System

Abstract

A system is provided.SOLUTION: A system including means for collecting data related to personality diagnosis, aptitude diagnosis, and a change intention from each employee, means for storing the collected data, means for generating a mapping of human relations based on the stored data, means for proposing an optimal organization arrangement based on the mapping of human relations, means for notifying each employee and a manager of the proposed organization arrangement, means for collecting feedback in response to the notified proposal, and means for analyzing the collected feedback and reflecting the analyzed feedback in a generated AI.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 organizational placement systems, it is difficult to assign employees with sufficient consideration for their personality, aptitude, and willingness to transfer, resulting in frequent mismatches. Furthermore, it takes a great deal of time and effort to optimally assign new employees. Furthermore, there is a lack of a system for properly collecting and analyzing feedback and utilizing it in the next assignment. This limits the effectiveness of this system in improving the efficiency of the entire organization and employee satisfaction. [Means for solving the problem]

[0005] This invention provides a system that collects data from each employee regarding personality tests, aptitude tests, and transfer intentions, and then stores and analyzes that data. This system generates a human relationship mapping based on the collected data and uses generative AI to propose optimal organizational placement. It also notifies each employee and manager of the proposal, and then collects and analyzes feedback and reflects it in the generative AI to improve the accuracy of the next organizational placement proposal. This series of processes optimizes employee placement within the organization and maximizes overall results.

[0006] A "personality assessment" is the result of a test or questionnaire that quantifies or categorizes each employee's personality traits.

[0007] An "aptitude test" is the result of a diagnostic test or questionnaire to assess which tasks or roles each employee is suited to.

[0008] "Transfer intention" refers to whether an employee wishes to be transferred, or to the department or role they would like to transfer to.

[0009] "Data collection instrument" means a form, application, or system for obtaining data from each employee regarding personality assessment, aptitude assessment, and transfer intentions.

[0010] "Data storage means" refers to a system or database for safely and appropriately storing collected data about each employee.

[0011] "Relationship mapping" is a diagram or model of relationships for optimal organizational structure based on personality traits, aptitudes, and interactions between employees.

[0012] "Generative AI" refers to artificial intelligence used to analyze collected data and suggest optimal organizational placements.

[0013] The "organizational layout proposal means" is a system or algorithm for proposing optimal organizational layout based on the results of analysis by the generation AI.

[0014] The "notification means" is an email sending system or notification application for informing each employee and manager of the proposed organizational arrangement.

[0015] "Feedback collection instrument" refers to a form or survey system for collecting feedback regarding the proposed organizational arrangement.

[0016] A "feedback analysis means" is a system or algorithm that analyzes collected feedback and reflects the results in the generative AI. [Brief explanation of the drawings]

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

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

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

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

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

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

[0023] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

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

[0025] [First embodiment]

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

[0027] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

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

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

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

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

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

[0034] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0036] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

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

[0038] This invention is a system that proposes optimal organizational placement based on the personality, aptitude, and transfer intention of employees. Specific program processing of the system will be explained below in natural language.

[0039] Data collection

[0040] User (employee)

[0041] Each employee enters information about their personality and aptitude tests, as well as their intention to transfer, via a web form or dedicated application. For example, employee A answers the personality test, is judged to be "suited to creative work" through the aptitude test, and enters their intention to transfer as "to the marketing department."

[0042] Device (employee's PC or smartphone)

[0043] The device sends the collected data to the server, where it is encrypted to ensure the security of the information.

[0044] server

[0045] The server analyzes the received data and stores it in an internal database. For example, it stores the personality test results, aptitude test results, and transfer request information of employee A in the database.

[0046] Data Processing and Training

[0047] server

[0048] The server cleanses the data collected from all employees (deleting invalid data and filling in missing data) and then starts the learning process using generative AI. This learning process generates a mapping of human relationships. For example, a mapping is performed that takes into account employee A's personality traits and aptitudes, and it calculates which team employee A would be best suited to.

[0049] Organizational Arrangement Proposal

[0050] server

[0051] Based on the generated interpersonal relationship mapping, the server uses generative AI to create optimal organizational placement proposals. The proposals include the member composition of each team and the placement of new employees. For example, a proposal is generated to place employee A as the leader of a new project team in the marketing department.

[0052] Communicating suggestions and gathering feedback

[0053] server

[0054] The server then sends an email to each employee and manager notifying them of the proposed organizational placement, along with a link to a dedicated portal site where they can review the proposal.

[0055] Users (employees and administrators)

[0056] The user checks the notified organizational placement proposal and provides feedback on the dedicated portal site. For example, employee A checks the transfer proposal and submits feedback on the timing of the transfer.

[0057] server

[0058] The server analyzes the collected feedback and reflects the results in the generating AI, thereby improving the accuracy of the next organizational placement proposal.

[0059] In this way, this system proposes optimal organizational placement based on employee data and uses feedback to improve the accuracy of placement, thereby maximizing the efficiency and results of the entire organization.

[0060] The processing flow will be explained below.

[0061] Step 1:

[0062] User (employee)

[0063] Each employee enters information about their personality and aptitude tests, as well as their intention to transfer, via a web form or dedicated application. For example, employee A answers the personality test, is judged to be "suited to creative work" through the aptitude test, and enters their intention to transfer as "to the marketing department."

[0064] Step 2:

[0065] Device (employee's PC or smartphone)

[0066] The terminal sends the input data to the server. At this time, the data is encrypted before being sent, so the security of the information is maintained.

[0067] Step 3:

[0068] server

[0069] The server analyzes the received data and stores it in an internal database. For example, it stores the personality test results, aptitude test results, and transfer request information of employee A in the database.

[0070] Step 4:

[0071] server

[0072] The server cleanses the data collected from all employees, which includes removing invalid data and filling in missing data, a process that ensures data accuracy.

[0073] Step 5:

[0074] server

[0075] Based on the cleansed data, the generative AI begins the learning process. The generative AI analyzes information such as personality traits and aptitudes to generate a mapping of human relationships. For example, a mapping is performed that takes into account the personality traits and aptitudes of employee A, and it calculates which team employee A would be best suited to.

[0076] Step 6:

[0077] server

[0078] The server then creates optimal organizational placement proposals based on the generated interpersonal relationship mapping. The generation AI considers employee compatibility, team balance, and job content when proposing placements. For example, a proposal is generated to place employee A as the leader of a new project team in the marketing department.

[0079] Step 7:

[0080] server

[0081] The generated organizational placement proposal will be notified to each employee and manager via email, and a link to a dedicated portal site will be provided so that the proposal can be viewed in detail.

[0082] Step 8:

[0083] Users (employees and administrators)

[0084] The user checks the notified organizational placement proposal and provides feedback on the dedicated portal site. For example, employee A checks the transfer proposal and submits feedback on the timing of the transfer.

[0085] Step 9:

[0086] server

[0087] The server analyzes the collected feedback and reflects the results in the generating AI. This feedback analysis improves the accuracy of the next organizational placement proposal.

[0088] In this way, this system proposes optimal organizational placement based on employee data and uses feedback to improve the accuracy of placement, thereby maximizing the efficiency and results of the entire organization.

[0089] Example 1

[0090] 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."

[0091] With conventional organizational placement systems, it is difficult to make optimal organizational placements that properly reflect employees' personality traits, aptitudes, and transfer intentions. Furthermore, there is no fully established mechanism for effectively utilizing feedback to improve the accuracy of the next placement proposal. As a result, effective organizational placements that maximize employee capabilities are not realized, resulting in issues such as a decline in the efficiency and performance of the entire organization.

[0092] 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.

[0093] In this invention, the server includes means for collecting data on personality assessment, aptitude assessment, and transfer intention from each employee, means for encrypting and storing the collected data, means for cleansing the stored data, means for performing learning using a generative AI model based on the cleansed data and generating a human relationship mapping, means for proposing an optimal organizational placement based on the human relationship mapping, means for notifying each employee and manager of the proposed organizational placement, means for collecting feedback on the notified proposal, and means for analyzing the collected feedback and reflecting it in the generative AI model. This allows for optimal organizational placement that appropriately considers employees' personality traits, aptitude, and transfer intention, and further makes it possible to improve the accuracy of subsequent proposals by utilizing the collected feedback.

[0094] A "personality assessment" is a test or questionnaire used to evaluate an employee's personality traits.

[0095] An "aptitude test" is a test or assessment used to evaluate an employee's job aptitude and skills.

[0096] "Transfer intention" refers to information that indicates the department or job content to which an employee wishes to be transferred.

[0097] "Means of collecting data" refers to the methods and tools used to collect information from employees regarding personality tests, aptitude tests, and transfer intentions.

[0098] "Means for encryption and storage" refers to the method of storing collected data using encryption technology.

[0099] "Cleansing means" refers to a method for removing invalid data from stored data and for filling in missing data.

[0100] "Generative AI model" refers to a generative model using AI, a technology used to generate organizational placement proposals.

[0101] "Interpersonal relationship mapping" is a map that illustrates the interrelationships between employees based on their personality traits and aptitudes.

[0102] "Means for proposing optimal organizational placement" refers to methods and tools that make proposals for placing employees in the optimal departments or teams based on interpersonal relationship mapping.

[0103] "Means of notification" refers to the methods and tools used to inform each employee and manager of the proposed organizational arrangement.

[0104] "Feedback collection measures" are methods and tools used to gather input from employees and managers about proposed organizational arrangements.

[0105] "Means of analysis" refers to the method of analyzing the collected feedback and incorporating the results into the generative AI model.

[0106] The present invention is a system for proposing optimal organizational placement based on the personality traits, aptitudes, and transfer intentions of employees. Specific embodiments for carrying out the present invention will be described below.

[0107] Data collection

[0108] User (employee)

[0109] Each employee uses a dedicated application or web form to enter information about their personality and aptitude tests, as well as their intention to transfer. For example, an employee opens the dedicated application, answers "creative" to the personality test, answers "suitable for creative work" to the aptitude test, and enters "hope to transfer to the marketing department" as their intention to transfer.

[0110] Device (employee's PC or smartphone)

[0111] The terminal receives the data entered by the user, encrypts it using AES encryption technology, and sends it to the server. For example, the personality test result "creative," the aptitude test result "suitable for creative work," and the transfer intention "hope to be in the marketing department" are encrypted and sent.

[0112] Receiving and storing data

[0113] server

[0114] The server receives the data sent from the terminal, decrypts it, and saves it in a database (e.g., MySQL). For example, it saves the employee's personality test results, aptitude test results, and transfer request information in the employees_data table.

[0115] Data Cleansing

[0116] server

[0117] The server uses Python's Pandas library to cleanse the data. Specifically, it removes invalid data and fills in missing data to generate the final dataset. For example, it automatically removes or fills in incorrectly entered data or missing data, cleansing the data for all employees.

[0118] Data Analysis and Learning

[0119] server

[0120] The server then inputs the cleansed data into a generative AI model (e.g., GPT-4) and begins the learning process. Specifically, it generates a relationship mapping that takes into account personality traits, aptitudes, and transfer preferences. For example, the AI ​​can be trained on a dataset containing employee data to determine which employees are best suited to which teams.

[0121] Organizational Arrangement Proposal

[0122] server

[0123] The server creates optimal organizational placement proposals based on the human relationship mapping generated by the generative AI model. For example, the AI ​​may determine that "Employee A is the best suited person to be a project leader in the marketing department," and then compile that placement proposal.

[0124] Proposal Notification

[0125] server

[0126] The server notifies each employee and manager of the generated organizational placement proposal by email. It also includes a link to a dedicated portal site where the proposal can be viewed. For example, an email notification of "Project Leader Placement Proposal in the Marketing Department" is sent to employees and managers, along with the URL of the dedicated portal site.

[0127] Collecting feedback

[0128] Users (employees and administrators)

[0129] Users can review the proposed organizational placement and provide feedback on a dedicated portal site. For example, an employee can review the proposal and submit feedback such as "I would like the transfer to occur next month" in a dedicated form.

[0130] Analyzing and incorporating feedback

[0131] server

[0132] The server analyzes the collected feedback and reflects the results in the generative AI model. This improves the accuracy of the next organizational placement proposal. For example, a user's feedback such as "I would like the transfer to occur next month" is analyzed and added to the AI ​​model's learning dataset.

[0133] Prompt Sentence Examples

[0134] As an example of an input prompt for a generative AI model, we will use the following sentence:

[0135] "Based on the following data, please suggest the optimal organizational arrangement.

[0136] data:

[0137] Employee A: Personality test result: "Creative, team player"; Aptitude test result: "Suitable for creative work"; Intention to transfer: "Want to move to the marketing department"

[0138] It also includes detailed data of other employees.”

[0139] In this way, the system of the present invention proposes optimal organizational placement based on employees' personality traits, aptitudes, and intentions for transfer, and by reflecting feedback in a timely manner, it is possible to maximize the efficiency and results of the entire organization.

[0140] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0141] Step 1:

[0142] User (employee)

[0143] Each employee uses a dedicated application or web form to enter information about their personality and aptitude tests, as well as their intention to transfer. For example, employee A enters their personality test result of "creative," their aptitude test result of "suited for creative work," and their intention to transfer to the marketing department.

[0144] Input and Output

[0145] Input: Personality test results, aptitude test results, intention to transfer

[0146] Output: Information entered into an application or web form

[0147] Step 2:

[0148] Device (employee's PC or smartphone)

[0149] The terminal receives the information entered by the user in real time and encrypts it using AES encryption technology. For example, if employee A enters the personality test result "creative," the aptitude test result "suitable for creative work," and the transfer intention "hope to be transferred to the marketing department," the information is encrypted.

[0150] Input and Output

[0151] Input: Personality test results, aptitude test results, intention to transfer

[0152] Output: Encrypted information

[0153] Step 3:

[0154] Device (employee's PC or smartphone)

[0155] The terminal sends the encrypted information to the server. For example, the information of employee A is encrypted and sent to the server.

[0156] Input and Output

[0157] Input: Encrypted information

[0158] Output: Encrypted information sent to the server

[0159] Step 4:

[0160] server

[0161] The server receives the encrypted information sent from the terminal, decrypts it, and stores it in a database. For example, the personality test results, aptitude test results, and transfer request information for employee A are stored in the employees_data table of a database (e.g., MySQL).

[0162] Input and Output

[0163] Input: Encrypted information

[0164] Output: Decrypted database entries

[0165] Step 5:

[0166] server

[0167] The server uses the Python Pandas library to cleanse the data, remove invalid data, impute missing data, and generate a final cleansed dataset, e.g., impute missing data and remove incorrectly entered data.

[0168] Input and Output

[0169] Input: Employee data

[0170] Output: A cleansed dataset

[0171] Step 6:

[0172] server

[0173] The server inputs the cleansed data into a generative AI model (e.g., GPT-4) and begins the learning process. Specifically, it generates a relationship mapping that takes into account personality traits, aptitude, and transfer preferences. For example, based on a dataset containing employee A's data, the generative AI generates a relationship mapping and determines which team employee A would be best suited to.

[0174] Input and Output

[0175] Input: Cleansed dataset

[0176] Output: Relationship mapping

[0177] Step 7:

[0178] server

[0179] The server creates optimal organizational placement proposals based on the human relationship mapping generated by the generative AI model. For example, the AI ​​may determine that "Employee A is the best suited person to be a project leader in the marketing department," and then compile that placement proposal.

[0180] Input and Output

[0181] Input: Relationship mapping

[0182] Output: Organization layout proposal

[0183] Step 8:

[0184] server

[0185] The server notifies each employee and manager of the generated organizational placement proposal by email. It also provides a link to a dedicated portal site where the proposal can be viewed. For example, a notification email for "Project Leader Placement Proposal in the Marketing Department" is sent to employee A and the manager.

[0186] Input and Output

[0187] Input: Organizational Allocation Proposal

[0188] Output: Notification email and portal link

[0189] Step 9:

[0190] Users (employees and administrators)

[0191] The user checks the proposed organizational placement and provides feedback on a dedicated portal site. For example, employee A enters feedback such as "I would like to be transferred next month" into a dedicated form and submits it.

[0192] Input and Output

[0193] Input: Feedback on the proposal

[0194] Output: Feedback data

[0195] Step 10:

[0196] server

[0197] The server analyzes the collected feedback and reflects the results in the generative AI model, improving the accuracy of the next organizational placement proposal. For example, the feedback content can be added to the AI ​​model's learning dataset, resulting in more accurate next placement proposals.

[0198] Input and Output

[0199] Input: Feedback data

[0200] Output: Updated AI model training dataset

[0201] In this way, each processing step is carried out in a concrete manner, and the optimal organizational placement is proposed based on the employee's personality traits, aptitude, and intention to transfer, and feedback can be used to improve the accuracy of the proposal.

[0202] (Application example 1)

[0203] 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."

[0204] Currently, factories are not sufficiently optimizing the placement and task allocation of robots and workers, resulting in a decline in production efficiency. Furthermore, placement that reflects personality assessment, aptitude assessment, and transfer intentions has not been realized, leaving problems in improving the work environment and worker satisfaction. The objective of this invention is to solve these problems and provide a system that realizes optimal placement and task allocation of robots and workers in a factory.

[0205] 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.

[0206] In this invention, the server includes means for collecting data on personality tests, aptitude tests, and transfer intentions from each worker, means for saving the collected data, means for generating a human relationship mapping based on the saved data, means for proposing an optimal organizational placement based on the human relationship mapping, means for notifying each worker and manager of the proposed organizational placement, means for collecting feedback on the notified proposal, means for analyzing the collected feedback and reflecting it in the generation AI, and means for proposing an optimal placement and task assignment of robots in the factory based on the characteristics and capabilities of each worker. This enables placement and task assignment that takes into account the personality traits, aptitude, and transfer intentions of workers.

[0207] "Workers" are personnel who operate and manage robots within a factory, and are people whose personality tests, aptitude tests, and willingness to transfer are taken into consideration.

[0208] A "personality assessment" is a test or analytical tool used to evaluate a worker's personality traits, a process used to determine a worker's aptitude and job suitability.

[0209] An "aptitude test" is a test or assessment tool used to evaluate a worker's suitability for a job, and is a process used to determine what tasks or roles a worker is suited to.

[0210] "Transfer intention" refers to data that indicates a worker's intention or desire to move to a different department or role, and is one of the factors reflected in organizational placement proposals.

[0211] "Factory robots" are mechanical devices used to automate factory work and production processes, and optimizing their placement and task assignment is important to ensure that they perform various tasks efficiently.

[0212] "Means of collecting data" refers to methods and tools for collecting data on workers' personality tests, aptitude tests, and intentions to transfer, and specifically includes questionnaires, tests, sensors, etc.

[0213] "Means for storing data" means methods and systems for safely and efficiently storing collected data, including databases, cloud storage, and file systems.

[0214] "Means for generating a mapping of human relationships" refers to methods and technologies for analyzing stored data and visualizing the relationships and aptitudes between workers, and specifically includes generative AI algorithms and clustering methods.

[0215] "Means for proposing optimal organizational placement" refers to methods and systems for proposing optimal placement and task allocation of workers and robots based on human relationship mapping, and specifically includes AI models and simulation software.

[0216] "Means for notifying proposals" means a method or system for notifying each worker and manager of the generated placement and task assignment proposals, and specifically includes email, a notification application, and a portal site.

[0217] "Means for collecting feedback" means methods or systems for collecting feedback from workers or managers regarding the notified proposal, and specifically includes forms, questionnaires, and opinion collection systems.

[0218] "Generative AI" refers to artificial intelligence technology for data analysis, mapping generation, placement proposals, etc., and includes advanced algorithms and learning models.

[0219] "Task allocation" is the process of assigning specific tasks to workers or robots, and is a necessary step for achieving efficient work.

[0220] The present invention provides a system for proposing optimal placement and task allocation for robots and workers in a factory. Specific embodiments of the system will be described below.

[0221] 1. Data Collection

[0222] Collecting data from workers

[0223] The user, a worker, enters information about the personality test, aptitude test, and transfer intention through a dedicated application or web form. For example, worker A answers the personality test, is judged to have "aptitude for speedy work" through the aptitude test, and enters "hope for welding department" as his / her intention to transfer.

[0224] Data transmission and storage

[0225] The terminal (the worker's PC or smartphone) encrypts the collected data and sends it to the server. The server analyzes the received data and stores it securely in an internal database. For example, the results of worker A's personality test, aptitude test, and transfer request information are stored in the database.

[0226] 2. Data processing and human relationship mapping

[0227] The server cleanses the data collected from all workers, deleting invalid data and filling in missing data. The generative AI then analyzes the data and begins the learning process to generate a relationship mapping. For example, it determines which team Worker A is best suited to, taking into account Worker A's personality traits and aptitudes.

[0228] 3. Proposal for placement and task allocation

[0229] Based on the generated mapping results of human relationships, the server uses generative AI to create optimal robot and worker placement proposals and task assignments. For example, a proposal is generated to place Worker A as a member of a new project team in the welding department.

[0230] 4. Proposal notification and feedback collection

[0231] Proposal Notification

[0232] The server notifies each worker and manager of the generated placement and task assignment proposals via email, and also provides a URL for a dedicated portal site where the proposals can be viewed.

[0233] Collecting and analyzing feedback

[0234] The users, i.e., workers and managers, can check the notified placement proposal on a dedicated portal site and provide feedback there. For example, worker A checks the placement proposal and submits his / her opinion on the contents of the proposal as feedback.

[0235] The server analyzes the collected feedback and reflects the results in the generating AI to improve the accuracy of the next placement proposal.

[0236] Examples of concrete examples and prompts

[0237] For example, you might enter the following prompt for a generative AI model:

[0238] "Please tell me a method to propose optimal placement and task assignment based on the characteristics and capabilities of each robot in a factory. In particular, I would like to know a concrete example using K-means clustering."

[0239] By using this system, production efficiency within the factory can be significantly improved, and worker satisfaction can also be increased.

[0240] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0241] Step 1:

[0242] Data on personality and aptitude tests and transfer intentions are collected from each worker. The user enters this information through a dedicated application or a web form. The entered data includes the worker's personality traits, aptitude for the job, and transfer wishes. The terminal temporarily stores this data, encrypts it, and sends it to the server.

[0243] Step 2:

[0244] The server receives the collected data and stores it in a database. At this time, it performs data cleansing, deletes invalid data, and fills in missing data. The server safely stores the received personality test results, aptitude test results, and transfer request information in the database.

