system

Abstract

Provide a system. A user input means for inputting employee information; server means for receiving the input employee information and storing it in a database; a user input means for inputting business task information; a server means for receiving input business task information and storing it in a database; An analytical means for analyzing employee information and work task information stored in the database and selecting the most suitable employee for each task; an output means for presenting the selection results to the user; A system including:

Description

[Technical Field]

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

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

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

[0004] To improve work efficiency, it is necessary to quickly and accurately assign optimal work tasks to employees based on their individual skill sets. However, traditional methods require manual skill analysis and task assignment, which is time-consuming and labor-intensive and therefore inefficient. Furthermore, because the accuracy of assignment depends on human judgment, it is difficult to always guarantee optimal matching. This leads to problems such as delays in work progress and wasted resources. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides a system including a user input means for inputting employee information, a server means for receiving the input employee information and storing it in a database, a user input means for inputting work task information, a server means for receiving the input work task information and storing it in a database, an analysis means for analyzing the employee information and work task information stored in the database and selecting the optimal employee for each task, and an output means for presenting the selection results to the user. This system automates the assignment of optimal work tasks based on employees' skill sets, significantly reducing time and effort compared to manual assignment and achieving optimal matching with high accuracy. Furthermore, the use of a machine learning algorithm further improves the accuracy and efficiency of matching employees and work tasks.

[0006] "Employee information" refers to detailed information such as an employee's name, department, and skill set.

[0007] The "user input means" is an interface that allows the user to input employee information and business task information.

[0008] "Server means" refers to a computing device that receives input data, stores it in a database, and further processes the data.

[0009] "Business task information" is detailed information such as the content of the required business, the required skills, and deadlines.

[0010] A "database" is a data storage area for storing employee information and business task information.

[0011] The "analysis means" refers to an algorithm or program that analyzes employee information and business task information stored in the database and performs optimal task allocation.

[0012] The "output means" is an interface for presenting the results obtained by the analysis means to the user. [Brief explanation of the drawings]

[0013] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0014] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

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

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

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

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

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0021] [First embodiment]

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

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

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

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

[0026] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0034] The present invention is a system for automatically assigning optimal work tasks based on the skill sets of employees. Specific embodiments will be described below.

[0035] Enter employee information and skill sets

[0036] 1. User Action:

[0037] The user (administrator) enters employee information (such as name, department, skill set, etc.) in a dedicated input form. For example, the information for Employee A is entered as "Name: Employee A, Department: IT, Skills: Project management, Programming, Data analysis."

[0038] Receiving and storing data

[0039] 2. Terminal processing:

[0040] The terminal converts the employee information obtained from the input form into JSON format and sends the data to the server.

[0041] 3. Server processing:

[0042] The server receives the JSON data sent from the terminal and stores it in the database. For example, the information of employee A is saved in the corresponding table in the database.

[0043] Receive and store information about business tasks

[0044] 4. User Action:

[0045] The user enters the details of the work task (task name, required skills, deadline, etc.) into the system. For example, the user enters "Task name: Data analysis, Required skills: Data analysis, Deadline: End of October 2023."

[0046] 5. Terminal processing:

[0047] The device collects business task data, converts it into JSON format, and sends it to the server.

[0048] 6. Server Processing:

[0049] The server receives the business task information and stores it in a database, for example, the information of a data analysis task is stored in a corresponding table in the database.

[0050] Skill set analysis and personnel selection

[0051] 7. Server Processing:

[0052] The server retrieves employee and task information from the database and uses machine learning or rule-based algorithms to select the best employee for each task. For example, it may determine that Employee A, with his data analysis skills, is best suited for the data analysis task.

[0053] 8. Server Processing:

[0054] The selection results are formatted into a list and sent to the terminal in a data format such as JSON.

[0055] Generate and display a contact list

[0056] 9. Terminal Processing:

[0057] The terminal analyzes the list of people in charge received from the server and displays it on the user interface. For example, it displays "Employee A has been assigned to the data analysis task."

[0058] Specific examples

[0059] Specific examples are shown below.

[0060] input

[0061] Employee A: Skills (project management, programming, data analysis)

[0062] Employee B: Skills (system design, programming, security)

[0063] Employee C: Skills (writing, data analysis, marketing)

[0064] Business tasks

[0065] Task 1: Data Analysis

[0066] Task 2: System Development

[0067] Task 3: Report writing

[0068] Output

[0069] Task 1 (Data Analysis): Employee A

[0070] Task 2 (System Development): Employee B

[0071] Task 3 (Report Writing): Employee C

[0072] In this way, work tasks can be automatically assigned based on employees' skill sets, resulting in increased work efficiency.

[0073] The processing flow will be explained below.

[0074] Step 1:

[0075] The user enters employee information (such as name, department, skill set, etc.) using a dedicated input form.

[0076] For example, the user enters "Employee A, Department: IT, Skills: Project management, Programming, Data analysis."

[0077] Step 2:

[0078] The terminal takes the employee information entered by the user and converts it into JSON format or another appropriate data format.

[0079] Step 3:

[0080] The terminal transmits the converted data to the server.

[0081] Step 4:

[0082] The server parses the JSON data received from the device and breaks down the employee information into individual fields.

[0083] The server stores the parsed data in a database.

[0084] For example, store the information of "Employee A" in a corresponding table in the database.

[0085] Step 5:

[0086] The user enters details of the business task (task name, required skills, deadline, etc.) using a dedicated input form.

[0087] For example, enter "Task name: Data analysis, Required skills: Data analysis, Deadline: End of October 2023."

[0088] Step 6:

[0089] The device acquires the business task information entered by the user and converts it into JSON format or another appropriate data format.

[0090] Step 7:

[0091] The terminal transmits the converted data to the server.

[0092] Step 8:

[0093] The server analyzes the JSON data received from the terminal and breaks down the business task information into individual fields.

[0094] The server stores the parsed data in a database.

[0095] For example, the information of the "data analysis task" is stored in a corresponding table in the database.

[0096] Step 9:

[0097] The server retrieves employee information and work task information from the database.

[0098] Step 10:

[0099] The server uses machine learning or rule-based algorithms to select the best employee for each task.

[0100] For example, you might determine that "Employee A, who has data analysis skills, is best suited for the data analysis task."

[0101] Step 11:

[0102] The server formats the selection results into a list and sends it to the terminal in JSON format or other appropriate data format.

[0103] Step 12:

[0104] The terminal analyzes the list of personnel received from the server.

[0105] Step 13:

[0106] The terminal displays the analyzed list of personnel on the user interface.

[0107] For example, it might say, "Employee A has been assigned the data analysis task."

[0108] In this way, the system of the present invention automates the allocation of optimal business tasks based on the skill sets of employees through each processing step, thereby realizing efficient business operations.

[0109] Example 1

[0110] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0111] The process of selecting the most suitable work tasks based on employees' skill sets has traditionally been done manually, requiring time and effort. It also poses challenges in ensuring fair allocation of personnel and improving work efficiency. Furthermore, in large organizations, it is difficult to centrally manage employee skill information and work task information and quickly select the most suitable personnel.

