system

Abstract

We provide the system. [Solution] A means of inputting data that indicates an individual's abilities, A means of inputting data that shows the operational requirements of each department within the organization, A method for comparing individual ability data with departmental work requirements data and calculating the degree of agreement between them, A means of assigning each individual to the most appropriate department based on the degree of agreement, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a conventional personnel allocation system, the skills possessed by individual employees do not match the job requirements of each department appropriately, and employees are often not assigned to departments where they can发挥 their maximum capabilities. As a result, problems such as a decline in employee motivation and productivity have arisen. Solving such problems and enabling individual employees to make the most of their abilities is an issue.

Means for Solving the Problems

[0005] This invention provides a means for inputting data indicating an individual's abilities and data indicating the operational requirements of each department within an organization, comparing them, and calculating the degree of agreement. Specifically, the individual's ability data is represented as a list of skills, and the departmental operational requirements data is similarly represented as a list of skills. Based on this data, the degree of agreement is calculated, and the system provides a system for assigning each individual to the most appropriate department based on that degree of agreement. As a result, employees are transferred to departments where they can best utilize their abilities, and the overall productivity of the organization is improved.

[0006] "Individual competency data" refers to information that expresses an employee's skills and expertise in a list format.

[0007] "Business requirements data for each department within the organization" refers to information that expresses the skills and expertise required by each department in a list format.

[0008] "Calculating the degree of agreement" is the process of comparing an individual's ability data with the work requirements data of each department and expressing the degree of agreement numerically or in other ways.

[0009] "Matching methods" refer to methods and algorithms for assigning employees to the most suitable departments based on the degree of match between individual skill data and job requirements data.

[0010] A "skills list" is a data structure that enumerates the specific skills and knowledge that an individual or department possesses or needs.

[0011] The "number of intersection elements" refers to the number of elements that are common to both lists and is used in calculating the degree of similarity. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3]It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

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

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

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

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

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

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

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

[0020] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0033] The embodiments for carrying out the present invention will be described in detail below. This system uses individual capability data and operational requirements data for each department within an organization to assign each individual to the most appropriate department.

[0034] System Configuration

[0035] This system consists of the following main components:

[0036] 1. Employee Information Input Method: This is a method by which users input employee names and skill lists using a terminal.

[0037] 2. Department Information Input Method: This is a method by which users use a terminal to input the name of each department and a list of the skills required by that department.

[0038] 3. Skill Matching Calculation Method: This method involves the server comparing the employee's skill list with the department's required skill list and calculating the degree of match.

[0039] 4. Optimal Department Assignment Method: This method assigns each employee to the most suitable department based on the calculated degree of matching by the server.

[0040] 5. Result Output Means: This means by which the server calculates the optimal department assignment result and reports it to the user.

[0041] Program processing

[0042] Employee Information Input: Users input employee names and a list of their skills into the system via a terminal. For example, if an employee named "John" has the skills of "Python," "Data Analysis," and "Project Management," they would input that information.

[0043] Department Information Input: Users input the name of each department and a list of skills required by that department into the system via a terminal. For example, if the "Data Science" department requires "Python" and "Data Analysis" skills, they would input those.

[0044] Skill Match Calculation: The server compares the employee's skill list with the department's required skills list. Specifically, it calculates the number of intersection elements between the employee's skills and the skills required by the department. For example, if "Python" and "Data Analysis" from "John's" skill list match the required skills list for "Data Science," the match score is calculated as 2.

[0045] Optimal Department Assignment: The server assigns each employee to the most suitable department based on the calculated degree of match. For example, employee "John" is assigned to the "Data Science" department.

[0046] Result Output: The server calculates the optimal department assignment and reports it to the user. For example, it might output a result such as, "John has been assigned to the Data Science department."

[0047] Specific example

[0048] For example, consider a case where a user enters the following data.

[0049] Employee information:

[0050] Employee 1: Name "John", Skills "Python", "Data Analysis", "Project Management"

[0051] Employee 2: Name "Emily", Skills "Java (registered trademark)", "System Design", "Debugging"

[0052] Department information:

[0053] Department 1: "Data Science", Required skills: "Python", "Data Analysis"

[0054] Department 2: "Software Development", Required skills: "Java", "System Design"

[0055] The server calculates the skill match as follows:

[0056] "John" and "Data Science": Similarity level 2 ("Python" and "Data Analysis")

[0057] "Emily" and "Software Development": Match level 2 ("Java," "System Design")

[0058] Based on the calculation results, the server assigns "John" to the "Data Science" department and "Emily" to the "Software Development" department, and outputs the results to the user.

[0059] In this way, this system enables the placement of individual employees into departments where they can best utilize their abilities.

[0060] The following describes the processing flow.

[0061] Step 1:

[0062] The user uses a terminal to enter a list of employee names and skills. For example, they might enter the name "John" and the skills "Python," "Data Analysis," and "Project Management." The entered information is then sent to the server.

[0063] Step 2:

[0064] The user uses a terminal to enter the names of each department and a list of the skills required by that department. For example, they might enter the department name "Data Science" and the required skills "Python" and "Data Analysis". The entered information is then sent to the server.

[0065] Step 3:

[0066] The server stores the entered employee and department information as lists. This ensures that each employee's skill list and each department's required skill list are saved in the database.

[0067] Step 4:

[0068] The server compares each employee's skill list with the required skill list for each department and calculates the number of matching skills as the degree of match. For example, if "Python" and "Data Analysis" match the required skill list for "Data Science" in "John's" skill list, the degree of match is calculated as 2.

[0069] Step 5:

[0070] The server calculates the degree of match for each employee with all departments and identifies the department with the highest degree of match. For example, in the case of "John," the server assigns "John" to the "Data Science" department because the degree of match is highest with the "Data Science" department.

[0071] Step 6:

[0072] The server generates the assignment result to the most suitable department and reports it to the user. For example, it might output a result such as, "John has been assigned to the Data Science department."

[0073] Step 7:

[0074] The user uses their terminal to check the results reported from the server and perform the necessary transfer procedures.

[0075] (Example 1)

[0076] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0077] Placing employees in the most suitable departments based on their skills and abilities is a critical challenge for many companies. However, traditional manual placement methods are time-consuming and sometimes result in inappropriate placements. Furthermore, there is a lack of automated systems to efficiently perform overall skill matching. Solving this challenge is essential to maximizing the use of internal resources and improving operational efficiency.

[0078] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0079] In this invention, the server includes means for inputting data indicating an individual's capabilities, means for inputting data indicating the operational requirements of each department within the organization, means for comparing the individual's capabilities data with the departmental operational requirements data and calculating the degree of agreement between them, means for assigning each individual to the most appropriate department based on the degree of agreement, and means for reporting the result of the assignment to the optimal department. This makes it possible to assign employees to departments based on their individual capabilities.

[0080] "Data indicating individual capabilities" refers to information that lists the skills, qualifications, experience, and other abilities that each individual possesses.

[0081] "Data showing the operational requirements of each department within an organization" refers to information that lists the operational requirements, such as the skills, qualifications, and experience needed for each department.

[0082] A "means for calculating the degree of agreement" refers to an algorithm or calculation system that compares individual ability data with departmental work requirements data and calculates the number of commonalities between the two.

[0083] The "assignment method" is a system that automatically places individuals in the most suitable departments based on a calculated degree of matching.

[0084] "Means of reporting" refers to systems or interfaces that notify users of the results of their placement into the most suitable department.

[0085] Modes for carrying out the invention

[0086] The embodiments for carrying out the present invention are described in detail below. This system utilizes individual capability data and operational requirements data for each department within an organization to assign each individual to the most appropriate department. The system consists of the following main components:

[0087] 1. Employee Information Input Method: This is a method by which users input employee names and skill lists using a terminal. Users use a dedicated form to input employee names and skills. For example, if an employee named "John" has the skills of "Python," "Data Analysis," and "Project Management," the user would input that information.

[0088] 2. Department Information Input Method: This is a method for users to input the name of each department and a list of skills required by that department using a terminal. Users use a dedicated form to input the department name and required skills. For example, if the "Data Science" department requires "Python" and "Data Analysis" skills, the user would input those.

[0089] 3. Skill Match Calculation Method: This method involves the server comparing an employee's skill list with the department's required skill list and calculating the degree of match. Specifically, the server calculates the number of common elements between the employee's skills and the department's required skills. This calculation is performed using a specific algorithm.

[0090] 4. Optimal Department Assignment Method: This method assigns each employee to the most suitable department based on the match score calculated by the server. The server selects the department with the highest match score for each employee and assigns the employee to that department.

[0091] 5. Result Output Method: This is a method by which the server calculates the optimal department assignment result and reports it to the user. For example, the user is provided with information such as, "John has been assigned to the Data Science department."

[0092] Specific usage instructions

[0093] Consider a scenario where the user enters the following data.

[0094] Employee information:

[0095] Employee 1: Name "John", Skills "Python", "Data Analysis", "Project Management"

[0096] Employee 2: Name "Emily", Skills "Java", "System Design", "Debugging"

[0097] Department information:

[0098] Department 1: "Data Science", Required skills: "Python", "Data Analysis"

[0099] Department 2: "Software Development", Required Skills: "Java", "System Design"

[0100] The server calculates skill match as follows:

[0101] "John" and "Data Science": Similarity level 2 ("Python" and "Data Analysis")

[0102] "Emily" and "Software Development": Match level 2 ("Java," "System Design")

[0103] Based on the calculation results, the server assigns "John" to the "Data Science" department and "Emily" to the "Software Development" department, and outputs the results to the user.

[0104] Examples of prompt statements

[0105] The following are specific examples of prompt statements for a generative AI model.

[0106] Employee information entry:

[0107] User: "Please enter an employee's skills list based on the following information. Example: Name: John, Skills: "Python", "Data Analysis", "Project Management""

[0108] In a specific action, the user enters employee information into a designated form on the terminal.

[0109] Department information entry:

[0110] User: "Please enter the department's skill list based on the following information. Example: Department: Data Science, Required Skills: "Python", "Data Analysis""

[0111] In a specific action, the user enters departmental information into a designated form on the terminal.

[0112] This system allows companies to efficiently assign employees to the most suitable departments based on their skills and qualifications. Furthermore, by automating the skills matching process, it significantly reduces the effort and time required.

[0113] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0114] Step 1:

[0115] Entering employee information

[0116] User: Enter the employee's name and a list of their skills via the terminal. For example, if an employee named "John" has the skills of "Python," "Data Analysis," and "Project Management," enter that information.

[0117] Input: Employee name "John", Skills "Python", "Data Analysis", "Project Management"

[0118] Specific action: The user enters the required employee information into the input form on the terminal and clicks the submit button.

[0119] Output: The entered employee information is sent to the server and stored in the database.

[0120] Step 2:

[0121] Entering department information

[0122] User: Enter the name of each department and a list of skills required by that department via the terminal. For example, if the "Data Science" department requires "Python" and "Data Analysis" skills, enter that information.

[0123] Input: Department name "Data Science", Required skills "Python", "Data Analysis"

[0124] Specific action: The user enters the required department information into the input form on the terminal and clicks the submit button.

[0125] Output: The entered department information is sent to the server and stored in the database.

[0126] Step 3:

[0127] Calculation of skill match

[0128] Server: Compares employee skill lists with departmental skill list requirements and calculates the degree of agreement. Specifically, it calculates the number of intersection elements.

[0129] Input: Skill list for each employee, required skill list for each department

[0130] Specific operation: The server retrieves employee skill lists and departmental skill list requirements from the database, compares them, and runs an algorithm to calculate the degree of match.

[0131] Output: A list of matching scores for each employee and department is generated.

[0132] Step 4:

[0133] Assignment to the optimal department

[0134] Server: Assigns employees to the most suitable department based on the calculated degree of match.

[0135] Input: List of employee and departmental matching levels

[0136] Specific operation: The server determines the optimal department for each employee based on the matching score list and automatically assigns them. Assignment priority is determined by the matching score value.

[0137] Output: A list of assignment results to the most suitable department is generated.

[0138] Step 5:

[0139] Output of results

[0140] Server: Reports the user the result of assigning them to the most suitable department. For example, it might output, "John has been assigned to the Data Science department."

[0141] Input: List of results for assignment to the optimal department

[0142] Specific operation: The server generates a list of assignment results as data to notify the user and sends it to the terminal. The terminal displays the received data in the user interface.

[0143] Output: The assignment results are displayed on the user's terminal.

[0144] This series of steps makes it possible to efficiently place individual employees in the departments where they can best utilize their abilities.

[0145] (Application Example 1)

[0146] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0147] As industry advances, factories and production sites require the appropriate placement of highly skilled robots for efficient work. However, optimally matching the skills of each robot with the requirements of each work location is difficult, and in many cases, it has to rely on human judgment, which is time-consuming and labor-intensive. Furthermore, if robots are not properly placed, work efficiency decreases and production costs increase.

[0148] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0149] In this invention, the server includes means for inputting data indicating an individual's capabilities, means for inputting data indicating the operational requirements of each department within the organization, means for comparing the individual's capabilities data with the departmental operational requirements data and calculating the degree of agreement between them, means for assigning each individual to the most appropriate department based on the degree of agreement, means for inputting data indicating the machine's skills, means for inputting data indicating the operational requirements of each work site, means for comparing the machine's skills data with the work site operational requirements data and calculating the degree of agreement, and means for assigning each machine to the most appropriate work site based on the degree of agreement. This enables the appropriate placement of robots, resulting in improved work efficiency and reduced production costs.

[0150] "Data demonstrating individual capabilities" refers to quantitative representations of the skills, knowledge, and experience of employees within an organization.

[0151] "Data showing the operational requirements of each department within an organization" refers to a quantitative representation of the skills, knowledge, experience, and other conditions required by a specific department within an organization.

[0152] "A method for comparing individual competency data with departmental job requirements data and calculating the degree of agreement" refers to a method of matching an employee's skill set with the department's required skill set and calculating the degree of agreement numerically.

[0153] "A means of assigning each individual to the most appropriate department based on the degree of agreement" refers to a method of assigning employees to the most suitable department based on a calculated degree of agreement.

[0154] "Data demonstrating machine skills" refers to a quantitative representation of the specific work capabilities and technologies possessed by robots and machines used in factories and production sites.

[0155] "Data indicating the operational requirements of each work location" refers to a quantitative representation of the work capabilities and technical requirements of a specific work station in a factory or production site.

[0156] "A method for comparing machine skill data with work site requirements data and calculating the degree of agreement" refers to a method of matching the skills of a machine with the skills required by the work station and calculating the degree of agreement numerically.

[0157] "Means of assigning each machine to the most appropriate work location based on the degree of match" refers to a method of assigning machines to the most suitable work station based on the calculated degree of match.

[0158] The embodiments for carrying out this invention will be described in detail below. This system automatically determines the most appropriate placement using individual ability data, machine skill data, and work requirement data for each department and work station within the organization.

[0159] System Configuration

[0160] This system consists of the following main components:

[0161] 1. Means of inputting data indicating individual capabilities:

[0162] This is a method for users to use a terminal to enter a list of employee names and skills. For example, if "Employee 1" has skills "Skill A" and "Skill B," then that information would be entered.

[0163] 2. Means of inputting data that shows the operational requirements of each department within the organization:

[0164] This is a method for users to use a terminal to input the names of each department and a list of the skills that department requires. For example, if "Department 1" requires skills "Skill A" and "Skill B," the user would input those skills.

[0165] 3. A means of comparing individual competency data with departmental work requirements data and calculating the degree of agreement between them:

[0166] The server compares employee skill data with departmental job requirements data and calculates the degree of agreement. Specifically, it calculates the number of intersection elements between the skills an employee possesses and the skills required by the department. For example, if "Skill A" and "Skill B" from "Employee 1's" skill list match the required skills list of "Department 1," the degree of agreement is calculated as 2.

[0167] 4. Means for assigning each individual to the most appropriate department based on the degree of agreement:

[0168] The server assigns each employee to the most suitable department based on the calculated degree of match. For example, employee "Employee 1" is assigned to "Department 1".

[0169] 5. Means of inputting data indicating machine skills:

[0170] This is a method for users to use a terminal to input the names of machines in the factory and a list of the skills they possess. For example, if "Machine 1" has skills "Skill X" and "Skill Y," the user would input those skills.

[0171] 6. Means for inputting data indicating the operational requirements of each work location:

[0172] This is a method by which users use a terminal to input the names of each work location and a list of skills required for that location. For example, if "Station 1" requires skills "Skill X" and "Skill Y," the user would input those skills.

[0173] 7. Means for comparing machine skill data and work site requirements data and calculating the degree of agreement:

[0174] The server compares the machine's skill data with the work site's job requirements data and calculates the degree of match. Specifically, it calculates the number of intersection elements between the skills the machine possesses and the skills required by the work site. For example, if "Skill X" and "Skill Y" from the "Machine 1" skill list match the required skills list of "Station 1," the degree of match is calculated as 2.

[0175] 8. Means for assigning each machine to the most appropriate work location based on its degree of matching:

[0176] The server assigns each machine to the most suitable work location based on the calculated match. For example, machine "Machine 1" is assigned to "Station 1".

[0177] Hardware and software configuration

[0178] This system uses the following hardware and software:

[0179] Hardware:

[0180] Factory work robots (PLC-compatible smart robots)

[0181] Factory management server

[0182] Input devices (e.g., tablets or PCs)

[0183] software:

[0184] Python 3.x

[0185] Database management tools (e.g., SQLite, MySQL®)

[0186] Examples

[0187] Employee information entry:

[0188] The user enters the employee's name and a list of their skills into the system via a terminal. For example, if "Employee 1" has "Skill A" and "Skill B," the user would enter that information.

[0189] Department information entry:

[0190] The user enters the name of each department and a list of skills required by that department into the system via a terminal. For example, if "Department 1" requires "Skill A" and "Skill B," the user would enter that information.

[0191] Skill Matching Calculation:

[0192] The server compares the employee's skill list with the department's required skill list. For example, if "Employee 1"'s skills "Skill A" and "Skill B" match the required skill list of "Department 1," the degree of match is calculated as 2.

[0193] Optimal department assignment:

[0194] The server assigns each employee to the most suitable department based on the calculated degree of match. For example, employee "Employee 1" is assigned to "Department 1".

[0195] Robot skill input:

[0196] The user enters the machine's name and its skill list into the system via a terminal. For example, if "Machine 1" has "Skill X" and "Skill Y," the user would enter that information.

[0197] Enter work location information:

[0198] The user enters the name of each work location and a list of skills required by that location into the system via a terminal. For example, if "Work Location 1" requires "Skill X" and "Skill Y," the user would enter that information.

[0199] Skill Matching Calculation:

[0200] The server compares the machine's skill list with the required skill list for the work location. For example, if "Skill X" and "Skill Y" from "Machine 1" match the required skill list for "Work Location 1", the degree of match is calculated as 2.

[0201] Optimal workspace assignment:

[0202] The server assigns each machine to the most suitable workspace based on the calculated match. For example, machine "Machine 1" is assigned to workspace "Workspace 1".

[0203] Example of a prompt

[0204] Robot "Machine 1" skills: Skill A, Skill B

[0205] Robot "Machine 2" skills: Skill C, Skill D

[0206] Robot "Machine 3" skills: Skill A, Skill C

[0207] Required skill for station "Workplace 1": Skill A

[0208] Required skill for Station "Workplace 2": Skill B

[0209] Required skills for Station "Workplace 3": Skill C

[0210] Use this data to assign each robot to the optimal work location.

[0211] This prompt allows the server to automatically generate a result that determines the optimal machine placement.

[0212] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0213] Step 1:

[0214] The user uses a terminal to enter the employee's name and a list of their skills. The input data includes the employee's name and a list of skills. For example, information such as "Employee 1 has skill A and skill B" might be entered. The system then retrieves the employee's competence data.

[0215] Step 2:

[0216] The user uses a terminal to enter the name of each department and a list of skills required by that department. The input data includes the department name and a list of required skills. For example, information such as "Department 1 requires skills A and B" might be entered. The system then retrieves the business requirements data for each department.

[0217] Step 3:

[0218] The server compares employee skill data with departmental job requirements data. First, it matches each employee's skill list with each department's required skill list and calculates the degree of match. Specifically, it calculates the number of intersection elements between the skills an employee possesses and the skills required by the department. For example, "If skills A and B from employee 1's skill list match the required skill list of department 1, the degree of match is calculated as 2."

[0219] Step 4:

[0220] The server assigns each employee to the most suitable department based on the calculated match score. Based on the entered match score data, the server selects the employee-department combination with the highest match score. For example, if the match score is highest, employee 1 will be assigned to department 1.

[0221] Step 5:

[0222] The user uses a terminal to input the names of machines in the factory and a list of skills they possess. The input data includes the machine name and a list of skills. For example, information such as "Machine 1 has skill X and skill Y" might be entered. The system then retrieves the machine's skill data.

[0223] Step 6:

[0224] The user uses a terminal to enter the name of each work location and a list of skills required for that location. The input data includes the name of the work location and a list of required skills. For example, information such as "Work Location 1 requires Skill X and Skill Y" might be entered. The system then retrieves the work requirements data for each work location.

[0225] Step 7:

[0226] The server compares the machine's skill data with the work site's operational requirements data. First, it matches the skill list for each machine with the required skill list for each work site and calculates the degree of match. Specifically, it calculates the number of intersection elements between the skills the machine possesses and the skills required by the work site. For example, "If skills X and Y from machine 1's skill list match the required skill list for work site 1, the degree of match is calculated as 2."

[0227] Step 8:

[0228] The server assigns each machine to the most suitable workspace based on the calculated match score. Based on the input match score data, the server selects the machine and workspace combination with the highest match score. For example, if the match score is highest, machine 1 is assigned to workspace 1.

[0229] By following these steps, the system can automatically determine the optimal placement of employees and machinery, thereby improving work efficiency and reducing production costs.