[0245] Step 3:

[0246] The server analyzes the stored data and generates a mapping of human relationships. Specifically, it uses generative AI to perform cluster analysis based on the personality traits and aptitudes of workers. Here, it extracts data features and recognizes patterns to visualize the relationships between workers. The mapping results are used as the base data for the next processing step.

[0247] Step 4:

[0248] The server generates optimal robot and worker placement proposals and task assignments based on the results of the interpersonal relationship mapping. Using a generative AI model, it outputs placement proposals that make the most of the characteristics of each worker and robot. The input data is the worker's personality traits, aptitude, and transfer intentions, and the output is a specific placement proposal and task assignment.

[0249] Step 5:

[0250] The server notifies each worker and manager of the generated placement and task assignment proposals. Notifications are sent via email, and the worker and manager are also provided with a link to a dedicated portal site. Details of the proposals can be viewed on the portal site.

[0251] Step 6:

[0252] The users, i.e., workers and managers, can check the placement proposals they have been notified of on a dedicated portal site and provide feedback, including how they accepted the proposals and their opinions on areas for improvement.

[0253] Step 7:

[0254] The server analyzes the collected feedback and reflects the results in the generative AI. This is used to improve the accuracy of the next placement proposal. Specifically, the feedback content is added to the dataset and the generative AI model is retrained to improve the system.

[0255] The above processing steps enable optimal placement and task allocation of robots and workers in a factory, thereby improving production efficiency and worker satisfaction.

[0256] 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.

[0257] This invention is a system that proposes optimal organizational placement based on employees' personalities, aptitudes, and transfer intentions, and by combining this with an emotion engine that recognizes the user's emotions, it achieves even more accurate placement. Below, the specific program processing of the system is explained in natural language.

[0258] Data collection

[0259] User (employee)

[0260] Each employee enters information about their personality and aptitude tests, as well as their intention to transfer, via a web form or dedicated application. For example, employee A answers the personality test, is judged to be "suited to creative work" through the aptitude test, and enters their intention to transfer as "to the marketing department."

[0261] Device (employee's PC or smartphone)

[0262] The terminal sends the input data to the server. At this time, the data is encrypted before being sent, so the security of the information is maintained.

[0263] server

[0264] The server analyzes the received data and stores it in an internal database. For example, it stores the personality test results, aptitude test results, and transfer request information of employee A in the database.

[0265] emotion recognition

[0266] server

[0267] During the data collection process, the device's camera and microphone are used to analyze the user's facial expressions and tone of voice, and the emotion engine recognizes the user's emotions. For example, if employee A feels irritated during a personality test, that emotion is recorded.

[0268] Data Processing and Training

[0269] server

[0270] The server cleanses the data collected from all employees, which includes removing invalid data and filling in missing data, a process that ensures data accuracy.

[0271] server

[0272] Based on the cleansed data, the generative AI begins the learning process. The generative AI analyzes personality traits, aptitudes, and recognized emotions to generate a mapping of human relationships. For example, a mapping is performed that takes into account Employee A's personality traits and aptitudes, and calculates which team Employee A would be best suited to.

[0273] Organizational Arrangement Proposal

[0274] server

[0275] The server then creates optimal organizational placement proposals based on the generated interpersonal relationship mapping. The generation AI considers employee compatibility, team balance, work content, and emotions when proposing placements. For example, a proposal is generated to place employee A as the leader of a new project team in the marketing department.

[0276] server

[0277] The proposed organizational relocation is notified to each employee and manager via email, and a link to a dedicated portal site is also provided. The content of the notification can also be adjusted based on the emotion recognition results. For example, if Employee A is feeling anxious about the transfer, a message to ease his / her feelings can be included.

[0278] Reviewing proposals and gathering feedback

[0279] Users (employees and administrators)

[0280] The user checks the notified organizational placement proposal and provides feedback on the dedicated portal site. For example, employee A checks the transfer proposal and submits feedback on the timing of the transfer.

[0281] server

[0282] The server analyzes the collected feedback and reflects it in the generation AI. This feedback analysis improves the accuracy of the next organizational placement proposal. The emotion engine recognizes the user's emotions even when providing feedback, and supports receiving appropriate feedback.

[0283] In this way, the system proposes optimal organizational placement based on employee data and emotions, and uses feedback to improve the accuracy of placement, thereby maximizing the efficiency and results of the entire organization.

[0284] The processing flow will be explained below.

[0285] Step 1:

[0286] User (employee)

[0287] Each employee enters information about their personality and aptitude tests, as well as their intention to transfer, via a web form or dedicated application. For example, employee A answers the personality test, is judged to be "suited to creative work" through the aptitude test, and enters their intention to transfer as "to the marketing department."

[0288] Step 2:

[0289] Device (employee's PC or smartphone)

[0290] The terminal sends the input data to the server. At this time, the data is encrypted before being sent, so the security of the information is maintained.

[0291] Step 3:

[0292] server

[0293] The server analyzes the received data and stores it in an internal database. For example, it stores the personality test results, aptitude test results, and transfer request information of employee A in the database.

[0294] Step 4:

[0295] Terminal

[0296] During the data collection process, the device's camera and microphone are used to analyze the user's facial expressions and tone of voice, and the emotion engine recognizes the user's emotions. For example, if employee A feels irritated during a personality test, that emotion is recorded.

[0297] Step 5:

[0298] server

[0299] The server cleanses the data collected from all employees, which includes removing invalid data and filling in missing data, a process that ensures data accuracy.

[0300] Step 6:

[0301] server

[0302] Based on the cleansed data, the generative AI begins the learning process. The generative AI analyzes personality traits, aptitudes, and recognized emotions to generate a mapping of human relationships. For example, a mapping is performed that takes into account Employee A's personality traits and aptitudes, and calculates which team Employee A would be best suited to.

[0303] Step 7:

[0304] server

[0305] The server then creates optimal organizational placement proposals based on the generated interpersonal relationship mapping. The generation AI considers employee compatibility, team balance, work content, and emotions when proposing placements. For example, a proposal is generated to place employee A as the leader of a new project team in the marketing department.

[0306] Step 8:

[0307] server

[0308] The proposed organizational relocation is notified to each employee and manager via email, and a link to a dedicated portal site is also provided. The content of the notification can also be adjusted based on the emotion recognition results. For example, if Employee A is feeling anxious about the transfer, a message to ease his / her feelings can be included.

[0309] Step 9:

[0310] Users (employees and administrators)

[0311] The user checks the notified organizational placement proposal and provides feedback on the dedicated portal site. For example, employee A checks the transfer proposal and submits feedback on the timing of the transfer.

[0312] Step 10:

[0313] server

[0314] The server analyzes the collected feedback and reflects it in the generation AI. This feedback analysis improves the accuracy of the next organizational placement proposal. The emotion engine recognizes the user's emotions even when providing feedback, and supports receiving appropriate feedback.

[0315] In this way, the system proposes optimal organizational placement based on employee data and emotions, and uses feedback to improve the accuracy of placement, thereby maximizing the efficiency and results of the entire organization.

[0316] Example 2

[0317] 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."

[0318] Conventional organizational placement systems were unable to make appropriate placement recommendations because they had difficulty taking into account employees' personality traits, aptitude, and transfer intentions. Furthermore, they lacked emotion recognition technology, making it impossible to reflect users' emotions in organizational placement. Furthermore, there was a lack of means to effectively utilize feedback, making it difficult to improve the accuracy of recommendations.

[0319] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for collecting data on personality assessment, aptitude assessment, and transfer intentions from each user; means for saving the collected data; means for analyzing the saved data and saving it in an internal database; means for analyzing the user's facial expressions and tone of voice using the device's camera and microphone and recognizing the user's emotions using an emotion engine; means for starting a learning process by the generation AI based on the cleansed data; means for analyzing personality traits, aptitude, and recognized emotions and generating a relationship mapping; means for creating an optimal organizational placement proposal based on the generated relationship mapping; means for notifying each user and administrator of the proposed organizational placement by email and providing a link to a dedicated portal site; means for collecting feedback on the notified proposal; and means for analyzing the collected feedback and reflecting it in the generation AI. This enables highly accurate organizational placement proposals that take into account employees' personality traits, aptitudes, and emotions.

[0320] "User" refers to individuals, especially employees, who use the system.

[0321] A "personality test" is an assessment test for measuring the personality traits of an individual user.

[0322] An "aptitude test" is a test that assesses how suited a user is to a particular job or role.

[0323] "Intention to transfer" refers to the user's intentions and wishes regarding the desired transfer destination and position.

[0324] "Collection methods" refers to software or hardware functions used to collect user data.

[0325] "Storage means" refers to the function for accumulating and safely storing collected data.

[0326] A "database" is a system for efficiently managing and accessing stored data.

[0327] A "camera" is an imaging device for capturing a user's facial expression.

[0328] A "microphone" is a device that picks up the user's voice and acquires it as audio data.

[0329] The "emotion engine" is software that analyzes the user's emotions from collected facial and voice data.

[0330] "Cleansing" is the process of removing invalid parts of data and filling in missing data.

[0331] "Generative AI" refers to artificial intelligence that analyzes data through a learning process and generates optimal suggestions.

[0332] The "learning process" is the process by which the generative AI repeatedly learns based on data.

[0333] "Relationship mapping" refers to a map that visually or abstractly represents the relationships between users.

[0334] An "organizational placement proposal" is an optimal placement proposal generated in consideration of the user's personality traits, aptitude, emotions, and the like.

[0335] "Notification means" refers to software or functions for informing each user or administrator of the proposed content.

[0336] "Portal Site" means a website that Users can access to review Proposals and provide Feedback.

[0337] "Feedback" refers to the opinions and thoughts that users provide regarding suggestions.

[0338] "Analysis" is the process of examining and understanding the collected data and feedback.

[0339] The present invention is a system that proposes optimal organizational placement based on the user's personality traits, aptitude, and transfer intentions, and improves the placement accuracy by combining it with an emotion recognition engine. This system is mainly operated by a server, terminals, and users.

[0340] Data collection

[0341] The user (employee) enters information about the personality test, aptitude test, and intention to transfer through a web form or dedicated application. This information is encrypted and sent to the server via the user's device (PC or smartphone). As a concrete example, employee A answers the personality test and enters the result that he or she is "outgoing and values ​​teamwork."

[0342] Data analysis and storage

[0343] The server analyzes the received data and stores it in an internal database. The stored data includes personality test results, aptitude test results, and transfer intentions. This allows detailed data on each user to be safely stored.

[0344] emotion recognition

[0345] The server analyzes the user's facial expressions and tone of voice using the device's camera and microphone. The emotion engine recognizes the user's emotions, and this data is also stored in an internal database. For example, if employee A feels dissatisfied during a personality test, the camera captures his or her facial expression, and the emotion engine identifies it as "dissatisfied."

[0346] Data Cleansing and Learning

[0347] The server cleanses the data collected from all users, which includes removing invalid data and filling in missing data, ensuring the accuracy of the data.

[0348] Next, the generative AI begins the learning process based on the cleansed data. The generative AI analyzes personality traits, aptitudes, and recognized emotions to generate a relationship mapping. For example, the mapping is performed to place employee A in the optimal team, taking into account employee A's personality traits and aptitudes.

[0349] Organizational Allocation Proposal and Notification

[0350] The server then creates optimal organizational placement proposals based on the generated interpersonal relationship mapping. The generation AI considers the user's compatibility, team balance, work content, and emotions when proposing placements. For example, a proposal is made to place employee A as the leader of a new project team in the marketing department.

[0351] The proposed organizational placement is notified to each user and administrator via email, and a link to a dedicated portal site is provided. The content of the notification may be adjusted based on the emotion recognition results. For example, if employee A is anxious about the transfer, a message to ease his or her anxiety may be included.

[0352] Collecting and analyzing feedback

[0353] The user checks the notified organizational placement proposal and provides feedback on the dedicated portal site. For example, employee A checks the transfer proposal and submits feedback on the timing of the transfer.

[0354] The server analyzes the collected feedback and reflects it in the generation AI. This process further improves the accuracy of the next organizational placement proposal. The emotion engine recognizes the user's emotions even when providing feedback, and supports them in receiving appropriate feedback.

[0355] Prompt Sentence Examples

[0356] Analyze the following user information and propose the optimal organizational layout.

[0357] Personality test result: Introverted and good at detailed work

[0358] Aptitude test result: Aptitude for data analysis

[0359] Willingness to transfer: Data analysis team preferred

[0360] Sentiment: Positive about the transfer

[0361] The present invention makes it possible to make highly accurate organizational placement proposals that take into account the user's personality traits, aptitudes, and emotions, thereby improving user satisfaction.

[0362] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0363] Step 1: Data entry

[0364] User (employee)

[0365] Input: Personality test, aptitude test, and information about transfer intentions

[0366] How it works: Users enter this information through a web form or a dedicated application.

[0367] Output: The input data is stored on the user's terminal.

[0368] Step 2: Send data

[0369] Device (employee's PC or smartphone)

[0370] Input: Personality test results, aptitude test results, and data regarding transfer intentions entered by the user

[0371] Operation: The terminal encrypts the entered data and sends it to the server.

[0372] Output: The encrypted data is sent to the server.

[0373] Step 3: Save Data

[0374] server

[0375] Input: Encrypted data sent from the device

[0376] How it works: The server decrypts the data it receives and stores it in an internal database.

[0377] Output: The decoded data is stored in the database.

[0378] Step 4: Emotion Recognition

[0379] server

[0380] Input: User facial expression and voice data collected through the device's camera and microphone

[0381] How it works: The server uses an emotion engine to analyze the user's facial expressions and tone of voice to recognize their emotions.

[0382] Output: The recognized emotion data is stored in the internal database.

[0383] Step 5: Data cleansing

[0384] server

[0385] Input: All collected user data (personality traits, aptitudes, emotions, etc.)

[0386] What it does: The server cleanses the data, removing invalid parts and filling in missing data.

[0387] Output: Cleansed and accurate data is produced.

[0388] Step 6: Generative AI training

[0389] server

[0390] Input: Cleansed data

[0391] How it works: The server initiates the learning process with the generative AI, analyzing personality traits, aptitudes, and emotions.

[0392] Output: Relationship mapping data is generated.

[0393] Step 7: Generate organizational alignment proposals

[0394] server

[0395] Input: Relationship mapping data

[0396] How it works: Creates optimal organizational placement proposals based on generative AI.

[0397] Output: Proposal data for optimal organizational placement is generated.

[0398] Step 8: Proposal Notification

[0399] server

[0400] Input: Proposal data for optimal organizational layout

[0401] How it works: The server notifies each user and administrator of the proposed data via email and provides a link to a dedicated portal site. The content of the notification may be adjusted based on the emotion recognition results.

[0402] Output: A notification email is sent to the user and administrator.

[0403] Step 9: Gather feedback

[0404] Users (employees and administrators)

[0405] Input: Notified organizational placement proposal

[0406] How it works: Users review suggestions and provide feedback on a dedicated portal site.

[0407] Output: The feedback data is sent to the server.

[0408] Step 10: Feedback analysis and implementation

[0409] server

[0410] Input: Collected feedback data

[0411] How it works: The server analyzes the feedback and applies it to the generative AI, which improves the accuracy of the next suggestion.

[0412] Output: The accuracy of the next organizational placement proposal will be improved.

[0413] (Application example 2)

[0414] 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."

[0415] Conventional organizational placement systems were limited to proposing placements based on employees' personality traits, aptitudes, and transfer intentions, and were unable to achieve optimal placements and role allocations that took into account the characteristics of the work environment and work machines. Furthermore, they lacked a mechanism for grasping the emotions and operating status of employees and work machines in real time and responding flexibly. These issues led to reduced factory efficiency and productivity, and risk of employee stress and health problems. To solve these problems, the present invention aims to provide a system that makes optimal placement proposals that comprehensively consider the characteristics and emotional states of employees and work machines.

[0416] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0417] In this invention, the server includes means for collecting data on personality diagnosis, aptitude diagnosis, and transfer intentions from each employee and work machine, means for storing the collected data and work machine operating status data, and means for generating a mapping of human relationships and work environments based on the stored data. This enables optimal organizational layout and work machine allocation / role allocation to maximize the efficiency and results of the entire organization.

[0418] A "personality assessment" is a test or analytical method for assessing and classifying each employee's personality traits.

[0419] "Aptitude diagnosis" refers to tests and analytical methods used to evaluate each employee's suitability and skills for their job.

[0420] "Intention to transfer" refers to an employee's desire to move from their current job or department to a new job or department.

[0421] "Work machine" refers to automated work equipment, including machines and robots, used in factories.

[0422] "Operating status" is information that indicates how the work machine is currently operating, whether it is operating or stopped, or its performance level, or other such status.

[0423] "Data collection means" refers to the systems and tools used to obtain necessary information from each employee and work machine.

[0424] "Data storage means" refers to the systems and databases used to safely store and manage collected data.

[0425] "Interpersonal relationship mapping" is a map that visually represents the interrelationships between employees based on their personality traits and aptitudes.

[0426] "Work environment mapping" is a map that visually summarizes the location and operating status of work machines.

[0427] "Organizational placement" refers to assigning employees to the most suitable departments and jobs based on their aptitude and personality traits.

[0428] "Role allocation" refers to the allocation of appropriate tasks to work machines and employees to achieve efficient work.

[0429] "Notification methods" are systems and tools used to inform employees and managers of proposed placements and role assignments.

[0430] An "emotion engine" is an algorithm or system that recognizes emotions by analyzing a user's facial expressions, tone of voice, etc.

[0431] "Feedback collection tools" are systems and tools used to collect opinions and reactions from employees and managers regarding notified proposals.

[0432] "Generative AI" is artificial intelligence that analyzes collected data, generates a mapping of human relationships and work environments, and proposes optimal placement and division of roles.

[0433] To implement this invention, the following system is required: This system includes a means for collecting and analyzing data from each employee and work machine, and a generative AI for proposing optimal organizational arrangements and role allocations.

[0434] First, the server collects data on personality and aptitude tests, as well as transfer intentions, from each employee and work machine. This data is collected through web forms and dedicated applications. For example, employee A answers a personality test, is judged to be "suited to creative work" through the aptitude test, and enters "I would like to be transferred to the marketing department" as his or her transfer intention.

[0435] Next, the device (employee's PC or smartphone) sends the collected data to the server. The data is encrypted before transmission, ensuring the security of the information. The server analyzes the received data and stores it in an internal database. For example, the results of employee A's personality test, aptitude test, and transfer request information are stored in the database.

[0436] Furthermore, during the data collection process, the server uses the device's camera and microphone to analyze the user's facial expressions and tone of voice, and uses an emotion engine to recognize the user's emotions. For example, if employee A feels irritated during the personality test, that emotion will be recorded.

[0437] Next, the server cleanses the data collected from all employees. This process includes deleting invalid data and filling in missing data to ensure data accuracy. Based on the cleansed data, the generative AI begins the learning process. The generative AI analyzes personality traits, aptitudes, and recognized emotions to generate a mapping of interpersonal relationships and work environments. For example, a mapping is performed taking into account Employee A's personality traits and aptitudes, and it calculates which team and work environment Employee A is best suited to.

[0438] The server then proposes optimal organizational layouts and machine placements and role allocations based on the generated mapping of human relationships and work environments. The generation AI proposes placements taking into consideration employee compatibility, team balance, work content, emotions, and the operating status and characteristics of machine placements. For example, employee A is placed as the leader of a new project team in the marketing department, and the server proposes optimal machine placements and role allocations.

[0439] The server then notifies each employee and manager of the proposed organizational layout, machine placement, and role allocation. The content of the notification can also be adjusted based on the emotion recognition results. For example, if employee A is feeling anxious about the transfer, the notification can include a message to ease his or her anxiety.

[0440] Users can review the proposed organizational transfer and provide feedback on a dedicated portal site. For example, employee A reviews the proposed transfer and submits feedback on the timing of the transfer.

[0441] Finally, the server analyzes the collected feedback and reflects it in the generating AI. This feedback analysis improves the accuracy of the next organizational layout and machine placement / role allocation proposals. The emotion engine recognizes the user's emotions even when providing feedback, and supports receiving appropriate feedback.

[0442] As a concrete example, by inputting the following prompt sentence into the generative AI model, optimal placement and division of roles will be suggested.

[0443] Example prompt sentence:

[0444] Robot data: ID=R123, Type=Welding robot, Performance=85, Operation status=In operation

[0445] Worker Emotions: Stress Level = High, Health Status = Good

[0446] This system is expected to improve factory work efficiency and productivity, and reduce employee stress and health problems.

[0447] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0448] Step 1:

[0449] Collect data from each employee and work machine.

[0450] Input: Personality test, aptitude test, and information about transfer intentions.

[0451] How it works: The server collects necessary data from each employee and work machine through web forms and dedicated applications. For example, employee A answers a personality test, is judged to be "suited for creative work" through an aptitude test, and enters his / her intention to transfer as "I would like to be transferred to the marketing department."

[0452] Output: Collected personality test results, aptitude test results, transfer request information, and operating status data of work machines.

[0453] Step 2:

[0454] Store the collected data.

[0455] Input: Collected data.

[0456] Operation: The device encrypts this data and sends it to the server, which then stores the received data in an internal database. For example, the results of the personality test, aptitude test, and transfer request information for employee A are stored in the database.

[0457] Output: Personality test results, aptitude test results, transfer request information, and operating status data of work machines stored in a database.

[0458] Step 3:

[0459] Recognize user emotions.

[0460] Input: facial expressions and tone of voice.

[0461] How it works: The server uses the device's camera and microphone to analyze the user's facial expressions and tone of voice in real time, and recognizes their emotions using an emotion engine. For example, if employee A feels irritated during a personality test, that emotion is recorded.

[0462] Output: Emotion recognition results.

[0463] Step 4:

[0464] Cleanse and process data.

[0465] Input: Stored data and emotion recognition results.

[0466] How it works: The server cleanses the collected data, which includes removing invalid parts of the data and filling in missing data. The generative AI model then begins the training process based on the cleansed data.

[0467] Output: Cleansed data and training results.

[0468] Step 5:

[0469] Generate a mapping of relationships and work environments.

[0470] Input: Cleansed data and training results.

[0471] How it works: The server uses the generative AI model to generate a mapping of interpersonal relationships and work environments, taking into account personality traits, aptitudes, and perceived emotions. For example, a mapping is generated that takes into account employee A's personality traits and aptitudes.

[0472] Output: Relationship map and work environment map.

[0473] Step 6:

[0474] We propose optimal organizational layout, machine placement, and division of roles.

[0475] Input: Relationship map and work environment map.