[0112] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0113] In this invention, the server includes user input means for inputting employee information, terminal means for receiving the input employee information, converting it into a data format and sending it to the server, server means for storing the received employee information in a database, user input means for inputting work task information, terminal means for receiving the input work task information, converting it into a data format and sending it to the server, server means for storing the received work task information in the database, analysis means for acquiring the employee information and work task information stored in the database and selecting the most suitable employee for each task, and output means for displaying the selection results on a user interface. This makes it possible to quickly and efficiently assign work tasks to the most suitable employees.

[0114] "Employee information" is data that includes basic attribute information and skill sets about employees.

[0115] A "user input means" is a device or interface through which a user inputs information, including, for example, web forms and input fields.

[0116] "Terminal means" refers to a device or function for collecting employee information and business task information, converting it into an appropriate format, and transmitting it to a server.

[0117] The "server means" is a device or system for processing information received from the terminal means and storing it in a database.

[0118] "Business task information" is data that includes details of a specific business task (such as task name, required skills, deadline, etc.).

[0119] A "data format" is a way of representing data in a particular format, such as JSON or XML.

[0120] A "database" is a system for efficiently storing, managing, and searching data, and includes, for example, MySQL (registered trademark) and PostgreSQL.

[0121] "Analysis means" refers to devices and algorithms used to retrieve and analyze information in a database.

[0122] "Output means" refers to a device or interface for displaying the analysis results to the user.

[0123] A "machine learning algorithm" is an algorithm that learns patterns and rules based on data and makes predictions and classifications.

[0124] The present invention is a system for automatically assigning work tasks based on the skill sets of employees. This system is operated by combining users, terminals, and a server.

[0125] Enter employee information and skill sets

[0126] The user enters employee information using a dedicated input form provided on the web browser. The information entered includes name, department, skill set, etc. For example, for employee A, the user enters "Name: Employee A, Department: IT, Skills: Project management, Programming, Data analysis."

[0127] Receiving and storing data

[0128] The terminal receives the employee information entered by the user and converts it into JSON format. The converted data is sent to the server as an HTTP POST request. The server receives this request, parses the JSON data, and stores the employee information in a database (for example, MySQL or PostgreSQL).

[0129] Receive and store information about business tasks

[0130] The user enters the details of the work task using a similarly provided input form. For example, they enter "Task name: Data analysis, Required skills: Data analysis, Deadline: End of October 2023." The device converts this work task information into JSON format and sends it to the server. The server analyzes the received JSON data and stores the work task information in a database.

[0131] Skill set analysis and personnel selection

[0132] The server retrieves employee and task information from the database using SQL SELECT statements. Based on the retrieved information, it applies machine learning or rule-based algorithms to select the best employee for each task. For example, it uses the Python scikit-learn library to determine that Employee A, with his data analysis skills, is best suited for a data analysis task.

[0133] Generate and display a contact list

[0134] The server formats the selection results into a list and converts it into a data format such as JSON. This data is sent to the terminal as an HTTP response. The terminal analyzes the list of people in charge received from the server and displays it in the user interface. For example, the browser displays "Employee A has been assigned to the data analysis task."

[0135] Specific examples

[0136] input

[0137] Employee A: Skills (project management, programming, data analysis)

[0138] Employee B: Skills (system design, programming, security)

[0139] Employee C: Skills (writing, data analysis, marketing)

[0140] Business tasks

[0141] Task 1: Data Analysis

[0142] Task 2: System Development

[0143] Task 3: Report writing

[0144] Prompt Sentence Examples

[0145] You can use the prompt text as follows:

[0146] Employee Information:

[0147] Name: Employee A

[0148] Department: IT

[0149] Skills: Project Management, Programming, Data Analysis

[0150] Business tasks:

[0151] Task Name: Data Analysis

[0152] Required skills: Data analysis

[0153] Deadline: End of October 2023

[0154] Using the information above, explain how to assign tasks to the appropriate employees.

[0155] This prompt can be fed into an AI model to generate instructions on how to assign the task to the best employee.

[0156] As such, the system streamlines the allocation of complex work tasks and makes the most of employee skill sets.

[0157] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0158] Step 1:

[0159] Entering employee information

[0160] Users access a dedicated input form on a web browser and enter employee information.

[0161] Specifically, the user fills in form fields with details such as the employee's name, department, and skill set.

[0162] Example: "Name: Employee A, Department: IT, Skills: Project management, programming, data analysis"

[0163] Input: Employee information entered by the user.

[0164] Output: Employee information entered into the input form.

[0165] Step 2:

[0166] Receiving employee information

[0167] The terminal receives the data entered by the user into the input form.

[0168] Converts input data into JSON format.

[0169] Input: Employee information entered by the user.

[0170] Output: Employee information converted to JSON format.

[0171] Step 3:

[0172] Transmitting and storing employee information

[0173] The device sends the converted JSON format data to the server as an HTTP POST request.

[0174] The server receives the HTTP POST request and parses the JSON data.

[0175] The server stores the parsed employee information in a database, for example, the employee information is inserted into the "Employees" table.

[0176] Input: Employee information in JSON format.

[0177] Output: Employee information stored in the database.

[0178] Step 4:

[0179] Entering work task information

[0180] The user opens a separate input form in a web browser and enters the details of the work task.

[0181] For example, enter "Task name: Data analysis, Required skills: Data analysis, Deadline: End of October 2023."

[0182] Input: Work task information entered by the user.

[0183] Output: Business task information entered in the input form.

[0184] Step 5:

[0185] Receiving business task information

[0186] The terminal receives the business task information entered by the user in the input form.

[0187] Converts input data into JSON format.

[0188] Input: Work task information entered by the user.

[0189] Output: Business task information converted to JSON format.

[0190] Step 6:

[0191] Sending and storing business task information

[0192] The device sends the converted JSON format data to the server as an HTTP POST request.

[0193] The server receives the HTTP POST request and parses the JSON data.

[0194] The server stores the analyzed business task information in a database. For example, the business task information is inserted into a "Tasks" table.

[0195] Input: Business task information in JSON format.

[0196] Output: Business task information stored in a database.

[0197] Step 7:

[0198] Obtaining employee and task information

[0199] The server retrieves employee and task information from the database using SQL SELECT statements.

[0200] The obtained information is passed to an analysis means.

[0201] Input: Employee and work task information stored in a database.

[0202] Output: Employee information and work task information captured for analysis.

[0203] Step 8:

[0204] Analysis and selection of personnel

[0205] The server analyzes the acquired information using an analysis means (machine learning algorithm or rule-based algorithm).

[0206] The analytical method matches employee skill sets with business task requirements and selects the most suitable employee.

[0207] For example, employee A, who has data analysis skills, is assigned to the data analysis task.

[0208] Input: Captured employee information and work task information.

[0209] Output: Selection of the best employee for each task.

[0210] Step 9:

[0211] Sending and displaying the selection results

[0212] The server formats the selection results into a list and converts them into JSON format.

[0213] The server sends this data to the terminal as an HTTP response.

[0214] The terminal analyzes the received list of people in charge and displays the results on the user interface, for example, "Employee A has been assigned to the data analysis task."

[0215] Input: JSON data of the selection result.

[0216] Output: The list of agents displayed in the user interface.