[0230] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0231] The embodiments for carrying out the present invention will be described in detail below. This system uses individual ability data and business requirements data of each department within an organization to assign each individual to the most appropriate department, and further incorporates an emotion engine that recognizes the user's emotions.

[0232] System Configuration

[0233] This system consists of the following main components:

[0234] 1. Employee Information Input Method: This is a method by which users input employee names and skill lists using a terminal.

[0235] 2. Department Information Input Method: This is a method by which users use a terminal to input the name of each department and a list of the skills required by that department.

[0236] 3. Skill Matching Calculation Method: This method involves the server comparing the employee's skill list with the department's required skill list and calculating the degree of match.

[0237] 4. Emotion Engine: A means of recognizing user emotions and collecting and analyzing that data.

[0238] 5. Emotional Data Evaluation Method: This method evaluates employee motivation and satisfaction based on user emotional data collected by an emotional engine.

[0239] 6. Optimal Department Assignment Method: This method assigns each employee to the most suitable department based on the server's calculated degree of agreement and sentiment data evaluation.

[0240] 7. Result Output Means: This means by which the server calculates the optimal department assignment result and reports it to the user.

[0241] Program processing

[0242] Employee Information Input: Users input employee names and a list of their skills into the system via a terminal. For example, if an employee named "John" has the skills of "Python," "Data Analysis," and "Project Management," they would input that information.

[0243] Department Information Input: Users input the name of each department and a list of skills required by that department into the system via a terminal. For example, if the "Data Science" department requires "Python" and "Data Analysis" skills, they would input those.

[0244] Skill Match Calculation: The server compares the employee's skill list with the department's required skills list. Specifically, it calculates the number of intersection elements between the employee's skills and the skills required by the department. For example, if "Python" and "Data Analysis" from "John's" skill list match the required skills list for "Data Science," the match score is calculated as 2.

[0245] Emotional Data Collection: The emotion engine collects emotional data from employees and managers through the user's device. This data reflects employee motivation and satisfaction, specifically analyzing emotions from facial expressions, voice tone, and other factors.

[0246] Emotional Data Evaluation: The server analyzes emotional data collected from the emotion engine to evaluate employee motivation and satisfaction. For example, if "John's" emotional data indicates high motivation and satisfaction, that data is recorded.

[0247] Optimal Department Assignment: The server assigns each employee to the most suitable department based on calculated skill match and sentiment data evaluation. For example, in the case of "John," his skill match is high in the "Data Science" department, and in addition, his sentiment data evaluation shows high motivation, so he is assigned to the "Data Science" department.

[0248] Result Output: The server generates the result of assigning John to the most suitable department and reports it to the user. For example, it might output a result such as "John has been assigned to the Data Science department."

[0249] Specific example

[0250] The following are specific examples of its use.

[0251] Employee information:

[0252] Employee 1: Name "John", Skills "Python", "Data Analysis", "Project Management"

[0253] Employee 2: Name "Emily", Skills "Java", "System Design", "Debugging"

[0254] Department information:

[0255] Department 1: "Data Science", Required skills: "Python", "Data Analysis"

[0256] Department 2: "Software Development", Required skills: "Java", "System Design"

[0257] Emotional data collection:

[0258] John's emotional data: High motivation and satisfaction

[0259] Emily's emotional data: Normal motivation and satisfaction

[0260] The server calculates the skill match as follows:

[0261] "John" and "Data Science": Similarity level 2 ("Python" and "Data Analysis")

[0262] "Emily" and "Software Development": Match level 2 ("Java," "System Design")

[0263] Taking sentiment data evaluation into consideration, the server assigns "John" to the "Data Science" department and "Emily" to the "Software Development" department. The results are then reported to the user.

[0264] In this way, the system achieves optimal placement by considering not only employee skills but also emotional data, thereby improving overall organizational productivity and employee satisfaction.

[0265] The following describes the processing flow.

[0266] Step 1:

[0267] The user uses a terminal to enter a list of employee names and skills. For example, they might enter the name "John" and the skills "Python," "Data Analysis," and "Project Management." The entered information is then sent to the server.

[0268] Step 2:

[0269] The user uses a terminal to enter the names of each department and a list of the skills required by that department. For example, they might enter the department name "Data Science" and the required skills "Python" and "Data Analysis". The entered information is then sent to the server.

[0270] Step 3:

[0271] The server stores the entered employee and department information as lists. This ensures that each employee's skill list and each department's required skill list are saved in the database.

[0272] Step 4:

[0273] The server compares each employee's skill list with the required skill list for each department and calculates the number of matching skills as the degree of match. For example, if "Python" and "Data Analysis" match the required skill list for "Data Science" in "John's" skill list, the degree of match is calculated as 2.

[0274] Step 5:

[0275] The server uses an emotion engine to collect user emotion data through the terminal. This data reflects employees' motivation and satisfaction levels, and analyzes emotions from facial expressions, voice tone, and other factors. For example, if "John's" emotion data indicates high motivation and satisfaction, that data is sent to the server.

[0276] Step 6:

[0277] The server analyzes emotional data collected from the emotion engine to evaluate employee motivation and satisfaction. For example, if the emotion engine recognizes high motivation and satisfaction from "John's" facial expressions and tone of voice, it records that evaluation.

[0278] Step 7:

[0279] The server assigns each employee to the most suitable department based on calculated skill match and sentiment data evaluation. For example, in the case of "John," his skill match is high in the "Data Science" department, and his sentiment data evaluation also shows high motivation, so he is assigned to the "Data Science" department.

[0280] Step 8:

[0281] The server generates the assignment result to the most suitable department and reports it to the user. For example, it might output a result such as, "John has been assigned to the Data Science department."

[0282] Step 9:

[0283] The user uses the terminal to check the results reported by the server and performs necessary transfer procedures. For example, procedures for officially transferring "John" to the "Data Science" department are carried out.

[0284] In this way, the system takes into account the skills and emotional data of employees, realizes optimal allocation, and improves the productivity of the entire organization and employee satisfaction.

[0285] (Example 2)

[0286] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart device 14 is referred to as the "terminal".

[0287] In the conventional system, only allocation based on personal ability data and department business requirement data is performed, and the work motivation and satisfaction of employees are not considered. Therefore, even if skill matching is suitable, there is a problem that the motivation and satisfaction of employees decrease and optimal allocation cannot be achieved.

[0288] The specific processing by the specific 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 inputting personal ability data, means for inputting business requirements of each department within the organization, means for comparing personal ability data and department business requirement data and calculating the degree of match for each, means for recognizing the emotions of the user, means for evaluating the collected emotion data as work motivation and satisfaction, and means for comprehensively judging the emotion data evaluation and skill match degree and allocating each individual to an optimal department. Thereby, optimal allocation considering not only the skills of employees but also emotion data becomes possible.

[0289] "Data indicating personal ability" is information representing the skills, knowledge, and experience possessed by an individual in numerical or list form.

[0290] "Data showing the operational requirements of each department within an organization" refers to information that expresses the skills, knowledge, and experience required by a specific department within an organization in numerical or list format.

[0291] "Means for comparing and calculating the degree of agreement" refers to methods or devices that analyze common elements between input individual ability data and departmental business requirements data, and quantify the degree of agreement.

[0292] "Means of recognizing user emotions" refers to methods or devices that analyze a user's emotions from their facial expressions, tone of voice, etc., and collect the results as data.

[0293] "Means for evaluating collected emotional data as work motivation and satisfaction" refers to methods or devices that analyze emotional data obtained by emotion recognition means and use that analysis to evaluate the user's work motivation and satisfaction.

[0294] "Methods for assigning individuals to the most suitable departments by comprehensively evaluating emotional data and skill match" refers to methods or devices that analyze both skill match and emotional evaluation data, and use these results to place individuals in the most appropriate departments.

[0295] "An individual's ability data is presented as a list of skills" refers to a method of clearly indicating an individual's skills and knowledge in a list format.

[0296] "The degree of agreement is calculated using the number of intersection elements between the individual's skill list and the department's required skill list" means that the degree of agreement is calculated using the number of common elements between the individual's skill list and the department's required skill list.

[0297] This invention is a system for assigning individuals to the most appropriate departments by utilizing individual capability data and business requirements data for each department within an organization, and further incorporates an emotion engine that recognizes user emotions.

[0298] System Configuration

[0299] This system consists of the following main components:

[0300] 1. Employee information input means:

[0301] The user uses the terminal to input the names and skill lists of employees. Specifically, the user inputs through the keyboard on the terminal screen.

[0302] 2. Department information input means:

[0303] The user uses the terminal to input the names of each department and the required skill lists. Specifically, the user inputs through the keyboard on the terminal screen.

[0304] 3. Skill matching degree calculation means:

[0305] The server compares the skill list of employees with the required skill list of the department and calculates the matching degree. Specifically, the processing device on the server executes a program to count the number of intersecting elements in the list.

[0306] 4. Emotion engine:

[0307] The emotion engine collects emotion data of employees and administrators through the user's terminal. This data is evaluated as work motivation and satisfaction. Specifically, face recognition software and voice analysis software are used.

[0308] 5. Emotion data evaluation means:

[0309] The server analyzes the emotion data collected from the emotion engine and evaluates work motivation and satisfaction. The analysis results are recorded inside the system.

[0310] 6. Optimal department assignment means:

[0311] The server assigns each employee to the most suitable department based on the calculated skill matching degree and emotion data evaluation.

[0312] 7. Means for outputting results:

[0313] The server generates the optimal department assignment result and reports it to the user.

[0314] Specific example

[0315] The following are specific examples of its use.

[0316] Employee information:

[0317] Employee 1: Name "Employee A", Skills "Python", "Data Analysis", "Project Management"

[0318] Employee 2: Name "Employee B", Skills "Java", "System Design", "Debugging"

[0319] Department information:

[0320] Department 1: "Data Science", Required skills: "Python", "Data Analysis"

[0321] Department 2: "Software Development", Required skills: "Java", "System Design"

[0322] Emotional data collection:

[0323] Emotional data for "Employee A": High motivation and satisfaction

[0324] Emotional data for "Employee B": Normal motivation and satisfaction

[0325] Example of assignment result:

[0326] The server calculates the following skill match:

[0327] "Employee A" and "Data Science": Matching score 2 ("Python" and "Data Analysis")

[0328] "Employee B" and "Software Development": Matching score 2 ("Java," "System Design")

[0329] Taking sentiment data evaluation into consideration, the server assigns "Employee A" to the "Data Science" department and "Employee B" to the "Software Development" department. It then reports the results to the user. For example, it might report, "Employee A has been assigned to the Data Science department, and Employee B has been assigned to the Software Development department."

[0330] Example of a prompt

[0331] The following are examples of prompt statements to input into a generative AI model:

[0332] Employee 1: Employee A, Skills: Python, Data Analysis, Project Management

[0333] Employee 2: Employee B, Skills: Java, System Design, Debugging

[0334] Department 1: Data Science, Required Skills: Python, Data Analysis

[0335] Department 2: Software Development, Required Skills: Java, System Design

[0336] Employee A's emotional data: High motivation and satisfaction

[0337] Employee B's emotional data: Normal motivation and satisfaction

[0338] Please assign each employee to the most suitable department.

[0339] In this way, the present invention takes into account both employee skills and emotional data to achieve optimal departmental placement, thereby improving overall organizational productivity and employee satisfaction.

[0340] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0341] Step 1:

[0342] Entering employee information

[0343] The user uses a terminal to enter the employee's name and skill list. For example, the user enters the name of employee "A" and the skills "Python," "Data Analysis," and "Project Management."

[0344] Input: Employee name and skill information

[0345] Output: Employee names and skill lists stored in the employee information database

[0346] Specific operation: The user uses the keyboard to fill in employee information in the input fields on the terminal and clicks the submit button. The server receives the data and saves it to the employee information database.

[0347] Step 2:

[0348] Entering department information

[0349] The user uses a terminal to enter the name of each department and a list of required skills. For example, the user enters the name of the "Data Science Department" and the required skills "Python" and "Data Analysis".

[0350] Input: Department name and required skills information

[0351] Output: Department names and required skill lists stored in the department information database.

[0352] Specific operation: The user uses the keyboard to enter department information into the input field on the terminal and clicks the submit button. The server receives the data and saves it to the department information database.

[0353] Step 3:

[0354] Calculation of skill match

[0355] The server compares the employee's skill list with the department's required skill list and calculates the degree of match. A processing unit on the server executes a program and counts the number of intersection elements in the lists.

[0356] Input: Employee skill list, departmental required skill list

[0357] Output: Numerical data on skill matching

[0358] Specific operation: The server retrieves the necessary data from the employee database and department database, counts the number of matching skills, and calculates the degree of match.

[0359] Step 4:

[0360] Collection of emotional data

[0361] The emotion engine collects emotional data from employees and managers through the user's device. The emotion engine analyzes this emotional data using facial recognition software and voice analysis software.

[0362] Input: Emotional data such as employee facial expressions and voice tone.

[0363] Output: Analyzed sentiment data

[0364] Specific operation: The emotion engine uses the device's camera and microphone to collect emotion data and sends it to the server. The server receives it and stores the analysis results in a database.

[0365] Step 5:

[0366] Evaluation of emotional data

[0367] The server analyzes emotional data collected from the emotion engine to evaluate employee motivation and satisfaction.

[0368] Input: Collected emotional data

[0369] Output: Evaluation results of work motivation and satisfaction

[0370] Specific operation: The server runs a program to analyze emotional data, evaluates work motivation and satisfaction as numerical values, and saves the evaluation results to a database.

[0371] Step 6:

[0372] Assignment to the most suitable department

[0373] The server assigns each employee to the most suitable department based on calculated skill match and emotional data evaluation.

[0374] Input: Skill matching, emotional data evaluation

[0375] Output: Results of optimal departmental assignment

[0376] Specific operation: The server comprehensively analyzes both skill match and sentiment data evaluation, and uses an assignment algorithm to determine the optimal department.

[0377] Step 7:

[0378] Output of results

[0379] The server generates the optimal department assignment results and reports them to the user.

[0380] Input: Result of optimal departmental assignment

[0381] Output: Report of user assignment results

[0382] Specific operation: The server generates the assignment result and reports it to the user via the terminal display or email. For example, a message such as "Employee A has been assigned to the Data Science Department" might be displayed.

[0383] The above outlines the specific processing steps of this system's program. In this way, the system takes into account employee skills and emotional data to achieve optimal placement.

[0384] (Application Example 2)

[0385] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0386] Traditional employee placement systems primarily rely on employee skill sets, neglecting to consider employee emotions or motivation. This can lead to situations where employees with suitable skills but low motivation are assigned to the wrong positions, resulting in decreased productivity. To address this, a system is needed that collects and analyzes employee emotional data, placing employees in the most suitable departments based on both skills and emotions.

[0387] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting data indicating an individual's abilities, means for inputting data indicating the work requirements of each department within the organization, means for comparing the individual's ability data with the department's work requirements data and calculating the degree of agreement between them, means for assigning each individual to the most appropriate department based on the degree of agreement, an emotion engine for collecting and analyzing emotion data from facial expressions, voice tone, etc., means for evaluating the emotion data and analyzing the employee's motivation and satisfaction level, and optimization means for assigning the employee to the most suitable department based on the evaluation results and degree of agreement of the emotion data. This makes it possible to make optimal placements that simultaneously consider the employee's skills and emotions.

[0388] "Individual competency data" refers to information that indicates the skills and knowledge possessed by individual employees.

[0389] "Departmental operational requirements data" refers to information that indicates the skills and knowledge required by each department within an organization.

[0390] "Conformance" is an indicator that shows the consistency between the competency data of individual employees and the operational requirements data of their department.

[0391] An "emotion engine" is a system component that collects and analyzes emotional data from facial expressions, voice tone, and other sources.

[0392] "Emotional data" refers to information that indicates employee motivation and satisfaction levels, derived from their facial expressions and voice.

[0393] An "optimization measure" is a method or system for assigning employees to the most suitable departments based on the evaluation results of agreement and sentiment data.

[0394] This invention is a system that assigns individuals to the most suitable departments based on their skills and emotional data. This system calculates the degree of match using employee skill data and organizational work requirements data, and further applies emotional data acquired by an emotional engine to achieve optimal placement that takes into account employee satisfaction and motivation.

[0395] System Configuration

[0396] The system consists of the following main components:

[0397] 1. Employee Information Input Method: This is a method by which users input employee competency data using a terminal. This data consists of the employee's name and a list of skills.

[0398] 2. Department Information Input Method: This is a method by which users input business requirement data for each department using a terminal. This data consists of the department name and a list of skills required by that department.

[0399] 3. Skill Match Calculation Method: The server compares the employee's skill list with the department's job requirements list and calculates the degree of match. This generates candidates for assignment to the most suitable department.

[0400] 4. Emotion Engine: Uses the device's camera and microphone to analyze employees' facial expressions and voice tone to collect emotional data.

[0401] 5. Emotional Data Evaluation Method: The server analyzes the collected emotional data to evaluate employees' work motivation and satisfaction.

[0402] 6. Optimal Department Assignment Method: The server assigns each employee to the most appropriate department based on skill match and sentiment evaluation data.

[0403] 7. Result output means: The server generates the allocation results and reports them to the user.

[0404] Program processing

[0405] Employee information entry:

[0406] Users input employee names and skill lists into the system via a terminal. For example, "Employee A" has the skills of "Python" and "Data Analysis".

[0407] Department information entry:

[0408] The user enters the name of each department and a list of skills required by that department. For example, "Department 1" requires "Python" and "Data Analysis".

[0409] Skill Matching Calculation:

[0410] The server compares the employee's skill list with the department's skill list and calculates the degree of match for each. It counts the number of matches between employee A's skill list and department 1's skill list and calculates the degree of match.

[0411] Emotional data collection:

[0412] The system collects employee emotional data using the terminal's camera and microphone. It performs facial recognition and voice analysis using libraries such as OpenCV.

[0413] Sentiment data evaluation:

[0414] The server analyzes collected emotional data to evaluate employees' motivation and satisfaction levels. This helps determine whether employees are likely to adapt well to their respective departments.

[0415] Optimal department assignment:

[0416] The server assigns each employee to the most suitable department based on skill match and emotional data evaluation. It selects the combination with the highest skill match and emotional evaluation score.

[0417] Result output:

[0418] The server generates the assignment result for the most suitable department and reports it to the user. For example, the output might say, "Employee A has been assigned to Department 1."

[0419] Specific example

[0420] The following are specific examples of its use.

[0421] Employee information:

[0422] Employee A: Name "Employee A", Skills "Python", "Data Analysis"

[0423] Employee B: Name "Employee B", Skills "Java", "System Design"

[0424] Department information:

[0425] Department 1: Name "Data Science", Required skills "Python", "Data Analysis"

[0426] Department 2: Name "Software Development", Required skills "Java", "System Design"

[0427] Emotional data collection:

[0428] Employee A's emotional data: High motivation and satisfaction

[0429] Employee B's emotional data: Normal motivation and satisfaction

[0430] The server calculates skill match as follows:

[0431] "Employee A" and "Data Science": Concord score 2 ("Python", "Data Analysis")

[0432] "Employee B" and "Software Development": Matching score 2 ("Java," "System Design")

[0433] The optimal placement is as follows:

[0434] Assign "Employee A" to the "Data Science" department.

[0435] Assign "Employee B" to the "Software Development" department.

[0436] Example of a prompt:

[0437] "Enter the employee or robot's name and skill set, calculate the degree of match with the required skills for each work station, and collect emotional data to assess work motivation and determine the optimal placement."

[0438] This system enables optimal placement by considering both employee skills and emotions simultaneously. This, in turn, improves overall organizational productivity and employee satisfaction.

[0439] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0440] Step 1:

[0441] Employee Information Input: Users input employee names and skill lists via a terminal. The entered data is stored on the server side. For example, "Employee A" has the skills "Python" and "Data Analysis." This input data is sent to the system and stored in an internal database.

[0442] Step 2:

[0443] Department Information Input: Users input the name of each department and a list of skills required by that department via a terminal. The entered data is stored on the server side. For example, "Department 1" requires "Python" and "Data Analysis" skills. This input data is sent to the system and stored in an internal database.

[0444] Step 3:

[0445] Skill Match Calculation: The server compares the employee's skill list with the department's skill list and calculates the match score for each. The employee's skill list and the department's skill list are used as input. The match score is calculated based on the number of intersection elements between the skill lists. For example, if "Python" and "Data Analysis" from "Employee A's" skill list match the required skills list of "Department 1," the match score would be 2. The match score results are stored on the server.

[0446] Step 4:

[0447] Emotional Data Collection: Using the terminal's camera and microphone, the system analyzes employees' facial expressions and voice tone to collect emotional data. The collected emotional data is sent to the server. Using a facial recognition library (e.g., OpenCV) or voice analysis software, the raw image and voice data are converted into emotional data. This allows the emotional data to be quantified.

[0448] Step 5:

[0449] Emotional Data Evaluation: The server analyzes collected emotional data to evaluate employee motivation and satisfaction. Emotional data is used as input, and employee motivation scores are obtained as output. The server uses an emotional engine to classify the emotional data and quantify motivation and satisfaction.

[0450] Step 6:

[0451] Optimal Department Assignment: The server assigns each employee to the most suitable department based on skill match and sentiment data evaluation. Match data and sentiment evaluation data are used as input, and the optimal department assignment result is obtained as output. The server calculates a weighted average of skill match and sentiment score and determines assignment to the department with the highest score.

[0452] Step 7:

[0453] Result Output: The server generates and reports the optimal department assignment results to the user. The results are output in a report format that includes assignment information. For example, the result "Employee A has been assigned to Department 1" might be displayed on the terminal. This output information is provided in a format that is easy for the user to understand.

