance data" is information about the specific performance and capabilities of a robot or device.

[1405] "Matching" is the process of finding the best combination based on specific conditions.

[1406] A "server" is a central control device that processes information and manages data.

[1407] A "terminal" is a device that is connected to a server and can be directly operated by a user.

[1408] "Real time" refers to the immediacy in which state changes and data processing occur almost simultaneously.

[1409] The "best fit" is the work that can be performed most efficiently and effectively based on skill sets, experience, and performance data.

[1410] The present invention is a system that assigns optimal tasks and departments to employees and robots based on their skills, experience, and personality traits. This system operates in cooperation with a server, terminals, and users.

[1411] Server Processing

[1412] 1. Data Collection and Storage

[1413] The server collects employee and robot information and stores it in a database. This information includes resumes, qualifications, experience, personality traits, skill sets, and performance data. It also collects job descriptions and desired personnel data from each department and stores them in the database.

[1414] 2. Data analysis and matching

[1415] The server uses generative AI to analyze the collected employee information and work data. The AI ​​comprehensively evaluates the skill sets, experience, personality traits, and performance data of employees and robots, scoring and matching them with the most suitable jobs and departments.

[1416] 3. Generate and submit a proposal

[1417] Based on the analysis results, the server creates a specific placement proposal, which indicates which department and task each employee and robot is suited to. The proposal is then sent to the terminals of the person in charge and the target employee and robot.

[1418] Terminal handling

[1419] 1. Receiving and Displaying Proposals

[1420] The terminal receives the placement proposal sent from the server and displays it on the screen. Employees and personnel can review the proposal and provide feedback if necessary.

[1421] 2. Gathering feedback

[1422] The terminal provides a UI for employees and staff to input their opinions and requests regarding the proposal. Once the feedback is entered, it is sent to the server.

[1423] User Action

[1424] 1. Confirmation of proposal

[1425] Users (employees, staff, and robot managers) check the proposals from the server via their terminals. The proposals contain specific details about the tasks and departments that are best suited for them or the robots they are responsible for.

[1426] 2. Providing Feedback

[1427] Users input their opinions and requests regarding the proposals and send them to the server via their terminals. This feedback is used as an important factor in the final decision-making process.

[1428] Specific examples

[1429] For example, consider the launch of a new production line. This project requires human resources and robots with specific skill sets and experience. First, the project manager inputs project details (e.g., required skills, number of people, deadline) into the server. Based on this information, the server uses a generative AI model to select the most suitable employees and robots. The server then creates a list of candidates and robots evaluated by the AI ​​model and sends it to the project manager and stakeholders. The project manager and stakeholders review the proposals on their own devices and provide feedback. Based on this feedback, the server reevaluates and makes a final placement decision. The project manager and employees / robots are notified of the final placement results, allowing them to quickly transition to new tasks and roles.

[1430] Example prompts for generative AI models

[1431] "Robot A's skill set: [welding accuracy, speed, energy consumption]. Required tasks: Welding, part assembly, quality inspection. Please assign the most appropriate task to it."

[1432] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1433] Step 1:

[1434] The server collects employee information and robot information. Specifically, it stores employee resumes, qualifications, experience, personality traits, skill sets, and robot performance data in a database. This information is treated as input data. The input data is stored in the database and used in subsequent processes.

[1435] Step 2:

[1436] The server collects job descriptions and desired personnel data from each department and stores it in a database. This information includes the required skills, experience, work content, and specific performance requirements. The input data are job details, which are also stored in the database.

[1437] Step 3:

[1438] The server uses generative AI to analyze the collected employee information and business data as input. This analysis comprehensively evaluates each employee and robot's skill set, experience, personality traits, and performance data. Data calculations are used to calculate and score the optimal employee-job match. The output is a list of optimal matches.

[1439] Step 4:

[1440] The server then creates a specific placement proposal based on the analysis results. This placement proposal specifically indicates which department and task each employee and robot is suited to. The server then creates the placement proposal as output and sends it to the terminal.