[0476] Operation: The server uses the generative AI model to propose optimal organizational arrangements and machine allocations and role assignments, taking into consideration employee compatibility, team balance, work content, emotions, and the operating status and characteristics of machine tools. For example, employee A is assigned as the leader of a new project team in the marketing department, and the server proposes optimal machine allocations and role assignments.

[0477] Output: Placement suggestion.

[0478] Step 7:

[0479] Notify proposals.

[0480] Input: Placement proposal.

[0481] Operation: The server notifies each employee and manager of the proposed organizational layout, machine placement, and role allocation. The content of the notification can also be adjusted based on the emotion recognition results. For example, if employee A is feeling anxious about the transfer, the notification can include a message to ease his / her anxiety.

[0482] Output: Informational message.

[0483] Step 8:

[0484] Collect and analyze feedback.

[0485] Input: Feedback for the notification.

[0486] Operation: Users review the proposed organizational reassignment and provide feedback on a dedicated portal site. For example, employee A reviews the proposed transfer and submits feedback on the timing of the transfer. The server analyzes the collected feedback and reflects it in the next proposal.

[0487] Output: Feedback and analysis results.

[0488] 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.

[0489] 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.

[0490] 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.

[0491] [Second embodiment]

[0492] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0493] 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.

[0494] 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).

[0495] 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.

[0496] 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.

[0497] 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).

[0498] 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.

[0499] 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.

[0500] 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.

[0501] 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.

[0502] 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.

[0503] 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."

[0504] This invention is a system that proposes optimal organizational placement based on the personality, aptitude, and transfer intention of employees. Specific program processing of the system will be explained below in natural language.

[0505] Data collection

[0506] User (employee)

[0507] Each employee enters information about their personality and aptitude tests, as well as their intention to transfer, via a web form or dedicated application. For example, employee A answers the personality test, is judged to be "suited to creative work" through the aptitude test, and enters their intention to transfer as "to the marketing department."

[0508] Device (employee's PC or smartphone)

[0509] The device sends the collected data to the server, where it is encrypted to ensure the security of the information.

[0510] server

[0511] The server analyzes the received data and stores it in an internal database. For example, it stores the personality test results, aptitude test results, and transfer request information of employee A in the database.

[0512] Data Processing and Training

[0513] server

[0514] The server cleanses the data collected from all employees (deleting invalid data and filling in missing data) and then starts the learning process using generative AI. This learning process generates a mapping of human relationships. For example, a mapping is performed that takes into account employee A's personality traits and aptitudes, and it calculates which team employee A would be best suited to.

[0515] Organizational layout proposal

[0516] server

[0517] Based on the generated interpersonal relationship mapping, the server uses generative AI to create optimal organizational placement proposals. The proposals include the member composition of each team and the placement of new employees. For example, a proposal is generated to place employee A as the leader of a new project team in the marketing department.

[0518] Communicating suggestions and gathering feedback

[0519] server

[0520] The server then sends an email to each employee and manager notifying them of the proposed organizational placement, along with a link to a dedicated portal site where they can review the proposal.

[0521] Users (employees and administrators)

[0522] The user checks the notified organizational placement proposal and provides feedback on the dedicated portal site. For example, employee A checks the transfer proposal and submits feedback on the timing of the transfer.

[0523] server

[0524] The server analyzes the collected feedback and reflects the results in the generating AI, thereby improving the accuracy of the next organizational placement proposal.

[0525] In this way, this system proposes optimal organizational placement based on employee data and uses feedback to improve the accuracy of placement, thereby maximizing the efficiency and results of the entire organization.

[0526] The processing flow will be explained below.

[0527] Step 1:

[0528] User (employee)

[0529] Each employee enters information about their personality and aptitude tests, as well as their intention to transfer, via a web form or dedicated application. For example, employee A answers the personality test, is judged to be "suited to creative work" through the aptitude test, and enters their intention to transfer as "to the marketing department."

[0530] Step 2:

[0531] Device (employee's PC or smartphone)

[0532] The terminal sends the entered data to the server. At this time, the data is encrypted before being sent, so the security of the information is maintained.

[0533] Step 3:

[0534] server

[0535] The server analyzes the received data and stores it in an internal database. For example, it stores the personality test results, aptitude test results, and transfer request information of employee A in the database.

[0536] Step 4:

[0537] server

[0538] The server cleanses the data collected from all employees, which includes removing invalid data and filling in missing data, a process that ensures data accuracy.

[0539] Step 5:

[0540] server

[0541] Based on the cleansed data, the generative AI begins the learning process. The generative AI analyzes information such as personality traits and aptitudes to generate a mapping of human relationships. For example, a mapping is performed that takes into account the personality traits and aptitudes of employee A, and it calculates which team employee A would be best suited to.

[0542] Step 6:

[0543] server

[0544] The server then creates optimal organizational placement proposals based on the generated interpersonal relationship mapping. The generation AI considers employee compatibility, team balance, and job content when proposing placements. For example, a proposal is generated to place employee A as the leader of a new project team in the marketing department.

[0545] Step 7:

[0546] server

[0547] The generated organizational placement proposal will be notified to each employee and manager via email, and a link to a dedicated portal site will be provided so that the proposal can be viewed in detail.

[0548] Step 8:

[0549] Users (employees and administrators)

[0550] The user checks the notified organizational placement proposal and provides feedback on the dedicated portal site. For example, employee A checks the transfer proposal and submits feedback on the timing of the transfer.

[0551] Step 9:

[0552] server

[0553] The server analyzes the collected feedback and reflects the results in the generating AI. This feedback analysis improves the accuracy of the next organizational placement proposal.

[0554] In this way, this system proposes optimal organizational placement based on employee data and uses feedback to improve the accuracy of placement, thereby maximizing the efficiency and results of the entire organization.

[0555] Example 1

[0556] 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."

[0557] With conventional organizational placement systems, it is difficult to make optimal organizational placements that properly reflect employees' personality traits, aptitudes, and transfer intentions. Furthermore, there is no fully established mechanism for effectively utilizing feedback to improve the accuracy of the next placement proposal. As a result, effective organizational placements that maximize employee capabilities are not realized, resulting in issues such as a decline in the efficiency and performance of the entire organization.

[0558] 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.

[0559] In this invention, the server includes means for collecting data on personality assessment, aptitude assessment, and transfer intention from each employee, means for encrypting and storing the collected data, means for cleansing the stored data, means for performing learning using a generative AI model based on the cleansed data and generating a human relationship mapping, means for proposing an optimal organizational placement based on the human relationship mapping, means for notifying each employee and manager of the proposed organizational placement, means for collecting feedback on the notified proposal, and means for analyzing the collected feedback and reflecting it in the generative AI model. This allows for optimal organizational placement that appropriately considers employees' personality traits, aptitude, and transfer intention, and further makes it possible to improve the accuracy of subsequent proposals by utilizing the collected feedback.

[0560] A "personality assessment" is a test or questionnaire used to evaluate an employee's personality traits.

[0561] An "aptitude test" is a test or assessment used to evaluate an employee's job aptitude and skills.

[0562] "Transfer intention" refers to information that indicates the department or job content to which an employee wishes to be transferred.

[0563] "Means of collecting data" refers to the methods and tools used to collect information from employees regarding personality tests, aptitude tests, and transfer intentions.

[0564] "Means for encryption and storage" refers to the method of storing collected data using encryption technology.

[0565] "Cleansing means" refers to a method for removing invalid data from stored data and for filling in missing data.

[0566] "Generative AI model" refers to a generative model using AI, a technology used to generate organizational placement proposals.

[0567] "Interpersonal relationship mapping" is a map that illustrates the interrelationships between employees based on their personality traits and aptitudes.

[0568] "Means for proposing optimal organizational placement" refers to methods and tools that make proposals for placing employees in the optimal departments or teams based on interpersonal relationship mapping.

[0569] "Means of notification" refers to the methods and tools used to inform each employee and manager of the proposed organizational arrangement.

[0570] "Feedback collection measures" are methods and tools used to gather input from employees and managers about proposed organizational arrangements.

[0571] "Means of analysis" refers to the method of analyzing the collected feedback and incorporating the results into the generative AI model.

[0572] The present invention is a system for proposing optimal organizational placement based on the personality traits, aptitudes, and transfer intentions of employees. Specific embodiments for carrying out the present invention will be described below.

[0573] Data collection

[0574] User (employee)

[0575] Each employee uses a dedicated application or web form to enter information about their personality and aptitude tests, as well as their intention to transfer. For example, an employee opens the dedicated application, answers "creative" to the personality test, answers "suitable for creative work" to the aptitude test, and enters "hope to transfer to the marketing department" as their intention to transfer.

[0576] Device (employee's PC or smartphone)

[0577] The terminal receives the data entered by the user, encrypts it using AES encryption technology, and sends it to the server. For example, the personality test result "creative," the aptitude test result "suitable for creative work," and the transfer intention "hope to be in the marketing department" are encrypted and sent.

[0578] Receiving and storing data

[0579] server

[0580] The server receives the data sent from the terminal, decrypts it, and saves it in a database (e.g., MySQL). For example, it saves the employee's personality test results, aptitude test results, and transfer request information in the employees_data table.

[0581] Data Cleansing

[0582] server

[0583] The server uses Python's Pandas library to cleanse the data. Specifically, it removes invalid data and fills in missing data to generate the final dataset. For example, it automatically removes or fills in incorrectly entered data or missing data, cleansing the data for all employees.

[0584] Data Analysis and Learning

[0585] server

[0586] The server then inputs the cleansed data into a generative AI model (e.g., GPT-4) and begins the learning process. Specifically, it generates a relationship mapping that takes into account personality traits, aptitudes, and transfer preferences. For example, the AI ​​can be trained on a dataset containing employee data to determine which employees are best suited to which teams.

[0587] Organizational layout proposal

[0588] server

[0589] The server creates optimal organizational placement proposals based on the human relationship mapping generated by the generative AI model. For example, the AI ​​may determine that "Employee A is the best suited person to be a project leader in the marketing department," and then compile that placement proposal.

[0590] Proposal Notification

[0591] server

[0592] The server notifies each employee and manager of the generated organizational placement proposal by email. It also includes a link to a dedicated portal site where the proposal can be viewed. For example, an email notification of "Project Leader Placement Proposal in the Marketing Department" is sent to employees and managers, along with the URL of the dedicated portal site.

[0593] Gathering feedback

[0594] Users (employees and administrators)

[0595] Users can review the proposed organizational placement and provide feedback on a dedicated portal site. For example, an employee can review the proposal and submit feedback such as "I would like the transfer to occur next month" in a dedicated form.

[0596] Analyzing and incorporating feedback

[0597] server

[0598] The server analyzes the collected feedback and reflects the results in the generative AI model. This improves the accuracy of the next organizational placement proposal. For example, a user's feedback such as "I would like the transfer to occur next month" is analyzed and added to the AI ​​model's learning dataset.

[0599] Prompt Sentence Examples

[0600] As an example of an input prompt for a generative AI model, we will use the following sentence:

[0601] "Based on the following data, please suggest the optimal organizational arrangement.

[0602] data:

[0603] Employee A: Personality test result: "Creative, team player", aptitude test result: "Suitable for creative work", transfer intention: "Want to move to the marketing department"

[0604] It also includes detailed data of other employees.”

[0605] In this way, the system of the present invention proposes optimal organizational placement based on employees' personality traits, aptitudes, and intentions for transfer, and by reflecting feedback in a timely manner, it is possible to maximize the efficiency and results of the entire organization.

[0606] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0607] Step 1:

[0608] User (employee)

[0609] Each employee uses a dedicated application or web form to enter information about their personality and aptitude tests, as well as their intention to transfer. For example, employee A enters their personality test result of "creative," their aptitude test result of "suited for creative work," and their intention to transfer to the marketing department.

[0610] Input and Output

[0611] Input: Personality test results, aptitude test results, intention to transfer

[0612] Output: Information entered into an application or web form

[0613] Step 2:

[0614] Device (employee's PC or smartphone)

[0615] The terminal receives the information entered by the user in real time and encrypts it using AES encryption technology. For example, if employee A enters the personality test result "creative," the aptitude test result "suitable for creative work," and the transfer intention "hope to be transferred to the marketing department," the information is encrypted.

[0616] Input and Output

[0617] Input: Personality test results, aptitude test results, intention to transfer

[0618] Output: Encrypted information

[0619] Step 3:

[0620] Device (employee's PC or smartphone)

[0621] The terminal sends the encrypted information to the server. For example, the information of employee A is encrypted and sent to the server.

[0622] Input and Output

[0623] Input: Encrypted information

[0624] Output: Encrypted information sent to the server

[0625] Step 4:

[0626] server

[0627] The server receives the encrypted information sent from the terminal, decrypts it, and stores it in a database. For example, the personality test results, aptitude test results, and transfer request information for employee A are stored in the employees_data table of a database (e.g., MySQL).

[0628] Input and Output

[0629] Input: Encrypted information

[0630] Output: Decrypted database entries

[0631] Step 5:

[0632] server

[0633] The server uses the Python Pandas library to cleanse the data, remove invalid data, impute missing data, and generate a final cleansed dataset, e.g., impute missing data and remove incorrectly entered data.

[0634] Input and Output

[0635] Input: Employee data

[0636] Output: A cleansed dataset

[0637] Step 6:

[0638] server

[0639] The server inputs the cleansed data into a generative AI model (e.g., GPT-4) and begins the learning process. Specifically, it generates a relationship mapping that takes into account personality traits, aptitude, and transfer preferences. For example, based on a dataset containing employee A's data, the generative AI generates a relationship mapping and determines which team employee A would be best suited to.

[0640] Input and Output

[0641] Input: Cleansed dataset

[0642] Output: Relationship mapping

[0643] Step 7:

[0644] server

[0645] The server creates optimal organizational placement proposals based on the human relationship mapping generated by the generative AI model. For example, the AI ​​may determine that "Employee A is the best suited person to be a project leader in the marketing department," and then compile that placement proposal.

[0646] Input and Output

[0647] Input: Relationship mapping

[0648] Output: Organizational layout proposal

[0649] Step 8:

[0650] server

[0651] The server notifies each employee and manager of the generated organizational placement proposal by email. It also provides a link to a dedicated portal site where the proposal can be viewed. For example, a notification email for "Project Leader Placement Proposal in the Marketing Department" is sent to employee A and the manager.

[0652] Input and Output

[0653] Input: Organizational Allocation Proposal

[0654] Output: Notification email and portal link

[0655] Step 9:

[0656] Users (employees and administrators)

[0657] The user checks the proposed organizational placement and provides feedback on a dedicated portal site. For example, employee A enters feedback such as "I would like to be transferred next month" into a dedicated form and submits it.

[0658] Input and Output

[0659] Input: Feedback on the proposal

[0660] Output: Feedback data

[0661] Step 10:

[0662] server

[0663] The server analyzes the collected feedback and reflects the results in the generative AI model, improving the accuracy of the next organizational placement proposal. For example, the feedback content can be added to the AI ​​model's learning dataset, resulting in more accurate next placement proposals.

[0664] Input and Output

[0665] Input: Feedback data

[0666] Output: Updated AI model training dataset

[0667] In this way, each processing step is carried out in a concrete manner, and the optimal organizational placement is proposed based on the employee's personality traits, aptitude, and intention to transfer, and feedback can be used to improve the accuracy of the proposal.

[0668] (Application example 1)

[0669] 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."

[0670] Currently, factories are not sufficiently optimizing the placement and task allocation of robots and workers, resulting in a decline in production efficiency. Furthermore, placement that reflects personality assessment, aptitude assessment, and transfer intentions has not been realized, leaving problems in improving the work environment and worker satisfaction. The objective of this invention is to solve these problems and provide a system that realizes optimal placement and task allocation of robots and workers in a factory.

[0671] 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.

[0672] In this invention, the server includes means for collecting data on personality tests, aptitude tests, and transfer intentions from each worker, means for saving the collected data, means for generating a human relationship mapping based on the saved data, means for proposing an optimal organizational placement based on the human relationship mapping, means for notifying each worker and manager of the proposed organizational placement, means for collecting feedback on the notified proposal, means for analyzing the collected feedback and reflecting it in the generation AI, and means for proposing an optimal placement and task assignment of robots in the factory based on the characteristics and capabilities of each worker. This enables placement and task assignment that takes into account the personality traits, aptitude, and transfer intentions of workers.

[0673] "Workers" are personnel who operate and manage robots within a factory, and are people whose personality tests, aptitude tests, and willingness to transfer are taken into consideration.

[0674] A "personality assessment" is a test or analytical tool used to evaluate a worker's personality traits, a process used to determine a worker's aptitude and job suitability.

[0675] An "aptitude test" is a test or assessment tool used to evaluate a worker's suitability for a job, and is a process used to determine what tasks or roles a worker is suited to.

[0676] "Transfer intention" refers to data that indicates a worker's intention or desire to move to a different department or role, and is one of the factors reflected in organizational placement proposals.

[0677] "Factory robots" are mechanical devices used to automate factory work and production processes, and optimizing their placement and task assignment is important to efficiently perform various tasks.

[0678] "Means of collecting data" refers to methods and tools for collecting data on workers' personality tests, aptitude tests, and intentions to transfer, and specifically includes questionnaires, tests, sensors, etc.

[0679] "Means for storing data" means methods and systems for safely and efficiently storing collected data, including databases, cloud storage, and file systems.

[0680] "Means for generating a mapping of human relationships" refers to methods and technologies for analyzing stored data and visualizing the relationships and aptitudes between workers, and specifically includes generative AI algorithms and clustering methods.

[0681] "Means for proposing optimal organizational placement" refers to methods and systems for proposing optimal placement and task allocation of workers and robots based on human relationship mapping, and specifically includes AI models and simulation software.

[0682] "Means for notifying proposals" means a method or system for notifying each worker and manager of the generated placement and task assignment proposals, and specifically includes email, a notification application, and a portal site.

[0683] "Means for collecting feedback" means methods or systems for collecting feedback from workers or managers regarding the notified proposal, and specifically includes forms, questionnaires, and opinion collection systems.

[0684] "Generative AI" refers to artificial intelligence technology for data analysis, mapping generation, placement proposals, etc., and includes advanced algorithms and learning models.

[0685] "Task allocation" is the process of assigning specific tasks to workers or robots, and is a necessary step for achieving efficient work.

[0686] The present invention provides a system for proposing optimal placement and task allocation for robots and workers in a factory. Specific embodiments of the system will be described below.

[0687] 1. Data Collection

[0688] Collecting data from workers

[0689] The user, a worker, enters information about the personality test, aptitude test, and transfer intention through a dedicated application or web form. For example, worker A answers the personality test, is judged to have "aptitude for speedy work" through the aptitude test, and enters "hope for welding department" as his / her intention to transfer.

[0690] Data transmission and storage

[0691] The terminal (the worker's PC or smartphone) encrypts the collected data and sends it to the server. The server analyzes the received data and stores it securely in an internal database. For example, the results of worker A's personality test, aptitude test, and transfer request information are stored in the database.

[0692] 2. Data processing and human relationship mapping

[0693] The server cleanses the data collected from all workers, deleting invalid data and filling in missing data. The generative AI then analyzes the data and begins the learning process to generate a relationship mapping. For example, it determines which team Worker A is best suited to, taking into account Worker A's personality traits and aptitudes.

[0694] 3. Proposal for placement and task allocation

[0695] Based on the generated mapping results of human relationships, the server uses generative AI to create optimal robot and worker placement proposals and task assignments. For example, a proposal is generated to place Worker A as a member of a new project team in the welding department.

[0696] 4. Proposal notification and feedback collection

[0697] Proposal Notification

[0698] The server notifies each worker and manager of the generated placement and task assignment proposals via email, and also provides a URL for a dedicated portal site where the proposals can be viewed.

[0699] Collecting and analyzing feedback

[0700] The users, i.e., workers and managers, can check the notified placement proposal on a dedicated portal site and provide feedback there. For example, worker A checks the placement proposal and submits his / her opinion on the contents of the proposal as feedback.

[0701] The server analyzes the collected feedback and reflects the results in the generating AI to improve the accuracy of the next placement proposal.

[0702] Examples of specific examples and prompts

[0703] For example, you might enter the following prompt for a generative AI model:

[0704] "Please tell me a method to propose optimal placement and task assignment based on the characteristics and capabilities of each robot in a factory. In particular, I would like to know a concrete example using K-means clustering."

[0705] By using this system, production efficiency within the factory can be significantly improved, and worker satisfaction can also be increased.

[0706] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0707] Step 1:

[0708] Data on personality and aptitude tests and transfer intentions are collected from each worker. The user enters this information through a dedicated application or a web form. The entered data includes the worker's personality traits, aptitude for the job, and transfer wishes. The device temporarily stores this data, encrypts it, and sends it to the server.

[0709] Step 2:

[0710] The server receives the collected data and stores it in a database. At this time, it performs data cleansing, deletes invalid data, and fills in missing data. The server safely stores the received personality test results, aptitude test results, and transfer request information in the database.

[0711] Step 3:

[0712] The server analyzes the stored data and generates a mapping of human relationships. Specifically, it uses generative AI to perform cluster analysis based on the personality traits and aptitudes of workers. Here, it extracts data features and recognizes patterns to visualize the relationships between workers. The mapping results are used as the base data for the next processing step.

[0713] Step 4:

[0714] The server generates optimal robot and worker placement proposals and task assignments based on the results of the interpersonal relationship mapping. Using a generative AI model, it outputs placement proposals that make the most of the characteristics of each worker and robot. The input data is the worker's personality traits, aptitude, and transfer intentions, and the output is a specific placement proposal and task assignment.

[0715] Step 5:

[0716] The server notifies each worker and manager of the generated placement and task assignment proposals. Notifications are sent via email, and the worker and manager are also provided with a link to a dedicated portal site. Details of the proposals can be viewed on the portal site.

[0717] Step 6:

[0718] The users, i.e., workers and managers, can check the placement proposals they have been notified of on a dedicated portal site and provide feedback, including how they accepted the proposals and their opinions on areas for improvement.

[0719] Step 7:

[0720] The server analyzes the collected feedback and reflects the results in the generative AI. This is used to improve the accuracy of the next placement proposal. Specifically, the feedback content is added to the dataset and the generative AI model is retrained to improve the system.

[0721] The above processing steps enable optimal placement and task allocation of robots and workers in a factory, thereby improving production efficiency and worker satisfaction.

[0722] 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.

[0723] This invention is a system that proposes optimal organizational placement based on employees' personalities, aptitudes, and transfer intentions, and by combining this with an emotion engine that recognizes the user's emotions, it achieves even more accurate placement. Below, the specific program processing of the system is explained in natural language.