[0454] In this way, the entire system processes data to ensure optimal placement in departments, taking into account employees' skills and emotions. This is expected to improve overall organizational productivity and employee satisfaction.

[0455] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0456] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0457] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0458] [Second Embodiment]

[0459] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0460] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0461] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0462] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0463] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0464] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0465] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0466] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0467] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0468] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0469] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0470] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0471] The embodiments for carrying out the present invention will be described in detail below. This system uses individual capability data and operational requirements data for each department within an organization to assign each individual to the most appropriate department.

[0472] System Configuration

[0473] This system consists of the following main components:

[0474] 1. Employee Information Input Method: This is a method by which users input employee names and skill lists using a terminal.

[0475] 2. Department Information Input Method: This is a method by which users use a terminal to input the name of each department and a list of the skills required by that department.

[0476] 3. Skill Matching Calculation Method: This method involves the server comparing the employee's skill list with the department's required skill list and calculating the degree of match.

[0477] 4. Optimal Department Assignment Method: This method assigns each employee to the most suitable department based on the calculated degree of matching by the server.

[0478] 5. Result Output Means: This means by which the server calculates the optimal department assignment result and reports it to the user.

[0479] Program processing

[0480] Employee Information Input: Users input employee names and a list of their skills into the system via a terminal. For example, if an employee named "John" has the skills of "Python," "Data Analysis," and "Project Management," they would input that information.

[0481] Department Information Input: Users input the name of each department and a list of skills required by that department into the system via a terminal. For example, if the "Data Science" department requires "Python" and "Data Analysis" skills, they would input those.

[0482] Skill Match Calculation: The server compares the employee's skill list with the department's required skills list. Specifically, it calculates the number of intersection elements between the employee's skills and the skills required by the department. For example, if "Python" and "Data Analysis" from "John's" skill list match the required skills list for "Data Science," the match score is calculated as 2.

[0483] Optimal Department Assignment: The server assigns each employee to the most suitable department based on the calculated degree of match. For example, employee "John" is assigned to the "Data Science" department.

[0484] Result Output: The server calculates the optimal department assignment and reports it to the user. For example, it might output a result such as, "John has been assigned to the Data Science department."

[0485] Specific example

[0486] For example, consider a case where a user enters the following data.

[0487] Employee information:

[0488] Employee 1: Name "John", Skills "Python", "Data Analysis", "Project Management"

[0489] Employee 2: Name "Emily", Skills "Java", "System Design", "Debugging"

[0490] Department information:

[0491] Department 1: "Data Science", Required skills: "Python", "Data Analysis"

[0492] Department 2: "Software Development", Required skills: "Java", "System Design"

[0493] The server calculates the skill match as follows:

[0494] "John" and "Data Science": Similarity level 2 ("Python" and "Data Analysis")

[0495] "Emily" and "Software Development": Match level 2 ("Java," "System Design")

[0496] Based on the calculation results, the server assigns "John" to the "Data Science" department and "Emily" to the "Software Development" department, and outputs the results to the user.

[0497] In this way, this system enables the placement of individual employees into departments where they can best utilize their abilities.

[0498] The following describes the processing flow.

[0499] Step 1:

[0500] The user uses a terminal to enter a list of employee names and skills. For example, they might enter the name "John" and the skills "Python," "Data Analysis," and "Project Management." The entered information is then sent to the server.

[0501] Step 2:

[0502] The user uses a terminal to enter the names of each department and a list of the skills required by that department. For example, they might enter the department name "Data Science" and the required skills "Python" and "Data Analysis". The entered information is then sent to the server.

[0503] Step 3:

[0504] The server stores the entered employee and department information as lists. This ensures that each employee's skill list and each department's required skill list are saved in the database.

[0505] Step 4:

[0506] The server compares each employee's skill list with the required skill list for each department and calculates the number of matching skills as the degree of match. For example, if "Python" and "Data Analysis" match the required skill list for "Data Science" in "John's" skill list, the degree of match is calculated as 2.

[0507] Step 5:

[0508] The server calculates the degree of match for each employee with all departments and identifies the department with the highest degree of match. For example, in the case of "John," the server assigns "John" to the "Data Science" department because the degree of match is highest with the "Data Science" department.

[0509] Step 6:

[0510] The server generates the assignment result to the most suitable department and reports it to the user. For example, it might output a result such as, "John has been assigned to the Data Science department."

[0511] Step 7:

[0512] The user uses their terminal to check the results reported from the server and perform the necessary transfer procedures.

[0513] (Example 1)

[0514] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0515] Placing employees in the most suitable departments based on their skills and abilities is a critical challenge for many companies. However, traditional manual placement methods are time-consuming and sometimes result in inappropriate placements. Furthermore, there is a lack of automated systems to efficiently perform overall skill matching. Solving this challenge is essential to maximizing the use of internal resources and improving operational efficiency.

[0516] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0517] In this invention, the server includes means for inputting data indicating an individual's capabilities, means for inputting data indicating the operational requirements of each department within the organization, means for comparing the individual's capabilities data with the departmental operational requirements data and calculating the degree of agreement between them, means for assigning each individual to the most appropriate department based on the degree of agreement, and means for reporting the result of the assignment to the optimal department. This makes it possible to assign employees to departments based on their individual capabilities.

[0518] "Data indicating individual capabilities" refers to information that lists the skills, qualifications, experience, and other abilities that each individual possesses.

[0519] "Data showing the operational requirements of each department within an organization" refers to information that lists the operational requirements, such as the skills, qualifications, and experience needed for each department.

[0520] A "means for calculating the degree of agreement" refers to an algorithm or calculation system that compares individual ability data with departmental work requirements data and calculates the number of commonalities between the two.

[0521] The "assignment method" is a system that automatically places individuals in the most suitable departments based on a calculated degree of matching.

[0522] "Means of reporting" refers to systems or interfaces that notify users of the results of their placement into the most suitable department.

[0523] Modes for carrying out the invention

[0524] The embodiments for carrying out the present invention are described in detail below. This system utilizes individual capability data and operational requirements data for each department within an organization to assign each individual to the most appropriate department. The system consists of the following main components:

[0525] 1. Employee Information Input Method: This is a method by which users input employee names and skill lists using a terminal. Users use a dedicated form to input employee names and skills. For example, if an employee named "John" has the skills of "Python," "Data Analysis," and "Project Management," the user would input that information.

[0526] 2. Department Information Input Method: This is a method for users to input the name of each department and a list of skills required by that department using a terminal. Users use a dedicated form to input the department name and required skills. For example, if the "Data Science" department requires "Python" and "Data Analysis" skills, the user would input those.

[0527] 3. Skill Match Calculation Method: This method involves the server comparing an employee's skill list with the department's required skill list and calculating the degree of match. Specifically, the server calculates the number of common elements between the employee's skills and the department's required skills. This calculation is performed using a specific algorithm.

[0528] 4. Optimal Department Assignment Method: This method assigns each employee to the most suitable department based on the match score calculated by the server. The server selects the department with the highest match score for each employee and assigns the employee to that department.

[0529] 5. Result Output Method: This is a method by which the server calculates the optimal department assignment result and reports it to the user. For example, the user is provided with information such as, "John has been assigned to the Data Science department."

[0530] Specific usage instructions

[0531] Consider a scenario where the user enters the following data.

[0532] Employee information:

[0533] Employee 1: Name "John", Skills "Python", "Data Analysis", "Project Management"

[0534] Employee 2: Name "Emily", Skills "Java", "System Design", "Debugging"

[0535] Department information:

[0536] Department 1: "Data Science", Required skills: "Python", "Data Analysis"

[0537] Department 2: "Software Development", Required Skills: "Java", "System Design"

[0538] The server calculates skill match as follows:

[0539] "John" and "Data Science": Similarity level 2 ("Python" and "Data Analysis")

[0540] "Emily" and "Software Development": Match level 2 ("Java," "System Design")

[0541] Based on the calculation results, the server assigns "John" to the "Data Science" department and "Emily" to the "Software Development" department, and outputs the results to the user.

[0542] Examples of prompt statements

[0543] The following are specific examples of prompt statements for a generative AI model.

[0544] Employee information entry:

[0545] User: "Please enter an employee's skills list based on the following information. Example: Name: John, Skills: "Python", "Data Analysis", "Project Management""

[0546] In a specific action, the user enters employee information into a designated form on the terminal.

[0547] Department information entry:

[0548] User: "Please enter the department's skill list based on the following information. Example: Department: Data Science, Required Skills: "Python", "Data Analysis""

[0549] In a specific action, the user enters departmental information into a designated form on the terminal.

[0550] This system allows companies to efficiently assign employees to the most suitable departments based on their skills and qualifications. Furthermore, by automating the skills matching process, it significantly reduces the effort and time required.

[0551] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0552] Step 1:

[0553] Entering employee information

[0554] User: Enter the employee's name and a list of their skills via the terminal. For example, if an employee named "John" has the skills of "Python," "Data Analysis," and "Project Management," enter that information.

[0555] Input: Employee name "John", Skills "Python", "Data Analysis", "Project Management"

[0556] Specific action: The user enters the required employee information into the input form on the terminal and clicks the submit button.

[0557] Output: The entered employee information is sent to the server and stored in the database.

[0558] Step 2:

[0559] Entering department information

[0560] User: Enter the name of each department and a list of skills required by that department via the terminal. For example, if the "Data Science" department requires "Python" and "Data Analysis" skills, enter that information.

[0561] Input: Department name "Data Science", Required skills "Python", "Data Analysis"

[0562] Specific action: The user enters the required department information into the input form on the terminal and clicks the submit button.

[0563] Output: The entered department information is sent to the server and stored in the database.

[0564] Step 3:

[0565] Calculation of skill match

[0566] Server: Compares employee skill lists with departmental skill list requirements and calculates the degree of agreement. Specifically, it calculates the number of intersection elements.

[0567] Input: Skill list for each employee, required skill list for each department

[0568] Specific operation: The server retrieves employee skill lists and departmental skill list requirements from the database, compares them, and runs an algorithm to calculate the degree of match.

[0569] Output: A list of matching scores for each employee and department is generated.

[0570] Step 4:

[0571] Assignment to the optimal department

[0572] Server: Assigns employees to the most suitable department based on the calculated degree of match.

[0573] Input: List of employee and departmental matching levels

[0574] Specific operation: The server determines the optimal department for each employee based on the matching score list and automatically assigns them. Assignment priority is determined by the matching score value.

[0575] Output: A list of assignment results to the most suitable department is generated.

[0576] Step 5:

[0577] Output of results

[0578] Server: Reports the user the result of assigning them to the most suitable department. For example, it might output, "John has been assigned to the Data Science department."

[0579] Input: List of results for assignment to the optimal department

[0580] Specific operation: The server generates a list of assignment results as data to notify the user and sends it to the terminal. The terminal displays the received data in the user interface.

[0581] Output: The assignment results are displayed on the user's terminal.

[0582] This series of steps makes it possible to efficiently place individual employees in the departments where they can best utilize their abilities.

[0583] (Application Example 1)

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

[0585] As industry advances, factories and production sites require the appropriate placement of highly skilled robots for efficient work. However, optimally matching the skills of each robot with the requirements of each work location is difficult, and in many cases, it has to rely on human judgment, which is time-consuming and labor-intensive. Furthermore, if robots are not properly placed, work efficiency decreases and production costs increase.

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

[0587] In this invention, the server includes means for inputting data indicating an individual's capabilities, means for inputting data indicating the operational requirements of each department within the organization, means for comparing the individual's capabilities data with the departmental operational requirements data and calculating the degree of agreement between them, means for assigning each individual to the most appropriate department based on the degree of agreement, means for inputting data indicating the machine's skills, means for inputting data indicating the operational requirements of each work site, means for comparing the machine's skills data with the work site operational requirements data and calculating the degree of agreement, and means for assigning each machine to the most appropriate work site based on the degree of agreement. This enables the appropriate placement of robots, resulting in improved work efficiency and reduced production costs.

[0588] "Data demonstrating individual capabilities" refers to quantitative representations of the skills, knowledge, and experience of employees within an organization.

[0589] "Data showing the operational requirements of each department within an organization" refers to a quantitative representation of the skills, knowledge, experience, and other conditions required by a specific department within an organization.

[0590] "A method for comparing individual competency data with departmental job requirements data and calculating the degree of agreement" refers to a method of matching an employee's skill set with the department's required skill set and calculating the degree of agreement numerically.

[0591] "A means of assigning each individual to the most appropriate department based on the degree of agreement" refers to a method of assigning employees to the most suitable department based on a calculated degree of agreement.

[0592] "Data demonstrating machine skills" refers to a quantitative representation of the specific work capabilities and technologies possessed by robots and machines used in factories and production sites.

[0593] "Data indicating the operational requirements of each work location" refers to a quantitative representation of the work capabilities and technical requirements of a specific work station in a factory or production site.

[0594] "A method for comparing machine skill data with work site requirements data and calculating the degree of agreement" refers to a method of matching the skills of a machine with the skills required by the work station and calculating the degree of agreement numerically.

[0595] "Means of assigning each machine to the most appropriate work location based on the degree of match" refers to a method of assigning machines to the most suitable work station based on the calculated degree of match.

[0596] The embodiments for carrying out this invention will be described in detail below. This system automatically determines the most appropriate placement using individual ability data, machine skill data, and work requirement data for each department and work station within the organization.

[0597] System Configuration

[0598] This system consists of the following main components:

[0599] 1. Means of inputting data indicating individual capabilities:

[0600] This is a method for users to use a terminal to enter a list of employee names and skills. For example, if "Employee 1" has skills "Skill A" and "Skill B," then that information would be entered.

[0601] 2. Means of inputting data that shows the operational requirements of each department within the organization:

[0602] This is a method for users to use a terminal to input the names of each department and a list of the skills that department requires. For example, if "Department 1" requires skills "Skill A" and "Skill B," the user would input those skills.

[0603] 3. A means of comparing individual competency data with departmental work requirements data and calculating the degree of agreement between them:

[0604] The server compares employee skill data with departmental job requirements data and calculates the degree of agreement. Specifically, it calculates the number of intersection elements between the skills an employee possesses and the skills required by the department. For example, if "Skill A" and "Skill B" from "Employee 1's" skill list match the required skills list of "Department 1," the degree of agreement is calculated as 2.

[0605] 4. Means for assigning each individual to the most appropriate department based on the degree of agreement:

[0606] The server assigns each employee to the most suitable department based on the calculated degree of match. For example, employee "Employee 1" is assigned to "Department 1".

[0607] 5. Means of inputting data indicating machine skills:

[0608] This is a method for users to use a terminal to input the names of machines in the factory and a list of the skills they possess. For example, if "Machine 1" has skills "Skill X" and "Skill Y," the user would input those skills.

[0609] 6. Means for inputting data indicating the operational requirements of each work location:

[0610] This is a method by which users use a terminal to input the names of each work location and a list of skills required for that location. For example, if "Station 1" requires skills "Skill X" and "Skill Y," the user would input those skills.

[0611] 7. Means for comparing machine skill data and work site requirements data and calculating the degree of agreement:

[0612] The server compares the machine's skill data with the work site's job requirements data and calculates the degree of match. Specifically, it calculates the number of intersection elements between the skills the machine possesses and the skills required by the work site. For example, if "Skill X" and "Skill Y" from the "Machine 1" skill list match the required skills list of "Station 1," the degree of match is calculated as 2.

[0613] 8. Means for assigning each machine to the most appropriate work location based on its degree of matching:

[0614] The server assigns each machine to the most suitable work location based on the calculated match. For example, machine "Machine 1" is assigned to "Station 1".

[0615] Hardware and software configuration

[0616] This system uses the following hardware and software:

[0617] Hardware:

[0618] Factory work robots (PLC-compatible smart robots)

[0619] Factory management server

[0620] Input devices (e.g., tablets or PCs)

[0621] software:

[0622] Python 3.x

[0623] Database management tools (e.g., SQLite, MySQL)

[0624] Examples

[0625] Employee information entry:

[0626] The user enters the employee's name and a list of their skills into the system via a terminal. For example, if "Employee 1" has "Skill A" and "Skill B," the user would enter that information.

[0627] Department information entry:

[0628] The user enters the name of each department and a list of skills required by that department into the system via a terminal. For example, if "Department 1" requires "Skill A" and "Skill B," the user would enter that information.

[0629] Skill Matching Calculation:

[0630] The server compares the employee's skill list with the department's required skill list. For example, if "Employee 1"'s skills "Skill A" and "Skill B" match the required skill list of "Department 1," the degree of match is calculated as 2.

[0631] Optimal department assignment:

[0632] The server assigns each employee to the most suitable department based on the calculated degree of match. For example, employee "Employee 1" is assigned to "Department 1".

[0633] Robot skill input:

[0634] The user enters the machine's name and its skill list into the system via a terminal. For example, if "Machine 1" has "Skill X" and "Skill Y," the user would enter that information.

[0635] Enter work location information:

[0636] The user enters the name of each work location and a list of skills required by that location into the system via a terminal. For example, if "Work Location 1" requires "Skill X" and "Skill Y," the user would enter that information.

[0637] Skill Matching Calculation:

[0638] The server compares the machine's skill list with the required skill list for the work location. For example, if "Skill X" and "Skill Y" from "Machine 1" match the required skill list for "Work Location 1", the degree of match is calculated as 2.

[0639] Optimal workspace assignment:

[0640] The server assigns each machine to the most suitable workspace based on the calculated match. For example, machine "Machine 1" is assigned to workspace "Workspace 1".

[0641] Example of a prompt

[0642] Robot "Machine 1" skills: Skill A, Skill B

[0643] Robot "Machine 2" skills: Skill C, Skill D

[0644] Robot "Machine 3" skills: Skill A, Skill C

[0645] Required skill for station "Workplace 1": Skill A

[0646] Required skill for Station "Workplace 2": Skill B

[0647] Required skills for Station "Workplace 3": Skill C

[0648] Use this data to assign each robot to the optimal work location.

[0649] This prompt allows the server to automatically generate a result that determines the optimal machine placement.

[0650] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0651] Step 1:

[0652] The user uses a terminal to enter the employee's name and a list of their skills. The input data includes the employee's name and a list of skills. For example, information such as "Employee 1 has skill A and skill B" might be entered. The system then retrieves the employee's competence data.

[0653] Step 2:

[0654] The user uses a terminal to enter the name of each department and a list of skills required by that department. The input data includes the department name and a list of required skills. For example, information such as "Department 1 requires skills A and B" might be entered. The system then retrieves the business requirements data for each department.

[0655] Step 3:

[0656] The server compares employee skill data with departmental job requirements data. First, it matches each employee's skill list with each department's required skill list and calculates the degree of match. Specifically, it calculates the number of intersection elements between the skills an employee possesses and the skills required by the department. For example, "If skills A and B from employee 1's skill list match the required skill list of department 1, the degree of match is calculated as 2."

[0657] Step 4:

[0658] The server assigns each employee to the most suitable department based on the calculated match score. Based on the entered match score data, the server selects the employee-department combination with the highest match score. For example, if the match score is highest, employee 1 will be assigned to department 1.

[0659] Step 5:

[0660] The user uses a terminal to input the names of machines in the factory and a list of skills they possess. The input data includes the machine name and a list of skills. For example, information such as "Machine 1 has skill X and skill Y" might be entered. The system then retrieves the machine's skill data.

[0661] Step 6:

[0662] The user uses a terminal to enter the name of each work location and a list of skills required for that location. The input data includes the name of the work location and a list of required skills. For example, information such as "Work Location 1 requires Skill X and Skill Y" might be entered. The system then retrieves the work requirements data for each work location.

[0663] Step 7:

[0664] The server compares the machine's skill data with the work site's operational requirements data. First, it matches the skill list for each machine with the required skill list for each work site and calculates the degree of match. Specifically, it calculates the number of intersection elements between the skills the machine possesses and the skills required by the work site. For example, "If skills X and Y from machine 1's skill list match the required skill list for work site 1, the degree of match is calculated as 2."

[0665] Step 8:

[0666] The server assigns each machine to the most suitable workspace based on the calculated match score. Based on the input match score data, the server selects the machine and workspace combination with the highest match score. For example, if the match score is highest, machine 1 is assigned to workspace 1.

[0667] By following these steps, the system can automatically determine the optimal placement of employees and machinery, thereby improving work efficiency and reducing production costs.

[0668] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0669] The embodiments for carrying out the present invention will be described in detail below. This system uses individual ability data and business requirements data of each department within an organization to assign each individual to the most appropriate department, and further incorporates an emotion engine that recognizes the user's emotions.

[0670] System Configuration

[0671] This system consists of the following main components:

[0672] 1. Employee Information Input Method: This is a method by which users input employee names and skill lists using a terminal.

[0673] 2. Department Information Input Method: This is a method by which users use a terminal to input the name of each department and a list of the skills required by that department.

[0674] 3. Skill Matching Calculation Method: This method involves the server comparing the employee's skill list with the department's required skill list and calculating the degree of match.

[0675] 4. Emotion Engine: A means of recognizing user emotions and collecting and analyzing that data.

[0676] 5. Emotional Data Evaluation Method: This method evaluates employee motivation and satisfaction based on user emotional data collected by an emotional engine.

[0677] 6. Optimal Department Assignment Method: This method assigns each employee to the most suitable department based on the server's calculated degree of agreement and sentiment data evaluation.

[0678] 7. Result Output Means: This means by which the server calculates the optimal department assignment result and reports it to the user.

[0679] Program processing

[0680] Employee Information Input: Users input employee names and a list of their skills into the system via a terminal. For example, if an employee named "John" has the skills of "Python," "Data Analysis," and "Project Management," they would input that information.

[0681] Department Information Input: Users input the name of each department and a list of skills required by that department into the system via a terminal. For example, if the "Data Science" department requires "Python" and "Data Analysis" skills, they would input those.