[1441] Step 5:

[1442] The terminal receives the placement proposal sent from the server and displays it on the screens of the target employee and robot administrator. This proposal includes specific work content and reasons for the placement. The user receives this information as input data and confirms it.

[1443] Step 6:

[1444] The terminal provides a UI for employees and robot administrators to input their opinions and requests regarding the proposals, and collects feedback. The input feedback is sent from the terminal to the server, and the feedback data is obtained as output.

[1445] Step 7:

[1446] The server performs a re-evaluation based on the collected feedback data, which may result in modifications to the initial placement proposal. The re-evaluation process uses the feedback as input to adjust the evaluation logic and finalize the optimal placement. The output includes the finalized placement proposal.

[1447] Step 8:

[1448] The server notifies the final placement results and sends them to the terminals of the employees and robots. This allows each employee and robot to quickly transition to new tasks and roles. The input is the re-evaluated placement plan, and the output is the notified final placement result.

[1449] As a specific example, a prompt sentence for a generative AI model is used: "Robot A's skill set: [welding accuracy, speed, energy consumption]. Required tasks: Please assign the most appropriate task from among welding, part assembly, and quality inspection."

[1450] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1451] This invention is a system that assigns optimal tasks and departments based on employees' skills, experience, personality traits, and emotions. This system operates in cooperation with a server, terminals, users, and an emotion engine.

[1452] Server Processing

[1453] 1. Data Collection and Storage

[1454] The server first collects employee information and stores it in a database. This information includes resumes, qualifications, experience, personality traits, skill sets, etc. It also collects job descriptions and desired personnel data from each department and stores it in the database.

[1455] 2. Emotional Data Recognition

[1456] The server uses an emotion engine to recognize the emotions of employees and staff in real time, and this emotion data is used as important information when evaluating employees and collecting feedback.

[1457] 3. Data analysis and matching

[1458] The server inputs the collected employee information, work data, and emotional data into the generative AI, which then comprehensively evaluates each employee's skill set, experience, personality traits, emotions, etc., and scores and matches them with the department and job that best suits them.

[1459] 4. Generate and submit proposals

[1460] Based on the analysis results, the server creates specific placement proposals, which indicate which departments and tasks each employee is suited to. These proposals also take into account emotional data. The proposals are then sent to the devices of the person in charge and the target employee.

[1461] Terminal handling

[1462] 1. Receiving and Displaying Proposals

[1463] The terminal receives the placement proposal sent from the server and displays it on the screen. Employees and personnel can review the proposal and provide feedback if necessary.

[1464] 2. Gathering feedback

[1465] The terminal provides a UI for employees and staff to input their opinions and requests regarding the proposal. Once the feedback is entered, the terminal sends it to the server.

[1466] User Action

[1467] 1. Confirmation of proposal

[1468] Users (employees and staff) check the proposals from the server through their terminals. The proposals include specific details about the tasks and departments that are best suited to them.

[1469] 2. Providing Feedback

[1470] Users input their opinions and requests regarding the proposals and send them to the server via their devices. This feedback includes emotional data recognized by the emotion engine.

[1471] Specific examples

[1472] For example, imagine a new project is being launched. This project requires people with a specific skill set and experience. The project manager enters the project details (skills needed, number of people, deadline, etc.) into the server. The server uses this information to select the best candidates using a database of current employees and emotional data from an emotion engine.

[1473] The server creates a list of candidates evaluated by the AI ​​model and emotion engine and sends it to the project manager and candidate's devices. The project manager and candidate review the proposals on their respective devices and provide feedback. Based on this feedback, the server reevaluates and makes the final placement decision.

[1474] The final allocation results are notified to the project manager and employee, allowing them to quickly transition to new tasks and roles. Using an emotion engine, allocation can be made taking into account the emotional state of employees, contributing to improved employee satisfaction and performance. This system ensures unbiased and fair personnel allocation, providing an environment where employees can perform to their full potential, while also improving the efficiency and performance of the entire organization.

[1475] The processing flow will be explained below.