[0217] (Application example 1)

[0218] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0219] Although systems already exist that can efficiently assign optimal work tasks to employees based on their skill sets, there is a lack of a way to quickly notify employees of the results of the selection process. This means that employees are unable to immediately know about the assigned tasks and take action, resulting in a decline in work efficiency in factories and offices.

[0220] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0221] In this invention, the server includes user input means for inputting employee information, server means for receiving the input employee information and storing it in a database, user input means for inputting job content information, server means for receiving the input job content information and storing it in a database, analysis means for analyzing the employee information and job content information stored in the database and selecting the employee most suitable for each job, output means for presenting the selection results to the user, and communication means for notifying the employee's communication terminal of the selection results, thereby making it possible to promptly notify the employee of the selection results.

[0222] "Employee information" includes information such as the employee's name, department, and skill set.

[0223] The "user input means" is an interface that allows employees or managers to input employee information and job content information.

[0224] The "server means" is a server having a function for receiving input data and storing it in a database as necessary.

[0225] "Work content information" is information such as the name of the work task, the required skills, and the deadline.

[0226] The "analysis means" is a system that has the function of analyzing employee information and job content information stored in the database and selecting the most suitable employee for each job.

[0227] The "output means" is an interface for presenting the analysis results and selection results to the user.

[0228] "Communication means" refers to a system that has the function of notifying the selection results to the employee's communication terminal.

[0229] A "machine learning algorithm" is an algorithm that learns patterns from data and makes predictions and classifications.

[0230] The present invention is a system that automatically selects the most suitable employee based on the employee's skill set and job content information in order to improve work efficiency in factories and offices, and notifies the results to the employee's communication terminal. A specific implementation method is shown below.

[0231] System configuration

[0232] Hardware

[0233] User terminal: A device (e.g., tablet, smart glasses) for inputting employee information and job content information.

[0234] Server: A server system for receiving, storing, and analyzing data.

[0235] Communication terminal: Employee device (e.g., smartphone, smart glasses) for receiving the selection results.

[0236] software

[0237] Input form: An input interface for employee information and job content information installed on the user's terminal.

[0238] Receiving and storing data: A server-side script that converts the received data into JSON format and stores it in a database.

[0239] Machine learning algorithm: Python's scikit-learn library is used to analyze employee information and job description information and select the most suitable employees.

[0240] Notification system: An API for pushing the selection results to employees' communication devices.

[0241] Program processing explanation

[0242] The server receives employee information and job details sent from the user's device in JSON format and stores the data in a database. It then uses a machine learning algorithm to select the most suitable employee, formats the selection results, and notifies the employee's communication device.

[0243] Data processing and calculation

[0244] Analyze employee information: Map each employee's skill set to a numerical value and feed it into machine learning algorithms.

[0245] Analyze job information: Map required skills to a numerical value and compare it with employee skill sets to find the best match.

[0246] Sending the selection results: Using the push notification API, the selection results are notified to the employee's communication device.

[0247] As a specific example, if Employee A's skill set "project management, programming, data analysis" and Task 1 "data analysis" are entered, the server will match the skill set with the task and determine that Employee A is the best person for this task. The result will be notified to Employee A's smartphone.

[0248] Generative AI model prompt example

[0249] Create an application that automatically selects the best workers to improve work efficiency. Enter employee skill sets and work task information, and the system will select the best employee for each task. The output will be displayed in a list format, including the selected workers and the task names.

[0250] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0251] Step 1:

[0252] A user uses a tablet or smart glasses to enter employee information, including the employee's name, department, and skill set, which is then converted into JSON format.

[0253] Step 2:

[0254] The entered JSON data is sent from the terminal to the server, which receives it and stores it in a database for analysis. When stored in the database, each employee's information is saved as a separate record.

[0255] Step 3:

[0256] The user again uses a tablet or smart glasses to input task information, including the task name, required skills, and deadline, which is then converted to JSON format.

[0257] Step 4:

[0258] The JSON data of the job content information is sent from the terminal to the server. The server receives this data and stores it in a database. Information about each job content is saved as an individual record.

[0259] Step 5:

[0260] The server retrieves employee information and job information from the database. It uses a machine learning algorithm (K-nearest neighbor method) to process and calculate the data to select the best employee for each task. Specifically, it maps the employee's skill set and the skills required for the task to a numerical value, and generates a dataset for analysis.

[0261] Step 6:

[0262] The machine learning model selects the best employee, and the matching results between the selected employee and the task are formatted in JSON format and presented to the user. This generates a list of the best people to assign each task.

[0263] Step 7:

[0264] The server sends a push notification to not only the user but also the employee's communication device (smartphone or smart glasses). The notification system API is used to deliver the selection results to the employee's device.

[0265] Step 8:

[0266] Employees receive notifications via their communication devices and check the details of their assigned tasks. The notifications include the task name, required skills, deadline, and the name of the person in charge. They can immediately begin working on the assigned task.

[0267] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0268] The present invention combines a system that automatically assigns optimal work tasks based on the skill sets of employees with an emotion engine that recognizes the emotions of users. Specific embodiments will be described below.

[0269] Enter employee information and skill sets

[0270] User operations

[0271] The user (administrator) enters employee information (such as name, department, skill set, etc.) in a dedicated input form. For example, the information for Employee A is entered as "Name: Employee A, Department: IT, Skills: Project management, Programming, Data analysis."

[0272] Receiving and storing data

[0273] Terminal handling

[0274] The terminal converts the employee information obtained from the input form into JSON format and sends it to the server.

[0275] Server Processing

[0276] The server receives the JSON data sent from the terminal and stores it in the database. For example, the information of employee A is saved in the corresponding table in the database.

[0277] Receive and store information about business tasks

[0278] User operations

[0279] The user enters the details of the work task (task name, required skills, deadline, etc.) into the system. For example, the user enters "Task name: Data analysis, Required skills: Data analysis, Deadline: End of October 2023."

[0280] Terminal handling

[0281] The device collects data on business tasks, converts it into JSON format, and sends it to the server.

[0282] Server Processing

[0283] The server receives the business task information and stores it in a database, for example, the information of a data analysis task is stored in a corresponding table in the database.

[0284] Skill set analysis and personnel selection

[0285] Server Processing

[0286] The server retrieves employee and task information from the database and uses machine learning or rule-based algorithms to select the best employee for each task. For example, it may determine that Employee A, with his data analysis skills, is best suited for the data analysis task.

[0287] Introducing the Emotion Engine

[0288] Emotion Engine Functions

[0289] The emotion engine monitors the emotional state of employees in real time and provides that information to analytical tools. The emotion engine evaluates the emotional state of employees, for example, through facial expression recognition and voice analysis.

[0290] Collaboration between the server and emotion engine

[0291] The server stores the emotional information obtained from the emotion engine in a database. For example, information such as "Employee A's current emotional state: high stress level" is saved.

[0292] Adjustment based on emotional state

[0293] The server analyzes the emotional and skill information stored in the database and reflects this information in task allocation. For example, if employee A has a high stress level, the server will adjust the allocation to avoid high-stress tasks.

[0294] Generate and display a contact list

[0295] Server Processing

[0296] The server generates a list of optimal agents that reflects the emotional information and sends it to the terminal in a data format such as JSON.