[0682] Skill Match Calculation: The server compares the employee's skill list with the department's required skills list. Specifically, it calculates the number of intersection elements between the employee's skills and the skills required by the department. For example, if "Python" and "Data Analysis" from "John's" skill list match the required skills list for "Data Science," the match score is calculated as 2.

[0683] Emotional Data Collection: The emotion engine collects emotional data from employees and managers through the user's device. This data reflects employee motivation and satisfaction, specifically analyzing emotions from facial expressions, voice tone, and other factors.

[0684] Emotional Data Evaluation: The server analyzes emotional data collected from the emotion engine to evaluate employee motivation and satisfaction. For example, if "John's" emotional data indicates high motivation and satisfaction, that data is recorded.

[0685] Optimal Department Assignment: The server assigns each employee to the most suitable department based on calculated skill match and sentiment data evaluation. For example, in the case of "John," his skill match is high in the "Data Science" department, and in addition, his sentiment data evaluation shows high motivation, so he is assigned to the "Data Science" department.

[0686] Result Output: The server generates the result of assigning John to the most suitable department and reports it to the user. For example, it might output a result such as "John has been assigned to the Data Science department."

[0687] Specific example

[0688] The following are specific examples of its use.

[0689] Employee information:

[0690] Employee 1: Name "John", Skills "Python", "Data Analysis", "Project Management"

[0691] Employee 2: Name "Emily", Skills "Java", "System Design", "Debugging"

[0692] Department information:

[0693] Department 1: "Data Science", Required skills: "Python", "Data Analysis"

[0694] Department 2: "Software Development", Required skills: "Java", "System Design"

[0695] Emotional data collection:

[0696] John's emotional data: High motivation and satisfaction

[0697] Emily's emotional data: Normal motivation and satisfaction

[0698] The server calculates the skill match as follows:

[0699] "John" and "Data Science": Similarity level 2 ("Python" and "Data Analysis")

[0700] "Emily" and "Software Development": Match level 2 ("Java," "System Design")

[0701] Taking sentiment data evaluation into consideration, the server assigns "John" to the "Data Science" department and "Emily" to the "Software Development" department. The results are then reported to the user.

[0702] In this way, the system achieves optimal placement by considering not only employee skills but also emotional data, thereby improving overall organizational productivity and employee satisfaction.

[0703] The following describes the processing flow.

[0704] Step 1:

[0705] The user uses a terminal to enter a list of employee names and skills. For example, they might enter the name "John" and the skills "Python," "Data Analysis," and "Project Management." The entered information is then sent to the server.

[0706] Step 2:

[0707] The user uses a terminal to enter the names of each department and a list of the skills required by that department. For example, they might enter the department name "Data Science" and the required skills "Python" and "Data Analysis". The entered information is then sent to the server.

[0708] Step 3:

[0709] The server stores the entered employee and department information as lists. This ensures that each employee's skill list and each department's required skill list are saved in the database.

[0710] Step 4:

[0711] The server compares each employee's skill list with the required skill list for each department and calculates the number of matching skills as the degree of match. For example, if "Python" and "Data Analysis" match the required skill list for "Data Science" in "John's" skill list, the degree of match is calculated as 2.

[0712] Step 5:

[0713] The server uses an emotion engine to collect user emotion data through the terminal. This data reflects employees' motivation and satisfaction levels, and analyzes emotions from facial expressions, voice tone, and other factors. For example, if "John's" emotion data indicates high motivation and satisfaction, that data is sent to the server.

[0714] Step 6:

[0715] The server analyzes emotional data collected from the emotion engine to evaluate employee motivation and satisfaction. For example, if the emotion engine recognizes high motivation and satisfaction from "John's" facial expressions and tone of voice, it records that evaluation.

[0716] Step 7:

[0717] The server assigns each employee to the most suitable department based on calculated skill match and sentiment data evaluation. For example, in the case of "John," his skill match is high in the "Data Science" department, and his sentiment data evaluation also shows high motivation, so he is assigned to the "Data Science" department.

[0718] Step 8:

[0719] The server generates the assignment result to the most suitable department and reports it to the user. For example, it might output a result such as, "John has been assigned to the Data Science department."

[0720] Step 9:

[0721] The user uses a terminal to review the results reported from the server and perform the necessary transfer procedures. For example, they might formally transfer "John" to the "Data Science" department.

[0722] In this way, the system takes employee skills and emotional data into consideration to achieve optimal placement, thereby improving overall organizational productivity and employee satisfaction.

[0723] (Example 2)

[0724] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0725] Traditional systems assigned employees based solely on individual skill data and departmental work requirements data, without considering employee motivation or satisfaction. Therefore, even if skill matching was appropriate, employee motivation and satisfaction could decline, preventing optimal placement.

[0726] The identification processing performed 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 inputting individual ability data, means for inputting the business requirements of each department within the organization, means for comparing individual ability data with departmental business requirement data and calculating the degree of agreement between them, means for recognizing the user's emotions, means for evaluating the collected emotional data as motivation and satisfaction, and means for comprehensively determining the emotional data evaluation and the degree of skill agreement to assign each individual to the most suitable department. This makes it possible to make optimal placements that take into account not only the skills of employees but also their emotional data.

[0727] "Data demonstrating an individual's capabilities" refers to information that expresses an individual's skills, knowledge, and experience in numerical or list format.

[0728] "Data showing the operational requirements of each department within an organization" refers to information that expresses the skills, knowledge, and experience required by a specific department within an organization in numerical or list format.

[0729] "Means for comparing and calculating the degree of agreement" refers to methods or devices that analyze common elements between input individual ability data and departmental business requirements data, and quantify the degree of agreement.

[0730] "Means of recognizing user emotions" refers to methods or devices that analyze a user's emotions from their facial expressions, tone of voice, etc., and collect the results as data.

[0731] "Means for evaluating collected emotional data as work motivation and satisfaction" refers to methods or devices that analyze emotional data obtained by emotion recognition means and use that analysis to evaluate the user's work motivation and satisfaction.

[0732] "Methods for assigning individuals to the most suitable departments by comprehensively evaluating emotional data and skill match" refers to methods or devices that analyze both skill match and emotional evaluation data, and use these results to place individuals in the most appropriate departments.

[0733] "An individual's ability data is presented as a list of skills" refers to a method of clearly indicating an individual's skills and knowledge in a list format.

[0734] "The degree of agreement is calculated using the number of intersection elements between the individual's skill list and the department's required skill list" means that the degree of agreement is calculated using the number of common elements between the individual's skill list and the department's required skill list.

[0735] This invention is a system for assigning individuals to the most appropriate departments by utilizing individual capability data and business requirements data for each department within an organization, and further incorporates an emotion engine that recognizes user emotions.

[0736] System Configuration

[0737] This system consists of the following main components:

[0738] 1. Employee information input method:

[0739] The user enters the employee's name and skill list using a terminal. Specifically, the user enters the information on the terminal screen via the keyboard.

[0740] 2. Department information input method:

[0741] The user uses a terminal to input the name of each department and a list of required skills. Specifically, the user inputs the information on the terminal screen using the keyboard.

[0742] 3. Skill Matching Calculation Method:

[0743] The server compares the employee's skill list with the department's required skill list and calculates the degree of match. Specifically, a processing unit on the server executes a program to count the number of intersection elements in the lists.

[0744] 4. Emotional Engine:

[0745] The emotion engine collects emotional data from employees and managers through the user's device. This data is then evaluated as motivation and satisfaction. Specifically, it uses facial recognition software and voice analysis software.

[0746] 5. Means for evaluating emotion data:

[0747] The server analyzes emotional data collected from the emotion engine to evaluate work motivation and satisfaction. The analysis results are recorded within the system.

[0748] 6. Means for optimal department assignment:

[0749] The server assigns each employee to the most suitable department based on calculated skill match and emotional data evaluation.

[0750] 7. Means for outputting results:

[0751] The server generates the optimal department assignment result and reports it to the user.

[0752] Specific example

[0753] The following are specific examples of its use.

[0754] Employee information:

[0755] Employee 1: Name "Employee A", Skills "Python", "Data Analysis", "Project Management"

[0756] Employee 2: Name "Employee B", Skills "Java", "System Design", "Debugging"

[0757] Department information:

[0758] Department 1: "Data Science", Required skills: "Python", "Data Analysis"

[0759] Department 2: "Software Development", Required skills: "Java", "System Design"

[0760] Emotional data collection:

[0761] Emotional data for "Employee A": High motivation and satisfaction

[0762] Emotional data for "Employee B": Normal motivation and satisfaction

[0763] Example of assignment result:

[0764] The server calculates the following skill match:

[0765] "Employee A" and "Data Science": Matching score 2 ("Python" and "Data Analysis")

[0766] "Employee B" and "Software Development": Matching score 2 ("Java," "System Design")

[0767] Taking sentiment data evaluation into consideration, the server assigns "Employee A" to the "Data Science" department and "Employee B" to the "Software Development" department. It then reports the results to the user. For example, it might report, "Employee A has been assigned to the Data Science department, and Employee B has been assigned to the Software Development department."

[0768] Example of a prompt

[0769] The following are examples of prompt statements to input into a generative AI model:

[0770] Employee 1: Employee A, Skills: Python, Data Analysis, Project Management

[0771] Employee 2: Employee B, Skills: Java, System Design, Debugging

[0772] Department 1: Data Science, Required Skills: Python, Data Analysis

[0773] Department 2: Software Development, Required Skills: Java, System Design

[0774] Employee A's emotional data: High motivation and satisfaction

[0775] Employee B's emotional data: Normal motivation and satisfaction

[0776] Please assign each employee to the most suitable department.

[0777] In this way, the present invention takes into account both employee skills and emotional data to achieve optimal departmental placement, thereby improving overall organizational productivity and employee satisfaction.

[0778] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0779] Step 1:

[0780] Entering employee information

[0781] The user uses a terminal to enter the employee's name and skill list. For example, the user enters the name of employee "A" and the skills "Python," "Data Analysis," and "Project Management."

[0782] Input: Employee name and skill information

[0783] Output: Employee names and skill lists stored in the employee information database

[0784] Specific operation: The user uses the keyboard to fill in employee information in the input fields on the terminal and clicks the submit button. The server receives the data and saves it to the employee information database.

[0785] Step 2:

[0786] Entering department information

[0787] The user uses a terminal to enter the name of each department and a list of required skills. For example, the user enters the name of the "Data Science Department" and the required skills "Python" and "Data Analysis".

[0788] Input: Department name and required skills information

[0789] Output: Department names and required skill lists stored in the department information database.

[0790] Specific operation: The user uses the keyboard to enter department information into the input field on the terminal and clicks the submit button. The server receives the data and saves it to the department information database.

[0791] Step 3:

[0792] Calculation of skill match

[0793] The server compares the employee's skill list with the department's required skill list and calculates the degree of match. A processing unit on the server executes a program and counts the number of intersection elements in the lists.

[0794] Input: Employee skill list, departmental required skill list

[0795] Output: Numerical data on skill matching

[0796] Specific operation: The server retrieves the necessary data from the employee database and department database, counts the number of matching skills, and calculates the degree of match.

[0797] Step 4:

[0798] Collection of emotional data

[0799] The emotion engine collects emotional data from employees and managers through the user's device. The emotion engine analyzes this emotional data using facial recognition software and voice analysis software.

[0800] Input: Emotional data such as employee facial expressions and voice tone.

[0801] Output: Analyzed sentiment data

[0802] Specific operation: The emotion engine uses the device's camera and microphone to collect emotion data and sends it to the server. The server receives it and stores the analysis results in a database.

[0803] Step 5:

[0804] Evaluation of emotional data

[0805] The server analyzes emotional data collected from the emotion engine to evaluate employee motivation and satisfaction.

[0806] Input: Collected emotional data

[0807] Output: Evaluation results of work motivation and satisfaction

[0808] Specific operation: The server runs a program to analyze emotional data, evaluates work motivation and satisfaction as numerical values, and saves the evaluation results to a database.

[0809] Step 6:

[0810] Assignment to the most suitable department

[0811] The server assigns each employee to the most suitable department based on calculated skill match and emotional data evaluation.

[0812] Input: Skill matching, emotional data evaluation

[0813] Output: Results of optimal departmental assignment

[0814] Specific operation: The server comprehensively analyzes both skill match and sentiment data evaluation, and uses an assignment algorithm to determine the optimal department.

[0815] Step 7:

[0816] Output of results

[0817] The server generates the optimal department assignment results and reports them to the user.

[0818] Input: Result of optimal departmental assignment

[0819] Output: Report of user assignment results

[0820] Specific operation: The server generates the assignment result and reports it to the user via the terminal display or email. For example, a message such as "Employee A has been assigned to the Data Science Department" might be displayed.

[0821] The above outlines the specific processing steps of this system's program. In this way, the system takes into account employee skills and emotional data to achieve optimal placement.

[0822] (Application Example 2)

[0823] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0824] Traditional employee placement systems primarily rely on employee skill sets, neglecting to consider employee emotions or motivation. This can lead to situations where employees with suitable skills but low motivation are assigned to the wrong positions, resulting in decreased productivity. To address this, a system is needed that collects and analyzes employee emotional data, placing employees in the most suitable departments based on both skills and emotions.

[0825] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting data indicating an individual's abilities, means for inputting data indicating the work requirements of each department within the organization, means for comparing the individual's ability data with the department's work requirements data and calculating the degree of agreement between them, means for assigning each individual to the most appropriate department based on the degree of agreement, an emotion engine for collecting and analyzing emotion data from facial expressions, voice tone, etc., means for evaluating the emotion data and analyzing the employee's motivation and satisfaction level, and optimization means for assigning the employee to the most suitable department based on the evaluation results and degree of agreement of the emotion data. This makes it possible to make optimal placements that simultaneously consider the employee's skills and emotions.

[0826] "Individual competency data" refers to information that indicates the skills and knowledge possessed by individual employees.

[0827] "Departmental operational requirements data" refers to information that indicates the skills and knowledge required by each department within an organization.

[0828] "Conformance" is an indicator that shows the consistency between the competency data of individual employees and the operational requirements data of their department.

[0829] An "emotion engine" is a system component that collects and analyzes emotional data from facial expressions, voice tone, and other sources.

[0830] "Emotional data" refers to information that indicates employee motivation and satisfaction levels, derived from their facial expressions and voice.

[0831] An "optimization measure" is a method or system for assigning employees to the most suitable departments based on the evaluation results of agreement and sentiment data.

[0832] This invention is a system that assigns individuals to the most suitable departments based on their skills and emotional data. This system calculates the degree of match using employee skill data and organizational work requirements data, and further applies emotional data acquired by an emotional engine to achieve optimal placement that takes into account employee satisfaction and motivation.

[0833] System Configuration

[0834] The system consists of the following main components:

[0835] 1. Employee Information Input Method: This is a method by which users input employee competency data using a terminal. This data consists of the employee's name and a list of skills.

[0836] 2. Department Information Input Method: This is a method by which users input business requirement data for each department using a terminal. This data consists of the department name and a list of skills required by that department.

[0837] 3. Skill Match Calculation Method: The server compares the employee's skill list with the department's job requirements list and calculates the degree of match. This generates candidates for assignment to the most suitable department.

[0838] 4. Emotion Engine: Uses the device's camera and microphone to analyze employees' facial expressions and voice tone to collect emotional data.

[0839] 5. Emotional Data Evaluation Method: The server analyzes the collected emotional data to evaluate employees' work motivation and satisfaction.

[0840] 6. Optimal Department Assignment Method: The server assigns each employee to the most appropriate department based on skill match and sentiment evaluation data.

[0841] 7. Result output means: The server generates the allocation results and reports them to the user.

[0842] Program processing

[0843] Employee information entry:

[0844] Users input employee names and skill lists into the system via a terminal. For example, "Employee A" has the skills of "Python" and "Data Analysis".

[0845] Department information entry:

[0846] The user enters the name of each department and a list of skills required by that department. For example, "Department 1" requires "Python" and "Data Analysis".

[0847] Skill Matching Calculation:

[0848] The server compares the employee's skill list with the department's skill list and calculates the degree of match for each. It counts the number of matches between employee A's skill list and department 1's skill list and calculates the degree of match.

[0849] Emotional data collection:

[0850] The system collects employee emotional data using the terminal's camera and microphone. It performs facial recognition and voice analysis using libraries such as OpenCV.

[0851] Sentiment data evaluation:

[0852] The server analyzes collected emotional data to evaluate employees' motivation and satisfaction levels. This helps determine whether employees are likely to adapt well to their respective departments.

[0853] Optimal department assignment:

[0854] The server assigns each employee to the most suitable department based on skill match and emotional data evaluation. It selects the combination with the highest skill match and emotional evaluation score.

[0855] Result output:

[0856] The server generates the assignment result for the most suitable department and reports it to the user. For example, the output might say, "Employee A has been assigned to Department 1."

[0857] Specific example

[0858] The following are specific examples of its use.

[0859] Employee information:

[0860] Employee A: Name "Employee A", Skills "Python", "Data Analysis"

[0861] Employee B: Name "Employee B", Skills "Java", "System Design"

[0862] Department information:

[0863] Department 1: Name "Data Science", Required skills "Python", "Data Analysis"

[0864] Department 2: Name "Software Development", Required skills "Java", "System Design"

[0865] Emotional data collection:

[0866] Employee A's emotional data: High motivation and satisfaction

[0867] Employee B's emotional data: Normal motivation and satisfaction

[0868] The server calculates skill match as follows:

[0869] "Employee A" and "Data Science": Concord score 2 ("Python", "Data Analysis")

[0870] "Employee B" and "Software Development": Matching score 2 ("Java," "System Design")

[0871] The optimal placement is as follows:

[0872] Assign "Employee A" to the "Data Science" department.

[0873] Assign "Employee B" to the "Software Development" department.

[0874] Example of a prompt:

[0875] "Enter the employee or robot's name and skill set, calculate the degree of match with the required skills for each work station, and collect emotional data to assess work motivation and determine the optimal placement."

[0876] This system enables optimal placement by considering both employee skills and emotions simultaneously. This, in turn, improves overall organizational productivity and employee satisfaction.

[0877] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0878] Step 1:

[0879] Employee Information Input: Users input employee names and skill lists via a terminal. The entered data is stored on the server side. For example, "Employee A" has the skills "Python" and "Data Analysis." This input data is sent to the system and stored in an internal database.

[0880] Step 2:

[0881] Department Information Input: Users input the name of each department and a list of skills required by that department via a terminal. The entered data is stored on the server side. For example, "Department 1" requires "Python" and "Data Analysis" skills. This input data is sent to the system and stored in an internal database.

[0882] Step 3:

[0883] Skill Match Calculation: The server compares the employee's skill list with the department's skill list and calculates the match score for each. The employee's skill list and the department's skill list are used as input. The match score is calculated based on the number of intersection elements between the skill lists. For example, if "Python" and "Data Analysis" from "Employee A's" skill list match the required skills list of "Department 1," the match score would be 2. The match score results are stored on the server.

[0884] Step 4:

[0885] Emotional Data Collection: Using the terminal's camera and microphone, the system analyzes employees' facial expressions and voice tone to collect emotional data. The collected emotional data is sent to the server. Using a facial recognition library (e.g., OpenCV) or voice analysis software, the raw image and voice data are converted into emotional data. This allows the emotional data to be quantified.

[0886] Step 5:

[0887] Emotional Data Evaluation: The server analyzes collected emotional data to evaluate employee motivation and satisfaction. Emotional data is used as input, and employee motivation scores are obtained as output. The server uses an emotional engine to classify the emotional data and quantify motivation and satisfaction.

[0888] Step 6:

[0889] Optimal Department Assignment: The server assigns each employee to the most suitable department based on skill match and sentiment data evaluation. Match data and sentiment evaluation data are used as input, and the optimal department assignment result is obtained as output. The server calculates a weighted average of skill match and sentiment score and determines assignment to the department with the highest score.

[0890] Step 7:

[0891] Result Output: The server generates and reports the optimal department assignment results to the user. The results are output in a report format that includes assignment information. For example, the result "Employee A has been assigned to Department 1" might be displayed on the terminal. This output information is provided in a format that is easy for the user to understand.

[0892] In this way, the entire system processes data to ensure optimal placement in departments, taking into account employees' skills and emotions. This is expected to improve overall organizational productivity and employee satisfaction.

[0893] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0894] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0895] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0896] [Third Embodiment]

[0897] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0898] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0899] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0900] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0901] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0902] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0903] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0904] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0905] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0906] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0907] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0908] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0909] The embodiments for carrying out the present invention will be described in detail below. This system uses individual capability data and operational requirements data for each department within an organization to assign each individual to the most appropriate department.

[0910] System Configuration

[0911] This system consists of the following main components:

[0912] 1. Employee Information Input Method: This is a method by which users input employee names and skill lists using a terminal.

[0913] 2. Department Information Input Method: This is a method by which users use a terminal to input the name of each department and a list of the skills required by that department.

[0914] 3. Skill Matching Calculation Method: This method involves the server comparing the employee's skill list with the department's required skill list and calculating the degree of match.

[0915] 4. Optimal Department Assignment Method: This method assigns each employee to the most suitable department based on the calculated degree of matching by the server.

[0916] 5. Result Output Means: This means by which the server calculates the optimal department assignment result and reports it to the user.

[0917] Program processing

[0918] Employee Information Input: Users input employee names and a list of their skills into the system via a terminal. For example, if an employee named "John" has the skills of "Python," "Data Analysis," and "Project Management," they would input that information.

[0919] Department Information Input: Users input the name of each department and a list of skills required by that department into the system via a terminal. For example, if the "Data Science" department requires "Python" and "Data Analysis" skills, they would input those.

[0920] Skill Match Calculation: The server compares the employee's skill list with the department's required skills list. Specifically, it calculates the number of intersection elements between the employee's skills and the skills required by the department. For example, if "Python" and "Data Analysis" from "John's" skill list match the required skills list for "Data Science," the match score is calculated as 2.