[0724] Data collection

[0725] User (employee)

[0726] Each employee enters information about their personality and aptitude tests, as well as their intention to transfer, via a web form or dedicated application. For example, employee A answers the personality test, is judged to be "suited to creative work" through the aptitude test, and enters their intention to transfer as "to the marketing department."

[0727] Device (employee's PC or smartphone)

[0728] The terminal sends the entered data to the server. At this time, the data is encrypted before being sent, so the security of the information is maintained.

[0729] server

[0730] The server analyzes the received data and stores it in an internal database. For example, it stores the personality test results, aptitude test results, and transfer request information of employee A in the database.

[0731] emotion recognition

[0732] server

[0733] During the data collection process, the device's camera and microphone are used to analyze the user's facial expressions and tone of voice, and the emotion engine recognizes the user's emotions. For example, if employee A feels irritated during a personality test, that emotion is recorded.

[0734] Data Processing and Training

[0735] server

[0736] The server cleanses the data collected from all employees, which includes removing invalid data and filling in missing data, a process that ensures data accuracy.

[0737] server

[0738] Based on the cleansed data, the generative AI begins the learning process. The generative AI analyzes personality traits, aptitudes, and recognized emotions to generate a mapping of human relationships. For example, a mapping is performed that takes into account Employee A's personality traits and aptitudes, and calculates which team Employee A would be best suited to.

[0739] Organizational layout proposal

[0740] server

[0741] The server then creates optimal organizational placement proposals based on the generated interpersonal relationship mapping. The generation AI considers employee compatibility, team balance, work content, and emotions when proposing placements. For example, a proposal is generated to place employee A as the leader of a new project team in the marketing department.

[0742] server

[0743] The proposed organizational relocation is notified to each employee and manager via email, and a link to a dedicated portal site is also provided. The content of the notification can also be adjusted based on the emotion recognition results. For example, if Employee A is feeling anxious about the transfer, a message to ease his / her feelings can be included.

[0744] Reviewing proposals and gathering feedback

[0745] Users (employees and administrators)

[0746] The user checks the notified organizational placement proposal and provides feedback on the dedicated portal site. For example, employee A checks the transfer proposal and submits feedback on the timing of the transfer.

[0747] server

[0748] The server analyzes the collected feedback and reflects it in the generation AI. This feedback analysis improves the accuracy of the next organizational placement proposal. The emotion engine recognizes the user's emotions even when providing feedback, and supports them in receiving appropriate feedback.

[0749] In this way, the system proposes optimal organizational placement based on employee data and emotions, and uses feedback to improve the accuracy of placement, thereby maximizing the efficiency and results of the entire organization.

[0750] The processing flow will be explained below.

[0751] Step 1:

[0752] User (employee)

[0753] Each employee enters information about their personality and aptitude tests, as well as their intention to transfer, via a web form or dedicated application. For example, employee A answers the personality test, is judged to be "suited to creative work" through the aptitude test, and enters their intention to transfer as "to the marketing department."

[0754] Step 2:

[0755] Device (employee's PC or smartphone)

[0756] The terminal sends the entered data to the server. At this time, the data is encrypted before being sent, so the security of the information is maintained.

[0757] Step 3:

[0758] server

[0759] The server analyzes the received data and stores it in an internal database. For example, it stores the personality test results, aptitude test results, and transfer request information of employee A in the database.

[0760] Step 4:

[0761] Terminal

[0762] During the data collection process, the device's camera and microphone are used to analyze the user's facial expressions and tone of voice, and the emotion engine recognizes the user's emotions. For example, if employee A feels irritated during a personality test, that emotion is recorded.

[0763] Step 5:

[0764] server

[0765] The server cleanses the data collected from all employees, which includes removing invalid data and filling in missing data, a process that ensures data accuracy.

[0766] Step 6:

[0767] server

[0768] Based on the cleansed data, the generative AI begins the learning process. The generative AI analyzes personality traits, aptitudes, and recognized emotions to generate a mapping of human relationships. For example, a mapping is performed that takes into account Employee A's personality traits and aptitudes, and calculates which team Employee A would be best suited to.

[0769] Step 7:

[0770] server

[0771] The server then creates optimal organizational placement proposals based on the generated interpersonal relationship mapping. The generation AI considers employee compatibility, team balance, work content, and emotions when proposing placements. For example, a proposal is generated to place employee A as the leader of a new project team in the marketing department.

[0772] Step 8:

[0773] server

[0774] The proposed organizational relocation is notified to each employee and manager via email, and a link to a dedicated portal site is also provided. The content of the notification can also be adjusted based on the emotion recognition results. For example, if Employee A is feeling anxious about the transfer, a message to ease his / her feelings can be included.

[0775] Step 9:

[0776] Users (employees and administrators)

[0777] The user checks the notified organizational placement proposal and provides feedback on the dedicated portal site. For example, employee A checks the transfer proposal and submits feedback on the timing of the transfer.

[0778] Step 10:

[0779] server

[0780] The server analyzes the collected feedback and reflects it in the generation AI. This feedback analysis improves the accuracy of the next organizational placement proposal. The emotion engine recognizes the user's emotions even when providing feedback, and supports them in receiving appropriate feedback.

[0781] In this way, the system proposes optimal organizational placement based on employee data and emotions, and uses feedback to improve the accuracy of placement, thereby maximizing the efficiency and results of the entire organization.

[0782] Example 2

[0783] 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."

[0784] Conventional organizational placement systems were unable to make appropriate placement recommendations because they had difficulty taking into account employees' personality traits, aptitude, and transfer intentions. Furthermore, they lacked emotion recognition technology, making it impossible to reflect users' emotions in organizational placement. Furthermore, there was a lack of means to effectively utilize feedback, making it difficult to improve the accuracy of recommendations.

[0785] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for collecting data on personality assessment, aptitude assessment, and transfer intentions from each user; means for saving the collected data; means for analyzing the saved data and saving it in an internal database; means for analyzing the user's facial expressions and tone of voice using the device's camera and microphone and recognizing the user's emotions using an emotion engine; means for starting a learning process by the generation AI based on the cleansed data; means for analyzing personality traits, aptitude, and recognized emotions and generating a relationship mapping; means for creating an optimal organizational placement proposal based on the generated relationship mapping; means for notifying each user and administrator of the proposed organizational placement by email and providing a link to a dedicated portal site; means for collecting feedback on the notified proposal; and means for analyzing the collected feedback and reflecting it in the generation AI. This enables highly accurate organizational placement proposals that take into account employees' personality traits, aptitudes, and emotions.

[0786] "User" refers to individuals, especially employees, who use the system.

[0787] A "personality test" is an assessment test for measuring the personality traits of an individual user.

[0788] An "aptitude test" is a test that assesses how suited a user is to a particular job or role.

[0789] "Intention to transfer" refers to the user's intentions and wishes regarding the desired transfer destination and position.

[0790] "Collection methods" refers to software or hardware functions used to collect user data.

[0791] "Storage means" refers to the function for accumulating and safely storing collected data.

[0792] A "database" is a system for efficiently managing and accessing stored data.

[0793] A "camera" is an imaging device for capturing a user's facial expression.

[0794] A "microphone" is a device that picks up the user's voice and acquires it as audio data.

[0795] The "emotion engine" is software that analyzes the user's emotions from collected facial and voice data.

[0796] "Cleansing" is the process of removing invalid parts of data and filling in missing data.

[0797] "Generative AI" refers to artificial intelligence that analyzes data through a learning process and generates optimal suggestions.

[0798] The "learning process" is the process by which the generative AI repeatedly learns based on data.

[0799] "Relationship mapping" refers to a map that visually or abstractly represents the relationships between users.

[0800] An "organizational placement proposal" is an optimal placement proposal generated in consideration of the user's personality traits, aptitude, emotions, and the like.

[0801] "Notification means" refers to software or functions for informing each user or administrator of the proposed content.

[0802] "Portal Site" means a website that Users can access to review Proposals and provide Feedback.

[0803] "Feedback" refers to the opinions and thoughts that users provide regarding suggestions.

[0804] "Analysis" is the process of examining and understanding the collected data and feedback.

[0805] The present invention is a system that proposes optimal organizational placement based on the user's personality traits, aptitude, and transfer intentions, and improves the placement accuracy by combining it with an emotion recognition engine. This system is mainly operated by a server, terminals, and users.

[0806] Data collection

[0807] The user (employee) enters information about the personality test, aptitude test, and intention to transfer through a web form or dedicated application. This information is encrypted and sent to the server via the user's device (PC or smartphone). As a concrete example, employee A answers the personality test and enters the result that he or she is "outgoing and values ​​teamwork."

[0808] Data analysis and storage

[0809] The server analyzes the received data and stores it in an internal database. The stored data includes personality test results, aptitude test results, and transfer intentions. This allows detailed data on each user to be safely stored.

[0810] emotion recognition

[0811] The server analyzes the user's facial expressions and tone of voice using the device's camera and microphone. The emotion engine recognizes the user's emotions, and this data is also stored in an internal database. For example, if employee A feels dissatisfied during a personality test, the camera captures his or her facial expression, and the emotion engine identifies it as "dissatisfied."

[0812] Data Cleansing and Learning

[0813] The server cleanses the data collected from all users, which includes removing invalid data and filling in missing data, ensuring the accuracy of the data.

[0814] Next, the generative AI begins the learning process based on the cleansed data. The generative AI analyzes personality traits, aptitudes, and recognized emotions to generate a relationship mapping. For example, the mapping is performed to place employee A in the optimal team, taking into account employee A's personality traits and aptitudes.

[0815] Organizational Allocation Proposal and Notification

[0816] The server then creates optimal organizational placement proposals based on the generated interpersonal relationship mapping. The generation AI considers the user's compatibility, team balance, work content, and emotions when proposing placements. For example, a proposal is made to place employee A as the leader of a new project team in the marketing department.

[0817] The proposed organizational placement is notified to each user and administrator via email, and a link to a dedicated portal site is provided. The content of the notification may be adjusted based on the results of emotion recognition. For example, if employee A is anxious about the transfer, a message to ease his or her anxiety may be included.

[0818] Collecting and analyzing feedback

[0819] The user checks the notified organizational placement proposal and provides feedback on the dedicated portal site. For example, employee A checks the transfer proposal and submits feedback on the timing of the transfer.

[0820] The server analyzes the collected feedback and reflects it in the generating AI. This process further improves the accuracy of the next organizational placement proposal. The emotion engine recognizes the user's emotions even when providing feedback, and supports them in receiving appropriate feedback.

[0821] Prompt Sentence Examples

[0822] Analyze the following user information and propose the optimal organizational layout.

[0823] Personality test result: Introverted and good at detailed work

[0824] Aptitude test result: Aptitude for data analysis

[0825] Willingness to transfer: Data analysis team preferred

[0826] Sentiment: Positive about the transfer

[0827] The present invention makes it possible to make highly accurate organizational placement proposals that take into account the user's personality traits, aptitudes, and emotions, thereby improving user satisfaction.

[0828] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0829] Step 1: Data entry

[0830] User (employee)

[0831] Input: Personality test, aptitude test, and information about transfer intentions

[0832] How it works: Users enter this information through a web form or a dedicated application.

[0833] Output: The input data is stored on the user's terminal.

[0834] Step 2: Send data

[0835] Device (employee's PC or smartphone)

[0836] Input: Personality test results, aptitude test results, and data regarding transfer intentions entered by the user

[0837] Operation: The terminal encrypts the entered data and sends it to the server.

[0838] Output: The encrypted data is sent to the server.

[0839] Step 3: Save Data

[0840] server

[0841] Input: Encrypted data sent from the device

[0842] How it works: The server decrypts the data it receives and stores it in an internal database.

[0843] Output: The decoded data is stored in the database.

[0844] Step 4: Emotion Recognition

[0845] server

[0846] Input: User facial expression and voice data collected through the device's camera and microphone

[0847] How it works: The server uses an emotion engine to analyze the user's facial expressions and tone of voice to recognize their emotions.

[0848] Output: The recognized emotion data is stored in the internal database.

[0849] Step 5: Data cleansing

[0850] server

[0851] Input: All collected user data (personality traits, aptitudes, emotions, etc.)

[0852] How it works: The server cleanses the data, removing invalid parts and filling in missing data.

[0853] Output: Cleansed and accurate data is produced.

[0854] Step 6: Generative AI training

[0855] server

[0856] Input: Cleansed data

[0857] How it works: The server initiates the learning process with the generative AI, analyzing personality traits, aptitudes, and emotions.

[0858] Output: Relationship mapping data is generated.

[0859] Step 7: Generate organizational alignment proposals

[0860] server

[0861] Input: Relationship mapping data

[0862] How it works: Creates optimal organizational placement proposals based on generative AI.

[0863] Output: Proposal data for optimal organizational placement is generated.

[0864] Step 8: Proposal Notification

[0865] server

[0866] Input: Proposal data for optimal organizational layout

[0867] How it works: The server notifies each user and administrator of the proposed data via email and provides a link to a dedicated portal site. The content of the notification may be adjusted based on the emotion recognition results.

[0868] Output: A notification email is sent to the user and administrator.

[0869] Step 9: Gather feedback

[0870] Users (employees and administrators)

[0871] Input: Notified organizational placement proposal

[0872] How it works: Users review suggestions and provide feedback on a dedicated portal site.

[0873] Output: The feedback data is sent to the server.

[0874] Step 10: Feedback analysis and implementation

[0875] server

[0876] Input: Collected feedback data

[0877] How it works: The server analyzes the feedback and applies it to the generative AI, which improves the accuracy of the next suggestion.

[0878] Output: The accuracy of the next organizational placement proposal will be improved.

[0879] (Application example 2)

[0880] 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."

[0881] Conventional organizational placement systems were limited to proposing placements based on employees' personality traits, aptitudes, and transfer intentions, and were unable to achieve optimal placements and role allocations that took into account the characteristics of the work environment and work machines. Furthermore, they lacked a mechanism for grasping the emotions and operating status of employees and work machines in real time and responding flexibly. These issues led to reduced factory efficiency and productivity, and risk of employee stress and health problems. To solve these problems, the present invention aims to provide a system that makes optimal placement proposals that comprehensively consider the characteristics and emotional states of employees and work machines.

[0882] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0883] In this invention, the server includes means for collecting data on personality diagnosis, aptitude diagnosis, and transfer intentions from each employee and work machine, means for storing the collected data and work machine operating status data, and means for generating a mapping of human relationships and work environments based on the stored data. This enables optimal organizational layout and work machine allocation / role allocation to maximize the efficiency and results of the entire organization.

[0884] A "personality assessment" is a test or analytical method for assessing and classifying each employee's personality traits.

[0885] "Aptitude diagnosis" refers to tests and analytical methods used to evaluate each employee's suitability and skills for their job.

[0886] "Intention to transfer" refers to an employee's desire to move from their current job or department to a new job or department.

[0887] "Work machine" refers to automated work equipment, including machines and robots, used in factories.

[0888] "Operating status" is information that indicates how the work machine is currently operating, whether it is operating or stopped, or its performance level, or other such status.

[0889] "Data collection means" refers to the systems and tools used to obtain necessary information from each employee and work machine.

[0890] "Data storage means" refers to the systems and databases used to safely store and manage collected data.

[0891] "Interpersonal relationship mapping" is a map that visually represents the interrelationships between employees based on their personality traits and aptitudes.

[0892] "Work environment mapping" is a map that visually summarizes the location and operating status of work machines.

[0893] "Organizational placement" refers to assigning employees to the most suitable departments and jobs based on their aptitude and personality traits.

[0894] "Role allocation" refers to the allocation of appropriate tasks to work machines and employees to achieve efficient work.

[0895] "Notification methods" are systems and tools used to inform employees and managers of proposed placements and role assignments.

[0896] An "emotion engine" is an algorithm or system that recognizes emotions by analyzing a user's facial expressions, tone of voice, etc.

[0897] "Feedback collection tools" are systems and tools used to collect opinions and reactions from employees and managers regarding notified proposals.

[0898] "Generative AI" is artificial intelligence that analyzes collected data, generates a mapping of human relationships and work environments, and proposes optimal placement and division of roles.

[0899] To implement this invention, the following system is required: This system includes a means for collecting and analyzing data from each employee and work machine, and a generative AI for proposing optimal organizational arrangements and role allocations.

[0900] First, the server collects data on personality and aptitude tests, as well as transfer intentions, from each employee and work machine. This data is collected through web forms and dedicated applications. For example, employee A answers a personality test, is judged to be "suited to creative work" through the aptitude test, and enters "I would like to be transferred to the marketing department" as his or her transfer intention.

[0901] Next, the device (employee's PC or smartphone) sends the collected data to the server. The data is encrypted before transmission, ensuring the security of the information. The server analyzes the received data and stores it in an internal database. For example, the results of employee A's personality test, aptitude test, and transfer request information are stored in the database.

[0902] Furthermore, during the data collection process, the server uses the device's camera and microphone to analyze the user's facial expressions and tone of voice, and uses an emotion engine to recognize the user's emotions. For example, if employee A feels irritated during the personality test, that emotion will be recorded.

[0903] Next, the server cleanses the data collected from all employees. This process includes deleting invalid data and filling in missing data to ensure data accuracy. Based on the cleansed data, the generative AI begins the learning process. The generative AI analyzes personality traits, aptitudes, and recognized emotions to generate a mapping of interpersonal relationships and work environments. For example, a mapping is performed taking into account Employee A's personality traits and aptitudes, and it calculates which team and work environment Employee A is best suited to.

[0904] The server then proposes optimal organizational layouts and machine placements and role allocations based on the generated mapping of human relationships and work environments. The generation AI proposes placements taking into consideration employee compatibility, team balance, work content, emotions, and the operating status and characteristics of machine placements. For example, employee A is placed as the leader of a new project team in the marketing department, and the server proposes optimal machine placements and role allocations.

[0905] The server then notifies each employee and manager of the proposed organizational layout, machine placement, and role allocation. The content of the notification can also be adjusted based on the emotion recognition results. For example, if employee A is feeling anxious about the transfer, the notification can include a message to ease his or her anxiety.

[0906] Users can review the proposed organizational transfer and provide feedback on the dedicated portal site. For example, employee A reviews the proposed transfer and submits feedback on the timing of the transfer.

[0907] Finally, the server analyzes the collected feedback and reflects it in the generating AI. This feedback analysis improves the accuracy of the next organizational layout and machine placement / role allocation proposals. The emotion engine recognizes the user's emotions even when providing feedback, and supports receiving appropriate feedback.

[0908] As a concrete example, by inputting the following prompt sentence into the generative AI model, optimal placement and division of roles will be suggested.

[0909] Example prompt sentence:

[0910] Robot data: ID=R123, Type=Welding robot, Performance=85, Operation status=In operation

[0911] Worker Emotions: Stress Level = High, Health Status = Good

[0912] This system is expected to improve factory work efficiency and productivity, and reduce employee stress and health problems.

[0913] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0914] Step 1:

[0915] Collect data from each employee and work machine.

[0916] Input: Personality test, aptitude test, and information about transfer intentions.

[0917] How it works: The server collects necessary data from each employee and work machine through web forms and dedicated applications. For example, employee A answers a personality test, is judged to be "suited for creative work" through an aptitude test, and enters his / her intention to transfer as "I would like to be transferred to the marketing department."

[0918] Output: Collected personality test results, aptitude test results, transfer request information, and operating status data of work machines.

[0919] Step 2:

[0920] Store the collected data.

[0921] Input: Collected data.

[0922] Operation: The device encrypts this data and sends it to the server, which then stores the received data in an internal database. For example, the results of employee A's personality test, aptitude test, and transfer request information are stored in the database.

[0923] Output: Personality test results, aptitude test results, transfer request information, and operating status data of work machines stored in a database.

[0924] Step 3:

[0925] Recognize user emotions.

[0926] Input: facial expressions and tone of voice.

[0927] How it works: The server uses the device's camera and microphone to analyze the user's facial expressions and tone of voice in real time, and recognizes their emotions using an emotion engine. For example, if employee A feels irritated during a personality test, that emotion is recorded.

[0928] Output: Emotion recognition results.

[0929] Step 4:

[0930] Cleanse and process data.

[0931] Input: Stored data and emotion recognition results.

[0932] How it works: The server cleanses the collected data, which includes removing invalid parts of the data and filling in missing data. The generative AI model then begins the training process based on the cleansed data.

[0933] Output: Cleansed data and training results.

[0934] Step 5:

[0935] Generate a mapping of relationships and work environments.

[0936] Input: Cleansed data and training results.

[0937] How it works: The server uses the generative AI model to generate a mapping of interpersonal relationships and work environments, taking into account personality traits, aptitudes, and perceived emotions. For example, a mapping is generated that takes into account employee A's personality traits and aptitudes.

[0938] Output: Relationship map and work environment map.

[0939] Step 6:

[0940] We propose optimal organizational layout, machine placement, and division of roles.

[0941] Input: Relationship map and work environment map.

[0942] Operation: The server uses a generative AI model to propose optimal organizational arrangements and machine allocations and role assignments, taking into consideration employee compatibility, team balance, work content, emotions, and the operating status and characteristics of machine tools. For example, employee A is assigned as the leader of a new project team in the marketing department, and the server proposes optimal machine allocations and role assignments.

[0943] Output: Placement suggestion.

[0944] Step 7:

[0945] Notify proposals.

[0946] Input: Placement proposal.

[0947] Operation: The server notifies each employee and manager of the proposed organizational layout, machine placement, and role allocation. The content of the notification can also be adjusted based on the emotion recognition results. For example, if employee A is feeling anxious about the transfer, the notification can include a message to ease his / her anxiety.

[0948] Output: Informational message.

[0949] Step 8:

[0950] Collect and analyze feedback.

[0951] Input: Feedback for the notification.

[0952] Operation: Users review the proposed organizational reassignment and provide feedback on a dedicated portal site. For example, employee A reviews the proposed transfer and submits feedback on the timing of the transfer. The server analyzes the collected feedback and reflects it in the next proposal.

[0953] Output: Feedback and analysis results.

[0954] 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.

[0955] 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.

[0956] 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.

[0957] [Third embodiment]

[0958] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0959] 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.

[0960] 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).

[0961] 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.

[0962] 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.

[0963] 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).

[0964] 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.

[0965] 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.

[0966] 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.

[0967] 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.

[0968] 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.

[0969] 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."