[0297] Terminal handling

[0298] The terminal analyzes the list of people in charge received from the server and displays it on the user interface. For example, it displays "Employee B has been assigned to the data analysis task."

[0299] Specific examples

[0300] Specific examples are shown below.

[0301] input

[0302] Employee A: Skills (project management, programming, data analysis), Emotional state (high stress level)

[0303] Employee B: Skills (system design, programming, security), emotional state (low stress level)

[0304] Employee C: Skills (Writing, Data Analysis, Marketing), Emotional State (Medium Stress Level)

[0305] Business tasks

[0306] Task 1: Data Analysis

[0307] Task 2: System Development

[0308] Task 3: Report writing

[0309] Output

[0310] Task 1 (Data Analysis): Employee C

[0311] Task 2 (System Development): Employee B

[0312] Task 3 (Report Writing): Employee C

[0313] In this way, by combining emotion engines, optimal task allocation can be achieved while taking into consideration the emotional state of employees, enabling even more efficient business operations.

[0314] The processing flow will be explained below.

[0315] Step 1:

[0316] The user enters employee information (such as name, department, skill set, etc.) using a dedicated input form.

[0317] For example, the user enters "Employee A, Department: IT, Skills: Project management, Programming, Data analysis."

[0318] Step 2:

[0319] The terminal takes the employee information entered by the user and converts it into JSON format or another appropriate data format.

[0320] Step 3:

[0321] The terminal transmits the converted data to the server.

[0322] Step 4:

[0323] The server parses the JSON data received from the device and breaks down the employee information into individual fields.

[0324] The server stores the parsed data in a database.

[0325] For example, store the information of "Employee A" in a corresponding table in the database.

[0326] Step 5:

[0327] The user enters details of the business task (task name, required skills, deadline, etc.) using a dedicated input form.

[0328] For example, enter "Task name: Data analysis, Required skills: Data analysis, Deadline: End of October 2023."

[0329] Step 6:

[0330] The device acquires the business task information entered by the user and converts it into JSON format or another appropriate data format.

[0331] Step 7:

[0332] The terminal transmits the converted data to the server.

[0333] Step 8:

[0334] The server analyzes the JSON data received from the terminal and breaks down the business task information into individual fields.

[0335] The server stores the parsed data in a database.

[0336] For example, the information of the "data analysis task" is stored in a corresponding table in the database.

[0337] Step 9:

[0338] The server retrieves employee information and work task information from the database.

[0339] Step 10:

[0340] The server requests the emotion engine for the emotion information of the current employee.

[0341] Step 11:

[0342] The emotion engine monitors the emotional state of employees in real time and sends that information to the server.

[0343] For example, information such as "Employee A's current emotional state: stress level is high" is sent.

[0344] Step 12:

[0345] The server stores the emotion information obtained from the emotion engine in a database.

[0346] Step 13:

[0347] The server uses machine learning or rule-based algorithms to select the best employee for each task based on the employee's skill set and emotional state.

[0348] For example, you might determine that "Employee A, who has data analysis skills, would be well suited to the data analysis task, but because he has a high stress level, Employee C would be the best choice instead."

[0349] Step 14:

[0350] The server formats the selection results into a list and sends it to the terminal in JSON format or other appropriate data format.

[0351] Step 15:

[0352] The terminal analyzes the list of personnel received from the server and displays it on the user interface.

[0353] For example, it might say, "Taking into consideration stress levels, employee C has been assigned the data analysis task."

[0354] In this way, the system of the present invention automates the allocation of optimal work tasks based on the employee's skill set and emotional state throughout each processing step, thereby achieving efficient business operations and reducing the psychological burden on employees.

[0355] Example 2

[0356] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0357] While conventional task allocation systems can assign tasks based on employees' skill sets, they are unable to take into account the emotional state of employees, which can lead to issues such as employees with high stress levels being assigned more demanding tasks, making it difficult to achieve efficient business operations.

[0358] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a user input means for inputting employee information, a server means for receiving the input employee information and storing it in a database, a user input means for inputting work task information, a server means for receiving the input work task information and storing it in the database, an analysis means for analyzing the employee information and work task information stored in the database and selecting the optimal employee for each task, an emotion engine means for monitoring the emotional states of employees in real time and providing that information, an analysis means for adjusting task assignments based on the emotional information, and an output means for presenting the selection results to the user. This enables optimal work task assignments that take into account not only the employee's skill set but also their emotional state.

[0359] "Employee information" refers to information such as an employee's name, department, and skill set.

[0360] "User input means" refers to an interface through which a user inputs employee information and business task information.

[0361] "Server means" refers to a server having the function of receiving input data and storing it in a database.

[0362] "Business task information" refers to information about the details of a business task (task name, required skills, deadline, etc.).

[0363] "Database" refers to a data management system for storing employee information and business task information.

[0364] "Analysis means" refers to the function for analyzing the information stored in the database and determining the best employee for each task.

[0365] The "emotion engine means" refers to a function for monitoring the emotional state of employees in real time and providing that information to the server.

[0366] "Analysis means" refers to the function for adjusting task allocation by reflecting emotional information.

[0367] "Output means" refers to a function for presenting the results of task allocation to the user.

[0368] The present invention relates to a system that manages employee information and work task information, and assigns optimal work tasks to employees by taking into consideration their skill sets and emotional states. The system efficiently and effectively assigns tasks by using a user input means, a server means, an analysis means, an emotion engine means, an analysis means, and an output means.

[0369] Hardware and software used

[0370] Hardware: Web server, client terminal (PC, tablet, etc.)

[0371] software:

[0372] Database: MySQL

[0373] Programming language: Python

[0374] Machine learning library: Scikit-Learn

[0375] Sentiment analysis engine: Microsoft® Azure® Face API, Google® Cloud Speech-to-Text

[0376] Front-end: HTML, CSS, JavaScript (registered trademark)

[0377] System configuration

[0378] Enter employee information and skill sets

[0379] The user (administrator) uses a dedicated input form to enter employee information (name, department, skill set). For example, for employee A, enter "Name: Employee A, Department: IT, Skills: Project management, Programming, Data analysis." Then, the user clicks the "Submit" button to complete the input.

[0380] Receiving and storing data

[0381] The terminal receives the employee information entered by the user and converts it into JSON format. The converted data is sent to the server, which then receives the data and stores it in the database. For example, the information of employee A is saved in the corresponding table in the database.

[0382] Entering and storing business task information

[0383] The user accesses the system's task input form and enters the details of the work task (task name, required skills, deadline, etc.). For example, the user enters "Task name: Data analysis, required skills: Data analysis, deadline: End of October 2023." The user then clicks the "Submit" button.

[0384] The device collects data on business tasks, converts it into JSON format, and sends it to the server, which receives the data and stores it in a database.

[0385] Skill set analysis and personnel selection

[0386] The server retrieves employee and task information from the database and uses machine learning or rule-based algorithms to select the best employee for each task. For example, it may determine that an employee with data analysis skills is best suited for a data analysis task.

[0387] Introducing the Emotion Engine

[0388] The emotion engine monitors the emotional state of employees in real time and provides emotional information to the server. The emotion engine recognizes the employee's facial expressions and voice to evaluate their emotional state.

[0389] The server stores the emotion information provided by the emotion engine in a database. For example, information such as "Employee A's current emotional state: high stress level" is saved.