[0921] Optimal Department Assignment: The server assigns each employee to the most suitable department based on the calculated degree of match. For example, employee "John" is assigned to the "Data Science" department.

[0922] Result Output: The server calculates the optimal department assignment and reports it to the user. For example, it might output a result such as, "John has been assigned to the Data Science department."

[0923] Specific example

[0924] For example, consider a case where a user enters the following data.

[0925] Employee information:

[0926] Employee 1: Name "John", Skills "Python", "Data Analysis", "Project Management"

[0927] Employee 2: Name "Emily", Skills "Java", "System Design", "Debugging"

[0928] Department information:

[0929] Department 1: "Data Science", Required skills: "Python", "Data Analysis"

[0930] Department 2: "Software Development", Required skills: "Java", "System Design"

[0931] The server calculates the skill match as follows:

[0932] "John" and "Data Science": Similarity level 2 ("Python" and "Data Analysis")

[0933] "Emily" and "Software Development": Match level 2 ("Java," "System Design")

[0934] Based on the calculation results, the server assigns "John" to the "Data Science" department and "Emily" to the "Software Development" department, and outputs the results to the user.

[0935] In this way, this system enables the placement of individual employees into departments where they can best utilize their abilities.

[0936] The following describes the processing flow.

[0937] Step 1:

[0938] The user uses a terminal to enter a list of employee names and skills. For example, they might enter the name "John" and the skills "Python," "Data Analysis," and "Project Management." The entered information is then sent to the server.

[0939] Step 2:

[0940] The user uses a terminal to enter the names of each department and a list of the skills required by that department. For example, they might enter the department name "Data Science" and the required skills "Python" and "Data Analysis". The entered information is then sent to the server.

[0941] Step 3:

[0942] The server stores the entered employee and department information as lists. This ensures that each employee's skill list and each department's required skill list are saved in the database.

[0943] Step 4:

[0944] The server compares each employee's skill list with the required skill list for each department and calculates the number of matching skills as the degree of match. For example, if "Python" and "Data Analysis" match the required skill list for "Data Science" in "John's" skill list, the degree of match is calculated as 2.

[0945] Step 5:

[0946] The server calculates the degree of match for each employee with all departments and identifies the department with the highest degree of match. For example, in the case of "John," the server assigns "John" to the "Data Science" department because the degree of match is highest with the "Data Science" department.

[0947] Step 6:

[0948] The server generates the assignment result to the most suitable department and reports it to the user. For example, it might output a result such as, "John has been assigned to the Data Science department."

[0949] Step 7:

[0950] The user uses their terminal to check the results reported from the server and perform the necessary transfer procedures.

[0951] (Example 1)

[0952] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0953] Placing employees in the most suitable departments based on their skills and abilities is a critical challenge for many companies. However, traditional manual placement methods are time-consuming and sometimes result in inappropriate placements. Furthermore, there is a lack of automated systems to efficiently perform overall skill matching. Solving this challenge is essential to maximizing the use of internal resources and improving operational efficiency.

[0954] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0955] In this invention, the server includes means for inputting data indicating an individual's capabilities, means for inputting data indicating the operational requirements of each department within the organization, means for comparing the individual's capabilities data with the departmental operational requirements data and calculating the degree of agreement between them, means for assigning each individual to the most appropriate department based on the degree of agreement, and means for reporting the result of the assignment to the optimal department. This makes it possible to assign employees to departments based on their individual capabilities.

[0956] "Data indicating individual capabilities" refers to information that lists the skills, qualifications, experience, and other abilities that each individual possesses.

[0957] "Data showing the operational requirements of each department within an organization" refers to information that lists the operational requirements, such as the skills, qualifications, and experience needed for each department.

[0958] A "means for calculating the degree of agreement" refers to an algorithm or calculation system that compares individual ability data with departmental work requirements data and calculates the number of commonalities between the two.

[0959] The "assignment method" is a system that automatically places individuals in the most suitable departments based on a calculated degree of matching.

[0960] "Means of reporting" refers to systems or interfaces that notify users of the results of their placement into the most suitable department.

[0961] Modes for carrying out the invention

[0962] The embodiments for carrying out the present invention are described in detail below. This system utilizes individual capability data and operational requirements data for each department within an organization to assign each individual to the most appropriate department. The system consists of the following main components:

[0963] 1. Employee Information Input Method: This is a method by which users input employee names and skill lists using a terminal. Users use a dedicated form to input employee names and skills. For example, if an employee named "John" has the skills of "Python," "Data Analysis," and "Project Management," the user would input that information.

[0964] 2. Department Information Input Method: This is a method for users to input the name of each department and a list of skills required by that department using a terminal. Users use a dedicated form to input the department name and required skills. For example, if the "Data Science" department requires "Python" and "Data Analysis" skills, the user would input those.

[0965] 3. Skill Match Calculation Method: This method involves the server comparing an employee's skill list with the department's required skill list and calculating the degree of match. Specifically, the server calculates the number of common elements between the employee's skills and the department's required skills. This calculation is performed using a specific algorithm.

[0966] 4. Optimal Department Assignment Method: This method assigns each employee to the most suitable department based on the match score calculated by the server. The server selects the department with the highest match score for each employee and assigns the employee to that department.

[0967] 5. Result Output Method: This is a method by which the server calculates the optimal department assignment result and reports it to the user. For example, the user is provided with information such as, "John has been assigned to the Data Science department."

[0968] Specific usage instructions

[0969] Consider a scenario where the user enters the following data.

[0970] Employee information:

[0971] Employee 1: Name "John", Skills "Python", "Data Analysis", "Project Management"

[0972] Employee 2: Name "Emily", Skills "Java", "System Design", "Debugging"

[0973] Department information:

[0974] Department 1: "Data Science", Required skills: "Python", "Data Analysis"

[0975] Department 2: "Software Development", Required Skills: "Java", "System Design"

[0976] The server calculates skill match as follows:

[0977] "John" and "Data Science": Similarity level 2 ("Python" and "Data Analysis")

[0978] "Emily" and "Software Development": Match level 2 ("Java," "System Design")

[0979] Based on the calculation results, the server assigns "John" to the "Data Science" department and "Emily" to the "Software Development" department, and outputs the results to the user.

[0980] Examples of prompt statements

[0981] The following are specific examples of prompt statements for a generative AI model.

[0982] Employee information entry:

[0983] User: "Please enter an employee's skills list based on the following information. Example: Name: John, Skills: "Python", "Data Analysis", "Project Management""

[0984] In a specific action, the user enters employee information into a designated form on the terminal.

[0985] Department information entry:

[0986] User: "Please enter the department's skill list based on the following information. Example: Department: Data Science, Required Skills: "Python", "Data Analysis""

[0987] In a specific action, the user enters departmental information into a designated form on the terminal.

[0988] This system allows companies to efficiently assign employees to the most suitable departments based on their skills and qualifications. Furthermore, by automating the skills matching process, it significantly reduces the effort and time required.

[0989] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0990] Step 1:

[0991] Entering employee information

[0992] User: Enter the employee's name and a list of their skills via the terminal. For example, if an employee named "John" has the skills of "Python," "Data Analysis," and "Project Management," enter that information.

[0993] Input: Employee name "John", Skills "Python", "Data Analysis", "Project Management"

[0994] Specific action: The user enters the required employee information into the input form on the terminal and clicks the submit button.

[0995] Output: The entered employee information is sent to the server and stored in the database.

[0996] Step 2:

[0997] Entering department information

[0998] User: Enter the name of each department and a list of skills required by that department via the terminal. For example, if the "Data Science" department requires "Python" and "Data Analysis" skills, enter that information.

[0999] Input: Department name "Data Science", Required skills "Python", "Data Analysis"

[1000] Specific action: The user enters the required department information into the input form on the terminal and clicks the submit button.

[1001] Output: The entered department information is sent to the server and stored in the database.

[1002] Step 3:

[1003] Calculation of skill match

[1004] Server: Compares employee skill lists with departmental skill list requirements and calculates the degree of agreement. Specifically, it calculates the number of intersection elements.

[1005] Input: Skill list for each employee, required skill list for each department

[1006] Specific operation: The server retrieves employee skill lists and departmental skill list requirements from the database, compares them, and runs an algorithm to calculate the degree of match.

[1007] Output: A list of matching scores for each employee and department is generated.

[1008] Step 4:

[1009] Assignment to the optimal department

[1010] Server: Assigns employees to the most suitable department based on the calculated degree of match.

[1011] Input: List of employee and departmental matching levels

[1012] Specific operation: The server determines the optimal department for each employee based on the matching score list and automatically assigns them. Assignment priority is determined by the matching score value.

[1013] Output: A list of assignment results to the most suitable department is generated.

[1014] Step 5:

[1015] Output of results

[1016] Server: Reports the user the result of assigning them to the most suitable department. For example, it might output, "John has been assigned to the Data Science department."

[1017] Input: List of results for assignment to the optimal department

[1018] Specific operation: The server generates a list of assignment results as data to notify the user and sends it to the terminal. The terminal displays the received data in the user interface.

[1019] Output: The assignment results are displayed on the user's terminal.

[1020] This series of steps makes it possible to efficiently place individual employees in the departments where they can best utilize their abilities.

[1021] (Application Example 1)

[1022] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1023] As industry advances, factories and production sites require the appropriate placement of highly skilled robots for efficient work. However, optimally matching the skills of each robot with the requirements of each work location is difficult, and in many cases, it has to rely on human judgment, which is time-consuming and labor-intensive. Furthermore, if robots are not properly placed, work efficiency decreases and production costs increase.

[1024] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1025] In this invention, the server includes means for inputting data indicating an individual's capabilities, means for inputting data indicating the operational requirements of each department within the organization, means for comparing the individual's capabilities data with the departmental operational requirements data and calculating the degree of agreement between them, means for assigning each individual to the most appropriate department based on the degree of agreement, means for inputting data indicating the machine's skills, means for inputting data indicating the operational requirements of each work site, means for comparing the machine's skills data with the work site operational requirements data and calculating the degree of agreement, and means for assigning each machine to the most appropriate work site based on the degree of agreement. This enables the appropriate placement of robots, resulting in improved work efficiency and reduced production costs.

[1026] "Data demonstrating individual capabilities" refers to quantitative representations of the skills, knowledge, and experience of employees within an organization.

[1027] "Data showing the operational requirements of each department within an organization" refers to a quantitative representation of the skills, knowledge, experience, and other conditions required by a specific department within an organization.

[1028] "A method for comparing individual competency data with departmental job requirements data and calculating the degree of agreement" refers to a method of matching an employee's skill set with the department's required skill set and calculating the degree of agreement numerically.

[1029] "A means of assigning each individual to the most appropriate department based on the degree of agreement" refers to a method of assigning employees to the most suitable department based on a calculated degree of agreement.

[1030] "Data demonstrating machine skills" refers to a quantitative representation of the specific work capabilities and technologies possessed by robots and machines used in factories and production sites.

[1031] "Data indicating the operational requirements of each work location" refers to a quantitative representation of the work capabilities and technical requirements of a specific work station in a factory or production site.

[1032] "A method for comparing machine skill data with work site requirements data and calculating the degree of agreement" refers to a method of matching the skills of a machine with the skills required by the work station and calculating the degree of agreement numerically.

[1033] "Means of assigning each machine to the most appropriate work location based on the degree of match" refers to a method of assigning machines to the most suitable work station based on the calculated degree of match.

[1034] The embodiments for carrying out this invention will be described in detail below. This system automatically determines the most appropriate placement using individual ability data, machine skill data, and work requirement data for each department and work station within the organization.

[1035] System Configuration

[1036] This system consists of the following main components:

[1037] 1. Means of inputting data indicating individual capabilities:

[1038] This is a method for users to use a terminal to enter a list of employee names and skills. For example, if "Employee 1" has skills "Skill A" and "Skill B," then that information would be entered.

[1039] 2. Means of inputting data that shows the operational requirements of each department within the organization:

[1040] This is a method for users to use a terminal to input the names of each department and a list of the skills that department requires. For example, if "Department 1" requires skills "Skill A" and "Skill B," the user would input those skills.

[1041] 3. A means of comparing individual competency data with departmental work requirements data and calculating the degree of agreement between them:

[1042] The server compares employee skill data with departmental job requirements data and calculates the degree of agreement. Specifically, it calculates the number of intersection elements between the skills an employee possesses and the skills required by the department. For example, if "Skill A" and "Skill B" from "Employee 1's" skill list match the required skills list of "Department 1," the degree of agreement is calculated as 2.

[1043] 4. Means for assigning each individual to the most appropriate department based on the degree of agreement:

[1044] The server assigns each employee to the most suitable department based on the calculated degree of match. For example, employee "Employee 1" is assigned to "Department 1".

[1045] 5. Means of inputting data indicating machine skills:

[1046] This is a method for users to use a terminal to input the names of machines in the factory and a list of the skills they possess. For example, if "Machine 1" has skills "Skill X" and "Skill Y," the user would input those skills.

[1047] 6. Means for inputting data indicating the operational requirements of each work location:

[1048] This is a method by which users use a terminal to input the names of each work location and a list of skills required for that location. For example, if "Station 1" requires skills "Skill X" and "Skill Y," the user would input those skills.

[1049] 7. Means for comparing machine skill data and work site requirements data and calculating the degree of agreement:

[1050] The server compares the machine's skill data with the work site's job requirements data and calculates the degree of match. Specifically, it calculates the number of intersection elements between the skills the machine possesses and the skills required by the work site. For example, if "Skill X" and "Skill Y" from the "Machine 1" skill list match the required skills list of "Station 1," the degree of match is calculated as 2.

[1051] 8. Means for assigning each machine to the most appropriate work location based on its degree of matching:

[1052] The server assigns each machine to the most suitable work location based on the calculated match. For example, machine "Machine 1" is assigned to "Station 1".

[1053] Hardware and software configuration

[1054] This system uses the following hardware and software:

[1055] Hardware:

[1056] Factory work robots (PLC-compatible smart robots)

[1057] Factory management server

[1058] Input devices (e.g., tablets or PCs)

[1059] software:

[1060] Python 3.x

[1061] Database management tools (e.g., SQLite, MySQL)

[1062] Examples

[1063] Employee information entry:

[1064] The user enters the employee's name and a list of their skills into the system via a terminal. For example, if "Employee 1" has "Skill A" and "Skill B," the user would enter that information.

[1065] Department information entry:

[1066] The user enters the name of each department and a list of skills required by that department into the system via a terminal. For example, if "Department 1" requires "Skill A" and "Skill B," the user would enter that information.

[1067] Skill Matching Calculation:

[1068] The server compares the employee's skill list with the department's required skill list. For example, if "Employee 1"'s skills "Skill A" and "Skill B" match the required skill list of "Department 1," the degree of match is calculated as 2.

[1069] Optimal department assignment:

[1070] The server assigns each employee to the most suitable department based on the calculated degree of match. For example, employee "Employee 1" is assigned to "Department 1".

[1071] Robot skill input:

[1072] The user enters the machine's name and its skill list into the system via a terminal. For example, if "Machine 1" has "Skill X" and "Skill Y," the user would enter that information.

[1073] Enter work location information:

[1074] The user enters the name of each work location and a list of skills required by that location into the system via a terminal. For example, if "Work Location 1" requires "Skill X" and "Skill Y," the user would enter that information.

[1075] Skill Matching Calculation:

[1076] The server compares the machine's skill list with the required skill list for the work location. For example, if "Skill X" and "Skill Y" from "Machine 1" match the required skill list for "Work Location 1", the degree of match is calculated as 2.

[1077] Optimal workspace assignment:

[1078] The server assigns each machine to the most suitable workspace based on the calculated match. For example, machine "Machine 1" is assigned to workspace "Workspace 1".

[1079] Example of a prompt

[1080] Robot "Machine 1" skills: Skill A, Skill B

[1081] Robot "Machine 2" skills: Skill C, Skill D

[1082] Robot "Machine 3" skills: Skill A, Skill C

[1083] Required skill for station "Workplace 1": Skill A

[1084] Required skill for Station "Workplace 2": Skill B

[1085] Required skills for Station "Workplace 3": Skill C

[1086] Use this data to assign each robot to the optimal work location.

[1087] This prompt allows the server to automatically generate a result that determines the optimal machine placement.

[1088] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1089] Step 1:

[1090] The user uses a terminal to enter the employee's name and a list of their skills. The input data includes the employee's name and a list of skills. For example, information such as "Employee 1 has skill A and skill B" might be entered. The system then retrieves the employee's competence data.

[1091] Step 2:

[1092] The user uses a terminal to enter the name of each department and a list of skills required by that department. The input data includes the department name and a list of required skills. For example, information such as "Department 1 requires skills A and B" might be entered. The system then retrieves the business requirements data for each department.

[1093] Step 3:

[1094] The server compares employee skill data with departmental job requirements data. First, it matches each employee's skill list with each department's required skill list and calculates the degree of match. Specifically, it calculates the number of intersection elements between the skills an employee possesses and the skills required by the department. For example, "If skills A and B from employee 1's skill list match the required skill list of department 1, the degree of match is calculated as 2."

[1095] Step 4:

[1096] The server assigns each employee to the most suitable department based on the calculated match score. Based on the entered match score data, the server selects the employee-department combination with the highest match score. For example, if the match score is highest, employee 1 will be assigned to department 1.

[1097] Step 5:

[1098] The user uses a terminal to input the names of machines in the factory and a list of skills they possess. The input data includes the machine name and a list of skills. For example, information such as "Machine 1 has skill X and skill Y" might be entered. The system then retrieves the machine's skill data.

[1099] Step 6:

[1100] The user uses a terminal to enter the name of each work location and a list of skills required for that location. The input data includes the name of the work location and a list of required skills. For example, information such as "Work Location 1 requires Skill X and Skill Y" might be entered. The system then retrieves the work requirements data for each work location.

[1101] Step 7:

[1102] The server compares the machine's skill data with the work site's operational requirements data. First, it matches the skill list for each machine with the required skill list for each work site and calculates the degree of match. Specifically, it calculates the number of intersection elements between the skills the machine possesses and the skills required by the work site. For example, "If skills X and Y from machine 1's skill list match the required skill list for work site 1, the degree of match is calculated as 2."

[1103] Step 8:

[1104] The server assigns each machine to the most suitable workspace based on the calculated match score. Based on the input match score data, the server selects the machine and workspace combination with the highest match score. For example, if the match score is highest, machine 1 is assigned to workspace 1.

[1105] By following these steps, the system can automatically determine the optimal placement of employees and machinery, thereby improving work efficiency and reducing production costs.

[1106] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1107] The embodiments for carrying out the present invention will be described in detail below. This system uses individual ability data and business requirements data of each department within an organization to assign each individual to the most appropriate department, and further incorporates an emotion engine that recognizes the user's emotions.

[1108] System Configuration

[1109] This system consists of the following main components:

[1110] 1. Employee Information Input Method: This is a method by which users input employee names and skill lists using a terminal.

[1111] 2. Department Information Input Method: This is a method by which users use a terminal to input the name of each department and a list of the skills required by that department.

[1112] 3. Skill Matching Calculation Method: This method involves the server comparing the employee's skill list with the department's required skill list and calculating the degree of match.

[1113] 4. Emotion Engine: A means of recognizing user emotions and collecting and analyzing that data.

[1114] 5. Emotional Data Evaluation Method: This method evaluates employee motivation and satisfaction based on user emotional data collected by an emotional engine.

[1115] 6. Optimal Department Assignment Method: This method assigns each employee to the most suitable department based on the server's calculated degree of agreement and sentiment data evaluation.

[1116] 7. Result Output Means: This means by which the server calculates the optimal department assignment result and reports it to the user.

[1117] Program processing

[1118] Employee Information Input: Users input employee names and a list of their skills into the system via a terminal. For example, if an employee named "John" has the skills of "Python," "Data Analysis," and "Project Management," they would input that information.

[1119] Department Information Input: Users input the name of each department and a list of skills required by that department into the system via a terminal. For example, if the "Data Science" department requires "Python" and "Data Analysis" skills, they would input those.

[1120] Skill Match Calculation: The server compares the employee's skill list with the department's required skills list. Specifically, it calculates the number of intersection elements between the employee's skills and the skills required by the department. For example, if "Python" and "Data Analysis" from "John's" skill list match the required skills list for "Data Science," the match score is calculated as 2.

[1121] Emotional Data Collection: The emotion engine collects emotional data from employees and managers through the user's device. This data reflects employee motivation and satisfaction, specifically analyzing emotions from facial expressions, voice tone, and other factors.

[1122] Emotional Data Evaluation: The server analyzes emotional data collected from the emotion engine to evaluate employee motivation and satisfaction. For example, if "John's" emotional data indicates high motivation and satisfaction, that data is recorded.

[1123] Optimal Department Assignment: The server assigns each employee to the most suitable department based on calculated skill match and sentiment data evaluation. For example, in the case of "John," his skill match is high in the "Data Science" department, and in addition, his sentiment data evaluation shows high motivation, so he is assigned to the "Data Science" department.

[1124] Result Output: The server generates the result of assigning John to the most suitable department and reports it to the user. For example, it might output a result such as "John has been assigned to the Data Science department."

[1125] Specific example

[1126] The following are specific examples of its use.

[1127] Employee information:

[1128] Employee 1: Name "John", Skills "Python", "Data Analysis", "Project Management"

[1129] Employee 2: Name "Emily", Skills "Java", "System Design", "Debugging"

[1130] Department information:

[1131] Department 1: "Data Science", Required skills: "Python", "Data Analysis"

[1132] Department 2: "Software Development", Required skills: "Java", "System Design"

[1133] Emotional data collection:

[1134] John's emotional data: High motivation and satisfaction

[1135] Emily's emotional data: Normal motivation and satisfaction

[1136] The server calculates the skill match as follows:

[1137] "John" and "Data Science": Similarity level 2 ("Python" and "Data Analysis")

[1138] "Emily" and "Software Development": Match level 2 ("Java," "System Design")

[1139] Taking sentiment data evaluation into consideration, the server assigns "John" to the "Data Science" department and "Emily" to the "Software Development" department. The results are then reported to the user.