[1476] Program processing steps

[1477] Step 1: Collect and store data

[1478] Server Operation

[1479] The server collects information from employees, such as their resumes, qualifications, experience, personality traits, and skill sets.

[1480] The server stores the collected employee information in a database.

[1481] The server collects data from each department about the work content and the personnel required.

[1482] The server stores the collected job details and desired personnel data in a database.

[1483] Step 2: Recognizing and storing emotion data

[1484] Server Operation

[1485] The server uses an emotion engine to recognize the emotion data of employees and personnel in real time.

[1486] The server organizes the recognized emotion data for each employee and stores it in a database.

[1487] Step 3: Analyze and match data

[1488] Server Operation

[1489] The server inputs employee information, business data, and emotional data stored in a database into the generative AI.

[1490] The server uses generative AI to comprehensively assess an employee's skill set, experience, personality traits, and emotional state.

[1491] Based on the evaluation results obtained from the AI ​​model, the server calculates scores for the departments and tasks that are most suitable for each employee and creates a matching list.

[1492] Step 4: Generate and submit a proposal

[1493] Server Operation

[1494] The server generates specific placement proposals based on the matching list, which also reflect the emotional data.

[1495] The server transmits the generated placement proposal to the terminals of the person in charge and employees.

[1496] Step 5: Receive and view proposals

[1497] Device behavior

[1498] The terminal receives the placement proposal sent from the server.

[1499] The terminal displays placement proposals on the screen so that employees and staff can review them.

[1500] Step 6: Provide feedback

[1501] User Actions

[1502] Users (employees and staff) check the placement proposals displayed on their terminals.

[1503] The user inputs their opinions and requests regarding the proposal into the terminal.

[1504] Device behavior

[1505] The terminal transmits the input feedback to the server.

[1506] Step 7: Collect and analyze feedback

[1507] Server Operation

[1508] The server stores the received feedback and analyzes it, including emotional data.

[1509] Based on the results of the feedback analysis, the server adjusts the evaluation logic and reflects it in the database.

[1510] Step 8: Reassess and finalize

[1511] Server Operation

[1512] Based on the adjusted evaluation logic, the server calls the generation AI again and performs a re-evaluation.

[1513] The server will make a final decision on the optimal placement based on the results of the re-evaluation.

[1514] The server sends the final decision to the person in charge and to the employee's terminal.

[1515] Step 9: Notification and confirmation of final placement results

[1516] Device behavior

[1517] The terminal receives the final placement result and displays it on the screen.

[1518] The terminal notifies relevant personnel and employees.

[1519] Example 2

[1520] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1521] While traditional employee placement systems can take into account employee skills, experience, and personality traits, they have difficulty taking into account employees' emotional state. As a result, employee satisfaction and performance decline, making it difficult to achieve optimal personnel placement. Furthermore, the process of collecting feedback and reevaluating it is inefficient, making it difficult to make quick placement decisions.

[1522] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1523] In this invention, the server includes means for collecting employee information and storing it in a database, means for collecting job content and desired personnel data and storing it in a database, means for recognizing and evaluating emotional data in real time, means for analyzing employee information, job data, and emotional data using generative AI and calculating the optimal employee-job match, means for generating optimal placement proposals and sending and displaying them to the terminal, means for collecting feedback from the terminal and storing it in a database, and means for reevaluating based on the feedback and emotional data, and for finalizing and notifying the optimal placement. This enables optimal personnel placement that takes employees' emotional states into consideration, thereby improving employee satisfaction and performance. Furthermore, the efficient collection of feedback and reevaluation process enables rapid placement decisions.

[1524] "Employee Information" means key data relating to an employee, such as an employee's biography, qualifications, experience, personality traits, and skill set.

[1525] "Job Description" refers to data that details the specific tasks an employee will be responsible for, as well as the skill set and experience required for those tasks.

[1526] "Desired talent data" refers to data that defines the characteristics of an ideal candidate, such as the skills, experience, and personality traits required for a specific job or department.