[0390] Adjustment based on emotional state

[0391] The server analyzes the emotional and skill information stored in the database in an integrated manner and reflects this in task allocation. For example, if employee A has a high stress level, it will adjust the allocation so that high-stress tasks are not assigned to him.

[0392] Generate and display a contact list

[0393] The server generates a list of optimal agents that reflects the emotional information and sends it to the device in a data format such as JSON. The device analyzes the list of agents received from the server and displays it on the user interface. For example, it displays "Employee B has been assigned to the data analysis task."

[0394] Specific examples

[0395] Specific examples are shown below.

[0396] Employee A: Skills (project management, programming, data analysis), Emotional state (high stress level)

[0397] Employee B: Skills (system design, programming, security), emotional state (low stress level)

[0398] Employee C: Skills (Writing, Data Analysis, Marketing), Emotional State (Medium Stress Level)

[0399] Business tasks

[0400] Task 1: Data Analysis

[0401] Task 2: System Development

[0402] Task 3: Report writing

[0403] Output

[0404] Task 1 (Data Analysis): Employee C

[0405] Task 2 (System Development): Employee B

[0406] Task 3 (Report Writing): Employee C

[0407] Prompt Sentence Examples

[0408] "Assign the most appropriate work tasks based on employee information and their skill set. Also, consider the employee's current emotional state (high, low, medium stress level) and adjust accordingly. For example, assign Task 1 (data analysis) to Employee C (data analysis skills, medium stress level) rather than Employee A (data analysis skills, high stress level)."

[0409] In this way, by combining the emotion engine, the system can achieve optimal task allocation that takes into account the emotional state of employees, enabling more efficient business operations.

[0410] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0411] Step 1:

[0412] User operations

[0413] The user (administrator) accesses a dedicated input form, where they enter the employee's name, department, and skill set. For example, the information for employee A is entered as "Name: Employee A, Department: IT, Skills: Project management, Programming, Data analysis." After entering the information, the user clicks the "Submit" button.

[0414] Input: Employee information (name, department, skill set)

[0415] Output: Employee information passed to the terminal

[0416] Specific operation: When the user fills in the form and presses the "Submit" button, the data is passed to the terminal.

[0417] Step 2:

[0418] Terminal handling

[0419] The terminal acquires employee information entered by the user, converts the acquired data into JSON format, and sends the converted JSON data to the server.

[0420] Input: Employee information (name, department, skill set)

[0421] Output: JSON data sent to the server

[0422] Specific operation: The terminal obtains employee information from the input form, converts it into JSON format, and sends it to the server.

[0423] Step 3:

[0424] Server Processing

[0425] The server receives the JSON data sent from the terminal and stores it in the database. For example, the information of employee A is saved in the corresponding table in the database.

[0426] Input: Employee data in JSON format

[0427] Output: Employee information stored in a database

[0428] Specific operation: The server receives the JSON data and stores it in the database.

[0429] Step 4:

[0430] User operations

[0431] The user accesses the system's task input form and enters the details of the work task (task name, required skills, deadline, etc.). For example, they might enter "Task name: Data analysis, required skills: Data analysis, deadline: End of October 2023." After entering the information, the user clicks the "Submit" button.

[0432] Input: Work task information (task name, required skills, deadline)

[0433] Output: Task information passed to the terminal

[0434] Specific operation: When the user enters work task information into the form and presses the "Submit" button, the data is passed to the terminal.

[0435] Step 5:

[0436] Terminal handling

[0437] The device collects data on business tasks, converts it into JSON format, and sends it to the server.

[0438] Input: Work task information (task name, required skills, deadline)

[0439] Output: JSON data sent to the server

[0440] Specific operation: The terminal acquires business task information, converts it into JSON format, and sends it to the server.

[0441] Step 6:

[0442] Server Processing

[0443] The server receives the business task information sent from the terminal and stores it in a database. For example, information about a data analysis task is saved in a corresponding table in the database.

[0444] Input: JSON formatted business task information

[0445] Output: Business task information stored in a database

[0446] Specific operation: The server receives the JSON data and stores the business task information in a database.

[0447] Step 7:

[0448] Server Processing

[0449] The server retrieves employee and task information from a database and uses machine learning or rule-based algorithms to select the best employee for each task.

[0450] Input: Employee information and work task information stored in a database

[0451] Output: Optimal employee-task pairs

[0452] Specific operation: The server analyzes the information and selects the most suitable employee.

[0453] Step 8:

[0454] Emotion engine processing

[0455] The emotion engine monitors the emotional state of employees in real time and provides that information to the server. The emotion engine recognizes the employee's facial expressions and voice to evaluate their emotional state.

[0456] Input: Employee facial expressions and voice data

[0457] Output: Employee sentiment information

[0458] Specific operation: The emotion engine analyzes facial expressions and voice to generate emotional information.

[0459] Step 9:

[0460] Collaboration between the server and emotion engine

[0461] The server stores the emotion information provided by the emotion engine in a database. For example, information such as "Employee A's current emotional state: high stress level" is saved.

[0462] Input: Employee emotional information

[0463] Output: Emotion information stored in a database

[0464] Specific operation: The server receives the emotion information and stores it in a database.

[0465] Step 10:

[0466] Server Processing

[0467] The server analyzes the emotional and skill information stored in the database and adjusts task allocation accordingly. For example, if employee A has a high stress level, it will avoid assigning high-stress tasks to him.

[0468] Input: Emotion information and skill information stored in the database

[0469] Output: Adjusted task assignments

[0470] Specific operation: The server comprehensively analyzes the information and adjusts task allocation.

[0471] Step 11:

[0472] Server Processing

[0473] The server generates a list of optimal agents that reflects the emotional information and sends it to the terminal in a data format such as JSON.

[0474] Input: Adjusted task assignment information

[0475] Output: A list of contacts sent to the terminal

[0476] Specific operation: The server generates a list of personnel and sends it to the terminal.

[0477] Step 12:

[0478] Terminal handling

[0479] The terminal analyzes the list of people in charge received from the server and displays it on the user interface. For example, it displays "Employee B has been assigned to the data analysis task."

[0480] Input: List of agents received from the server

[0481] Output: A list of agents displayed in the user interface

[0482] Specific behavior: The device parses and displays the list.

[0483] (Application example 2)

[0484] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0485] Conventional work task allocation systems based on employee skill sets were unable to consider the emotional state of employees, and tasks were assigned without regard for employee stress or motivation. This resulted in lower employee productivity and satisfaction, and was unable to contribute to work efficiency. This problem was particularly pronounced in high-stress environments such as logistics centers.

[0486] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0487] In this invention, the server includes user input means for inputting employee information, server means for receiving the input employee information and storing it in a database, user input means for inputting work task information, server means for receiving the input work task information and storing it in the database, analysis means for analyzing the employee information and work task information stored in the database and selecting the employee most suitable for each task, emotion engine means for detecting the emotional state of employees and storing the emotion information in the database, adjustment means for integrating the emotion information and the analysis results and optimizing task allocation, and output means for presenting the selection results to the user. This enables optimal task allocation taking into account the skill sets and emotional states of employees.

[0488] The "user input means for inputting employee information" is a means for providing an interface for the manager to input employee information.