[1140] In this way, the system achieves optimal placement by considering not only employee skills but also emotional data, thereby improving overall organizational productivity and employee satisfaction.

[1141] The following describes the processing flow.

[1142] Step 1:

[1143] The user uses a terminal to enter a list of employee names and skills. For example, they might enter the name "John" and the skills "Python," "Data Analysis," and "Project Management." The entered information is then sent to the server.

[1144] Step 2:

[1145] The user uses a terminal to enter the names of each department and a list of the skills required by that department. For example, they might enter the department name "Data Science" and the required skills "Python" and "Data Analysis". The entered information is then sent to the server.

[1146] Step 3:

[1147] The server stores the entered employee and department information as lists. This ensures that each employee's skill list and each department's required skill list are saved in the database.

[1148] Step 4:

[1149] The server compares each employee's skill list with the required skill list for each department and calculates the number of matching skills as the degree of match. For example, if "Python" and "Data Analysis" match the required skill list for "Data Science" in "John's" skill list, the degree of match is calculated as 2.

[1150] Step 5:

[1151] The server uses an emotion engine to collect user emotion data through the terminal. This data reflects employees' motivation and satisfaction levels, and analyzes emotions from facial expressions, voice tone, and other factors. For example, if "John's" emotion data indicates high motivation and satisfaction, that data is sent to the server.

[1152] Step 6:

[1153] The server analyzes emotional data collected from the emotion engine to evaluate employee motivation and satisfaction. For example, if the emotion engine recognizes high motivation and satisfaction from "John's" facial expressions and tone of voice, it records that evaluation.

[1154] Step 7:

[1155] The server assigns each employee to the most suitable department based on calculated skill match and sentiment data evaluation. For example, in the case of "John," his skill match is high in the "Data Science" department, and his sentiment data evaluation also shows high motivation, so he is assigned to the "Data Science" department.

[1156] Step 8:

[1157] The server generates the assignment result to the most suitable department and reports it to the user. For example, it might output a result such as, "John has been assigned to the Data Science department."

[1158] Step 9:

[1159] The user uses a terminal to review the results reported from the server and perform the necessary transfer procedures. For example, they might formally transfer "John" to the "Data Science" department.

[1160] In this way, the system takes employee skills and emotional data into consideration to achieve optimal placement, thereby improving overall organizational productivity and employee satisfaction.

[1161] (Example 2)

[1162] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1163] Traditional systems assigned employees based solely on individual skill data and departmental work requirements data, without considering employee motivation or satisfaction. Therefore, even if skill matching was appropriate, employee motivation and satisfaction could decline, preventing optimal placement.

[1164] The identification processing performed 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 inputting individual ability data, means for inputting the business requirements of each department within the organization, means for comparing individual ability data with departmental business requirement data and calculating the degree of agreement between them, means for recognizing the user's emotions, means for evaluating the collected emotional data as motivation and satisfaction, and means for comprehensively determining the emotional data evaluation and the degree of skill agreement to assign each individual to the most suitable department. This makes it possible to make optimal placements that take into account not only the skills of employees but also their emotional data.

[1165] "Data demonstrating an individual's capabilities" refers to information that expresses an individual's skills, knowledge, and experience in numerical or list format.

[1166] "Data showing the operational requirements of each department within an organization" refers to information that expresses the skills, knowledge, and experience required by a specific department within an organization in numerical or list format.

[1167] "Means for comparing and calculating the degree of agreement" refers to methods or devices that analyze common elements between input individual ability data and departmental business requirements data, and quantify the degree of agreement.

[1168] "Means of recognizing user emotions" refers to methods or devices that analyze a user's emotions from their facial expressions, tone of voice, etc., and collect the results as data.

[1169] "Means for evaluating collected emotional data as work motivation and satisfaction" refers to methods or devices that analyze emotional data obtained by emotion recognition means and use that analysis to evaluate the user's work motivation and satisfaction.

[1170] "Methods for assigning individuals to the most suitable departments by comprehensively evaluating emotional data and skill match" refers to methods or devices that analyze both skill match and emotional evaluation data, and use these results to place individuals in the most appropriate departments.

[1171] "An individual's ability data is presented as a list of skills" refers to a method of clearly indicating an individual's skills and knowledge in a list format.

[1172] "The degree of agreement is calculated using the number of intersection elements between the individual's skill list and the department's required skill list" means that the degree of agreement is calculated using the number of common elements between the individual's skill list and the department's required skill list.

[1173] This invention is a system for assigning individuals to the most appropriate departments by utilizing individual capability data and business requirements data for each department within an organization, and further incorporates an emotion engine that recognizes user emotions.

[1174] System Configuration

[1175] This system consists of the following main components:

[1176] 1. Employee information input method:

[1177] The user enters the employee's name and skill list using a terminal. Specifically, the user enters the information on the terminal screen via the keyboard.

[1178] 2. Department information input method:

[1179] The user uses a terminal to input the name of each department and a list of required skills. Specifically, the user inputs the information on the terminal screen using the keyboard.

[1180] 3. Skill Matching Calculation Method:

[1181] The server compares the employee's skill list with the department's required skill list and calculates the degree of match. Specifically, a processing unit on the server executes a program to count the number of intersection elements in the lists.

[1182] 4. Emotional Engine:

[1183] The emotion engine collects emotional data from employees and managers through the user's device. This data is then evaluated as motivation and satisfaction. Specifically, it uses facial recognition software and voice analysis software.

[1184] 5. Means for evaluating emotion data:

[1185] The server analyzes emotional data collected from the emotion engine to evaluate work motivation and satisfaction. The analysis results are recorded within the system.

[1186] 6. Means for optimal department assignment:

[1187] The server assigns each employee to the most suitable department based on calculated skill match and emotional data evaluation.

[1188] 7. Means for outputting results:

[1189] The server generates the optimal department assignment result and reports it to the user.

[1190] Specific example

[1191] The following are specific examples of its use.

[1192] Employee information:

[1193] Employee 1: Name "Employee A", Skills "Python", "Data Analysis", "Project Management"

[1194] Employee 2: Name "Employee B", Skills "Java", "System Design", "Debugging"

[1195] Department information:

[1196] Department 1: "Data Science", Required skills: "Python", "Data Analysis"

[1197] Department 2: "Software Development", Required skills: "Java", "System Design"

[1198] Emotional data collection:

[1199] Emotional data for "Employee A": High motivation and satisfaction

[1200] Emotional data for "Employee B": Normal motivation and satisfaction

[1201] Example of assignment result:

[1202] The server calculates the following skill match:

[1203] "Employee A" and "Data Science": Matching score 2 ("Python" and "Data Analysis")

[1204] "Employee B" and "Software Development": Matching score 2 ("Java," "System Design")

[1205] Taking sentiment data evaluation into consideration, the server assigns "Employee A" to the "Data Science" department and "Employee B" to the "Software Development" department. It then reports the results to the user. For example, it might report, "Employee A has been assigned to the Data Science department, and Employee B has been assigned to the Software Development department."

[1206] Example of a prompt

[1207] The following are examples of prompt statements to input into a generative AI model:

[1208] Employee 1: Employee A, Skills: Python, Data Analysis, Project Management

[1209] Employee 2: Employee B, Skills: Java, System Design, Debugging

[1210] Department 1: Data Science, Required Skills: Python, Data Analysis

[1211] Department 2: Software Development, Required Skills: Java, System Design

[1212] Employee A's emotional data: High motivation and satisfaction

[1213] Employee B's emotional data: Normal motivation and satisfaction

[1214] Please assign each employee to the most suitable department.

[1215] In this way, the present invention takes into account both employee skills and emotional data to achieve optimal departmental placement, thereby improving overall organizational productivity and employee satisfaction.

[1216] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1217] Step 1:

[1218] Entering employee information

[1219] The user uses a terminal to enter the employee's name and skill list. For example, the user enters the name of employee "A" and the skills "Python," "Data Analysis," and "Project Management."

[1220] Input: Employee name and skill information

[1221] Output: Employee names and skill lists stored in the employee information database

[1222] Specific operation: The user uses the keyboard to fill in employee information in the input fields on the terminal and clicks the submit button. The server receives the data and saves it to the employee information database.

[1223] Step 2:

[1224] Entering department information

[1225] The user uses a terminal to enter the name of each department and a list of required skills. For example, the user enters the name of the "Data Science Department" and the required skills "Python" and "Data Analysis".

[1226] Input: Department name and required skills information

[1227] Output: Department names and required skill lists stored in the department information database.

[1228] Specific operation: The user uses the keyboard to enter department information into the input field on the terminal and clicks the submit button. The server receives the data and saves it to the department information database.

[1229] Step 3:

[1230] Calculation of skill match

[1231] The server compares the employee's skill list with the department's required skill list and calculates the degree of match. A processing unit on the server executes a program and counts the number of intersection elements in the lists.

[1232] Input: Employee skill list, departmental required skill list

[1233] Output: Numerical data on skill matching

[1234] Specific operation: The server retrieves the necessary data from the employee database and department database, counts the number of matching skills, and calculates the degree of match.

[1235] Step 4:

[1236] Collection of emotional data

[1237] The emotion engine collects emotional data from employees and managers through the user's device. The emotion engine analyzes this emotional data using facial recognition software and voice analysis software.

[1238] Input: Emotional data such as employee facial expressions and voice tone.

[1239] Output: Analyzed sentiment data

[1240] Specific operation: The emotion engine uses the device's camera and microphone to collect emotion data and sends it to the server. The server receives it and stores the analysis results in a database.

[1241] Step 5:

[1242] Evaluation of emotional data

[1243] The server analyzes emotional data collected from the emotion engine to evaluate employee motivation and satisfaction.

[1244] Input: Collected emotional data

[1245] Output: Evaluation results of work motivation and satisfaction

[1246] Specific operation: The server runs a program to analyze emotional data, evaluates work motivation and satisfaction as numerical values, and saves the evaluation results to a database.

[1247] Step 6:

[1248] Assignment to the most suitable department

[1249] The server assigns each employee to the most suitable department based on calculated skill match and emotional data evaluation.

[1250] Input: Skill matching, emotional data evaluation

[1251] Output: Results of optimal departmental assignment

[1252] Specific operation: The server comprehensively analyzes both skill match and sentiment data evaluation, and uses an assignment algorithm to determine the optimal department.

[1253] Step 7:

[1254] Output of results

[1255] The server generates the optimal department assignment results and reports them to the user.

[1256] Input: Result of optimal departmental assignment

[1257] Output: Report of user assignment results

[1258] Specific operation: The server generates the assignment result and reports it to the user via the terminal display or email. For example, a message such as "Employee A has been assigned to the Data Science Department" might be displayed.

[1259] The above outlines the specific processing steps of this system's program. In this way, the system takes into account employee skills and emotional data to achieve optimal placement.

[1260] (Application Example 2)

[1261] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1262] Traditional employee placement systems primarily rely on employee skill sets, neglecting to consider employee emotions or motivation. This can lead to situations where employees with suitable skills but low motivation are assigned to the wrong positions, resulting in decreased productivity. To address this, a system is needed that collects and analyzes employee emotional data, placing employees in the most suitable departments based on both skills and emotions.

[1263] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting data indicating an individual's abilities, means for inputting data indicating the work requirements of each department within the organization, means for comparing the individual's ability data with the department's work requirements data and calculating the degree of agreement between them, means for assigning each individual to the most appropriate department based on the degree of agreement, an emotion engine for collecting and analyzing emotion data from facial expressions, voice tone, etc., means for evaluating the emotion data and analyzing the employee's motivation and satisfaction level, and optimization means for assigning the employee to the most suitable department based on the evaluation results and degree of agreement of the emotion data. This makes it possible to make optimal placements that simultaneously consider the employee's skills and emotions.

[1264] "Individual competency data" refers to information that indicates the skills and knowledge possessed by individual employees.

[1265] "Departmental operational requirements data" refers to information that indicates the skills and knowledge required by each department within an organization.

[1266] "Conformance" is an indicator that shows the consistency between the competency data of individual employees and the operational requirements data of their department.

[1267] An "emotion engine" is a system component that collects and analyzes emotional data from facial expressions, voice tone, and other sources.

[1268] "Emotional data" refers to information that indicates employee motivation and satisfaction levels, derived from their facial expressions and voice.

[1269] An "optimization measure" is a method or system for assigning employees to the most suitable departments based on the evaluation results of agreement and sentiment data.

[1270] This invention is a system that assigns individuals to the most suitable departments based on their skills and emotional data. This system calculates the degree of match using employee skill data and organizational work requirements data, and further applies emotional data acquired by an emotional engine to achieve optimal placement that takes into account employee satisfaction and motivation.

[1271] System Configuration

[1272] The system consists of the following main components:

[1273] 1. Employee Information Input Method: This is a method by which users input employee competency data using a terminal. This data consists of the employee's name and a list of skills.

[1274] 2. Department Information Input Method: This is a method by which users input business requirement data for each department using a terminal. This data consists of the department name and a list of skills required by that department.

[1275] 3. Skill Match Calculation Method: The server compares the employee's skill list with the department's job requirements list and calculates the degree of match. This generates candidates for assignment to the most suitable department.

[1276] 4. Emotion Engine: Uses the device's camera and microphone to analyze employees' facial expressions and voice tone to collect emotional data.

[1277] 5. Emotional Data Evaluation Method: The server analyzes the collected emotional data to evaluate employees' work motivation and satisfaction.

[1278] 6. Optimal Department Assignment Method: The server assigns each employee to the most appropriate department based on skill match and sentiment evaluation data.

[1279] 7. Result output means: The server generates the allocation results and reports them to the user.

[1280] Program processing

[1281] Employee information entry:

[1282] Users input employee names and skill lists into the system via a terminal. For example, "Employee A" has the skills of "Python" and "Data Analysis".

[1283] Department information entry:

[1284] The user enters the name of each department and a list of skills required by that department. For example, "Department 1" requires "Python" and "Data Analysis".

[1285] Skill Matching Calculation:

[1286] The server compares the employee's skill list with the department's skill list and calculates the degree of match for each. It counts the number of matches between employee A's skill list and department 1's skill list and calculates the degree of match.

[1287] Emotional data collection:

[1288] The system collects employee emotional data using the terminal's camera and microphone. It performs facial recognition and voice analysis using libraries such as OpenCV.

[1289] Sentiment data evaluation:

[1290] The server analyzes collected emotional data to evaluate employees' motivation and satisfaction levels. This helps determine whether employees are likely to adapt well to their respective departments.

[1291] Optimal department assignment:

[1292] The server assigns each employee to the most suitable department based on skill match and emotional data evaluation. It selects the combination with the highest skill match and emotional evaluation score.

[1293] Result output:

[1294] The server generates the assignment result for the most suitable department and reports it to the user. For example, the output might say, "Employee A has been assigned to Department 1."

[1295] Specific example

[1296] The following are specific examples of its use.

[1297] Employee information:

[1298] Employee A: Name "Employee A", Skills "Python", "Data Analysis"

[1299] Employee B: Name "Employee B", Skills "Java", "System Design"

[1300] Department information:

[1301] Department 1: Name "Data Science", Required skills "Python", "Data Analysis"

[1302] Department 2: Name "Software Development", Required skills "Java", "System Design"

[1303] Emotional data collection:

[1304] Employee A's emotional data: High motivation and satisfaction

[1305] Employee B's emotional data: Normal motivation and satisfaction

[1306] The server calculates skill match as follows:

[1307] "Employee A" and "Data Science": Concord score 2 ("Python", "Data Analysis")

[1308] "Employee B" and "Software Development": Matching score 2 ("Java," "System Design")

[1309] The optimal placement is as follows:

[1310] Assign "Employee A" to the "Data Science" department.

[1311] Assign "Employee B" to the "Software Development" department.

[1312] Example of a prompt:

[1313] "Enter the employee or robot's name and skill set, calculate the degree of match with the required skills for each work station, and collect emotional data to assess work motivation and determine the optimal placement."

[1314] This system enables optimal placement by considering both employee skills and emotions simultaneously. This, in turn, improves overall organizational productivity and employee satisfaction.

[1315] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1316] Step 1:

[1317] Employee Information Input: Users input employee names and skill lists via a terminal. The entered data is stored on the server side. For example, "Employee A" has the skills "Python" and "Data Analysis." This input data is sent to the system and stored in an internal database.

[1318] Step 2:

[1319] Department Information Input: Users input the name of each department and a list of skills required by that department via a terminal. The entered data is stored on the server side. For example, "Department 1" requires "Python" and "Data Analysis" skills. This input data is sent to the system and stored in an internal database.

[1320] Step 3:

[1321] Skill Match Calculation: The server compares the employee's skill list with the department's skill list and calculates the match score for each. The employee's skill list and the department's skill list are used as input. The match score is calculated based on the number of intersection elements between the skill lists. For example, if "Python" and "Data Analysis" from "Employee A's" skill list match the required skills list of "Department 1," the match score would be 2. The match score results are stored on the server.

[1322] Step 4:

[1323] Emotional Data Collection: Using the terminal's camera and microphone, the system analyzes employees' facial expressions and voice tone to collect emotional data. The collected emotional data is sent to the server. Using a facial recognition library (e.g., OpenCV) or voice analysis software, the raw image and voice data are converted into emotional data. This allows the emotional data to be quantified.

[1324] Step 5:

[1325] Emotional Data Evaluation: The server analyzes collected emotional data to evaluate employee motivation and satisfaction. Emotional data is used as input, and employee motivation scores are obtained as output. The server uses an emotional engine to classify the emotional data and quantify motivation and satisfaction.

[1326] Step 6:

[1327] Optimal Department Assignment: The server assigns each employee to the most suitable department based on skill match and sentiment data evaluation. Match data and sentiment evaluation data are used as input, and the optimal department assignment result is obtained as output. The server calculates a weighted average of skill match and sentiment score and determines assignment to the department with the highest score.

[1328] Step 7:

[1329] Result Output: The server generates and reports the optimal department assignment results to the user. The results are output in a report format that includes assignment information. For example, the result "Employee A has been assigned to Department 1" might be displayed on the terminal. This output information is provided in a format that is easy for the user to understand.

[1330] In this way, the entire system processes data to ensure optimal placement in departments, taking into account employees' skills and emotions. This is expected to improve overall organizational productivity and employee satisfaction.

[1331] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1332] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1333] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1334] [Fourth Embodiment]

[1335] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1336] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1337] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1338] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1339] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1340] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1341] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1342] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1343] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1344] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1345] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1346] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1347] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1348] The embodiments for carrying out the present invention will be described in detail below. This system uses individual capability data and operational requirements data for each department within an organization to assign each individual to the most appropriate department.

[1349] System Configuration

[1350] This system consists of the following main components:

[1351] 1. Employee Information Input Method: This is a method by which users input employee names and skill lists using a terminal.

[1352] 2. Department Information Input Method: This is a method by which users use a terminal to input the name of each department and a list of the skills required by that department.

[1353] 3. Skill Matching Calculation Method: This method involves the server comparing the employee's skill list with the department's required skill list and calculating the degree of match.

[1354] 4. Optimal Department Assignment Method: This method assigns each employee to the most suitable department based on the calculated degree of matching by the server.

[1355] 5. Result Output Means: This means by which the server calculates the optimal department assignment result and reports it to the user.

[1356] Program processing

[1357] Employee Information Input: Users input employee names and a list of their skills into the system via a terminal. For example, if an employee named "John" has the skills of "Python," "Data Analysis," and "Project Management," they would input that information.

[1358] Department Information Input: Users input the name of each department and a list of skills required by that department into the system via a terminal. For example, if the "Data Science" department requires "Python" and "Data Analysis" skills, they would input those.

[1359] Skill Match Calculation: The server compares the employee's skill list with the department's required skills list. Specifically, it calculates the number of intersection elements between the employee's skills and the skills required by the department. For example, if "Python" and "Data Analysis" from "John's" skill list match the required skills list for "Data Science," the match score is calculated as 2.

[1360] Optimal Department Assignment: The server assigns each employee to the most suitable department based on the calculated degree of match. For example, employee "John" is assigned to the "Data Science" department.

[1361] Result Output: The server calculates the optimal department assignment and reports it to the user. For example, it might output a result such as, "John has been assigned to the Data Science department."

[1362] Specific example

[1363] For example, consider a case where a user enters the following data.

[1364] Employee information:

[1365] Employee 1: Name "John", Skills "Python", "Data Analysis", "Project Management"

[1366] Employee 2: Name "Emily", Skills "Java", "System Design", "Debugging"

[1367] Department information:

[1368] Department 1: "Data Science", Required skills: "Python", "Data Analysis"

[1369] Department 2: "Software Development", Required skills: "Java", "System Design"

[1370] The server calculates the skill match as follows:

[1371] "John" and "Data Science": Similarity level 2 ("Python" and "Data Analysis")

[1372] "Emily" and "Software Development": Match level 2 ("Java," "System Design")

[1373] Based on the calculation results, the server assigns "John" to the "Data Science" department and "Emily" to the "Software Development" department, and outputs the results to the user.

[1374] In this way, this system enables the placement of individual employees into departments where they can best utilize their abilities.

[1375] The following describes the processing flow.

[1376] Step 1:

[1377] The user uses a terminal to enter a list of employee names and skills. For example, they might enter the name "John" and the skills "Python," "Data Analysis," and "Project Management." The entered information is then sent to the server.

[1378] Step 2:

[1379] The user uses a terminal to enter the names of each department and a list of the skills required by that department. For example, they might enter the department name "Data Science" and the required skills "Python" and "Data Analysis". The entered information is then sent to the server.