[1527] "Emotion data" refers to real-time assessment of the current emotional state of employees and personnel, and is obtained by the emotion engine.

[1528] "Generative AI" is an artificial intelligence that can analyze input data and generate optimal information, and is a model used to match employees with jobs and generate placement proposals.

[1529] A "database" is a system for centrally storing and managing collected employee information, job descriptions, desired personnel data, emotional data, feedback, etc.

[1530] "Terminal" refers to a device that employees and staff use to receive and display proposals from the server and to input and send opinions and requests as feedback.

[1531] "Feedback" refers to opinions and requests provided by employees and staff via terminals, and is information used for reevaluation and optimal placement decisions.

[1532] "Reevaluation" refers to the process of re-analyzing the collected feedback and sentiment data, and is a key step in arriving at a final placement decision.

[1533] "Placement proposals" refer to recommendations made by generative AI based on the analysis results, indicating which department or job each employee would be best suited to.

[1534] This invention is a system that assigns optimal tasks and departments to employees based on their skills, experience, personality traits, and emotions. This system is primarily composed of a server, terminals, and users, and operates through the cooperation of an emotion engine and a generative AI model. A specific embodiment of this system is shown below.

[1535] Server Features

[1536] Data Collection and Storage

[1537] The server periodically collects employee information (resume, qualifications, experience, personality traits, skill sets, etc.) and stores it in a database. To do this, the server implements an API to retrieve data from various sources and centrally manages the information using database technologies such as SQLite or RDS.

[1538] Emotional Data Recognition

[1539] The server uses an emotion engine (e.g., IBM Watson's emotion analysis API) to recognize the emotion data of employees and personnel in real time. The emotion data is sent to the emotion engine via the API when text is entered, and the evaluation results are stored in the database.

[1540] Data analysis and matching

[1541] The server inputs the collected employee information, business data, and emotional data into a generative AI model (e.g., GPT-4). The generative AI model uses this information to optimally match employees with jobs and assign scores. The model is primarily operated in a cloud environment such as Google Colab.

[1542] Generate and submit proposals

[1543] The server generates optimal placement proposals based on the analysis results of the generative AI model. These proposals are created in a format such as JSON and sent to the devices of employees and personnel in charge.

[1544] Device Features

[1545] Receiving and Viewing Suggestions

[1546] The terminal receives the placement proposal sent from the server and displays it on the user interface, allowing employees and personnel in charge to confirm the proposal.

[1547] Gathering feedback

[1548] The device provides a UI for users to input their opinions and requests regarding the proposal. The feedback entered by the user is sent to the server via the device and stored in a database.

[1549] User Roles

[1550] Review the proposal

[1551] Users (employees and staff) can check the proposals from the server through their terminals, which show the tasks and departments that are best suited to the user.

[1552] Providing feedback

[1553] Users input their opinions and requests regarding the suggestions and send them to the server via their devices. The feedback also includes emotional data recognized by the emotion engine.

[1554] Specific examples

[1555] For example, consider a new project that requires a specific skill set and experience, so the project manager inputs the project details (skills needed, number of people, deadline, etc.) into the server. The server uses this information to select the best candidates using the current employee database and sentiment data from the sentiment engine.

[1556] The candidate list, evaluated using the generative AI model and emotion engine, is sent from the server to the project manager's and candidate's devices. The project manager and candidate review the proposals on their respective devices and provide feedback. Based on this feedback, the server reevaluates and determines the final placement. The project manager and employee are notified of the final placement results, enabling them to quickly transition to new tasks and roles.

[1557] Prompt Sentence Examples

[1558] "Select the best candidates for a new project. The project requires the following skills: programming, project management, and communication. The deadline is within 3 months. Use an emotion engine to take the candidate's emotions into account when selecting candidates."

[1559] In this way, the system achieves optimal personnel placement by linking the server, terminals, users, emotion engine, and generative AI model to evaluate employee skills and emotions in a balanced manner.