[0489] The "server means" is a server device that has the function of receiving input information and storing it in a database.

[0490] The "user input means for inputting business task information" is a means for providing an interface for the administrator to input business task information.

[0491] The "analysis means" is a means having a function for analyzing employee information and business task information stored in the database and selecting the most suitable employee for each task.

[0492] The "emotion engine means" is a means having a function for detecting the emotional state of an employee and storing the information in a database.

[0493] The "adjustment means" is a means that has the function of integrating emotional information and analysis results to optimize task allocation.

[0494] The "output means" is a means for providing an interface for presenting the selection results to the user.

[0495] The present invention relates to an optimal work task allocation system that takes into account the skill sets and emotional states of employees in a logistics center. This system is implemented using a server, terminals, and an emotion engine. Specific embodiments are described below.

[0496] Enter employee information and skill sets

[0497] First, the administrator (user) uses a dedicated input interface to input employee information (such as name, skill set, and emotional state). For example, information about employee A is entered as "Name: Employee A, Skills: Programming, Data Analysis, Emotional State: High Stress Level."

[0498] Receiving and storing data

[0499] The terminal receives the entered employee information, converts it to JSON format, and sends it to the server. The server receives this information and stores it in a database. For example, the information of "Employee A" is saved in a database table.

[0500] Entering and storing business task information

[0501] Next, the administrator enters the details of the work task (task name, required skills, deadline, etc.) into the system. For example, "Task name: Data analysis, required skills: Data analysis, deadline: End of October 2023." The terminal converts this data into JSON format and sends it to the server, which then stores the information in a database.

[0502] Analysis and selection of personnel based on skill sets and emotional states

[0503] The server retrieves employee information and work task information stored in the database and uses machine learning or rule-based algorithms to select the employee best suited for each task. The server also takes into account the employee's emotional state, as provided by the emotion engine. For example, it determines that "Employee C" is best suited for "Task 1 (Data Analysis)" and "Employee B" is best suited for "Task 2 (System Development)."

[0504] Emotion Engine Functions

[0505] The emotion engine monitors employees' emotional states in real time and provides that information to the server. The emotion engine evaluates the employee's emotional state, for example, through facial expression recognition or voice analysis. The server stores this emotional information in a database and reflects it in task allocation. For example, if the emotional state of "Employee A" is evaluated as "high stress level," it will avoid assigning tasks that cause high stress.

[0506] Generate and view a contact list

[0507] Finally, the server generates a list of optimal agents that reflects the emotional information and sends it to the device in JSON format. The device then analyzes the received list and displays it on the user interface. For example, it displays that "Employee C" is assigned to "Task 1 (Data Analysis)."

[0508] Specific examples

[0509] Employee Information:

[0510] Employee A: Skills (programming, data analysis), emotional state (high stress level)

[0511] Employee B: Skills (system design, programming), emotional state (low stress level)

[0512] Employee C: Skills (Writing, Data Analysis, Marketing), Emotional State (Medium Stress Level)

[0513] Business tasks:

[0514] Task 1: Data Analysis

[0515] Task 2: System Development

[0516] Task 3: Report writing

[0517] Allocation results:

[0518] Task 1 (Data Analysis): Employee C

[0519] Task 2 (System Development): Employee B

[0520] Task 3 (Report Writing): Employee C

[0521] Example prompt sentence:

[0522] "Enter employee information (name, skill set, emotional state) and work tasks (task name, required skills) and generate code for a system that optimally assigns tasks based on the employee's emotional state."

[0523] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0524] Step 1:

[0525] The user uses a dedicated input interface to input employee information, including the employee's name, department, skill set, current emotional state, etc. This input information is converted into JSON format and sent to the terminal.

[0526] Input: Employee information (name, department, skill set, emotional state)

[0527] Output: Employee information in JSON format

[0528] Step 2:

[0529] The terminal sends the JSON-formatted employee information entered by the user to the server, and also correctly formats each employee entry and sends it as an HTTP request to the appropriate server endpoint.

[0530] Input: Employee information in JSON format

[0531] Output: HTTP request to the server

[0532] Step 3:

[0533] The server receives the employee information sent from the terminal and saves it in the database. Specifically, it parses the JSON format employee information and stores it in the corresponding table in the database.

[0534] Input: HTTP request from the terminal (employee information in JSON format)

[0535] Output: Employee information stored in the database

[0536] Step 4:

[0537] The user inputs task information using a dedicated input interface, including the task name, required skills, deadline, etc. This input information is converted into JSON format and sent to the device.

[0538] Input: Work task information (task name, required skills, deadline)

[0539] Output: JSON formatted business task information

[0540] Step 5:

[0541] The device sends the JSON-formatted work task information entered by the user to the server, and then correctly formats each piece of work task information and sends it as an HTTP request to the appropriate server endpoint.

[0542] Input: JSON formatted business task information

[0543] Output: HTTP request to the server

[0544] Step 6:

[0545] The server receives the business task information sent from the terminal and saves it in the database. Specifically, it parses the business task information in JSON format and stores it in the corresponding table in the database.

[0546] Input: HTTP request from the device (JSON formatted business task information)

[0547] Output: Business task information stored in the database

[0548] Step 7:

[0549] The server retrieves employee and task information stored in the database and analyzes it using analytical tools. Taking into account the employee's skill set and emotional state, the server selects the employee best suited for each task. It uses machine learning algorithms to find the optimal combination.

[0550] Input: Employee and task information in the database

[0551] Output: Optimal task assignment information

[0552] Step 8:

[0553] The emotion engine monitors the emotional state of employees in real time and sends the data to the server. The emotion engine uses facial expression recognition and voice analysis to evaluate the emotional state of employees in real time and sends the results in JSON format.

[0554] Input: Real-time employee emotion data (facial expressions, voice data)

[0555] Output: Emotion information in JSON format

[0556] Step 9:

[0557] The server stores the emotional information obtained from the emotion engine in a database, integrates this information with employee information, and adjusts task assignments based on the employee's emotional state.

[0558] Input: JSON format emotion information from the emotion engine

[0559] Output: Employee information and sentiment information integrated into a database

[0560] Step 10:

[0561] The server generates a list of optimal agents that reflects the emotional information and sends it to the device in JSON format. It then performs a final check of the analyzed information and sends it back to the device as a task assignment list.

[0562] Input: Parsed employee information and work task information

[0563] Output: HTTP request to the device (optimal task allocation information)

[0564] Step 11:

[0565] The terminal analyzes the list of people in charge received from the server and displays it on the user interface, specifically displaying the name of the employee assigned to each task and the task name.

[0566] Input: HTTP request from the server (optimal task allocation information)

[0567] Output: Optimal task assignment list on the user interface

[0568] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0569] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0570] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0571] [Second embodiment]

[0572] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0573] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0574] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0575] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0576] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0577] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0578] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0579] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0580] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0581] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0582] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0583] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0584] The present invention is a system for automatically assigning optimal work tasks based on the skill sets of employees. Specific embodiments will be described below.

[0585] Enter employee information and skill sets

[0586] 1. User Action:

[0587] The user (administrator) enters employee information (such as name, department, skill set, etc.) in a dedicated input form. For example, the information for Employee A is entered as "Name: Employee A, Department: IT, Skills: Project management, Programming, Data analysis."