[0970] This invention is a system that proposes optimal organizational placement based on the personality, aptitude, and transfer intention of employees. Specific program processing of the system will be explained below in natural language.

[0971] Data collection

[0972] User (employee)

[0973] Each employee enters information about their personality and aptitude tests, as well as their intention to transfer, via a web form or dedicated application. For example, employee A answers the personality test, is judged to be "suited to creative work" through the aptitude test, and enters their intention to transfer as "to the marketing department."

[0974] Device (employee's PC or smartphone)

[0975] The device sends the collected data to the server, where it is encrypted to ensure the security of the information.

[0976] server

[0977] The server analyzes the received data and stores it in an internal database. For example, it stores the personality test results, aptitude test results, and transfer request information of employee A in the database.

[0978] Data Processing and Training

[0979] server

[0980] The server cleanses the data collected from all employees (deleting invalid data and filling in missing data) and then starts the learning process using generative AI. This learning process generates a mapping of human relationships. For example, a mapping is performed that takes into account employee A's personality traits and aptitudes, and it calculates which team employee A would be best suited to.

[0981] Organizational layout proposal

[0982] server

[0983] Based on the generated interpersonal relationship mapping, the server uses generative AI to create optimal organizational placement proposals. The proposals include the member composition of each team and the placement of new employees. For example, a proposal is generated to place employee A as the leader of a new project team in the marketing department.

[0984] Communicating suggestions and gathering feedback

[0985] server

[0986] The server then sends an email to each employee and manager notifying them of the proposed organizational placement, along with a link to a dedicated portal site where they can review the proposal.

[0987] Users (employees and administrators)

[0988] The user checks the notified organizational placement proposal and provides feedback on the dedicated portal site. For example, employee A checks the transfer proposal and submits feedback on the timing of the transfer.

[0989] server

[0990] The server analyzes the collected feedback and reflects the results in the generating AI, thereby improving the accuracy of the next organizational placement proposal.

[0991] In this way, this system proposes optimal organizational placement based on employee data and uses feedback to improve the accuracy of placement, thereby maximizing the efficiency and results of the entire organization.

[0992] The processing flow will be explained below.

[0993] Step 1:

[0994] User (employee)

[0995] Each employee enters information about their personality and aptitude tests, as well as their intention to transfer, via a web form or dedicated application. For example, employee A answers the personality test, is judged to be "suited to creative work" through the aptitude test, and enters their intention to transfer as "to the marketing department."

[0996] Step 2:

[0997] Device (employee's PC or smartphone)

[0998] The terminal sends the entered data to the server. At this time, the data is encrypted before being sent, so the security of the information is maintained.

[0999] Step 3:

[1000] server

[1001] The server analyzes the received data and stores it in an internal database. For example, it stores the personality test results, aptitude test results, and transfer request information of employee A in the database.

[1002] Step 4:

[1003] server

[1004] The server cleanses the data collected from all employees, which includes removing invalid data and filling in missing data, a process that ensures data accuracy.

[1005] Step 5:

[1006] server

[1007] Based on the cleansed data, the generative AI begins the learning process. The generative AI analyzes information such as personality traits and aptitudes to generate a mapping of human relationships. For example, a mapping is performed that takes into account the personality traits and aptitudes of employee A, and it calculates which team employee A would be best suited to.

[1008] Step 6:

[1009] server

[1010] The server then creates optimal organizational placement proposals based on the generated interpersonal relationship mapping. The generation AI considers employee compatibility, team balance, and job content when proposing placements. For example, a proposal is generated to place employee A as the leader of a new project team in the marketing department.

[1011] Step 7:

[1012] server

[1013] The generated organizational placement proposal will be notified to each employee and manager via email, and a link to a dedicated portal site will be provided so that the proposal can be viewed in detail.

[1014] Step 8:

[1015] Users (employees and administrators)

[1016] The user checks the notified organizational placement proposal and provides feedback on the dedicated portal site. For example, employee A checks the transfer proposal and submits feedback on the timing of the transfer.

[1017] Step 9:

[1018] server

[1019] The server analyzes the collected feedback and reflects the results in the generating AI. This feedback analysis improves the accuracy of the next organizational placement proposal.

[1020] In this way, this system proposes optimal organizational placement based on employee data and uses feedback to improve the accuracy of placement, thereby maximizing the efficiency and results of the entire organization.

[1021] Example 1

[1022] 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."

[1023] With conventional organizational placement systems, it is difficult to make optimal organizational placements that properly reflect employees' personality traits, aptitudes, and transfer intentions. Furthermore, there is no fully established mechanism for effectively utilizing feedback to improve the accuracy of the next placement proposal. As a result, effective organizational placements that maximize employee capabilities are not realized, resulting in issues such as a decline in the efficiency and performance of the entire organization.

[1024] 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.

[1025] In this invention, the server includes means for collecting data on personality assessment, aptitude assessment, and transfer intention from each employee, means for encrypting and storing the collected data, means for cleansing the stored data, means for performing learning using a generative AI model based on the cleansed data and generating a human relationship mapping, means for proposing an optimal organizational placement based on the human relationship mapping, means for notifying each employee and manager of the proposed organizational placement, means for collecting feedback on the notified proposal, and means for analyzing the collected feedback and reflecting it in the generative AI model. This allows for optimal organizational placement that appropriately considers employees' personality traits, aptitude, and transfer intention, and further makes it possible to improve the accuracy of subsequent proposals by utilizing the collected feedback.

[1026] A "personality assessment" is a test or questionnaire used to evaluate an employee's personality traits.

[1027] An "aptitude test" is a test or assessment used to evaluate an employee's job aptitude and skills.

[1028] "Transfer intention" refers to information that indicates the department or job content to which an employee wishes to be transferred.

[1029] "Means of collecting data" refers to the methods and tools used to collect information from employees regarding personality tests, aptitude tests, and transfer intentions.

[1030] "Means for encryption and storage" refers to the method of storing collected data using encryption technology.

[1031] "Cleansing means" refers to a method for removing invalid data from stored data and for filling in missing data.

[1032] "Generative AI model" refers to a generative model using AI, a technology used to generate organizational placement proposals.

[1033] "Interpersonal relationship mapping" is a map that illustrates the interrelationships between employees based on their personality traits and aptitudes.

[1034] "Means for proposing optimal organizational placement" refers to methods and tools that make proposals for placing employees in the optimal departments or teams based on interpersonal relationship mapping.

[1035] "Means of notification" refers to the methods and tools used to inform each employee and manager of the proposed organizational arrangement.

[1036] "Feedback collection measures" are methods and tools used to gather input from employees and managers about proposed organizational arrangements.

[1037] "Means of analysis" refers to the method of analyzing the collected feedback and incorporating the results into the generative AI model.

[1038] The present invention is a system for proposing optimal organizational placement based on the personality traits, aptitudes, and transfer intentions of employees. Specific embodiments for carrying out the present invention will be described below.

[1039] Data collection

[1040] User (employee)

[1041] Each employee uses a dedicated application or web form to enter information about their personality and aptitude tests, as well as their intention to transfer. For example, an employee opens the dedicated application, answers "creative" to the personality test, answers "suitable for creative work" to the aptitude test, and enters "hope to transfer to the marketing department" as their intention to transfer.

[1042] Device (employee's PC or smartphone)

[1043] The terminal receives the data entered by the user, encrypts it using AES encryption technology, and sends it to the server. For example, the personality test result "creative," the aptitude test result "suitable for creative work," and the transfer intention "hope to be in the marketing department" are encrypted and sent.

[1044] Receiving and storing data

[1045] server

[1046] The server receives the data sent from the terminal, decrypts it, and saves it in a database (e.g., MySQL). For example, it saves the employee's personality test results, aptitude test results, and transfer request information in the employees_data table.

[1047] Data Cleansing

[1048] server

[1049] The server uses Python's Pandas library to cleanse the data. Specifically, it removes invalid data and fills in missing data to generate the final dataset. For example, it automatically removes or fills in incorrectly entered data or missing data, cleansing the data for all employees.

[1050] Data Analysis and Learning

[1051] server

[1052] The server then inputs the cleansed data into a generative AI model (e.g., GPT-4) and begins the learning process. Specifically, it generates a relationship mapping that takes into account personality traits, aptitudes, and transfer preferences. For example, the AI ​​can be trained on a dataset containing employee data to determine which employees are best suited to which teams.

[1053] Organizational layout proposal

[1054] server

[1055] The server creates optimal organizational placement proposals based on the human relationship mapping generated by the generative AI model. For example, the AI ​​may determine that "Employee A is the best suited person to be a project leader in the marketing department," and then compile that placement proposal.

[1056] Proposal Notification

[1057] server

[1058] The server notifies each employee and manager of the generated organizational placement proposal by email. It also includes a link to a dedicated portal site where the proposal can be viewed. For example, an email notification of "Project Leader Placement Proposal in the Marketing Department" is sent to employees and managers, along with the URL of the dedicated portal site.

[1059] Gathering feedback

[1060] Users (employees and administrators)

[1061] Users can review the proposed organizational placement and provide feedback on a dedicated portal site. For example, an employee can review the proposal and submit feedback such as "I would like the transfer to occur next month" in a dedicated form.

[1062] Analyzing and incorporating feedback

[1063] server

[1064] The server analyzes the collected feedback and reflects the results in the generative AI model. This improves the accuracy of the next organizational placement proposal. For example, a user's feedback such as "I would like the transfer to occur next month" is analyzed and added to the AI ​​model's learning dataset.

[1065] Prompt Sentence Examples

[1066] As an example of an input prompt for a generative AI model, we will use the following sentence:

[1067] "Based on the following data, please suggest the optimal organizational arrangement.

[1068] data:

[1069] Employee A: Personality test result: "Creative, team player", aptitude test result: "Suitable for creative work", transfer intention: "Want to move to the marketing department"

[1070] It also includes detailed data of other employees.”

[1071] In this way, the system of the present invention proposes optimal organizational placement based on employees' personality traits, aptitudes, and intentions for transfer, and by reflecting feedback in a timely manner, it is possible to maximize the efficiency and results of the entire organization.

[1072] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1073] Step 1:

[1074] User (employee)

[1075] Each employee uses a dedicated application or web form to enter information about their personality and aptitude tests, as well as their intention to transfer. For example, employee A enters their personality test result of "creative," their aptitude test result of "suited for creative work," and their intention to transfer to the marketing department.

[1076] Input and Output

[1077] Input: Personality test results, aptitude test results, intention to transfer

[1078] Output: Information entered into an application or web form

[1079] Step 2:

[1080] Device (employee's PC or smartphone)

[1081] The terminal receives the information entered by the user in real time and encrypts it using AES encryption technology. For example, if employee A enters the personality test result "creative," the aptitude test result "suitable for creative work," and the transfer intention "hope to be transferred to the marketing department," the information is encrypted.

[1082] Input and Output

[1083] Input: Personality test results, aptitude test results, intention to transfer

[1084] Output: Encrypted information

[1085] Step 3:

[1086] Device (employee's PC or smartphone)

[1087] The terminal sends the encrypted information to the server. For example, the information of employee A is encrypted and sent to the server.

[1088] Input and Output

[1089] Input: Encrypted information

[1090] Output: Encrypted information sent to the server

[1091] Step 4:

[1092] server

[1093] The server receives the encrypted information sent from the terminal, decrypts it, and stores it in a database. For example, the personality test results, aptitude test results, and transfer request information for employee A are stored in the employees_data table of a database (e.g., MySQL).

[1094] Input and Output

[1095] Input: Encrypted information

[1096] Output: Decrypted database entries

[1097] Step 5:

[1098] server

[1099] The server uses the Python Pandas library to cleanse the data, remove invalid data, impute missing data, and generate a final cleansed dataset, e.g., impute missing data and remove incorrectly entered data.

[1100] Input and Output

[1101] Input: Employee data

[1102] Output: A cleansed dataset

[1103] Step 6:

[1104] server

[1105] The server inputs the cleansed data into a generative AI model (e.g., GPT-4) and begins the learning process. Specifically, it generates a relationship mapping that takes into account personality traits, aptitude, and transfer preferences. For example, based on a dataset containing employee A's data, the generative AI generates a relationship mapping and determines which team employee A would be best suited to.

[1106] Input and Output

[1107] Input: Cleansed dataset

[1108] Output: Relationship mapping

[1109] Step 7:

[1110] server

[1111] The server creates optimal organizational placement proposals based on the human relationship mapping generated by the generative AI model. For example, the AI ​​may determine that "Employee A is the best suited person to be a project leader in the marketing department," and then compile that placement proposal.

[1112] Input and Output

[1113] Input: Relationship mapping

[1114] Output: Organizational layout proposal

[1115] Step 8:

[1116] server

[1117] The server notifies each employee and manager of the generated organizational placement proposal by email. It also provides a link to a dedicated portal site where the proposal can be viewed. For example, a notification email for "Project Leader Placement Proposal in the Marketing Department" is sent to employee A and the manager.

[1118] Input and Output

[1119] Input: Organizational Allocation Proposal

[1120] Output: Notification email and portal link

[1121] Step 9:

[1122] Users (employees and administrators)

[1123] The user checks the proposed organizational placement and provides feedback on a dedicated portal site. For example, employee A enters feedback such as "I would like to be transferred next month" into a dedicated form and submits it.

[1124] Input and Output

[1125] Input: Feedback on the proposal

[1126] Output: Feedback data

[1127] Step 10:

[1128] server

[1129] The server analyzes the collected feedback and reflects the results in the generative AI model, improving the accuracy of the next organizational placement proposal. For example, the feedback content can be added to the AI ​​model's learning dataset, resulting in a more accurate next placement proposal.

[1130] Input and Output

[1131] Input: Feedback data

[1132] Output: Updated AI model training dataset

[1133] In this way, each processing step is carried out in a concrete manner, and the optimal organizational placement is proposed based on the employee's personality traits, aptitude, and intention to transfer, and feedback can be used to improve the accuracy of the proposal.

[1134] (Application example 1)

[1135] 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."

[1136] Currently, factories are not sufficiently optimizing the placement and task allocation of robots and workers, resulting in a decline in production efficiency. Furthermore, placement that reflects personality assessment, aptitude assessment, and transfer intentions has not been realized, leaving problems in improving the work environment and worker satisfaction. The objective of this invention is to solve these problems and provide a system that realizes optimal placement and task allocation of robots and workers in a factory.

[1137] 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.

[1138] In this invention, the server includes means for collecting data on personality tests, aptitude tests, and transfer intentions from each worker, means for saving the collected data, means for generating a human relationship mapping based on the saved data, means for proposing an optimal organizational placement based on the human relationship mapping, means for notifying each worker and manager of the proposed organizational placement, means for collecting feedback on the notified proposal, means for analyzing the collected feedback and reflecting it in the generation AI, and means for proposing an optimal placement and task assignment of robots in the factory based on the characteristics and capabilities of each worker. This enables placement and task assignment that takes into account the personality traits, aptitude, and transfer intentions of workers.

[1139] "Workers" are personnel who operate and manage robots within a factory, and are people whose personality tests, aptitude tests, and willingness to transfer are taken into consideration.

[1140] A "personality assessment" is a test or analytical tool used to evaluate a worker's personality traits, a process used to determine a worker's aptitude and job suitability.

[1141] An "aptitude test" is a test or assessment tool used to evaluate a worker's suitability for a job, and is a process used to determine what tasks or roles a worker is suited to.

[1142] "Transfer intention" refers to data that indicates a worker's intention or desire to move to a different department or role, and is one of the factors reflected in organizational placement proposals.

[1143] "Factory robots" are mechanical devices used to automate factory work and production processes, and optimizing their placement and task assignment is important to efficiently perform various tasks.

[1144] "Means of collecting data" refers to methods and tools for collecting data on workers' personality tests, aptitude tests, and intentions to transfer, and specifically includes questionnaires, tests, sensors, etc.

[1145] "Means for storing data" means methods and systems for safely and efficiently storing collected data, including databases, cloud storage, and file systems.

[1146] "Means for generating a mapping of human relationships" refers to methods and technologies for analyzing stored data and visualizing the relationships and aptitudes between workers, and specifically includes generative AI algorithms and clustering methods.

[1147] "Means for proposing optimal organizational placement" refers to methods and systems for proposing optimal placement and task allocation of workers and robots based on human relationship mapping, and specifically includes AI models and simulation software.

[1148] "Means for notifying proposals" means a method or system for notifying each worker and manager of the generated placement and task assignment proposals, and specifically includes email, a notification application, and a portal site.

[1149] "Means for collecting feedback" means methods or systems for collecting feedback from workers or managers regarding the notified proposal, and specifically includes forms, questionnaires, and opinion collection systems.

[1150] "Generative AI" refers to artificial intelligence technology for data analysis, mapping generation, placement proposals, etc., and includes advanced algorithms and learning models.

[1151] "Task allocation" is the process of assigning specific tasks to workers or robots, and is a necessary step for achieving efficient work.

[1152] The present invention provides a system for proposing optimal placement and task allocation for robots and workers in a factory. Specific embodiments of the system will be described below.

[1153] 1. Data Collection

[1154] Collecting data from workers

[1155] The user, a worker, enters information about the personality test, aptitude test, and transfer intention through a dedicated application or web form. For example, worker A answers the personality test, is judged to have "aptitude for speedy work" through the aptitude test, and enters "hope for welding department" as his / her intention to transfer.

[1156] Data transmission and storage

[1157] The terminal (the worker's PC or smartphone) encrypts the collected data and sends it to the server. The server analyzes the received data and stores it securely in an internal database. For example, the results of worker A's personality test, aptitude test, and transfer request information are stored in the database.

[1158] 2. Data processing and human relationship mapping

[1159] The server cleanses the data collected from all workers, deleting invalid data and filling in missing data. The generative AI then analyzes the data and begins the learning process to generate a relationship mapping. For example, it determines which team Worker A is best suited to, taking into account Worker A's personality traits and aptitudes.

[1160] 3. Proposal for placement and task allocation

[1161] Based on the generated mapping results of human relationships, the server uses generative AI to create optimal robot and worker placement proposals and task assignments. For example, a proposal is generated to place Worker A as a member of a new project team in the welding department.

[1162] 4. Proposal notification and feedback collection

[1163] Proposal Notification

[1164] The server notifies each worker and manager of the generated placement and task assignment proposals via email, and also provides a URL for a dedicated portal site where the proposals can be viewed.

[1165] Collecting and analyzing feedback

[1166] The users, i.e., workers and managers, can check the notified placement proposal on a dedicated portal site and provide feedback there. For example, worker A checks the placement proposal and submits his / her opinion on the contents of the proposal as feedback.

[1167] The server analyzes the collected feedback and reflects the results in the generating AI to improve the accuracy of the next placement proposal.

[1168] Examples of specific examples and prompts

[1169] For example, you might enter the following prompt for a generative AI model:

[1170] "Please tell me a method to propose optimal placement and task assignment based on the characteristics and capabilities of each robot in a factory. In particular, I would like to know a concrete example using K-means clustering."

[1171] By using this system, production efficiency within the factory can be significantly improved, and worker satisfaction can also be increased.

[1172] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1173] Step 1:

[1174] Data on personality and aptitude tests and transfer intentions are collected from each worker. The user enters this information through a dedicated application or a web form. The entered data includes the worker's personality traits, aptitude for the job, and transfer wishes. The device temporarily stores this data, encrypts it, and sends it to the server.

[1175] Step 2:

[1176] The server receives the collected data and stores it in a database. At this time, it performs data cleansing, deletes invalid data, and fills in missing data. The server safely stores the received personality test results, aptitude test results, and transfer request information in the database.

[1177] Step 3:

[1178] The server analyzes the stored data and generates a mapping of human relationships. Specifically, it uses generative AI to perform cluster analysis based on the personality traits and aptitudes of workers. Here, it extracts data features and recognizes patterns to visualize the relationships between workers. The mapping results are used as the base data for the next processing step.

[1179] Step 4:

[1180] The server generates optimal robot and worker placement proposals and task assignments based on the results of the interpersonal relationship mapping. Using a generative AI model, it outputs placement proposals that make the most of the characteristics of each worker and robot. The input data is the worker's personality traits, aptitude, and transfer intentions, and the output is a specific placement proposal and task assignment.

[1181] Step 5:

[1182] The server notifies each worker and manager of the generated placement and task assignment proposals. Notifications are sent via email, and the worker and manager are also provided with a link to a dedicated portal site. Details of the proposals can be viewed on the portal site.

[1183] Step 6:

[1184] The users, i.e., workers and managers, can check the placement proposals they have been notified of on a dedicated portal site and provide feedback, including how they accepted the proposals and their opinions on areas for improvement.

[1185] Step 7:

[1186] The server analyzes the collected feedback and reflects the results in the generative AI. This is used to improve the accuracy of the next placement proposal. Specifically, the feedback content is added to the dataset and the generative AI model is retrained to improve the system.

[1187] The above processing steps enable optimal placement and task allocation of robots and workers in a factory, thereby improving production efficiency and worker satisfaction.

[1188] 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.

[1189] This invention is a system that proposes optimal organizational placement based on employees' personalities, aptitudes, and transfer intentions, and by combining this with an emotion engine that recognizes the user's emotions, it achieves even more accurate placement. Below, the specific program processing of the system is explained in natural language.

[1190] Data collection

[1191] User (employee)

[1192] Each employee enters information about their personality and aptitude tests, as well as their intention to transfer, via a web form or dedicated application. For example, employee A answers the personality test, is judged to be "suited to creative work" through the aptitude test, and enters their intention to transfer as "to the marketing department."

[1193] Device (employee's PC or smartphone)

[1194] The terminal sends the entered data to the server. At this time, the data is encrypted before being sent, so the security of the information is maintained.

[1195] server

[1196] The server analyzes the received data and stores it in an internal database. For example, it stores the personality test results, aptitude test results, and transfer request information of employee A in the database.

[1197] emotion recognition

[1198] server

[1199] During the data collection process, the device's camera and microphone are used to analyze the user's facial expressions and tone of voice, and the emotion engine recognizes the user's emotions. For example, if employee A feels irritated during a personality test, that emotion is recorded.

[1200] Data Processing and Training

[1201] server

[1202] The server cleanses the data collected from all employees, which includes removing invalid data and filling in missing data, a process that ensures data accuracy.

[1203] server

[1204] Based on the cleansed data, the generative AI begins the learning process. The generative AI analyzes personality traits, aptitudes, and recognized emotions to generate a mapping of human relationships. For example, a mapping is performed that takes into account Employee A's personality traits and aptitudes, and calculates which team Employee A would be best suited to.