[1560] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1561] Step 1: Collecting and storing employee information

[1562] Server Processing

[1563] The server collects employee resumes, qualifications, experience, personality traits, and skill sets and stores them in a database. The inputs include information periodically updated by employees and new employee data provided by the HR department. The server organizes this information and stores the data using SQLite or RDS. Specifically, when an employee enters new credentials from their terminal, the data is sent to the server and the database is updated.

[1564] Input: Employee biography, qualifications, experience, personality traits, skill set

[1565] Output: Employee information stored in the database

[1566] Step 2: Collecting and storing business and department information

[1567] Server Processing

[1568] The server collects job descriptions and required personnel data from each department and stores it in a database. It provides an interface where department personnel can enter the required skill sets, number of employees, and job descriptions. Specifically, when a department manager enters details of a new project (for example, "online marketing campaign"), the information is stored in the database.

[1569] Input: Required skill sets, number of people, job description

[1570] Output: Business and department information stored in the database

[1571] Step 3: Obtaining emotion data

[1572] Server Processing

[1573] The server uses an emotion engine to obtain emotional data from employees and staff in real time. Input includes text data such as feedback. The server sends this data to the emotion engine, which evaluates the emotional state. Specifically, if an employee types, "I'm confident about this project," the text data is sent to the emotion engine, which evaluates it as a positive emotion.

[1574] Input: Text data such as feedback

[1575] Output: Emotion data stored in a database

[1576] Step 4: Analyze and match data

[1577] Server Processing

[1578] The server inputs the collected employee information, job data, and emotional data into a generative AI model to match the optimal employee with the job. Inputs include the employee's skill set, experience, personality traits, and emotional data. The generative AI model (e.g., GPT-4) performs scoring and matching based on this data. Specifically, the server extracts employees who have Java programming qualifications and marketing experience, and inputs this into the generative AI model to match them with the optimal job.

[1579] Input: Employee information, business data, emotional data

[1580] Output: Matched optimal employee-job pairs

[1581] Step 5: Generate and submit a proposal

[1582] Server Processing

[1583] The server generates optimal placement proposals based on the analysis results of the generative AI model. The proposals are created in formats such as JSON and sent to the devices of employees and staff. Specifically, the server generates a proposal stating that "Employee A is ideal for the digital marketing campaign project" and sends the proposal data to the devices of Employee A and his manager.

[1584] Input: Analysis results

[1585] Output: Placement proposal sent to the device

[1586] Step 6: Review the proposal and gather feedback

[1587] Terminal handling

[1588] The terminal receives the placement proposal sent from the server and displays it on the user interface. Employees and personnel in charge review the proposal and input their own opinions and requests. Specifically, when Employee A reviews the proposal on the terminal and inputs feedback such as "I'm interested in this job," that information is sent to the server.

[1589] Input: Placement proposal

[1590] Output: Feedback sent to the server

[1591] Step 7: Reassess the feedback and finalize

[1592] Server Processing

[1593] The server inputs the collected feedback data back into the generation AI model and re-evaluates the employee, taking into account emotional data. It then makes a final placement decision and notifies each employee and the person in charge of the results. Specifically, the server receives feedback from employee A, re-evaluates the employee, generates the final placement result, and notifies the employee that "employee A has been officially assigned to the digital marketing campaign project."

[1594] Input: Feedback data, emotion data

[1595] Output: Notification of final placement result

[1596] (Application example 2)

[1597] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1598] While there were systems in place that assigned optimal tasks and departments to employees based on their skills, experience, personality traits, and emotions, there were no systems that proposed optimal placements for robots used in factories based on their skills, operating status, and environmental data. This made it difficult to maximize the robots' work efficiency, and improving the efficiency of the entire factory was a challenge.

[1599] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting employee information and storing it in a database, means for collecting data on job content and desired personnel and storing it in a database, means for analyzing employee information and job data using generative AI and calculating the optimal employee-job match, means for sending and displaying appropriate placement proposals to the terminal, means for collecting feedback from the terminal and storing it in a database, means for reevaluating based on the feedback and finalizing and notifying the optimal placement, means for collecting robot information and storing it in a database, and means for generating optimal placement proposals based on the robot's skills, operating data, and environmental data. This enables optimal placement of robots used in a factory and maximizes work efficiency.