[0588] Receiving and storing data

[0589] 2. Terminal processing:

[0590] The terminal converts the employee information obtained from the input form into JSON format and sends the data to the server.

[0591] 3. Server processing:

[0592] The server receives the JSON data sent from the terminal and stores it in the database. For example, the information of employee A is saved in the corresponding table in the database.

[0593] Receive and store information about business tasks

[0594] 4. User Action:

[0595] The user enters the details of the work task (task name, required skills, deadline, etc.) into the system. For example, the user enters "Task name: Data analysis, Required skills: Data analysis, Deadline: End of October 2023."

[0596] 5. Terminal processing:

[0597] The device collects business task data, converts it into JSON format, and sends it to the server.

[0598] 6. Server Processing:

[0599] The server receives the business task information and stores it in a database, for example, the information of a data analysis task is stored in a corresponding table in the database.

[0600] Skill set analysis and personnel selection

[0601] 7. Server Processing:

[0602] The server retrieves employee and task information from the database and uses machine learning or rule-based algorithms to select the best employee for each task. For example, it may determine that Employee A, with his data analysis skills, is best suited for the data analysis task.

[0603] 8. Server Processing:

[0604] The selection results are formatted into a list and sent to the terminal in a data format such as JSON.

[0605] Generate and display a contact list

[0606] 9. Terminal Processing:

[0607] The terminal analyzes the list of people in charge received from the server and displays it on the user interface. For example, it displays "Employee A has been assigned to the data analysis task."

[0608] Specific examples

[0609] Specific examples are shown below.

[0610] input

[0611] Employee A: Skills (project management, programming, data analysis)

[0612] Employee B: Skills (system design, programming, security)

[0613] Employee C: Skills (writing, data analysis, marketing)

[0614] Business tasks

[0615] Task 1: Data Analysis

[0616] Task 2: System Development

[0617] Task 3: Report writing

[0618] Output

[0619] Task 1 (Data Analysis): Employee A

[0620] Task 2 (System Development): Employee B

[0621] Task 3 (Report Writing): Employee C

[0622] In this way, work tasks can be automatically assigned based on employees' skill sets, resulting in increased work efficiency.

[0623] The processing flow will be explained below.

[0624] Step 1:

[0625] The user enters employee information (such as name, department, skill set, etc.) using a dedicated input form.

[0626] For example, the user enters "Employee A, Department: IT, Skills: Project management, Programming, Data analysis."

[0627] Step 2:

[0628] The terminal takes the employee information entered by the user and converts it into JSON format or another appropriate data format.

[0629] Step 3:

[0630] The terminal transmits the converted data to the server.

[0631] Step 4:

[0632] The server parses the JSON data received from the device and breaks down the employee information into individual fields.

[0633] The server stores the parsed data in a database.

[0634] For example, store the information of "Employee A" in a corresponding table in the database.

[0635] Step 5:

[0636] The user enters details of the business task (task name, required skills, deadline, etc.) using a dedicated input form.

[0637] For example, enter "Task name: Data analysis, Required skills: Data analysis, Deadline: End of October 2023."

[0638] Step 6:

[0639] The device acquires the business task information entered by the user and converts it into JSON format or another appropriate data format.

[0640] Step 7:

[0641] The terminal transmits the converted data to the server.

[0642] Step 8:

[0643] The server analyzes the JSON data received from the terminal and breaks down the business task information into individual fields.

[0644] The server stores the parsed data in a database.

[0645] For example, the information of the "data analysis task" is stored in a corresponding table in the database.

[0646] Step 9:

[0647] The server retrieves employee information and work task information from the database.

[0648] Step 10:

[0649] The server uses machine learning or rule-based algorithms to select the best employee for each task.

[0650] For example, you might determine that "Employee A, who has data analysis skills, is best suited for the data analysis task."

[0651] Step 11:

[0652] The server formats the selection results into a list and sends it to the terminal in JSON format or other appropriate data format.

[0653] Step 12:

[0654] The terminal analyzes the list of personnel received from the server.

[0655] Step 13:

[0656] The terminal displays the analyzed list of personnel on the user interface.

[0657] For example, it might say, "Employee A has been assigned the data analysis task."

[0658] In this way, the system of the present invention automates the allocation of optimal business tasks based on the skill sets of employees through each processing step, thereby realizing efficient business operations.

[0659] Example 1

[0660] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0661] The process of selecting the most suitable work tasks based on employees' skill sets has traditionally been done manually, requiring time and effort. It also poses challenges in ensuring fair allocation of personnel and improving work efficiency. Furthermore, in large organizations, it is difficult to centrally manage employee skill information and work task information and quickly select the most suitable personnel.

[0662] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0663] In this invention, the server includes user input means for inputting employee information, terminal means for receiving the input employee information, converting it into a data format and sending it to the server, server means for storing the received employee information in a database, user input means for inputting work task information, terminal means for receiving the input work task information, converting it into a data format and sending it to the server, server means for storing the received work task information in the database, analysis means for acquiring the employee information and work task information stored in the database and selecting the most suitable employee for each task, and output means for displaying the selection results on a user interface. This makes it possible to quickly and efficiently assign work tasks to the most suitable employees.

[0664] "Employee information" is data that includes basic attribute information and skill sets about employees.

[0665] A "user input means" is a device or interface through which a user inputs information, including, for example, web forms and input fields.

[0666] "Terminal means" refers to a device or function for collecting employee information and business task information, converting it into an appropriate format, and transmitting it to a server.

[0667] The "server means" is a device or system for processing information received from the terminal means and storing it in a database.

[0668] "Business task information" is data that includes details of a specific business task (such as task name, required skills, deadline, etc.).

[0669] A "data format" is a way of representing data in a particular format, such as JSON or XML.

[0670] A "database" is a system for efficiently storing, managing, and searching data, and examples include MySQL and PostgreSQL.

[0671] "Analysis means" refers to devices and algorithms used to retrieve and analyze information in a database.

[0672] "Output means" refers to a device or interface for displaying the analysis results to the user.

[0673] A "machine learning algorithm" is an algorithm that learns patterns and rules based on data and makes predictions and classifications.

[0674] The present invention is a system for automatically assigning work tasks based on the skill sets of employees. This system is operated by combining users, terminals, and a server.

[0675] Enter employee information and skill sets

[0676] The user enters employee information using a dedicated input form provided on the web browser. The information entered includes name, department, skill set, etc. For example, for employee A, the user enters "Name: Employee A, Department: IT, Skills: Project management, Programming, Data analysis."

[0677] Receiving and storing data

[0678] The terminal receives the employee information entered by the user and converts it into JSON format. The converted data is sent to the server as an HTTP POST request. The server receives this request, parses the JSON data, and stores the employee information in a database (for example, MySQL or PostgreSQL).

[0679] Receive and store information about business tasks

[0680] The user enters the details of the work task using a similarly provided input form. For example, they enter "Task name: Data analysis, Required skills: Data analysis, Deadline: End of October 2023." The device converts this work task information into JSON format and sends it to the server. The server analyzes the received JSON data and stores the work task information in a database.