[1380] Step 3:

[1381] The server stores the entered employee and department information as lists. This ensures that each employee's skill list and each department's required skill list are saved in the database.

[1382] Step 4:

[1383] The server compares each employee's skill list with the required skill list for each department and calculates the number of matching skills as the degree of match. For example, if "Python" and "Data Analysis" match the required skill list for "Data Science" in "John's" skill list, the degree of match is calculated as 2.

[1384] Step 5:

[1385] The server calculates the degree of match for each employee with all departments and identifies the department with the highest degree of match. For example, in the case of "John," the server assigns "John" to the "Data Science" department because the degree of match is highest with the "Data Science" department.

[1386] Step 6:

[1387] The server generates the assignment result to the most suitable department and reports it to the user. For example, it might output a result such as, "John has been assigned to the Data Science department."

[1388] Step 7:

[1389] The user uses their terminal to check the results reported from the server and perform the necessary transfer procedures.

[1390] (Example 1)

[1391] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1392] Placing employees in the most suitable departments based on their skills and abilities is a critical challenge for many companies. However, traditional manual placement methods are time-consuming and sometimes result in inappropriate placements. Furthermore, there is a lack of automated systems to efficiently perform overall skill matching. Solving this challenge is essential to maximizing the use of internal resources and improving operational efficiency.

[1393] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1394] In this invention, the server includes means for inputting data indicating an individual's capabilities, means for inputting data indicating the operational requirements of each department within the organization, means for comparing the individual's capabilities data with the departmental operational requirements data and calculating the degree of agreement between them, means for assigning each individual to the most appropriate department based on the degree of agreement, and means for reporting the result of the assignment to the optimal department. This makes it possible to assign employees to departments based on their individual capabilities.

[1395] "Data indicating individual capabilities" refers to information that lists the skills, qualifications, experience, and other abilities that each individual possesses.

[1396] "Data showing the operational requirements of each department within an organization" refers to information that lists the operational requirements, such as the skills, qualifications, and experience needed for each department.

[1397] A "means for calculating the degree of agreement" refers to an algorithm or calculation system that compares individual ability data with departmental work requirements data and calculates the number of commonalities between the two.

[1398] The "assignment method" is a system that automatically places individuals in the most suitable departments based on a calculated degree of matching.

[1399] "Means of reporting" refers to systems or interfaces that notify users of the results of their placement into the most suitable department.

[1400] Modes for carrying out the invention

[1401] The embodiments for carrying out the present invention are described in detail below. This system utilizes individual capability data and operational requirements data for each department within an organization to assign each individual to the most appropriate department. The system consists of the following main components:

[1402] 1. Employee Information Input Method: This is a method by which users input employee names and skill lists using a terminal. Users use a dedicated form to input employee names and skills. For example, if an employee named "John" has the skills of "Python," "Data Analysis," and "Project Management," the user would input that information.

[1403] 2. Department Information Input Method: This is a method for users to input the name of each department and a list of skills required by that department using a terminal. Users use a dedicated form to input the department name and required skills. For example, if the "Data Science" department requires "Python" and "Data Analysis" skills, the user would input those.

[1404] 3. Skill Match Calculation Method: This method involves the server comparing an employee's skill list with the department's required skill list and calculating the degree of match. Specifically, the server calculates the number of common elements between the employee's skills and the department's required skills. This calculation is performed using a specific algorithm.

[1405] 4. Optimal Department Assignment Method: This method assigns each employee to the most suitable department based on the match score calculated by the server. The server selects the department with the highest match score for each employee and assigns the employee to that department.

[1406] 5. Result Output Method: This is a method by which the server calculates the optimal department assignment result and reports it to the user. For example, the user is provided with information such as, "John has been assigned to the Data Science department."

[1407] Specific usage instructions

[1408] Consider a scenario where the user enters the following data.

[1409] Employee information:

[1410] Employee 1: Name "John", Skills "Python", "Data Analysis", "Project Management"

[1411] Employee 2: Name "Emily", Skills "Java", "System Design", "Debugging"

[1412] Department information:

[1413] Department 1: "Data Science", Required skills: "Python", "Data Analysis"

[1414] Department 2: "Software Development", Required Skills: "Java", "System Design"

[1415] The server calculates skill match as follows:

[1416] "John" and "Data Science": Similarity level 2 ("Python" and "Data Analysis")

[1417] "Emily" and "Software Development": Match level 2 ("Java," "System Design")

[1418] Based on the calculation results, the server assigns "John" to the "Data Science" department and "Emily" to the "Software Development" department, and outputs the results to the user.

[1419] Examples of prompt statements

[1420] The following are specific examples of prompt statements for a generative AI model.

[1421] Employee information entry:

[1422] User: "Please enter an employee's skills list based on the following information. Example: Name: John, Skills: "Python", "Data Analysis", "Project Management""

[1423] In a specific action, the user enters employee information into a designated form on the terminal.

[1424] Department information entry:

[1425] User: "Please enter the department's skill list based on the following information. Example: Department: Data Science, Required Skills: "Python", "Data Analysis""

[1426] In a specific action, the user enters departmental information into a designated form on the terminal.

[1427] This system allows companies to efficiently assign employees to the most suitable departments based on their skills and qualifications. Furthermore, by automating the skills matching process, it significantly reduces the effort and time required.

[1428] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1429] Step 1:

[1430] Entering employee information

[1431] User: Enter the employee's name and a list of their skills via the terminal. For example, if an employee named "John" has the skills of "Python," "Data Analysis," and "Project Management," enter that information.

[1432] Input: Employee name "John", Skills "Python", "Data Analysis", "Project Management"

[1433] Specific action: The user enters the required employee information into the input form on the terminal and clicks the submit button.

[1434] Output: The entered employee information is sent to the server and stored in the database.

[1435] Step 2:

[1436] Entering department information

[1437] User: Enter the name of each department and a list of skills required by that department via the terminal. For example, if the "Data Science" department requires "Python" and "Data Analysis" skills, enter that information.

[1438] Input: Department name "Data Science", Required skills "Python", "Data Analysis"

[1439] Specific action: The user enters the required department information into the input form on the terminal and clicks the submit button.

[1440] Output: The entered department information is sent to the server and stored in the database.

[1441] Step 3:

[1442] Calculation of skill match

[1443] Server: Compares employee skill lists with departmental skill list requirements and calculates the degree of agreement. Specifically, it calculates the number of intersection elements.

[1444] Input: Skill list for each employee, required skill list for each department

[1445] Specific operation: The server retrieves employee skill lists and departmental skill list requirements from the database, compares them, and runs an algorithm to calculate the degree of match.

[1446] Output: A list of matching scores for each employee and department is generated.

[1447] Step 4:

[1448] Assignment to the optimal department

[1449] Server: Assigns employees to the most suitable department based on the calculated degree of match.

[1450] Input: List of employee and departmental matching levels

[1451] Specific operation: The server determines the optimal department for each employee based on the matching score list and automatically assigns them. Assignment priority is determined by the matching score value.

[1452] Output: A list of assignment results to the most suitable department is generated.

[1453] Step 5:

[1454] Output of results

[1455] Server: Reports the user the result of assigning them to the most suitable department. For example, it might output, "John has been assigned to the Data Science department."

[1456] Input: List of results for assignment to the optimal department

[1457] Specific operation: The server generates a list of assignment results as data to notify the user and sends it to the terminal. The terminal displays the received data in the user interface.

[1458] Output: The assignment results are displayed on the user's terminal.

[1459] This series of steps makes it possible to efficiently place individual employees in the departments where they can best utilize their abilities.

[1460] (Application Example 1)

[1461] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1462] As industry advances, factories and production sites require the appropriate placement of highly skilled robots for efficient work. However, optimally matching the skills of each robot with the requirements of each work location is difficult, and in many cases, it has to rely on human judgment, which is time-consuming and labor-intensive. Furthermore, if robots are not properly placed, work efficiency decreases and production costs increase.

[1463] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1464] In this invention, the server includes means for inputting data indicating an individual's capabilities, means for inputting data indicating the operational requirements of each department within the organization, means for comparing the individual's capabilities data with the departmental operational requirements data and calculating the degree of agreement between them, means for assigning each individual to the most appropriate department based on the degree of agreement, means for inputting data indicating the machine's skills, means for inputting data indicating the operational requirements of each work site, means for comparing the machine's skills data with the work site operational requirements data and calculating the degree of agreement, and means for assigning each machine to the most appropriate work site based on the degree of agreement. This enables the appropriate placement of robots, resulting in improved work efficiency and reduced production costs.

[1465] "Data demonstrating individual capabilities" refers to quantitative representations of the skills, knowledge, and experience of employees within an organization.

[1466] "Data showing the operational requirements of each department within an organization" refers to a quantitative representation of the skills, knowledge, experience, and other conditions required by a specific department within an organization.

[1467] "A method for comparing individual competency data with departmental job requirements data and calculating the degree of agreement" refers to a method of matching an employee's skill set with the department's required skill set and calculating the degree of agreement numerically.

[1468] "A means of assigning each individual to the most appropriate department based on the degree of agreement" refers to a method of assigning employees to the most suitable department based on a calculated degree of agreement.

[1469] "Data demonstrating machine skills" refers to a quantitative representation of the specific work capabilities and technologies possessed by robots and machines used in factories and production sites.

[1470] "Data indicating the operational requirements of each work location" refers to a quantitative representation of the work capabilities and technical requirements of a specific work station in a factory or production site.

[1471] "A method for comparing machine skill data with work site requirements data and calculating the degree of agreement" refers to a method of matching the skills of a machine with the skills required by the work station and calculating the degree of agreement numerically.

[1472] "Means of assigning each machine to the most appropriate work location based on the degree of match" refers to a method of assigning machines to the most suitable work station based on the calculated degree of match.

[1473] The embodiments for carrying out this invention will be described in detail below. This system automatically determines the most appropriate placement using individual ability data, machine skill data, and work requirement data for each department and work station within the organization.

[1474] System Configuration

[1475] This system consists of the following main components:

[1476] 1. Means of inputting data indicating individual capabilities:

[1477] This is a method for users to use a terminal to enter a list of employee names and skills. For example, if "Employee 1" has skills "Skill A" and "Skill B," then that information would be entered.

[1478] 2. Means of inputting data that shows the operational requirements of each department within the organization:

[1479] This is a method for users to use a terminal to input the names of each department and a list of the skills that department requires. For example, if "Department 1" requires skills "Skill A" and "Skill B," the user would input those skills.

[1480] 3. A means of comparing individual competency data with departmental work requirements data and calculating the degree of agreement between them:

[1481] The server compares employee skill data with departmental job requirements data and calculates the degree of agreement. Specifically, it calculates the number of intersection elements between the skills an employee possesses and the skills required by the department. For example, if "Skill A" and "Skill B" from "Employee 1's" skill list match the required skills list of "Department 1," the degree of agreement is calculated as 2.

[1482] 4. Means for assigning each individual to the most appropriate department based on the degree of agreement:

[1483] The server assigns each employee to the most suitable department based on the calculated degree of match. For example, employee "Employee 1" is assigned to "Department 1".

[1484] 5. Means of inputting data indicating machine skills:

[1485] This is a method for users to use a terminal to input the names of machines in the factory and a list of the skills they possess. For example, if "Machine 1" has skills "Skill X" and "Skill Y," the user would input those skills.

[1486] 6. Means for inputting data indicating the operational requirements of each work location:

[1487] This is a method by which users use a terminal to input the names of each work location and a list of skills required for that location. For example, if "Station 1" requires skills "Skill X" and "Skill Y," the user would input those skills.

[1488] 7. Means for comparing machine skill data and work site requirements data and calculating the degree of agreement:

[1489] The server compares the machine's skill data with the work site's job requirements data and calculates the degree of match. Specifically, it calculates the number of intersection elements between the skills the machine possesses and the skills required by the work site. For example, if "Skill X" and "Skill Y" from the "Machine 1" skill list match the required skills list of "Station 1," the degree of match is calculated as 2.

[1490] 8. Means for assigning each machine to the most appropriate work location based on its degree of matching:

[1491] The server assigns each machine to the most suitable work location based on the calculated match. For example, machine "Machine 1" is assigned to "Station 1".

[1492] Hardware and software configuration

[1493] This system uses the following hardware and software:

[1494] Hardware:

[1495] Factory work robots (PLC-compatible smart robots)

[1496] Factory management server

[1497] Input devices (e.g., tablets or PCs)

[1498] software:

[1499] Python 3.x

[1500] Database management tools (e.g., SQLite, MySQL)

[1501] Examples

[1502] Employee information entry:

[1503] The user enters the employee's name and a list of their skills into the system via a terminal. For example, if "Employee 1" has "Skill A" and "Skill B," the user would enter that information.

[1504] Department information entry:

[1505] The user enters the name of each department and a list of skills required by that department into the system via a terminal. For example, if "Department 1" requires "Skill A" and "Skill B," the user would enter that information.

[1506] Skill Matching Calculation:

[1507] The server compares the employee's skill list with the department's required skill list. For example, if "Employee 1"'s skills "Skill A" and "Skill B" match the required skill list of "Department 1," the degree of match is calculated as 2.

[1508] Optimal department assignment:

[1509] The server assigns each employee to the most suitable department based on the calculated degree of match. For example, employee "Employee 1" is assigned to "Department 1".

[1510] Robot skill input:

[1511] The user enters the machine's name and its skill list into the system via a terminal. For example, if "Machine 1" has "Skill X" and "Skill Y," the user would enter that information.

[1512] Enter work location information:

[1513] The user enters the name of each work location and a list of skills required by that location into the system via a terminal. For example, if "Work Location 1" requires "Skill X" and "Skill Y," the user would enter that information.

[1514] Skill Matching Calculation:

[1515] The server compares the machine's skill list with the required skill list for the work location. For example, if "Skill X" and "Skill Y" from "Machine 1" match the required skill list for "Work Location 1", the degree of match is calculated as 2.

[1516] Optimal workspace assignment:

[1517] The server assigns each machine to the most suitable workspace based on the calculated match. For example, machine "Machine 1" is assigned to workspace "Workspace 1".

[1518] Example of a prompt

[1519] Robot "Machine 1" skills: Skill A, Skill B

[1520] Robot "Machine 2" skills: Skill C, Skill D

[1521] Robot "Machine 3" skills: Skill A, Skill C

[1522] Required skill for station "Workplace 1": Skill A

[1523] Required skill for Station "Workplace 2": Skill B

[1524] Required skills for Station "Workplace 3": Skill C

[1525] Use this data to assign each robot to the optimal work location.

[1526] This prompt allows the server to automatically generate a result that determines the optimal machine placement.

[1527] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1528] Step 1:

[1529] The user uses a terminal to enter the employee's name and a list of their skills. The input data includes the employee's name and a list of skills. For example, information such as "Employee 1 has skill A and skill B" might be entered. The system then retrieves the employee's competence data.

[1530] Step 2:

[1531] The user uses a terminal to enter the name of each department and a list of skills required by that department. The input data includes the department name and a list of required skills. For example, information such as "Department 1 requires skills A and B" might be entered. The system then retrieves the business requirements data for each department.

[1532] Step 3:

[1533] The server compares employee skill data with departmental job requirements data. First, it matches each employee's skill list with each department's required skill list and calculates the degree of match. Specifically, it calculates the number of intersection elements between the skills an employee possesses and the skills required by the department. For example, "If skills A and B from employee 1's skill list match the required skill list of department 1, the degree of match is calculated as 2."

[1534] Step 4:

[1535] The server assigns each employee to the most suitable department based on the calculated match score. Based on the entered match score data, the server selects the employee-department combination with the highest match score. For example, if the match score is highest, employee 1 will be assigned to department 1.

[1536] Step 5:

[1537] The user uses a terminal to input the names of machines in the factory and a list of skills they possess. The input data includes the machine name and a list of skills. For example, information such as "Machine 1 has skill X and skill Y" might be entered. The system then retrieves the machine's skill data.

[1538] Step 6:

[1539] The user uses a terminal to enter the name of each work location and a list of skills required for that location. The input data includes the name of the work location and a list of required skills. For example, information such as "Work Location 1 requires Skill X and Skill Y" might be entered. The system then retrieves the work requirements data for each work location.

[1540] Step 7:

[1541] The server compares the machine's skill data with the work site's operational requirements data. First, it matches the skill list for each machine with the required skill list for each work site and calculates the degree of match. Specifically, it calculates the number of intersection elements between the skills the machine possesses and the skills required by the work site. For example, "If skills X and Y from machine 1's skill list match the required skill list for work site 1, the degree of match is calculated as 2."

[1542] Step 8:

[1543] The server assigns each machine to the most suitable workspace based on the calculated match score. Based on the input match score data, the server selects the machine and workspace combination with the highest match score. For example, if the match score is highest, machine 1 is assigned to workspace 1.

[1544] By following these steps, the system can automatically determine the optimal placement of employees and machinery, thereby improving work efficiency and reducing production costs.

[1545] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1546] The embodiments for carrying out the present invention will be described in detail below. This system uses individual ability data and business requirements data of each department within an organization to assign each individual to the most appropriate department, and further incorporates an emotion engine that recognizes the user's emotions.

[1547] System Configuration

[1548] This system consists of the following main components:

[1549] 1. Employee Information Input Method: This is a method by which users input employee names and skill lists using a terminal.

[1550] 2. Department Information Input Method: This is a method by which users use a terminal to input the name of each department and a list of the skills required by that department.

[1551] 3. Skill Matching Calculation Method: This method involves the server comparing the employee's skill list with the department's required skill list and calculating the degree of match.

[1552] 4. Emotion Engine: A means of recognizing user emotions and collecting and analyzing that data.

[1553] 5. Emotional Data Evaluation Method: This method evaluates employee motivation and satisfaction based on user emotional data collected by an emotional engine.

[1554] 6. Optimal Department Assignment Method: This method assigns each employee to the most suitable department based on the server's calculated degree of agreement and sentiment data evaluation.

[1555] 7. Result Output Means: This means by which the server calculates the optimal department assignment result and reports it to the user.

[1556] Program processing

[1557] Employee Information Input: Users input employee names and a list of their skills into the system via a terminal. For example, if an employee named "John" has the skills of "Python," "Data Analysis," and "Project Management," they would input that information.

[1558] Department Information Input: Users input the name of each department and a list of skills required by that department into the system via a terminal. For example, if the "Data Science" department requires "Python" and "Data Analysis" skills, they would input those.

[1559] Skill Match Calculation: The server compares the employee's skill list with the department's required skills list. Specifically, it calculates the number of intersection elements between the employee's skills and the skills required by the department. For example, if "Python" and "Data Analysis" from "John's" skill list match the required skills list for "Data Science," the match score is calculated as 2.

[1560] Emotional Data Collection: The emotion engine collects emotional data from employees and managers through the user's device. This data reflects employee motivation and satisfaction, specifically analyzing emotions from facial expressions, voice tone, and other factors.

[1561] Emotional Data Evaluation: The server analyzes emotional data collected from the emotion engine to evaluate employee motivation and satisfaction. For example, if "John's" emotional data indicates high motivation and satisfaction, that data is recorded.

[1562] Optimal Department Assignment: The server assigns each employee to the most suitable department based on calculated skill match and sentiment data evaluation. For example, in the case of "John," his skill match is high in the "Data Science" department, and in addition, his sentiment data evaluation shows high motivation, so he is assigned to the "Data Science" department.

[1563] Result Output: The server generates the result of assigning John to the most suitable department and reports it to the user. For example, it might output a result such as "John has been assigned to the Data Science department."

[1564] Specific example

[1565] The following are specific examples of its use.

[1566] Employee information:

[1567] Employee 1: Name "John", Skills "Python", "Data Analysis", "Project Management"

[1568] Employee 2: Name "Emily", Skills "Java", "System Design", "Debugging"

[1569] Department information:

[1570] Department 1: "Data Science", Required skills: "Python", "Data Analysis"

[1571] Department 2: "Software Development", Required skills: "Java", "System Design"

[1572] Emotional data collection:

[1573] John's emotional data: High motivation and satisfaction

[1574] Emily's emotional data: Normal motivation and satisfaction

[1575] The server calculates the skill match as follows:

[1576] "John" and "Data Science": Similarity level 2 ("Python" and "Data Analysis")

[1577] "Emily" and "Software Development": Match level 2 ("Java," "System Design")

[1578] Taking sentiment data evaluation into consideration, the server assigns "John" to the "Data Science" department and "Emily" to the "Software Development" department. The results are then reported to the user.

[1579] In this way, the system achieves optimal placement by considering not only employee skills but also emotional data, thereby improving overall organizational productivity and employee satisfaction.

[1580] The following describes the processing flow.

[1581] Step 1:

[1582] The user uses a terminal to enter a list of employee names and skills. For example, they might enter the name "John" and the skills "Python," "Data Analysis," and "Project Management." The entered information is then sent to the server.

[1583] Step 2:

[1584] The user uses a terminal to enter the names of each department and a list of the skills required by that department. For example, they might enter the department name "Data Science" and the required skills "Python" and "Data Analysis". The entered information is then sent to the server.

[1585] Step 3:

[1586] The server stores the entered employee and department information as lists. This ensures that each employee's skill list and each department's required skill list are saved in the database.

[1587] Step 4:

[1588] The server compares each employee's skill list with the required skill list for each department and calculates the number of matching skills as the degree of match. For example, if "Python" and "Data Analysis" match the required skill list for "Data Science" in "John's" skill list, the degree of match is calculated as 2.

[1589] Step 5:

[1590] The server uses an emotion engine to collect user emotion data through the terminal. This data reflects employees' motivation and satisfaction levels, and analyzes emotions from facial expressions, voice tone, and other factors. For example, if "John's" emotion data indicates high motivation and satisfaction, that data is sent to the server.