[1600] "Employee Information" is information including an employee's biography, qualifications, experience, personality traits, and skill set.

[1601] A "database" is a system for systematically storing and managing collected information.

[1602] "Means" refers to a method or device used to achieve a particular purpose.

[1603] "Generative AI" refers to artificial intelligence that analyzes data and generates optimal conclusions and suggestions.

[1604] "Matching" refers to finding the optimal combination based on specific conditions.

[1605] "Terminal" refers to the device used by employees and administrators to interface with the system.

[1606] "Feedback" refers to opinions and requests provided regarding a system or proposal.

[1607] "Reassessment" is the process of repeating the initial assessment based on the feedback collected.

[1608] "Robot information" refers to information including the robot's skills, operating status, and environmental data.

[1609] "Operation data" is data that indicates how much work the robot has performed.

[1610] "Environmental data" refers to data including temperature, humidity, and other environmental conditions of the work area.

[1611] This invention is a system that proposes optimal placement of employees and robots based on their skills, experience, environmental information, etc. This system operates in cooperation with a server, terminals, users, and generative AI.

[1612] Server Processing

[1613] The server collects employee and robot information and stores it in a database. Employee information includes resumes, qualifications, experience, personality traits, and skill sets, while robot information includes skills, operating data, and environmental data (temperature, humidity, etc.). The server then uses generative AI to analyze the collected information and calculate the optimal employee-job match and optimal robot placement. These placement proposals are sent to and displayed on the terminal.

[1614] Terminal handling

[1615] The terminal receives the placement proposal sent from the server and displays it on the screen. Employees, personnel, and robot administrators can use the UI to review the proposal and provide feedback as needed. The provided feedback is sent from the terminal to the server.

[1616] User Action

[1617] Users (employees, staff, and robot administrators) review the proposals from the server through their terminals. The proposals specifically describe the tasks and placements that are best suited for them and the robots. Users input their opinions and requests regarding the proposals and send them to the server through their terminals. This feedback includes data that will be used to reassess the evaluation.

[1618] Specific examples

[1619] For example, consider the launch of a new project. This project requires human resources and robots with specific skill sets and experience. The project manager enters project details (required skills, number of people, deadline, etc.) into the server. Using this information, the server selects the most suitable candidates and robots using data from the current employee database and robot database. The server then creates a candidate list and robot placement proposals evaluated using generative AI and evaluation logic, and sends them to the project manager and each employee and robot administrator's device. The project manager, employee, and robot administrator review the proposals on their respective devices and provide feedback. Based on this feedback, the server reevaluates and makes the final placement decision.

[1620] Here are some examples of prompts for generative AI models:

[1621] text

[1622] Develop an application that proposes the optimal placement of robots in a factory by applying a system that assigns employees the most suitable tasks and departments based on their skills, experience, personality traits, and emotions. Please output the program code that generates the proposal using the following specific data:

[1623] Robot ID: 1, Skill Level: 7, Operating Time: 120, Area Temperature: 20, Area Humidity: 50

[1624] Robot ID: 2, Skill Level: 5, Run Time: 150, Area Temperature: 22, Area Humidity: 55

[1625] Robot ID: 3, Skill Level: 9, Operating Hours: 100, Area Temperature: 21, Area Humidity: 52

[1626] Robot ID: 4, Skill Level: 6, Operating Hours: 130, Area Temperature: 19, Area Humidity: 48

[1627] This invention enables optimal allocation of employees and robots and maximizes work efficiency.

[1628] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1629] Step 1:

[1630] The server collects employee information and robot information. Employee information includes resumes, qualifications, experience, personality traits, and skill sets, while robot information includes skills, operating data, and environmental data (temperature, humidity, etc.). This collected information is temporarily stored in a database. The input is employee information and robot information, and the output is the information stored in the database.