[1205] Organizational layout proposal

[1206] server

[1207] The server then creates optimal organizational placement proposals based on the generated interpersonal relationship mapping. The generation AI considers employee compatibility, team balance, work content, and emotions when proposing placements. For example, a proposal is generated to place employee A as the leader of a new project team in the marketing department.

[1208] server

[1209] The proposed organizational relocation is notified to each employee and manager via email, and a link to a dedicated portal site is also provided. The content of the notification can also be adjusted based on the emotion recognition results. For example, if Employee A is feeling anxious about the transfer, a message to ease his / her feelings can be included.

[1210] Reviewing proposals and gathering feedback

[1211] Users (employees and administrators)

[1212] The user checks the notified organizational placement proposal and provides feedback on the dedicated portal site. For example, employee A checks the transfer proposal and submits feedback on the timing of the transfer.

[1213] server

[1214] The server analyzes the collected feedback and reflects it in the generation AI. This feedback analysis improves the accuracy of the next organizational placement proposal. The emotion engine recognizes the user's emotions even when providing feedback, and supports them in receiving appropriate feedback.

[1215] In this way, the system proposes optimal organizational placement based on employee data and emotions, and uses feedback to improve the accuracy of placement, thereby maximizing the efficiency and results of the entire organization.

[1216] The processing flow will be explained below.

[1217] Step 1:

[1218] User (employee)

[1219] Each employee enters information about their personality and aptitude tests, as well as their intention to transfer, via a web form or dedicated application. For example, employee A answers the personality test, is judged to be "suited to creative work" through the aptitude test, and enters their intention to transfer as "to the marketing department."

[1220] Step 2:

[1221] Device (employee's PC or smartphone)

[1222] The terminal sends the entered data to the server. At this time, the data is encrypted before being sent, so the security of the information is maintained.

[1223] Step 3:

[1224] server

[1225] The server analyzes the received data and stores it in an internal database. For example, it stores the personality test results, aptitude test results, and transfer request information of employee A in the database.

[1226] Step 4:

[1227] Terminal

[1228] During the data collection process, the device's camera and microphone are used to analyze the user's facial expressions and tone of voice, and the emotion engine recognizes the user's emotions. For example, if employee A feels irritated during a personality test, that emotion is recorded.

[1229] Step 5:

[1230] server

[1231] The server cleanses the data collected from all employees, which includes removing invalid data and filling in missing data, a process that ensures data accuracy.

[1232] Step 6:

[1233] server

[1234] Based on the cleansed data, the generative AI begins the learning process. The generative AI analyzes personality traits, aptitudes, and recognized emotions to generate a mapping of human relationships. For example, a mapping is performed that takes into account Employee A's personality traits and aptitudes, and calculates which team Employee A would be best suited to.

[1235] Step 7:

[1236] server

[1237] The server then creates optimal organizational placement proposals based on the generated interpersonal relationship mapping. The generation AI considers employee compatibility, team balance, work content, and emotions when proposing placements. For example, a proposal is generated to place employee A as the leader of a new project team in the marketing department.

[1238] Step 8:

[1239] server

[1240] The proposed organizational relocation is notified to each employee and manager via email, and a link to a dedicated portal site is also provided. The content of the notification can also be adjusted based on the emotion recognition results. For example, if Employee A is feeling anxious about the transfer, a message to ease his / her feelings can be included.

[1241] Step 9:

[1242] Users (employees and administrators)

[1243] The user checks the notified organizational placement proposal and provides feedback on the dedicated portal site. For example, employee A checks the transfer proposal and submits feedback on the timing of the transfer.

[1244] Step 10:

[1245] server

[1246] The server analyzes the collected feedback and reflects it in the generation AI. This feedback analysis improves the accuracy of the next organizational placement proposal. The emotion engine recognizes the user's emotions even when providing feedback, and supports them in receiving appropriate feedback.

[1247] In this way, the system proposes optimal organizational placement based on employee data and emotions, and uses feedback to improve the accuracy of placement, thereby maximizing the efficiency and results of the entire organization.

[1248] Example 2

[1249] 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."

[1250] Conventional organizational placement systems were unable to make appropriate placement recommendations because they had difficulty taking into account employees' personality traits, aptitude, and transfer intentions. Furthermore, they lacked emotion recognition technology, making it impossible to reflect users' emotions in organizational placement. Furthermore, there was a lack of means to effectively utilize feedback, making it difficult to improve the accuracy of recommendations.

[1251] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for collecting data on personality assessment, aptitude assessment, and transfer intentions from each user; means for saving the collected data; means for analyzing the saved data and saving it in an internal database; means for analyzing the user's facial expressions and tone of voice using the device's camera and microphone and recognizing the user's emotions using an emotion engine; means for starting a learning process by the generation AI based on the cleansed data; means for analyzing personality traits, aptitude, and recognized emotions and generating a relationship mapping; means for creating an optimal organizational placement proposal based on the generated relationship mapping; means for notifying each user and administrator of the proposed organizational placement by email and providing a link to a dedicated portal site; means for collecting feedback on the notified proposal; and means for analyzing the collected feedback and reflecting it in the generation AI. This enables highly accurate organizational placement proposals that take into account employees' personality traits, aptitudes, and emotions.

[1252] "User" refers to individuals, especially employees, who use the system.

[1253] A "personality test" is an assessment test for measuring the personality traits of an individual user.

[1254] An "aptitude test" is a test that assesses how suited a user is to a particular job or role.

[1255] "Intention to transfer" refers to the user's intentions and wishes regarding the desired transfer destination and position.

[1256] "Collection methods" refers to software or hardware functions used to collect user data.

[1257] "Storage means" refers to the function for accumulating and safely storing collected data.

[1258] A "database" is a system for efficiently managing and accessing stored data.

[1259] A "camera" is an imaging device for capturing a user's facial expression.

[1260] A "microphone" is a device that picks up the user's voice and acquires it as audio data.

[1261] The "emotion engine" is software that analyzes the user's emotions from collected facial and voice data.

[1262] "Cleansing" is the process of removing invalid parts of data and filling in missing data.

[1263] "Generative AI" refers to artificial intelligence that analyzes data through a learning process and generates optimal suggestions.

[1264] The "learning process" is the process by which the generative AI repeatedly learns based on data.

[1265] "Relationship mapping" refers to a map that visually or abstractly represents the relationships between users.

[1266] An "organizational placement proposal" is an optimal placement proposal generated in consideration of the user's personality traits, aptitude, emotions, and the like.

[1267] "Notification means" refers to software or functions for informing each user or administrator of the proposed content.

[1268] "Portal Site" means a website that Users can access to review Proposals and provide Feedback.

[1269] "Feedback" refers to the opinions and thoughts that users provide regarding suggestions.

[1270] "Analysis" is the process of examining and understanding the collected data and feedback.

[1271] The present invention is a system that proposes optimal organizational placement based on the user's personality traits, aptitude, and transfer intentions, and improves the placement accuracy by combining it with an emotion recognition engine. This system is mainly operated by a server, terminals, and users.

[1272] Data collection

[1273] The user (employee) enters information about the personality test, aptitude test, and intention to transfer through a web form or dedicated application. This information is encrypted and sent to the server via the user's device (PC or smartphone). As a concrete example, employee A answers the personality test and enters the result that he or she is "outgoing and values ​​teamwork."

[1274] Data analysis and storage

[1275] The server analyzes the received data and stores it in an internal database. The stored data includes personality test results, aptitude test results, and transfer intentions. This allows detailed data on each user to be safely stored.

[1276] emotion recognition

[1277] The server analyzes the user's facial expressions and tone of voice using the device's camera and microphone. The emotion engine recognizes the user's emotions, and this data is also stored in an internal database. For example, if employee A feels dissatisfied during a personality test, the camera captures his or her facial expression, and the emotion engine identifies it as "dissatisfied."

[1278] Data Cleansing and Learning

[1279] The server cleanses the data collected from all users, which includes removing invalid data and filling in missing data, ensuring the accuracy of the data.

[1280] Next, the generative AI begins the learning process based on the cleansed data. The generative AI analyzes personality traits, aptitudes, and recognized emotions to generate a relationship mapping. For example, the mapping is performed to place employee A in the optimal team, taking into account employee A's personality traits and aptitudes.

[1281] Organizational Allocation Proposal and Notification

[1282] The server then creates optimal organizational placement proposals based on the generated interpersonal relationship mapping. The generation AI considers the user's compatibility, team balance, work content, and emotions when proposing placements. For example, a proposal is made to place employee A as the leader of a new project team in the marketing department.

[1283] The proposed organizational placement is notified to each user and administrator via email, and a link to a dedicated portal site is provided. The content of the notification may be adjusted based on the results of emotion recognition. For example, if employee A is anxious about the transfer, a message to ease his or her anxiety may be included.

[1284] Collecting and analyzing feedback

[1285] The user checks the notified organizational placement proposal and provides feedback on the dedicated portal site. For example, employee A checks the transfer proposal and submits feedback on the timing of the transfer.

[1286] The server analyzes the collected feedback and reflects it in the generating AI. This process further improves the accuracy of the next organizational placement proposal. The emotion engine recognizes the user's emotions even when providing feedback, and supports them in receiving appropriate feedback.

[1287] Prompt Sentence Examples

[1288] Analyze the following user information and propose the optimal organizational layout.

[1289] Personality test result: Introverted and good at detailed work

[1290] Aptitude test result: Aptitude for data analysis

[1291] Willingness to transfer: Data analysis team preferred

[1292] Sentiment: Positive about the transfer

[1293] The present invention makes it possible to make highly accurate organizational placement proposals that take into account the user's personality traits, aptitudes, and emotions, thereby improving user satisfaction.

[1294] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1295] Step 1: Data entry

[1296] User (employee)

[1297] Input: Personality test, aptitude test, and information about transfer intentions

[1298] How it works: Users enter this information through a web form or a dedicated application.

[1299] Output: The input data is stored on the user's terminal.

[1300] Step 2: Send data

[1301] Device (employee's PC or smartphone)

[1302] Input: Personality test results, aptitude test results, and data regarding transfer intentions entered by the user

[1303] Operation: The terminal encrypts the entered data and sends it to the server.

[1304] Output: The encrypted data is sent to the server.

[1305] Step 3: Save data

[1306] server

[1307] Input: Encrypted data sent from the device

[1308] How it works: The server decrypts the data it receives and stores it in an internal database.

[1309] Output: The decoded data is stored in the database.

[1310] Step 4: Emotion Recognition

[1311] server

[1312] Input: User facial expression and voice data collected through the device's camera and microphone

[1313] How it works: The server uses an emotion engine to analyze the user's facial expressions and tone of voice to recognize their emotions.

[1314] Output: The recognized emotion data is stored in the internal database.

[1315] Step 5: Data cleansing

[1316] server

[1317] Input: All collected user data (personality traits, aptitudes, emotions, etc.)

[1318] What it does: The server cleanses the data, removing invalid parts and filling in missing data.

[1319] Output: Cleansed and accurate data is produced.

[1320] Step 6: Generative AI training

[1321] server

[1322] Input: Cleansed data

[1323] How it works: The server initiates the learning process with the generative AI, analyzing personality traits, aptitudes, and emotions.

[1324] Output: Relationship mapping data is generated.

[1325] Step 7: Generate organizational alignment proposals

[1326] server

[1327] Input: Relationship mapping data

[1328] How it works: Creates optimal organizational placement proposals based on generative AI.

[1329] Output: Proposal data for optimal organizational placement is generated.

[1330] Step 8: Proposal Notification

[1331] server

[1332] Input: Data proposing optimal organizational layout

[1333] How it works: The server notifies each user and administrator of the proposed data via email and provides a link to a dedicated portal site. The content of the notification may be adjusted based on the emotion recognition results.

[1334] Output: A notification email is sent to the user and administrator.

[1335] Step 9: Gather feedback

[1336] Users (employees and administrators)

[1337] Input: Notified organizational placement proposal

[1338] How it works: Users review suggestions and provide feedback on a dedicated portal site.

[1339] Output: The feedback data is sent to the server.

[1340] Step 10: Feedback analysis and implementation

[1341] server

[1342] Input: Collected feedback data

[1343] How it works: The server analyzes the feedback and applies it to the generative AI, which improves the accuracy of the next suggestion.

[1344] Output: The accuracy of the next organizational placement proposal will be improved.

[1345] (Application example 2)

[1346] 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."

[1347] Conventional organizational placement systems were limited to proposing placements based on employees' personality traits, aptitudes, and transfer intentions, and were unable to achieve optimal placements and role allocations that took into account the characteristics of the work environment and work machines. Furthermore, they lacked a mechanism for grasping the emotions and operating status of employees and work machines in real time and responding flexibly. These issues led to reduced factory efficiency and productivity, and risk of employee stress and health problems. To solve these problems, the present invention aims to provide a system that makes optimal placement proposals that comprehensively consider the characteristics and emotional states of employees and work machines.

[1348] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1349] In this invention, the server includes means for collecting data on personality diagnosis, aptitude diagnosis, and transfer intentions from each employee and work machine, means for storing the collected data and work machine operating status data, and means for generating a mapping of human relationships and work environments based on the stored data. This enables optimal organizational layout and work machine allocation / role allocation to maximize the efficiency and results of the entire organization.

[1350] A "personality assessment" is a test or analytical method for assessing and classifying each employee's personality traits.

[1351] "Aptitude diagnosis" refers to tests and analytical methods used to evaluate each employee's suitability and skills for their job.

[1352] "Intention to transfer" refers to an employee's desire to move from their current job or department to a new job or department.

[1353] "Work machine" refers to automated work equipment, including machines and robots, used in factories.

[1354] "Operating status" is information that indicates how the work machine is currently operating, whether it is operating or stopped, or its performance level, or other such status.

[1355] "Data collection means" refers to the systems and tools used to obtain necessary information from each employee and work machine.

[1356] "Data storage means" refers to the systems and databases used to safely store and manage collected data.

[1357] "Interpersonal relationship mapping" is a map that visually represents the interrelationships between employees based on their personality traits and aptitudes.

[1358] "Work environment mapping" is a map that visually summarizes the location and operating status of work machines.

[1359] "Organizational placement" refers to assigning employees to the most suitable departments and jobs based on their aptitude and personality traits.

[1360] "Role allocation" refers to the allocation of appropriate tasks to work machines and employees to achieve efficient work.

[1361] "Notification methods" are systems and tools used to inform employees and managers of proposed placements and role assignments.

[1362] An "emotion engine" is an algorithm or system that recognizes emotions by analyzing a user's facial expressions, tone of voice, etc.

[1363] "Feedback collection tools" are systems and tools used to collect opinions and reactions from employees and managers regarding notified proposals.

[1364] "Generative AI" is artificial intelligence that analyzes collected data, generates a mapping of human relationships and work environments, and proposes optimal placement and division of roles.

[1365] To implement this invention, the following system is required: This system includes a means for collecting and analyzing data from each employee and work machine, and a generative AI for proposing optimal organizational arrangements and role allocations.

[1366] First, the server collects data on personality and aptitude tests, as well as transfer intentions, from each employee and work machine. This data is collected through web forms and dedicated applications. For example, employee A answers a personality test, is judged to be "suited to creative work" through the aptitude test, and enters "I would like to be transferred to the marketing department" as his or her transfer intention.

[1367] Next, the device (employee's PC or smartphone) sends the collected data to the server. The data is encrypted before transmission, ensuring the security of the information. The server analyzes the received data and stores it in an internal database. For example, the results of employee A's personality test, aptitude test, and transfer request information are stored in the database.

[1368] Furthermore, during the data collection process, the server uses the device's camera and microphone to analyze the user's facial expressions and tone of voice, and uses an emotion engine to recognize the user's emotions. For example, if employee A feels irritated during the personality test, that emotion will be recorded.

[1369] Next, the server cleanses the data collected from all employees. This process includes deleting invalid data and filling in missing data to ensure data accuracy. Based on the cleansed data, the generative AI begins the learning process. The generative AI analyzes personality traits, aptitudes, and recognized emotions to generate a mapping of interpersonal relationships and work environments. For example, a mapping is performed taking into account Employee A's personality traits and aptitudes, and it calculates which team and work environment Employee A is best suited to.

[1370] The server then proposes optimal organizational layouts and machine placements and role allocations based on the generated mapping of human relationships and work environments. The generation AI proposes placements taking into consideration employee compatibility, team balance, work content, emotions, and the operating status and characteristics of machine placements. For example, employee A is placed as the leader of a new project team in the marketing department, and the server proposes optimal machine placements and role allocations.

[1371] The server then notifies each employee and manager of the proposed organizational layout, machine placement, and role allocation. The content of the notification can also be adjusted based on the emotion recognition results. For example, if employee A is feeling anxious about the transfer, the notification can include a message to ease his or her anxiety.

[1372] Users can review the proposed organizational transfer and provide feedback on the dedicated portal site. For example, employee A reviews the proposed transfer and submits feedback on the timing of the transfer.

[1373] Finally, the server analyzes the collected feedback and reflects it in the generating AI. This feedback analysis improves the accuracy of the next organizational layout and machine placement / role allocation proposals. The emotion engine recognizes the user's emotions even when providing feedback, and supports receiving appropriate feedback.

[1374] As a concrete example, by inputting the following prompt sentence into the generative AI model, optimal placement and division of roles will be suggested.

[1375] Example prompt sentence:

[1376] Robot data: ID=R123, Type=Welding robot, Performance=85, Operation status=In operation

[1377] Worker Emotions: Stress Level = High, Health Status = Good

[1378] This system is expected to improve factory work efficiency and productivity, and reduce employee stress and health problems.

[1379] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1380] Step 1:

[1381] Collect data from each employee and work machine.

[1382] Input: Personality test, aptitude test, and information about transfer intentions.

[1383] How it works: The server collects necessary data from each employee and work machine through web forms and dedicated applications. For example, employee A answers a personality test, is judged to be "suited for creative work" through an aptitude test, and enters his / her intention to transfer as "I would like to be transferred to the marketing department."

[1384] Output: Collected personality test results, aptitude test results, transfer request information, and operating status data of work machines.

[1385] Step 2:

[1386] Store the collected data.

[1387] Input: Collected data.

[1388] Operation: The device encrypts this data and sends it to the server, which then stores the received data in an internal database. For example, the results of employee A's personality test, aptitude test, and transfer request information are stored in the database.

[1389] Output: Personality test results, aptitude test results, transfer request information, and operating status data of work machines stored in a database.

[1390] Step 3:

[1391] Recognize user emotions.

[1392] Input: facial expressions and tone of voice.

[1393] How it works: The server uses the device's camera and microphone to analyze the user's facial expressions and tone of voice in real time, and recognizes their emotions using an emotion engine. For example, if employee A feels irritated during a personality test, that emotion is recorded.

[1394] Output: Emotion recognition results.

[1395] Step 4:

[1396] Cleanse and process data.

[1397] Input: Stored data and emotion recognition results.

[1398] How it works: The server cleanses the collected data, which includes removing invalid parts of the data and filling in missing data. The generative AI model then begins the training process based on the cleansed data.

[1399] Output: Cleansed data and training results.

[1400] Step 5:

[1401] Generate a mapping of relationships and work environments.

[1402] Input: Cleansed data and training results.

[1403] How it works: The server uses the generative AI model to generate a mapping of interpersonal relationships and work environments, taking into account personality traits, aptitudes, and perceived emotions. For example, a mapping is generated that takes into account employee A's personality traits and aptitudes.

[1404] Output: Relationship map and work environment map.

[1405] Step 6:

[1406] We propose optimal organizational layout, machine placement, and division of roles.

[1407] Input: Relationship map and work environment map.

[1408] Operation: The server uses a generative AI model to propose optimal organizational arrangements and machine allocations and role assignments, taking into consideration employee compatibility, team balance, work content, emotions, and the operating status and characteristics of machine tools. For example, employee A is assigned as the leader of a new project team in the marketing department, and the server proposes optimal machine allocations and role assignments.

[1409] Output: Placement suggestion.

[1410] Step 7:

[1411] Notify proposals.

[1412] Input: Placement proposal.

[1413] Operation: The server notifies each employee and manager of the proposed organizational layout, machine placement, and role allocation. The content of the notification can also be adjusted based on the emotion recognition results. For example, if employee A is feeling anxious about the transfer, the notification can include a message to ease his / her anxiety.

[1414] Output: Informational message.

[1415] Step 8:

[1416] Collect and analyze feedback.

[1417] Input: Feedback for the notification.

[1418] Operation: Users review the proposed organizational reassignment and provide feedback on a dedicated portal site. For example, employee A reviews the proposed transfer and submits feedback on the timing of the transfer. The server analyzes the collected feedback and reflects it in the next proposal.

[1419] Output: Feedback and analysis results.

[1420] 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.

[1421] 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.

[1422] 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.

[1423] [Fourth embodiment]

[1424] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1425] 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.

[1426] 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).

[1427] 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.

[1428] 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.

[1429] 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).

[1430] 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.

[1431] 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.

[1432] 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.

[1433] 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.

[1434] 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.

[1435] 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.

[1436] 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."

[1437] This invention is a system that proposes optimal organizational placement based on the personality, aptitude, and transfer intention of employees. Specific program processing of the system will be explained below in natural language.

[1438] Data collection

[1439] User (employee)

[1440] Each employee enters information about their personality and aptitude tests, as well as their intention to transfer, via a web form or dedicated application. For example, employee A answers the personality test, is judged to be "suited to creative work" through the aptitude test, and enters their intention to transfer as "to the marketing department."

[1441] Device (employee's PC or smartphone)

[1442] The device sends the collected data to the server, where it is encrypted to ensure the security of the information.

[1443] server

[1444] The server analyzes the received data and stores it in an internal database. For example, it stores the personality test results, aptitude test results, and transfer request information of employee A in the database.

[1445] Data Processing and Training

[1446] server

[1447] The server cleanses the data collected from all employees (deleting invalid data and filling in missing data) and then starts the learning process using generative AI. This learning process generates a mapping of human relationships. For example, a mapping is performed that takes into account employee A's personality traits and aptitudes, and it calculates which team employee A would be best suited to.

[1448] Organizational layout proposal

[1449] server

[1450] Based on the generated interpersonal relationship mapping, the server uses generative AI to create optimal organizational placement proposals. The proposals include the member composition of each team and the placement of new employees. For example, a proposal is generated to place employee A as the leader of a new project team in the marketing department.

[1451] Communicating suggestions and gathering feedback

[1452] server

[1453] The server then sends an email to each employee and manager notifying them of the proposed organizational placement, along with a link to a dedicated portal site where they can review the proposal.