[1591] Step 6:

[1592] The server analyzes emotional data collected from the emotion engine to evaluate employee motivation and satisfaction. For example, if the emotion engine recognizes high motivation and satisfaction from "John's" facial expressions and tone of voice, it records that evaluation.

[1593] Step 7:

[1594] The server assigns each employee to the most suitable department based on calculated skill match and sentiment data evaluation. For example, in the case of "John," his skill match is high in the "Data Science" department, and his sentiment data evaluation also shows high motivation, so he is assigned to the "Data Science" department.

[1595] Step 8:

[1596] The server generates the assignment result to the most suitable department and reports it to the user. For example, it might output a result such as, "John has been assigned to the Data Science department."

[1597] Step 9:

[1598] The user uses a terminal to review the results reported from the server and perform the necessary transfer procedures. For example, they might formally transfer "John" to the "Data Science" department.

[1599] In this way, the system takes employee skills and emotional data into consideration to achieve optimal placement, thereby improving overall organizational productivity and employee satisfaction.

[1600] (Example 2)

[1601] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1602] Traditional systems assigned employees based solely on individual skill data and departmental work requirements data, without considering employee motivation or satisfaction. Therefore, even if skill matching was appropriate, employee motivation and satisfaction could decline, preventing optimal placement.

[1603] The identification processing performed 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 inputting individual ability data, means for inputting the business requirements of each department within the organization, means for comparing individual ability data with departmental business requirement data and calculating the degree of agreement between them, means for recognizing the user's emotions, means for evaluating the collected emotional data as motivation and satisfaction, and means for comprehensively determining the emotional data evaluation and the degree of skill agreement to assign each individual to the most suitable department. This makes it possible to make optimal placements that take into account not only the skills of employees but also their emotional data.

[1604] "Data demonstrating an individual's capabilities" refers to information that expresses an individual's skills, knowledge, and experience in numerical or list format.

[1605] "Data showing the operational requirements of each department within an organization" refers to information that expresses the skills, knowledge, and experience required by a specific department within an organization in numerical or list format.

[1606] "Means for comparing and calculating the degree of agreement" refers to methods or devices that analyze common elements between input individual ability data and departmental business requirements data, and quantify the degree of agreement.

[1607] "Means of recognizing user emotions" refers to methods or devices that analyze a user's emotions from their facial expressions, tone of voice, etc., and collect the results as data.

[1608] "Means for evaluating collected emotional data as work motivation and satisfaction" refers to methods or devices that analyze emotional data obtained by emotion recognition means and use that analysis to evaluate the user's work motivation and satisfaction.

[1609] "Methods for assigning individuals to the most suitable departments by comprehensively evaluating emotional data and skill match" refers to methods or devices that analyze both skill match and emotional evaluation data, and use these results to place individuals in the most appropriate departments.

[1610] "An individual's ability data is presented as a list of skills" refers to a method of clearly indicating an individual's skills and knowledge in a list format.

[1611] "The degree of agreement is calculated using the number of intersection elements between the individual's skill list and the department's required skill list" means that the degree of agreement is calculated using the number of common elements between the individual's skill list and the department's required skill list.

[1612] This invention is a system for assigning individuals to the most appropriate departments by utilizing individual capability data and business requirements data for each department within an organization, and further incorporates an emotion engine that recognizes user emotions.

[1613] System Configuration

[1614] This system consists of the following main components:

[1615] 1. Employee information input method:

[1616] The user enters the employee's name and skill list using a terminal. Specifically, the user enters the information on the terminal screen via the keyboard.

[1617] 2. Department information input method:

[1618] The user uses a terminal to input the name of each department and a list of required skills. Specifically, the user inputs the information on the terminal screen using the keyboard.

[1619] 3. Skill Matching Calculation Method:

[1620] The server compares the employee's skill list with the department's required skill list and calculates the degree of match. Specifically, a processing unit on the server executes a program to count the number of intersection elements in the lists.

[1621] 4. Emotional Engine:

[1622] The emotion engine collects emotional data from employees and managers through the user's device. This data is then evaluated as motivation and satisfaction. Specifically, it uses facial recognition software and voice analysis software.

[1623] 5. Means for evaluating emotion data:

[1624] The server analyzes emotional data collected from the emotion engine to evaluate work motivation and satisfaction. The analysis results are recorded within the system.

[1625] 6. Means for optimal department assignment:

[1626] The server assigns each employee to the most suitable department based on calculated skill match and emotional data evaluation.

[1627] 7. Means for outputting results:

[1628] The server generates the optimal department assignment result and reports it to the user.

[1629] Specific example

[1630] The following are specific examples of its use.

[1631] Employee information:

[1632] Employee 1: Name "Employee A", Skills "Python", "Data Analysis", "Project Management"

[1633] Employee 2: Name "Employee B", Skills "Java", "System Design", "Debugging"

[1634] Department information:

[1635] Department 1: "Data Science", Required skills: "Python", "Data Analysis"

[1636] Department 2: "Software Development", Required skills: "Java", "System Design"

[1637] Emotional data collection:

[1638] Emotional data for "Employee A": High motivation and satisfaction

[1639] Emotional data for "Employee B": Normal motivation and satisfaction

[1640] Example of assignment result:

[1641] The server calculates the following skill match:

[1642] "Employee A" and "Data Science": Matching score 2 ("Python" and "Data Analysis")

[1643] "Employee B" and "Software Development": Matching score 2 ("Java," "System Design")

[1644] Taking sentiment data evaluation into consideration, the server assigns "Employee A" to the "Data Science" department and "Employee B" to the "Software Development" department. It then reports the results to the user. For example, it might report, "Employee A has been assigned to the Data Science department, and Employee B has been assigned to the Software Development department."

[1645] Example of a prompt

[1646] The following are examples of prompt statements to input into a generative AI model:

[1647] Employee 1: Employee A, Skills: Python, Data Analysis, Project Management

[1648] Employee 2: Employee B, Skills: Java, System Design, Debugging

[1649] Department 1: Data Science, Required Skills: Python, Data Analysis

[1650] Department 2: Software Development, Required Skills: Java, System Design

[1651] Employee A's emotional data: High motivation and satisfaction

[1652] Employee B's emotional data: Normal motivation and satisfaction

[1653] Please assign each employee to the most suitable department.

[1654] In this way, the present invention takes into account both employee skills and emotional data to achieve optimal departmental placement, thereby improving overall organizational productivity and employee satisfaction.

[1655] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1656] Step 1:

[1657] Entering employee information

[1658] The user uses a terminal to enter the employee's name and skill list. For example, the user enters the name of employee "A" and the skills "Python," "Data Analysis," and "Project Management."

[1659] Input: Employee name and skill information

[1660] Output: Employee names and skill lists stored in the employee information database

[1661] Specific operation: The user uses the keyboard to fill in employee information in the input fields on the terminal and clicks the submit button. The server receives the data and saves it to the employee information database.

[1662] Step 2:

[1663] Entering department information

[1664] The user uses a terminal to enter the name of each department and a list of required skills. For example, the user enters the name of the "Data Science Department" and the required skills "Python" and "Data Analysis".

[1665] Input: Department name and required skills information

[1666] Output: Department names and required skill lists stored in the department information database.

[1667] Specific operation: The user uses the keyboard to enter department information into the input field on the terminal and clicks the submit button. The server receives the data and saves it to the department information database.

[1668] Step 3:

[1669] Calculation of skill match

[1670] The server compares the employee's skill list with the department's required skill list and calculates the degree of match. A processing unit on the server executes a program and counts the number of intersection elements in the lists.

[1671] Input: Employee skill list, departmental required skill list

[1672] Output: Numerical data on skill matching

[1673] Specific operation: The server retrieves the necessary data from the employee database and department database, counts the number of matching skills, and calculates the degree of match.

[1674] Step 4:

[1675] Collection of emotional data

[1676] The emotion engine collects emotional data from employees and managers through the user's device. The emotion engine analyzes this emotional data using facial recognition software and voice analysis software.

[1677] Input: Emotional data such as employee facial expressions and voice tone.

[1678] Output: Analyzed sentiment data

[1679] Specific operation: The emotion engine uses the device's camera and microphone to collect emotion data and sends it to the server. The server receives it and stores the analysis results in a database.

[1680] Step 5:

[1681] Evaluation of emotional data

[1682] The server analyzes emotional data collected from the emotion engine to evaluate employee motivation and satisfaction.

[1683] Input: Collected emotional data

[1684] Output: Evaluation results of work motivation and satisfaction

[1685] Specific operation: The server runs a program to analyze emotional data, evaluates work motivation and satisfaction as numerical values, and saves the evaluation results to a database.

[1686] Step 6:

[1687] Assignment to the most suitable department

[1688] The server assigns each employee to the most suitable department based on calculated skill match and emotional data evaluation.

[1689] Input: Skill matching, emotional data evaluation

[1690] Output: Results of optimal departmental assignment

[1691] Specific operation: The server comprehensively analyzes both skill match and sentiment data evaluation, and uses an assignment algorithm to determine the optimal department.

[1692] Step 7:

[1693] Output of results

[1694] The server generates the optimal department assignment results and reports them to the user.

[1695] Input: Result of optimal departmental assignment

[1696] Output: Report of user assignment results

[1697] Specific operation: The server generates the assignment result and reports it to the user via the terminal display or email. For example, a message such as "Employee A has been assigned to the Data Science Department" might be displayed.

[1698] The above outlines the specific processing steps of this system's program. In this way, the system takes into account employee skills and emotional data to achieve optimal placement.

[1699] (Application Example 2)

[1700] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1701] Traditional employee placement systems primarily rely on employee skill sets, neglecting to consider employee emotions or motivation. This can lead to situations where employees with suitable skills but low motivation are assigned to the wrong positions, resulting in decreased productivity. To address this, a system is needed that collects and analyzes employee emotional data, placing employees in the most suitable departments based on both skills and emotions.

[1702] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting data indicating an individual's abilities, means for inputting data indicating the work requirements of each department within the organization, means for comparing the individual's ability data with the department's work requirements data and calculating the degree of agreement between them, means for assigning each individual to the most appropriate department based on the degree of agreement, an emotion engine for collecting and analyzing emotion data from facial expressions, voice tone, etc., means for evaluating the emotion data and analyzing the employee's motivation and satisfaction level, and optimization means for assigning the employee to the most suitable department based on the evaluation results and degree of agreement of the emotion data. This makes it possible to make optimal placements that simultaneously consider the employee's skills and emotions.

[1703] "Individual competency data" refers to information that indicates the skills and knowledge possessed by individual employees.

[1704] "Departmental operational requirements data" refers to information that indicates the skills and knowledge required by each department within an organization.

[1705] "Conformance" is an indicator that shows the consistency between the competency data of individual employees and the operational requirements data of their department.

[1706] An "emotion engine" is a system component that collects and analyzes emotional data from facial expressions, voice tone, and other sources.

[1707] "Emotional data" refers to information that indicates employee motivation and satisfaction levels, derived from their facial expressions and voice.

[1708] An "optimization measure" is a method or system for assigning employees to the most suitable departments based on the evaluation results of agreement and sentiment data.

[1709] This invention is a system that assigns individuals to the most suitable departments based on their skills and emotional data. This system calculates the degree of match using employee skill data and organizational work requirements data, and further applies emotional data acquired by an emotional engine to achieve optimal placement that takes into account employee satisfaction and motivation.

[1710] System Configuration

[1711] The system consists of the following main components:

[1712] 1. Employee Information Input Method: This is a method by which users input employee competency data using a terminal. This data consists of the employee's name and a list of skills.

[1713] 2. Department Information Input Method: This is a method by which users input business requirement data for each department using a terminal. This data consists of the department name and a list of skills required by that department.

[1714] 3. Skill Match Calculation Method: The server compares the employee's skill list with the department's job requirements list and calculates the degree of match. This generates candidates for assignment to the most suitable department.

[1715] 4. Emotion Engine: Uses the device's camera and microphone to analyze employees' facial expressions and voice tone to collect emotional data.

[1716] 5. Emotional Data Evaluation Method: The server analyzes the collected emotional data to evaluate employees' work motivation and satisfaction.

[1717] 6. Optimal Department Assignment Method: The server assigns each employee to the most appropriate department based on skill match and sentiment evaluation data.

[1718] 7. Result output means: The server generates the allocation results and reports them to the user.

[1719] Program processing

[1720] Employee information entry:

[1721] Users input employee names and skill lists into the system via a terminal. For example, "Employee A" has the skills of "Python" and "Data Analysis".

[1722] Department information entry:

[1723] The user enters the name of each department and a list of skills required by that department. For example, "Department 1" requires "Python" and "Data Analysis".

[1724] Skill Matching Calculation:

[1725] The server compares the employee's skill list with the department's skill list and calculates the degree of match for each. It counts the number of matches between employee A's skill list and department 1's skill list and calculates the degree of match.

[1726] Emotional data collection:

[1727] The system collects employee emotional data using the terminal's camera and microphone. It performs facial recognition and voice analysis using libraries such as OpenCV.

[1728] Sentiment data evaluation:

[1729] The server analyzes collected emotional data to evaluate employees' motivation and satisfaction levels. This helps determine whether employees are likely to adapt well to their respective departments.

[1730] Optimal department assignment:

[1731] The server assigns each employee to the most suitable department based on skill match and emotional data evaluation. It selects the combination with the highest skill match and emotional evaluation score.

[1732] Result output:

[1733] The server generates the assignment result for the most suitable department and reports it to the user. For example, the output might say, "Employee A has been assigned to Department 1."

[1734] Specific example

[1735] The following are specific examples of its use.

[1736] Employee information:

[1737] Employee A: Name "Employee A", Skills "Python", "Data Analysis"

[1738] Employee B: Name "Employee B", Skills "Java", "System Design"

[1739] Department information:

[1740] Department 1: Name "Data Science", Required skills "Python", "Data Analysis"

[1741] Department 2: Name "Software Development", Required skills "Java", "System Design"

[1742] Emotional data collection:

[1743] Employee A's emotional data: High motivation and satisfaction

[1744] Employee B's emotional data: Normal motivation and satisfaction

[1745] The server calculates skill match as follows:

[1746] "Employee A" and "Data Science": Concord score 2 ("Python", "Data Analysis")

[1747] "Employee B" and "Software Development": Matching score 2 ("Java," "System Design")

[1748] The optimal placement is as follows:

[1749] Assign "Employee A" to the "Data Science" department.

[1750] Assign "Employee B" to the "Software Development" department.

[1751] Example of a prompt:

[1752] "Enter the employee or robot's name and skill set, calculate the degree of match with the required skills for each work station, and collect emotional data to assess work motivation and determine the optimal placement."

[1753] This system enables optimal placement by considering both employee skills and emotions simultaneously. This, in turn, improves overall organizational productivity and employee satisfaction.

[1754] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1755] Step 1:

[1756] Employee Information Input: Users input employee names and skill lists via a terminal. The entered data is stored on the server side. For example, "Employee A" has the skills "Python" and "Data Analysis." This input data is sent to the system and stored in an internal database.

[1757] Step 2:

[1758] Department Information Input: Users input the name of each department and a list of skills required by that department via a terminal. The entered data is stored on the server side. For example, "Department 1" requires "Python" and "Data Analysis" skills. This input data is sent to the system and stored in an internal database.

[1759] Step 3:

[1760] Skill Match Calculation: The server compares the employee's skill list with the department's skill list and calculates the match score for each. The employee's skill list and the department's skill list are used as input. The match score is calculated based on the number of intersection elements between the skill lists. For example, if "Python" and "Data Analysis" from "Employee A's" skill list match the required skills list of "Department 1," the match score would be 2. The match score results are stored on the server.

[1761] Step 4:

[1762] Emotional Data Collection: Using the terminal's camera and microphone, the system analyzes employees' facial expressions and voice tone to collect emotional data. The collected emotional data is sent to the server. Using a facial recognition library (e.g., OpenCV) or voice analysis software, the raw image and voice data are converted into emotional data. This allows the emotional data to be quantified.

[1763] Step 5:

[1764] Emotional Data Evaluation: The server analyzes collected emotional data to evaluate employee motivation and satisfaction. Emotional data is used as input, and employee motivation scores are obtained as output. The server uses an emotional engine to classify the emotional data and quantify motivation and satisfaction.

[1765] Step 6:

[1766] Optimal Department Assignment: The server assigns each employee to the most suitable department based on skill match and sentiment data evaluation. Match data and sentiment evaluation data are used as input, and the optimal department assignment result is obtained as output. The server calculates a weighted average of skill match and sentiment score and determines assignment to the department with the highest score.

[1767] Step 7:

[1768] Result Output: The server generates and reports the optimal department assignment results to the user. The results are output in a report format that includes assignment information. For example, the result "Employee A has been assigned to Department 1" might be displayed on the terminal. This output information is provided in a format that is easy for the user to understand.

[1769] In this way, the entire system processes data to ensure optimal placement in departments, taking into account employees' skills and emotions. This is expected to improve overall organizational productivity and employee satisfaction.

[1770] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1771] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1772] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1773] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1774] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1775] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1776] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1777] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1778] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1779] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1780] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1781] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1782] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1783] 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.

[1784] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1785] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1786] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1787] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1788] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1789] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1790] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[1791] The following is further disclosed regarding the embodiments described above.

[1792] (Claim 1)

[1793] A means of inputting data that indicates an individual's abilities,

[1794] A means of inputting data that shows the operational requirements of each department within the organization,

[1795] A method for comparing individual ability data with departmental work requirements data and calculating the degree of agreement between them,

[1796] A means of assigning each individual to the most appropriate department based on the degree of agreement,

[1797] A system that includes this.

[1798] (Claim 2)

[1799] The system according to claim 1, wherein an individual's ability data is represented as a list of skills.

[1800] (Claim 3)

[1801] The system according to claim 1, wherein the degree of agreement is calculated by the number of intersection elements between an individual's skill list and a department's required skill list.

[1802] "Example 1"

[1803] (Claim 1)

[1804] A means of inputting data that indicates an individual's abilities,

[1805] A means of inputting data that shows the operational requirements of each department within the organization,

[1806] A method for comparing individual ability data with departmental work requirements data and calculating the degree of agreement between them,

[1807] A means of assigning each individual to the most appropriate department based on the degree of agreement,

[1808] A means of reporting the results of assignment to the optimal department,

[1809] A system that includes this.

[1810] (Claim 2)

[1811] The system according to claim 1, wherein an individual's ability data is represented as a list of skills.

[1812] (Claim 3)

[1813] The system according to claim 1, wherein the degree of agreement is calculated by the number of intersection elements between an individual's skill list and a department's required skill list.

[1814] "Application Example 1"

[1815] (Claim 1)

[1816] A means of inputting data that indicates an individual's abilities,

[1817] A means of inputting data that shows the operational requirements of each department within the organization,

[1818] A method for comparing individual ability data with departmental work requirements data and calculating the degree of agreement between them,

[1819] A means of assigning each individual to the most appropriate department based on the degree of agreement,

[1820] A means of inputting data that indicates the machine's skills,

[1821] A means of inputting data indicating the operational requirements for each work location,

[1822] A method for comparing machine skill data and work site requirements data and calculating the degree of agreement,

[1823] A means of assigning each machine to the most appropriate work location based on the degree of matching,

[1824] A system that includes this.

[1825] (Claim 2)

[1826] The system according to claim 1, wherein individual ability data and machine skill data are represented as a list.

[1827] (Claim 3)

[1828] The system according to claim 1, wherein the degree of agreement is calculated by the number of intersection elements between the individual's skill list and the machine's skill list and the department's and workplace's required skill lists.

[1829] "Example 2 of combining an emotion engine"

[1830] (Claim 1)

[1831] A means of inputting data that indicates an individual's abilities,

[1832] A means of inputting data that shows the operational requirements of each department within the organization,

[1833] A method for comparing individual ability data with departmental work requirements data and calculating the degree of agreement between them,

[1834] A means of assigning each individual to the most appropriate department based on the degree of agreement,

[1835] Means of recognizing user emotions,

[1836] A method for evaluating collected emotional data as motivation and satisfaction at work,

[1837] A method for assigning each individual to the most suitable department by comprehensively evaluating emotional data and skill match,

[1838] A system that includes this.

[1839] (Claim 2)

[1840] The system according to claim 1, wherein an individual's ability data is represented as a list of skills.

[1841] (Claim 3)

[1842] The system according to claim 1, wherein the degree of agreement is calculated by the number of intersection elements between an individual's skill list and a department's required skill list.

[1843] "Application example 2 of combining emotional engines"

[1844] (Claim 1)

[1845] A means of inputting data that indicates an individual's abilities,

[1846] A means of inputting data that shows the operational requirements of each department within the organization,

[1847] A method for comparing individual ability data with departmental work requirements data and calculating the degree of agreement between them,

[1848] A means of assigning each individual to the most appropriate department based on the degree of agreement,

[1849] An emotion engine that collects and analyzes emotional data from facial expressions, voice tone, etc.

[1850] A method for evaluating emotional data and analyzing employee motivation and satisfaction,

[1851] An optimization method for assigning employees to the most suitable department based on the evaluation results and degree of agreement of emotional data,

[1852] A system that includes this.

[1853] (Claim 2)

[1854] The system according to claim 1, wherein an individual's ability data is represented as a list of skills.

[1855] (Claim 3)

[1856] The system according to claim 1, wherein the degree of agreement is calculated by the number of intersection elements between an individual's skill list and a department's required skill list. [Explanation of symbols]

[1857] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of inputting data that indicates an individual's abilities, A means of inputting data that shows the operational requirements of each department within the organization, A method for comparing individual ability data with departmental work requirements data and calculating the degree of agreement between them, A means of assigning each individual to the most appropriate department based on the degree of agreement, A system that includes this.

2. The system according to claim 1, wherein an individual's ability data is represented as a list of skills.

3. The system according to claim 1, wherein the degree of agreement is calculated by the number of intersection elements between an individual's skill list and a department's required skill list.

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