[1631] Step 2:

[1632] The server collects job descriptions and required personnel data and stores them in a database. The job descriptions include project details (required skills, number of people, deadline, etc.), and the required personnel data describes the skills and qualifications required for each job. The input is the job description and required personnel data, and the output is the job data stored in the database.

[1633] Step 3:

[1634] The server inputs the collected employee information, robot information, and business data into the generative AI. The generative AI comprehensively evaluates the employee skill sets, experience, personality traits, and emotions, as well as the robot's skills, operating status, and environmental data, to calculate the optimal employee-to-business match and optimal robot placement. The inputs are employee information, robot information, and business data, and the output is the optimal match and placement proposal.

[1635] Step 4:

[1636] The server sends the optimal placement proposal generated by the generative AI to the device. The user receives the proposal through the device and checks the proposal displayed on the screen. The input is the optimal placement proposal, and the output is the placement proposal displayed on the device.

[1637] Step 5:

[1638] The user inputs their opinions and requests about the proposal as feedback using the device's UI. This feedback is sent from the device to the server. The input is the feedback, and the output is the feedback sent to the server.

[1639] Step 6:

[1640] The server analyzes the collected feedback and adjusts the evaluation logic. After adjusting the evaluation logic, the server re-evaluates using generative AI to finally determine the optimal placement. The input is the feedback, and the output is the adjusted evaluation logic and the re-evaluated placement proposal.

[1641] Step 7:

[1642] The server notifies the terminal of the final placement proposal and notifies the user. The user can confirm the final placement proposal through the terminal and quickly transition to the new job or role. The input is the final placement proposal, and the output is the final proposal displayed on the terminal.

[1643] The above are the specific processing steps of the system that realizes this application example. This step-by-step processing makes it possible to achieve optimal allocation of employees and robots and maximize work efficiency.

[1644] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1645] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1646] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1647] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1648] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1649] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1650] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1651] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1652] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1653] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1654] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1655] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1656] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1657] 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.

[1658] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1659] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1660] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1661] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1662] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1663] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1664] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1665] The following is further disclosed regarding the above embodiment.

[1666] (Claim 1)

[1667] A means of collecting employee information and storing it in a database;

[1668] A means of collecting and storing data on job content and desired personnel in a database;

[1669] A method for analyzing employee information and work data using generative AI to calculate the optimal match between employees and work,

[1670] means for transmitting and displaying suitable placement suggestions to a terminal;

[1671] a means for collecting feedback from the device and storing it in a database;

[1672] A means to reassess based on feedback and finalize and inform optimal placement

[1673] A system including:

[1674] (Claim 2)

[1675] 10. The system of claim 1, further comprising means for analyzing collected feedback and adjusting the evaluation logic.

[1676] (Claim 3)

[1677] 10. The system of claim 1, further comprising means for calculating a score for each department for each employee based on employee and job skill set, experience, and personality traits.

[1678] "Example 1"

[1679] (Claim 1)

[1680] A means of collecting employee information and storing it in a database;

[1681] A means of collecting and storing data on job content and desired personnel in a database;

[1682] A method for analyzing employee information and work data using generative AI to calculate the optimal match between employees and work,

[1683] means for transmitting and displaying suitable placement suggestions to a terminal;

[1684] a means for collecting feedback from the device and storing it in a database;

[1685] A means to reassess based on feedback and finalize and communicate optimal placement;

[1686] Based on the collected employee information and business data, we evaluate each employee's skill set, experience, and personality traits, and then use these to find the best match.

[1687] a means for a user to input feedback via a terminal and transmit the feedback to a server;

[1688] Using generative AI models, we calculate scores for each employee and suggest the most suitable departments and tasks.

[1689] the means by which the collected feedback will be used to proceed with the reassessment and to make the final placement decision;

[1690] A means for displaying suggestions and providing a UI for inputting feedback;

[1691] A means of notifying the department and employee of the final placement results after reevaluation

[1692] A system including:

[1693] (Claim 2)

[1694] 10. The system of claim 1, further comprising means for analyzing collected feedback and adjusting the evaluation logic.