[1454] Users (employees and administrators)

[1455] The user checks the notified organizational placement proposal and provides feedback on the dedicated portal site. For example, employee A checks the transfer proposal and submits feedback on the timing of the transfer.

[1456] server

[1457] The server analyzes the collected feedback and reflects the results in the generating AI, thereby improving the accuracy of the next organizational placement proposal.

[1458] In this way, this system proposes optimal organizational placement based on employee data and uses feedback to improve the accuracy of placement, thereby maximizing the efficiency and results of the entire organization.

[1459] The processing flow will be explained below.

[1460] Step 1:

[1461] User (employee)

[1462] Each employee enters information about their personality and aptitude tests, as well as their intention to transfer, via a web form or dedicated application. For example, employee A answers the personality test, is judged to be "suited to creative work" through the aptitude test, and enters their intention to transfer as "to the marketing department."

[1463] Step 2:

[1464] Device (employee's PC or smartphone)

[1465] The terminal sends the entered data to the server. At this time, the data is encrypted before being sent, so the security of the information is maintained.

[1466] Step 3:

[1467] server

[1468] The server analyzes the received data and stores it in an internal database. For example, it stores the personality test results, aptitude test results, and transfer request information of employee A in the database.

[1469] Step 4:

[1470] server

[1471] The server cleanses the data collected from all employees, which includes removing invalid data and filling in missing data, a process that ensures data accuracy.

[1472] Step 5:

[1473] server

[1474] Based on the cleansed data, the generative AI begins the learning process. The generative AI analyzes information such as personality traits and aptitudes to generate a mapping of human relationships. For example, a mapping is performed that takes into account the personality traits and aptitudes of employee A, and it calculates which team employee A would be best suited to.

[1475] Step 6:

[1476] server

[1477] The server then creates optimal organizational placement proposals based on the generated interpersonal relationship mapping. The generation AI considers employee compatibility, team balance, and job content when proposing placements. For example, a proposal is generated to place employee A as the leader of a new project team in the marketing department.

[1478] Step 7:

[1479] server

[1480] The generated organizational placement proposal will be notified to each employee and manager via email, and a link to a dedicated portal site will be provided so that the proposal can be viewed in detail.

[1481] Step 8:

[1482] Users (employees and administrators)

[1483] The user checks the notified organizational placement proposal and provides feedback on the dedicated portal site. For example, employee A checks the transfer proposal and submits feedback on the timing of the transfer.

[1484] Step 9:

[1485] server

[1486] The server analyzes the collected feedback and reflects the results in the generating AI. This feedback analysis improves the accuracy of the next organizational placement proposal.

[1487] In this way, this system proposes optimal organizational placement based on employee data and uses feedback to improve the accuracy of placement, thereby maximizing the efficiency and results of the entire organization.

[1488] Example 1

[1489] 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."

[1490] With conventional organizational placement systems, it is difficult to make optimal organizational placements that properly reflect employees' personality traits, aptitudes, and transfer intentions. Furthermore, there is no fully established mechanism for effectively utilizing feedback to improve the accuracy of the next placement proposal. As a result, effective organizational placements that maximize employee capabilities are not realized, resulting in issues such as a decline in the efficiency and performance of the entire organization.

[1491] 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.

[1492] In this invention, the server includes means for collecting data on personality assessment, aptitude assessment, and transfer intention from each employee, means for encrypting and storing the collected data, means for cleansing the stored data, means for performing learning using a generative AI model based on the cleansed data and generating a human relationship mapping, means for proposing an optimal organizational placement based on the human relationship mapping, means for notifying each employee and manager of the proposed organizational placement, means for collecting feedback on the notified proposal, and means for analyzing the collected feedback and reflecting it in the generative AI model. This allows for optimal organizational placement that appropriately considers employees' personality traits, aptitude, and transfer intention, and further makes it possible to improve the accuracy of subsequent proposals by utilizing the collected feedback.

[1493] A "personality assessment" is a test or questionnaire used to evaluate an employee's personality traits.

[1494] An "aptitude test" is a test or assessment used to evaluate an employee's job aptitude and skills.

[1495] "Transfer intention" refers to information that indicates the department or job content to which an employee wishes to be transferred.

[1496] "Means of collecting data" refers to the methods and tools used to collect information from employees regarding personality tests, aptitude tests, and transfer intentions.

[1497] "Means for encryption and storage" refers to the method of storing collected data using encryption technology.

[1498] "Cleansing means" refers to a method for removing invalid data from stored data and for filling in missing data.

[1499] "Generative AI model" refers to a generative model using AI, a technology used to generate organizational placement proposals.

[1500] "Interpersonal relationship mapping" is a map that illustrates the interrelationships between employees based on their personality traits and aptitudes.

[1501] "Means for proposing optimal organizational placement" refers to methods and tools that make proposals for placing employees in the optimal departments or teams based on interpersonal relationship mapping.

[1502] "Means of notification" refers to the methods and tools used to inform each employee and manager of the proposed organizational arrangement.

[1503] "Feedback collection measures" are methods and tools used to gather input from employees and managers about proposed organizational arrangements.

[1504] "Means of analysis" refers to the method of analyzing the collected feedback and incorporating the results into the generative AI model.

[1505] The present invention is a system for proposing optimal organizational placement based on the personality traits, aptitudes, and transfer intentions of employees. Specific embodiments for carrying out the present invention will be described below.

[1506] Data collection

[1507] User (employee)

[1508] Each employee uses a dedicated application or web form to enter information about their personality and aptitude tests, as well as their intention to transfer. For example, an employee opens the dedicated application, answers "creative" to the personality test, answers "suitable for creative work" to the aptitude test, and enters "hope to transfer to the marketing department" as their intention to transfer.

[1509] Device (employee's PC or smartphone)

[1510] The terminal receives the data entered by the user, encrypts it using AES encryption technology, and sends it to the server. For example, the personality test result "creative," the aptitude test result "suitable for creative work," and the transfer intention "hope to be in the marketing department" are encrypted and sent.

[1511] Receiving and storing data

[1512] server

[1513] The server receives the data sent from the terminal, decrypts it, and saves it in a database (e.g., MySQL). For example, it saves the employee's personality test results, aptitude test results, and transfer request information in the employees_data table.

[1514] Data Cleansing

[1515] server

[1516] The server uses Python's Pandas library to cleanse the data. Specifically, it removes invalid data and fills in missing data to generate the final dataset. For example, it automatically removes or fills in incorrectly entered data or missing data, cleansing the data for all employees.

[1517] Data Analysis and Learning

[1518] server

[1519] The server then inputs the cleansed data into a generative AI model (e.g., GPT-4) and begins the learning process. Specifically, it generates a relationship mapping that takes into account personality traits, aptitudes, and transfer preferences. For example, the AI ​​can be trained on a dataset containing employee data to determine which employees are best suited to which teams.

[1520] Organizational layout proposal

[1521] server

[1522] The server creates optimal organizational placement proposals based on the human relationship mapping generated by the generative AI model. For example, the AI ​​may determine that "Employee A is the best suited person to be a project leader in the marketing department," and then compile that placement proposal.

[1523] Proposal Notification

[1524] server

[1525] The server notifies each employee and manager of the generated organizational placement proposal by email. It also includes a link to a dedicated portal site where the proposal can be viewed. For example, an email notification of "Project Leader Placement Proposal in the Marketing Department" is sent to employees and managers, along with the URL of the dedicated portal site.

[1526] Gathering feedback

[1527] Users (employees and administrators)

[1528] Users can review the proposed organizational placement and provide feedback on a dedicated portal site. For example, an employee can review the proposal and submit feedback such as "I would like the transfer to occur next month" in a dedicated form.

[1529] Analyzing and incorporating feedback

[1530] server

[1531] The server analyzes the collected feedback and reflects the results in the generative AI model. This improves the accuracy of the next organizational placement proposal. For example, a user's feedback such as "I would like the transfer to occur next month" is analyzed and added to the AI ​​model's learning dataset.

[1532] Prompt Sentence Examples

[1533] As an example of an input prompt for a generative AI model, we will use the following sentence:

[1534] "Based on the following data, please suggest the optimal organizational arrangement.

[1535] data:

[1536] Employee A: Personality test result: "Creative, team player", aptitude test result: "Suitable for creative work", transfer intention: "Want to move to the marketing department"

[1537] It also includes detailed data of other employees.”

[1538] In this way, the system of the present invention proposes optimal organizational placement based on employees' personality traits, aptitudes, and intentions for transfer, and by reflecting feedback in a timely manner, it is possible to maximize the efficiency and results of the entire organization.

[1539] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1540] Step 1:

[1541] User (employee)

[1542] Each employee uses a dedicated application or web form to enter information about their personality and aptitude tests, as well as their intention to transfer. For example, employee A enters their personality test result of "creative," their aptitude test result of "suited for creative work," and their intention to transfer to the marketing department.

[1543] Input and Output

[1544] Input: Personality test results, aptitude test results, intention to transfer

[1545] Output: Information entered into an application or web form

[1546] Step 2:

[1547] Device (employee's PC or smartphone)

[1548] The terminal receives the information entered by the user in real time and encrypts it using AES encryption technology. For example, if employee A enters the personality test result "creative," the aptitude test result "suitable for creative work," and the transfer intention "hope to be transferred to the marketing department," the information is encrypted.

[1549] Input and Output

[1550] Input: Personality test results, aptitude test results, intention to transfer

[1551] Output: Encrypted information

[1552] Step 3:

[1553] Device (employee's PC or smartphone)

[1554] The terminal sends the encrypted information to the server. For example, the information of employee A is encrypted and sent to the server.

[1555] Input and Output

[1556] Input: Encrypted information

[1557] Output: Encrypted information sent to the server

[1558] Step 4:

[1559] server

[1560] The server receives the encrypted information sent from the terminal, decrypts it, and stores it in a database. For example, the personality test results, aptitude test results, and transfer request information for employee A are stored in the employees_data table of a database (e.g., MySQL).

[1561] Input and Output

[1562] Input: Encrypted information

[1563] Output: Decrypted database entries

[1564] Step 5:

[1565] server

[1566] The server uses the Python Pandas library to cleanse the data, remove invalid data, impute missing data, and generate a final cleansed dataset, e.g., impute missing data and remove incorrectly entered data.

[1567] Input and Output

[1568] Input: Employee data

[1569] Output: A cleansed dataset

[1570] Step 6:

[1571] server

[1572] The server inputs the cleansed data into a generative AI model (e.g., GPT-4) and begins the learning process. Specifically, it generates a relationship mapping that takes into account personality traits, aptitude, and transfer preferences. For example, based on a dataset containing employee A's data, the generative AI generates a relationship mapping and determines which team employee A would be best suited to.

[1573] Input and Output

[1574] Input: Cleansed dataset

[1575] Output: Relationship mapping

[1576] Step 7:

[1577] server

[1578] The server creates optimal organizational placement proposals based on the human relationship mapping generated by the generative AI model. For example, the AI ​​may determine that "Employee A is the best suited person to be a project leader in the marketing department," and then compile that placement proposal.

[1579] Input and Output

[1580] Input: Relationship mapping

[1581] Output: Organizational layout proposal

[1582] Step 8:

[1583] server

[1584] The server notifies each employee and manager of the generated organizational placement proposal by email. It also provides a link to a dedicated portal site where the proposal can be viewed. For example, a notification email for "Project Leader Placement Proposal in the Marketing Department" is sent to employee A and the manager.

[1585] Input and Output

[1586] Input: Organizational Allocation Proposal

[1587] Output: Notification email and portal link

[1588] Step 9:

[1589] Users (employees and administrators)

[1590] The user checks the proposed organizational placement and provides feedback on a dedicated portal site. For example, employee A enters feedback such as "I would like to be transferred next month" into a dedicated form and submits it.

[1591] Input and Output

[1592] Input: Feedback on the proposal

[1593] Output: Feedback data

[1594] Step 10:

[1595] server

[1596] The server analyzes the collected feedback and reflects the results in the generative AI model, improving the accuracy of the next organizational placement proposal. For example, the feedback content can be added to the AI ​​model's learning dataset, resulting in a more accurate next placement proposal.

[1597] Input and Output

[1598] Input: Feedback data

[1599] Output: Updated AI model training dataset

[1600] In this way, each processing step is carried out in a concrete manner, and the optimal organizational placement is proposed based on the employee's personality traits, aptitude, and intention to transfer, and feedback can be used to improve the accuracy of the proposal.

[1601] (Application example 1)

[1602] 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."

[1603] Currently, factories are not sufficiently optimizing the placement and task allocation of robots and workers, resulting in a decline in production efficiency. Furthermore, placement that reflects personality assessment, aptitude assessment, and transfer intentions has not been realized, leaving problems in improving the work environment and worker satisfaction. The objective of this invention is to solve these problems and provide a system that realizes optimal placement and task allocation of robots and workers in a factory.

[1604] 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.

[1605] In this invention, the server includes means for collecting data on personality tests, aptitude tests, and transfer intentions from each worker, means for saving the collected data, means for generating a human relationship mapping based on the saved data, means for proposing an optimal organizational placement based on the human relationship mapping, means for notifying each worker and manager of the proposed organizational placement, means for collecting feedback on the notified proposal, means for analyzing the collected feedback and reflecting it in the generation AI, and means for proposing an optimal placement and task assignment of robots in the factory based on the characteristics and capabilities of each worker. This enables placement and task assignment that takes into account the personality traits, aptitude, and transfer intentions of workers.

[1606] "Workers" are personnel who operate and manage robots within a factory, and are people whose personality tests, aptitude tests, and willingness to transfer are taken into consideration.

[1607] A "personality assessment" is a test or analytical tool used to evaluate a worker's personality traits, a process used to determine a worker's aptitude and job suitability.

[1608] An "aptitude test" is a test or assessment tool used to evaluate a worker's suitability for a job, and is a process used to determine what tasks or roles a worker is suited to.

[1609] "Transfer intention" refers to data that indicates a worker's intention or desire to move to a different department or role, and is one of the factors reflected in organizational placement proposals.

[1610] "Factory robots" are mechanical devices used to automate factory work and production processes, and optimizing their placement and task assignment is important to efficiently perform various tasks.

[1611] "Means of collecting data" refers to methods and tools for collecting data on workers' personality tests, aptitude tests, and intentions to transfer, and specifically includes questionnaires, tests, sensors, etc.

[1612] "Means for storing data" means methods and systems for safely and efficiently storing collected data, including databases, cloud storage, and file systems.

[1613] "Means for generating a mapping of human relationships" refers to methods and technologies for analyzing stored data and visualizing the relationships and aptitudes between workers, and specifically includes generative AI algorithms and clustering methods.

[1614] "Means for proposing optimal organizational placement" refers to methods and systems for proposing optimal placement and task allocation of workers and robots based on human relationship mapping, and specifically includes AI models and simulation software.

[1615] "Means for notifying proposals" means a method or system for notifying each worker and manager of the generated placement and task assignment proposals, and specifically includes email, a notification application, and a portal site.

[1616] "Means for collecting feedback" means methods or systems for collecting feedback from workers or managers regarding the notified proposal, and specifically includes forms, questionnaires, and opinion collection systems.

[1617] "Generative AI" refers to artificial intelligence technology for data analysis, mapping generation, placement proposals, etc., and includes advanced algorithms and learning models.

[1618] "Task allocation" is the process of assigning specific tasks to workers or robots, and is a necessary step for achieving efficient work.

[1619] The present invention provides a system for proposing optimal placement and task allocation for robots and workers in a factory. Specific embodiments of the system will be described below.

[1620] 1. Data Collection

[1621] Collecting data from workers

[1622] The user, a worker, enters information about the personality test, aptitude test, and transfer intention through a dedicated application or web form. For example, worker A answers the personality test, is judged to have "aptitude for speedy work" through the aptitude test, and enters "hope for welding department" as his / her intention to transfer.

[1623] Data transmission and storage

[1624] The terminal (the worker's PC or smartphone) encrypts the collected data and sends it to the server. The server analyzes the received data and stores it securely in an internal database. For example, the results of worker A's personality test, aptitude test, and transfer request information are stored in the database.

[1625] 2. Data processing and human relationship mapping

[1626] The server cleanses the data collected from all workers, deleting invalid data and filling in missing data. The generative AI then analyzes the data and begins the learning process to generate a relationship mapping. For example, it determines which team Worker A is best suited to, taking into account Worker A's personality traits and aptitudes.

[1627] 3. Proposal for placement and task allocation

[1628] Based on the generated mapping results of human relationships, the server uses generative AI to create optimal robot and worker placement proposals and task assignments. For example, a proposal is generated to place Worker A as a member of a new project team in the welding department.

[1629] 4. Proposal notification and feedback collection

[1630] Proposal Notification

[1631] The server notifies each worker and manager of the generated placement and task assignment proposals via email, and also provides a URL for a dedicated portal site where the proposals can be viewed.

[1632] Collecting and analyzing feedback

[1633] The users, i.e., workers and managers, can check the notified placement proposal on a dedicated portal site and provide feedback there. For example, worker A checks the placement proposal and submits his / her opinion on the contents of the proposal as feedback.

[1634] The server analyzes the collected feedback and reflects the results in the generating AI to improve the accuracy of the next placement proposal.

[1635] Examples of specific examples and prompts

[1636] For example, you might enter the following prompt for a generative AI model:

[1637] "Please tell me a method to propose optimal placement and task assignment based on the characteristics and capabilities of each robot in a factory. In particular, I would like to know a concrete example using K-means clustering."

[1638] By using this system, production efficiency within the factory can be significantly improved, and worker satisfaction can also be increased.

[1639] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1640] Step 1:

[1641] Data on personality and aptitude tests and transfer intentions are collected from each worker. The user enters this information through a dedicated application or a web form. The entered data includes the worker's personality traits, aptitude for the job, and transfer wishes. The device temporarily stores this data, encrypts it, and sends it to the server.

[1642] Step 2:

[1643] The server receives the collected data and stores it in a database. At this time, it performs data cleansing, deletes invalid data, and fills in missing data. The server safely stores the received personality test results, aptitude test results, and transfer request information in the database.

[1644] Step 3:

[1645] The server analyzes the stored data and generates a mapping of human relationships. Specifically, it uses generative AI to perform cluster analysis based on the personality traits and aptitudes of workers. Here, it extracts data features and recognizes patterns to visualize the relationships between workers. The mapping results are used as the base data for the next processing step.

[1646] Step 4:

[1647] The server generates optimal robot and worker placement proposals and task assignments based on the results of the interpersonal relationship mapping. Using a generative AI model, it outputs placement proposals that make the most of the characteristics of each worker and robot. The input data is the worker's personality traits, aptitude, and transfer intentions, and the output is a specific placement proposal and task assignment.

[1648] Step 5:

[1649] The server notifies each worker and manager of the generated placement and task assignment proposals. Notifications are sent via email, and the worker and manager are also provided with a link to a dedicated portal site. Details of the proposals can be viewed on the portal site.

[1650] Step 6:

[1651] The users, i.e., workers and managers, can check the placement proposals they have been notified of on a dedicated portal site and provide feedback, including how they accepted the proposals and their opinions on areas for improvement.

[1652] Step 7:

[1653] The server analyzes the collected feedback and reflects the results in the generative AI. This is used to improve the accuracy of the next placement proposal. Specifically, the feedback content is added to the dataset and the generative AI model is retrained to improve the system.

[1654] The above processing steps enable optimal placement and task allocation of robots and workers in a factory, thereby improving production efficiency and worker satisfaction.

[1655] 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.

[1656] This invention is a system that proposes optimal organizational placement based on employees' personalities, aptitudes, and transfer intentions, and by combining this with an emotion engine that recognizes the user's emotions, it achieves even more accurate placement. Below, the specific program processing of the system is explained in natural language.

[1657] Data collection

[1658] User (employee)

[1659] Each employee enters information about their personality and aptitude tests, as well as their intention to transfer, via a web form or dedicated application. For example, employee A answers the personality test, is judged to be "suited to creative work" through the aptitude test, and enters their intention to transfer as "to the marketing department."

[1660] Device (employee's PC or smartphone)

[1661] The terminal sends the entered data to the server. At this time, the data is encrypted before being sent, so the security of the information is maintained.

[1662] server

[1663] The server analyzes the received data and stores it in an internal database. For example, it stores the personality test results, aptitude test results, and transfer request information of employee A in the database.

[1664] emotion recognition

[1665] server

[1666] During the data collection process, the device's camera and microphone are used to analyze the user's facial expressions and tone of voice, and the emotion engine recognizes the user's emotions. For example, if employee A feels irritated during a personality test, that emotion is recorded.

[1667] Data Processing and Training

[1668] server

[1669] The server cleanses the data collected from all employees, which includes removing invalid data and filling in missing data, a process that ensures data accuracy.

[1670] server

[1671] Based on the cleansed data, the generative AI begins the learning process. The generative AI analyzes personality traits, aptitudes, and recognized emotions to generate a mapping of human relationships. For example, a mapping is performed that takes into account Employee A's personality traits and aptitudes, and calculates which team Employee A would be best suited to.

[1672] Organizational layout proposal

[1673] server

[1674] The server then creates optimal organizational placement proposals based on the generated interpersonal relationship mapping. The generation AI considers employee compatibility, team balance, work content, and emotions when proposing placements. For example, a proposal is generated to place employee A as the leader of a new project team in the marketing department.

[1675] server

[1676] The proposed organizational relocation is notified to each employee and manager via email, and a link to a dedicated portal site is also provided. The content of the notification can also be adjusted based on the emotion recognition results. For example, if Employee A is feeling anxious about the transfer, a message to ease his / her feelings can be included.

[1677] Reviewing proposals and gathering feedback

[1678] Users (employees and administrators)

[1679] The user checks the notified organizational placement proposal and provides feedback on the dedicated portal site. For example, employee A checks the transfer proposal and submits feedback on the timing of the transfer.

[1680] server

[1681] The server analyzes the collected feedback and reflects it in the generation AI. This feedback analysis improves the accuracy of the next organizational placement proposal. The emotion engine recognizes the user's emotions even when providing feedback, and supports them in receiving appropriate feedback.

[1682] In this way, the system proposes optimal organizational placement based on employee data and emotions, and uses feedback to improve the accuracy of placement, thereby maximizing the efficiency and results of the entire organization.

[1683] The processing flow will be explained below.

[1684] Step 1:

[1685] User (employee)

[1686] Each employee enters information about their personality and aptitude tests, as well as their intention to transfer, via a web form or dedicated application. For example, employee A answers the personality test, is judged to be "suited to creative work" through the aptitude test, and enters their intention to transfer as "to the marketing department."

[1687] Step 2:

[1688] Device (employee's PC or smartphone)

[1689] The terminal sends the entered data to the server. At this time, the data is encrypted before being sent, so the security of the information is maintained.

[1690] Step 3:

[1691] server

[1692] The server analyzes the received data and stores it in an internal database. For example, it stores the personality test results, aptitude test results, and transfer request information of employee A in the database.

[1693] Step 4:

[1694] Terminal

[1695] During the data collection process, the device's camera and microphone are used to analyze the user's facial expressions and tone of voice, and the emotion engine recognizes the user's emotions. For example, if employee A feels irritated during a personality test, that emotion is recorded.

[1696] Step 5:

[1697] server

[1698] The server cleanses the data collected from all employees, which includes removing invalid data and filling in missing data, a process that ensures data accuracy.

[1699] Step 6:

[1700] server

[1701] Based on the cleansed data, the generative AI begins the learning process. The generative AI analyzes personality traits, aptitudes, and recognized emotions to generate a mapping of human relationships. For example, a mapping is performed that takes into account Employee A's personality traits and aptitudes, and calculates which team Employee A would be best suited to.

[1702] Step 7:

[1703] server

[1704] The server then creates optimal organizational placement proposals based on the generated interpersonal relationship mapping. The generation AI considers employee compatibility, team balance, work content, and emotions when proposing placements. For example, a proposal is generated to place employee A as the leader of a new project team in the marketing department.

[1705] Step 8:

[1706] server

[1707] The proposed organizational relocation is notified to each employee and manager via email, and a link to a dedicated portal site is also provided. The content of the notification can also be adjusted based on the emotion recognition results. For example, if Employee A is feeling anxious about the transfer, a message to ease his / her feelings can be included.

[1708] Step 9:

[1709] Users (employees and administrators)

[1710] The user checks the notified organizational placement proposal and provides feedback on the dedicated portal site. For example, employee A checks the transfer proposal and submits feedback on the timing of the transfer.

[1711] Step 10:

[1712] server

[1713] The server analyzes the collected feedback and reflects it in the generation AI. This feedback analysis improves the accuracy of the next organizational placement proposal. The emotion engine recognizes the user's emotions even when providing feedback, and supports them in receiving appropriate feedback.

[1714] In this way, the system proposes optimal organizational placement based on employee data and emotions, and uses feedback to improve the accuracy of placement, thereby maximizing the efficiency and results of the entire organization.

[1715] Example 2

[1716] 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."

[1717] Conventional organizational placement systems were unable to make appropriate placement recommendations because they had difficulty taking into account employees' personality traits, aptitude, and transfer intentions. Furthermore, they lacked emotion recognition technology, making it impossible to reflect users' emotions in organizational placement. Furthermore, there was a lack of means to effectively utilize feedback, making it difficult to improve the accuracy of recommendations.

[1718] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for collecting data on personality assessment, aptitude assessment, and transfer intentions from each user; means for saving the collected data; means for analyzing the saved data and saving it in an internal database; means for analyzing the user's facial expressions and tone of voice using the device's camera and microphone and recognizing the user's emotions using an emotion engine; means for starting a learning process by the generation AI based on the cleansed data; means for analyzing personality traits, aptitude, and recognized emotions and generating a relationship mapping; means for creating an optimal organizational placement proposal based on the generated relationship mapping; means for notifying each user and administrator of the proposed organizational placement by email and providing a link to a dedicated portal site; means for collecting feedback on the notified proposal; and means for analyzing the collected feedback and reflecting it in the generation AI. This enables highly accurate organizational placement proposals that take into account employees' personality traits, aptitudes, and emotions.

[1719] "User" refers to individuals, especially employees, who use the system.

[1720] A "personality test" is an assessment test for measuring the personality traits of an individual user.

[1721] An "aptitude test" is a test that assesses how suited a user is to a particular job or role.

[1722] "Intention to transfer" refers to the user's intentions and wishes regarding the desired transfer destination and position.

[1723] "Collection methods" refers to software or hardware functions used to collect user data.

[1724] "Storage means" refers to the function for accumulating and safely storing collected data.

[1725] A "database" is a system for efficiently managing and accessing stored data.

[1726] A "camera" is an imaging device for capturing a user's facial expression.

[1727] A "microphone" is a device that picks up the user's voice and acquires it as audio data.

[1728] The "emotion engine" is software that analyzes the user's emotions from collected facial and voice data.

[1729] "Cleansing" is the process of removing invalid parts of data and filling in missing data.

[1730] "Generative AI" refers to artificial intelligence that analyzes data through a learning process and generates optimal suggestions.

[1731] The "learning process" is the process by which the generative AI repeatedly learns based on data.

[1732] "Relationship mapping" refers to a map that visually or abstractly represents the relationships between users.

[1733] An "organizational placement proposal" is an optimal placement proposal generated in consideration of the user's personality traits, aptitude, emotions, and the like.

[1734] "Notification means" refers to software or functions for informing each user or administrator of the proposed content.

[1735] "Portal Site" means a website that Users can access to review Proposals and provide Feedback.

[1736] "Feedback" refers to the opinions and thoughts that users provide regarding suggestions.

[1737] "Analysis" is the process of examining and understanding the collected data and feedback.

[1738] The present invention is a system that proposes optimal organizational placement based on the user's personality traits, aptitude, and transfer intentions, and improves the placement accuracy by combining it with an emotion recognition engine. This system is mainly operated by a server, terminals, and users.

[1739] Data collection

[1740] The user (employee) enters information about the personality test, aptitude test, and intention to transfer through a web form or dedicated application. This information is encrypted and sent to the server via the user's device (PC or smartphone). As a concrete example, employee A answers the personality test and enters the result that he or she is "outgoing and values ​​teamwork."

[1741] Data analysis and storage

[1742] The server analyzes the received data and stores it in an internal database. The stored data includes personality test results, aptitude test results, and transfer intentions. This allows detailed data on each user to be safely stored.

[1743] emotion recognition

[1744] The server analyzes the user's facial expressions and tone of voice using the device's camera and microphone. The emotion engine recognizes the user's emotions, and this data is also stored in an internal database. For example, if employee A feels dissatisfied during a personality test, the camera captures his or her facial expression, and the emotion engine identifies it as "dissatisfied."

[1745] Data Cleansing and Learning

[1746] The server cleanses the data collected from all users, which includes removing invalid data and filling in missing data, ensuring the accuracy of the data.

[1747] Next, the generative AI begins the learning process based on the cleansed data. The generative AI analyzes personality traits, aptitudes, and recognized emotions to generate a relationship mapping. For example, the mapping is performed to place employee A in the optimal team, taking into account employee A's personality traits and aptitudes.

[1748] Organizational Allocation Proposal and Notification

[1749] The server then creates optimal organizational placement proposals based on the generated interpersonal relationship mapping. The generation AI considers the user's compatibility, team balance, work content, and emotions when proposing placements. For example, a proposal is made to place employee A as the leader of a new project team in the marketing department.

[1750] The proposed organizational placement is notified to each user and administrator via email, and a link to a dedicated portal site is provided. The content of the notification may be adjusted based on the emotion recognition results. For example, if employee A is anxious about the transfer, a message to ease his or her anxiety may be included.

[1751] Collecting and analyzing feedback

[1752] The user checks the notified organizational placement proposal and provides feedback on the dedicated portal site. For example, employee A checks the transfer proposal and submits feedback on the timing of the transfer.

[1753] The server analyzes the collected feedback and reflects it in the generation AI. This process further improves the accuracy of the next organizational placement proposal. The emotion engine recognizes the user's emotions even when providing feedback, and supports them in receiving appropriate feedback.

[1754] Prompt Sentence Examples

[1755] Analyze the following user information and propose the optimal organizational layout.

[1756] Personality test result: Introverted and good at detailed work

[1757] Aptitude test result: Aptitude for data analysis

[1758] Willingness to transfer: Data analysis team preferred

[1759] Sentiment: Positive about the transfer

[1760] The present invention makes it possible to make highly accurate organizational placement proposals that take into account the user's personality traits, aptitudes, and emotions, thereby improving user satisfaction.

[1761] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1762] Step 1: Data entry

[1763] User (employee)

[1764] Input: Personality test, aptitude test, and information about transfer intentions

[1765] How it works: Users enter this information through a web form or a dedicated application.

[1766] Output: The input data is stored on the user's terminal.

[1767] Step 2: Send data

[1768] Device (employee's PC or smartphone)

[1769] Input: Personality test results, aptitude test results, and data regarding transfer intentions entered by the user

[1770] Operation: The terminal encrypts the entered data and sends it to the server.

[1771] Output: The encrypted data is sent to the server.

[1772] Step 3: Save data

[1773] server

[1774] Input: Encrypted data sent from the device

[1775] How it works: The server decrypts the data it receives and stores it in an internal database.

[1776] Output: The decoded data is stored in the database.

[1777] Step 4: Emotion Recognition

[1778] server

[1779] Input: User facial expression and voice data collected through the device's camera and microphone

[1780] How it works: The server uses an emotion engine to analyze the user's facial expressions and tone of voice to recognize their emotions.

[1781] Output: The recognized emotion data is stored in the internal database.

[1782] Step 5: Data cleansing

[1783] server

[1784] Input: All collected user data (personality traits, aptitudes, emotions, etc.)

[1785] What it does: The server cleanses the data, removing invalid parts and filling in missing data.

[1786] Output: Cleansed and accurate data is produced.

[1787] Step 6: Generative AI training

[1788] server

[1789] Input: Cleansed data

[1790] How it works: The server initiates the learning process with the generative AI, analyzing personality traits, aptitudes, and emotions.

[1791] Output: Relationship mapping data is generated.

[1792] Step 7: Generate organizational alignment proposals

[1793] server

[1794] Input: Relationship mapping data

[1795] How it works: Creates optimal organizational placement proposals based on generative AI.

[1796] Output: Proposal data for optimal organizational placement is generated.

[1797] Step 8: Proposal Notification

[1798] server

[1799] Input: Data proposing optimal organizational layout

[1800] How it works: The server notifies each user and administrator of the proposed data via email and provides a link to a dedicated portal site. The content of the notification may be adjusted based on the emotion recognition results.

[1801] Output: A notification email is sent to the user and administrator.

[1802] Step 9: Gather feedback

[1803] Users (employees and administrators)

[1804] Input: Notified organizational placement proposal

[1805] How it works: Users review suggestions and provide feedback on a dedicated portal site.

[1806] Output: The feedback data is sent to the server.

[1807] Step 10: Feedback analysis and implementation

[1808] server

[1809] Input: Collected feedback data

[1810] How it works: The server analyzes the feedback and applies it to the generative AI, which improves the accuracy of the next suggestion.

[1811] Output: The accuracy of the next organizational placement proposal will be improved.

[1812] (Application example 2)

[1813] 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."

[1814] Conventional organizational placement systems were limited to proposing placements based on employees' personality traits, aptitudes, and transfer intentions, and were unable to achieve optimal placements and role allocations that took into account the characteristics of the work environment and work machines. Furthermore, they lacked a mechanism for grasping the emotions and operating status of employees and work machines in real time and responding flexibly. These issues led to reduced factory efficiency and productivity, and risk of employee stress and health problems. To solve these problems, the present invention aims to provide a system that makes optimal placement proposals that comprehensively consider the characteristics and emotional states of employees and work machines.

[1815] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1816] In this invention, the server includes means for collecting data on personality diagnosis, aptitude diagnosis, and transfer intentions from each employee and work machine, means for storing the collected data and work machine operating status data, and means for generating a mapping of human relationships and work environments based on the stored data. This enables optimal organizational layout and work machine allocation / role allocation to maximize the efficiency and results of the entire organization.

[1817] A "personality assessment" is a test or analytical method for assessing and classifying each employee's personality traits.

[1818] "Aptitude diagnosis" refers to tests and analytical methods used to evaluate each employee's suitability and skills for their job.

[1819] "Intention to transfer" refers to an employee's desire to move from their current job or department to a new job or department.

[1820] "Work machine" refers to automated work equipment, including machines and robots, used in factories.

[1821] "Operating status" is information that indicates how the work machine is currently operating, whether it is operating or stopped, or its performance level, or other such status.

[1822] "Data collection means" refers to the systems and tools used to obtain necessary information from each employee and work machine.

[1823] "Data storage means" refers to the systems and databases used to safely store and manage collected data.

[1824] "Interpersonal relationship mapping" is a map that visually represents the interrelationships between employees based on their personality traits and aptitudes.

[1825] "Work environment mapping" is a map that visually summarizes the location and operating status of work machines.

[1826] "Organizational placement" refers to assigning employees to the most suitable departments and jobs based on their aptitude and personality traits.

[1827] "Role allocation" refers to the allocation of appropriate tasks to work machines and employees to achieve efficient work.

[1828] "Notification methods" are systems and tools used to inform employees and managers of proposed placements and role assignments.

[1829] An "emotion engine" is an algorithm or system that recognizes emotions by analyzing a user's facial expressions, tone of voice, etc.

[1830] "Feedback collection tools" are systems and tools used to collect opinions and reactions from employees and managers regarding notified proposals.

[1831] "Generative AI" is artificial intelligence that analyzes collected data, generates a mapping of human relationships and work environments, and proposes optimal placement and division of roles.

[1832] To implement this invention, the following system is required: This system includes a means for collecting and analyzing data from each employee and work machine, and a generative AI for proposing optimal organizational arrangements and role allocations.

[1833] First, the server collects data on personality and aptitude tests, as well as transfer intentions, from each employee and work machine. This data is collected through web forms and dedicated applications. For example, employee A answers a personality test, is judged to be "suited to creative work" through the aptitude test, and enters "I would like to be transferred to the marketing department" as his or her transfer intention.

[1834] Next, the device (employee's PC or smartphone) sends the collected data to the server. The data is encrypted before transmission, ensuring the security of the information. The server analyzes the received data and stores it in an internal database. For example, the results of employee A's personality test, aptitude test, and transfer request information are stored in the database.

[1835] Furthermore, during the data collection process, the server uses the device's camera and microphone to analyze the user's facial expressions and tone of voice, and uses an emotion engine to recognize the user's emotions. For example, if employee A feels irritated during the personality test, that emotion will be recorded.

[1836] Next, the server cleanses the data collected from all employees. This process includes deleting invalid data and filling in missing data to ensure data accuracy. Based on the cleansed data, the generative AI begins the learning process. The generative AI analyzes personality traits, aptitudes, and recognized emotions to generate a mapping of interpersonal relationships and work environments. For example, a mapping is performed taking into account Employee A's personality traits and aptitudes, and it calculates which team and work environment Employee A is best suited to.

[1837] The server then proposes optimal organizational layouts and machine placements and role allocations based on the generated mapping of human relationships and work environments. The generation AI proposes placements taking into consideration employee compatibility, team balance, work content, emotions, and the operating status and characteristics of machine placements. For example, employee A is placed as the leader of a new project team in the marketing department, and the server proposes optimal machine placements and role allocations.

[1838] The server then notifies each employee and manager of the proposed organizational layout, machine placement, and role allocation. The content of the notification can also be adjusted based on the emotion recognition results. For example, if employee A is feeling anxious about the transfer, the notification can include a message to ease his or her anxiety.

[1839] Users can review the proposed organizational transfer and provide feedback on the dedicated portal site. For example, employee A reviews the proposed transfer and submits feedback on the timing of the transfer.

[1840] Finally, the server analyzes the collected feedback and reflects it in the generating AI. This feedback analysis improves the accuracy of the next organizational layout and machine placement / role allocation proposals. The emotion engine recognizes the user's emotions even when providing feedback, and supports receiving appropriate feedback.

[1841] As a concrete example, by inputting the following prompt sentence into the generative AI model, optimal placement and division of roles will be suggested.

[1842] Example prompt sentence:

[1843] Robot data: ID=R123, Type=Welding robot, Performance=85, Operation status=In operation

[1844] Worker Emotions: Stress Level = High, Health Status = Good

[1845] This system is expected to improve factory work efficiency and productivity, and reduce employee stress and health problems.

[1846] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1847] Step 1:

[1848] Collect data from each employee and work machine.

[1849] Input: Personality test, aptitude test, and information about transfer intentions.

[1850] How it works: The server collects necessary data from each employee and work machine through web forms and dedicated applications. For example, employee A answers a personality test, is judged to be "suited for creative work" through an aptitude test, and enters his / her intention to transfer as "I would like to be transferred to the marketing department."

[1851] Output: Collected personality test results, aptitude test results, transfer request information, and operating status data of work machines.

[1852] Step 2:

[1853] Store the collected data.

[1854] Input: Collected data.

[1855] Operation: The device encrypts this data and sends it to the server, which then stores the received data in an internal database. For example, the results of employee A's personality test, aptitude test, and transfer request information are stored in the database.

[1856] Output: Personality test results, aptitude test results, transfer request information, and operating status data of work machines stored in a database.

[1857] Step 3:

[1858] Recognize user emotions.

[1859] Input: facial expressions and tone of voice.

[1860] How it works: The server uses the device's camera and microphone to analyze the user's facial expressions and tone of voice in real time, and recognizes their emotions using an emotion engine. For example, if employee A feels irritated during a personality test, that emotion is recorded.

[1861] Output: Emotion recognition results.

[1862] Step 4:

[1863] Cleanse and process data.

[1864] Input: Stored data and emotion recognition results.

[1865] How it works: The server cleanses the collected data, which includes removing invalid parts of the data and filling in missing data. The generative AI model then begins the training process based on the cleansed data.

[1866] Output: Cleansed data and training results.

[1867] Step 5:

[1868] Generate a mapping of relationships and work environments.

[1869] Input: Cleansed data and training results.

[1870] How it works: The server uses the generative AI model to generate a mapping of interpersonal relationships and work environments, taking into account personality traits, aptitudes, and perceived emotions. For example, a mapping is generated that takes into account employee A's personality traits and aptitudes.

[1871] Output: Relationship map and work environment map.

[1872] Step 6:

[1873] We propose optimal organizational layout, machine placement, and division of roles.

[1874] Input: Relationship map and work environment map.

[1875] Operation: The server uses the generative AI model to propose optimal organizational arrangements and machine allocations and role assignments, taking into consideration employee compatibility, team balance, work content, emotions, and the operating status and characteristics of machine tools. For example, employee A is assigned as the leader of a new project team in the marketing department, and the server proposes optimal machine allocations and role assignments.

[1876] Output: Placement suggestion.

[1877] Step 7:

[1878] Notify proposals.

[1879] Input: Placement proposal.

[1880] Operation: The server notifies each employee and manager of the proposed organizational layout, machine placement, and role allocation. The content of the notification can also be adjusted based on the emotion recognition results. For example, if employee A is feeling anxious about the transfer, the notification can include a message to ease his / her anxiety.

[1881] Output: Informational message.

[1882] Step 8:

[1883] Collect and analyze feedback.

[1884] Input: Feedback for the notification.

[1885] Operation: Users review the proposed organizational reassignment and provide feedback on a dedicated portal site. For example, employee A reviews the proposed transfer and submits feedback on the timing of the transfer. The server analyzes the collected feedback and reflects it in the next proposal.

[1886] Output: Feedback and analysis results.

[1887] 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.

[1888] 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.

[1889] 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.

[1890] 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.

[1891] 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.

[1892] 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.

[1893] 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).

[1894] 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.

[1895] 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."

[1896] 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.

[1897] 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).

[1898] 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.

[1899] 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.

[1900] 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.

[1901] 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.

[1902] 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.

[1903] 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.

[1904] 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.

[1905] 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.

[1906] 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.

[1907] 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.

[1908] The following is further disclosed regarding the above embodiment.

[1909] (Claim 1)

[1910] A means of collecting data from each employee regarding personality test, aptitude test, and transfer intention;

[1911] a means of storing the collected data; and

[1912] A means for generating a mapping of human relationships based on the stored data;

[1913] A method to propose optimal organizational layout based on human relationship mapping,

[1914] a means of informing each employee and manager of the proposed organizational arrangement;

[1915] A means of gathering feedback on the notified proposals; and

[1916] A means to analyze the collected feedback and reflect it in the generative AI,

[1917] A system including:

[1918] (Claim 2)

[1919] 10. The system of claim 1, wherein the proposed organizational placement is generated taking into consideration the employee's personality traits, aptitude, and transfer intentions.

[1920] (Claim 3)

[1921] The system of claim 1, further comprising: improving accuracy of subsequent tissue placement suggestions based on said feedback.

[1922] "Example 1"

[1923] (Claim 1)

[1924] A means of collecting data from each employee regarding personality test, aptitude test, and transfer intention;

[1925] The means to encrypt and store the collected data;

[1926] a means for cleansing the stored data;

[1927] A means for generating a relationship mapping by using a generative AI model to learn from the cleansed data;

[1928] A method to propose optimal organizational layout based on human relationship mapping,

[1929] a means of informing each employee and manager of the proposed organizational arrangement;

[1930] A means of gathering feedback on the notified proposals; and

[1931] A means to analyze the collected feedback and incorporate it into the generative AI model;

[1932] A system including:

[1933] (Claim 2)

[1934] 10. The system of claim 1, wherein the proposed organizational placement is generated taking into consideration the employee's personality traits, aptitude, and transfer intentions.

[1935] (Claim 3)

[1936] The system of claim 1, further comprising: improving accuracy of subsequent tissue placement suggestions based on said feedback.

[1937] "Application Example 1"

[1938] (Claim 1)

[1939] A means for collecting data on personality test, aptitude test, and transfer intention from each worker;

[1940] a means of storing the collected data; and

[1941] A means for generating a mapping of human relationships based on the stored data;

[1942] A method to propose optimal organizational layout based on human relationship mapping,

[1943] a means for notifying each worker and manager of the proposed org...

Claims

1. A means of collecting data from each employee regarding personality test, aptitude test, and transfer intention; a means of storing the collected data; and A means for generating a mapping of human relationships based on the stored data; A method to propose optimal organizational layout based on human relationship mapping, a means of informing each employee and manager of the proposed organizational arrangement; A means of gathering feedback on the notified proposals; and A means to analyze the collected feedback and reflect it in the generative AI, A system including:

2. The system of claim 1 , wherein the proposed organizational placement is generated taking into consideration the employee's personality traits, aptitude, and transfer intentions.

3. The system of claim 1 , further comprising: a system for improving accuracy of subsequent tissue placement suggestions based on said feedback.

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