[1695] (Claim 3)

[1696] 10. The system of claim 1, further comprising means for calculating a score for each department for each employee based on employee and job skill set, experience, and personality traits.

[1697] "Application Example 1"

[1698] (Claim 1)

[1699] A means of collecting employee information and storing it in a database;

[1700] A means of collecting and storing data on job content and desired personnel in a database;

[1701] A method for analyzing employee information and work data using generative AI to calculate the optimal match between employees and work,

[1702] means for transmitting and displaying suitable placement suggestions to a terminal;

[1703] a means for collecting feedback from the device and storing it in a database;

[1704] A means to reassess based on feedback and finalize and communicate optimal placement; and

[1705] A means of collecting robot skill sets and performance data and matching them with the most suitable tasks;

[1706] A means of sending matching results to a server and notifying the robot of operational updates in real time

[1707] A system including:

[1708] (Claim 2)

[1709] 10. The system of claim 1, further comprising means for analyzing collected feedback and adjusting the evaluation logic.

[1710] (Claim 3)

[1711] A means to calculate a score for each employee and department based on the employee's and job's skill set, experience, and personality traits; and

[1712] A means of calculating a score for each robot for each task based on the robot's skill set and performance data

[1713] The system of claim 1 further comprising:

[1714] "Example 2: Combining Emotion Engines"

[1715] (Claim 1)

[1716] A means of collecting employee information and storing it in a database;

[1717] A means of collecting and storing data on job content and desired personnel in a database;

[1718] a means for recognizing and evaluating emotion data in real time;

[1719] A method for analyzing employee information, work data, and emotional data using generative AI to calculate the optimal match between employees and work;

[1720] means for generating an optimal placement proposal, transmitting it to a terminal, and displaying it;

[1721] a means for collecting feedback from the device and storing it in a database;

[1722] A means to reassess based on feedback and sentiment data to finalize and inform optimal placement

[1723] A system including:

[1724] (Claim 2)

[1725] 10. The system of claim 1, further comprising means for analyzing collected feedback and adjusting the evaluation logic.

[1726] (Claim 3)

[1727] 10. The system of claim 1, further comprising means for calculating a score for each department for each employee based on employee and job skill sets, experience, personality traits, and emotional data.

[1728] "Application example 2 when combining emotion engines"

[1729] (Claim 1)

[1730] A means of collecting employee information and storing it in a database;

[1731] A means of collecting and storing data on job content and desired personnel in a database;

[1732] A method for analyzing employee information and work data using generative AI to calculate the optimal match between employees and work,

[1733] means for transmitting and displaying suitable placement suggestions to a terminal;

[1734] a means for collecting feedback from the device and storing it in a database;

[1735] A means to reassess based on feedback and finalize and communicate optimal placement;

[1736] a means for collecting and storing robot information in a database;

[1737] A method for generating optimal placement proposals based on robot skills, operational data, and environmental data

[1738] A system including:

[1739] (Claim 2)

[1740] 10. The system of claim 1, further comprising means for analyzing collected feedback and adjusting the evaluation logic.

[1741] (Claim 3)

[1742] The system of claim 1 further comprising means for calculating a score for each department for each employee based on the employee and job skill set, experience, and personality traits, and means for calculating the optimal placement for each robot based on the robot's skill, operating status, and environmental data. [Explanation of symbols]

[1743] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting employee information and storing it in a database; A means of collecting and storing data on job content and desired personnel in a database; A method for analyzing employee information and work data using generative AI to calculate the optimal match between employees and work, means for transmitting and displaying suitable placement suggestions to a terminal; a means for collecting feedback from the device and storing it in a database; A means to reassess based on feedback and finalize and inform optimal placement A system including:

2. The system of claim 1 , further comprising means for analyzing collected feedback and adjusting evaluation logic.

3. 10. The system of claim 1, further comprising means for calculating a score for each employee and each department based on the employee's and job's skill set, experience, and personality traits.

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