[0681] Skill set analysis and personnel selection

[0682] The server retrieves employee and task information from the database using SQL SELECT statements. Based on the retrieved information, it applies machine learning or rule-based algorithms to select the best employee for each task. For example, it uses the Python scikit-learn library to determine that Employee A, with his data analysis skills, is best suited for a data analysis task.

[0683] Generate and display a contact list

[0684] The server formats the selection results into a list and converts it into a data format such as JSON. This data is sent to the terminal as an HTTP response. The terminal analyzes the list of people in charge received from the server and displays it in the user interface. For example, the browser displays "Employee A has been assigned to the data analysis task."

[0685] Specific examples

[0686] input

[0687] Employee A: Skills (project management, programming, data analysis)

[0688] Employee B: Skills (system design, programming, security)

[0689] Employee C: Skills (writing, data analysis, marketing)

[0690] Business tasks

[0691] Task 1: Data Analysis

[0692] Task 2: System Development

[0693] Task 3: Report writing

[0694] Prompt Sentence Examples

[0695] You can use the prompt text as follows:

[0696] Employee Information:

[0697] Name: Employee A

[0698] Department: IT

[0699] Skills: Project Management, Programming, Data Analysis

[0700] Business tasks:

[0701] Task Name: Data Analysis

[0702] Required skills: Data analysis

[0703] Deadline: End of October 2023

[0704] Using the information above, explain how to assign tasks to the appropriate employees.

[0705] This prompt can be fed into an AI model to generate instructions on how to assign the task to the best employee.

[0706] As such, the system streamlines the allocation of complex work tasks and makes the most of employee skill sets.

[0707] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0708] Step 1:

[0709] Entering employee information

[0710] Users access a dedicated input form on a web browser and enter employee information.

[0711] Specifically, the user fills in form fields with details such as the employee's name, department, and skill set.

[0712] Example: "Name: Employee A, Department: IT, Skills: Project management, programming, data analysis"

[0713] Input: Employee information entered by the user.

[0714] Output: Employee information entered into the input form.

[0715] Step 2:

[0716] Receiving employee information

[0717] The terminal receives the data entered by the user into the input form.

[0718] Converts input data into JSON format.

[0719] Input: Employee information entered by the user.

[0720] Output: Employee information converted to JSON format.

[0721] Step 3:

[0722] Transmitting and storing employee information

[0723] The device sends the converted JSON format data to the server as an HTTP POST request.

[0724] The server receives the HTTP POST request and parses the JSON data.

[0725] The server stores the parsed employee information in a database, for example, the employee information is inserted into the "Employees" table.

[0726] Input: Employee information in JSON format.

[0727] Output: Employee information stored in the database.

[0728] Step 4:

[0729] Entering work task information

[0730] The user opens a separate input form in a web browser and enters the details of the work task.

[0731] For example, enter "Task name: Data analysis, Required skills: Data analysis, Deadline: End of October 2023."

[0732] Input: Work task information entered by the user.

[0733] Output: Business task information entered in the input form.

[0734] Step 5:

[0735] Receiving business task information

[0736] The terminal receives the business task information entered by the user in the input form.

[0737] Converts input data into JSON format.

[0738] Input: Work task information entered by the user.

[0739] Output: Business task information converted to JSON format.

[0740] Step 6:

[0741] Sending and storing business task information

[0742] The device sends the converted JSON format data to the server as an HTTP POST request.

[0743] The server receives the HTTP POST request and parses the JSON data.

[0744] The server stores the analyzed business task information in a database. For example, the business task information is inserted into a "Tasks" table.

[0745] Input: Business task information in JSON format.

[0746] Output: Business task information stored in a database.

[0747] Step 7:

[0748] Obtaining employee and task information

[0749] The server retrieves employee and task information from the database using SQL SELECT statements.

[0750] The obtained information is passed to an analysis means.

[0751] Input: Employee and work task information stored in a database.

[0752] Output: Employee information and work task information captured for analysis.

[0753] Step 8:

[0754] Analysis and selection of personnel

[0755] The server analyzes the acquired information using an analysis means (machine learning algorithm or rule-based algorithm).

[0756] The analytical method matches employee skill sets with business task requirements and selects the most suitable employee.

[0757] For example, employee A, who has data analysis skills, is assigned to the data analysis task.

[0758] Input: Captured employee information and work task information.

[0759] Output: Selection of the best employee for each task.

[0760] Step 9:

[0761] Sending and displaying the selection results

[0762] The server formats the selection results into a list and converts them into JSON format.

[0763] The server sends this data to the terminal as an HTTP response.

[0764] The terminal analyzes the received list of people in charge and displays the results on the user interface, for example, "Employee A has been assigned to the data analysis task."

[0765] Input: JSON data of the selection result.

[0766] Output: The list of agents displayed in the user interface.

[0767] (Application example 1)

[0768] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0769] Although systems already exist that can efficiently assign optimal work tasks to employees based on their skill sets, there is a lack of a way to quickly notify employees of the results of the selection process. This means that employees are unable to immediately know about the assigned tasks and take action, resulting in a decline in work efficiency in factories and offices.

[0770] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0771] In this invention, the server includes user input means for inputting employee information, server means for receiving the input employee information and storing it in a database, user input means for inputting job content information, server means for receiving the input job content information and storing it in a database, analysis means for analyzing the employee information and job content information stored in the database and selecting the employee most suitable for each job, output means for presenting the selection results to the user, and communication means for notifying the employee's communication terminal of the selection results, thereby making it possible to promptly notify the employee of the selection results.

[0772] "Employee information" includes information such as the employee's name, department, and skill set.

[0773] The "user input means" is an interface that allows employees or managers to input employee information and job content information.

[0774] The "server means" is a server having a function for receiving input data and storing it in a database as necessary.

[0775] "Work content information" is information such as the name of the work task, the required skills, and the deadline.

[0776] The "analysis means" is a system that has the function of analyzing employee information and job content information stored in the database and selecting the most suitable employee for each job.

[0777] The "output means" is an interface for presenting the analysis results and selection results to the user.

[0778] "Communication means" refers to a system that has the function of notifying the selection results to the employee's communication terminal.

[0779] A "machine learning algorithm" is an algorithm that learns patterns from data and makes predictions and classifications.

[0780] The present invention is a system that automatically selects the most suitable employee based on the employee's skill set and job content information in order to improve work efficiency in factories and offices, and notifies the results to the employee's communication terminal. A specific implementation method is shown below.

[0781] System configuration

[0782] Hardware

[0783] User terminal: A device (e.g., tablet, smart glasses) for inputting employee information and job content information.

[0784] Server: A server system for receiving, storing, and analyzing data.

[0785] Communication terminal: Employee device (e.g., smartphone, smart glasses) for receiving the selection results.

[0786] software

[0787] Input form: An input interface for employee information and job content information installed on the user's terminal.

[0788] Receiving and storing data: A server-side script that converts the received data into JSON format and stores it in a database.

[0789] Machine learning algorithm: Python's scikit-learn library is used to analyze employee information and job description information and select the most suitable employees.

[0790] Notification system: An API for pushing the selection results to employees' communication devices.

[0791] Program processing explanation

[0792] The server receives employee information and job details sent from the user's device in JSON format and stores the data in a database. It then uses a machine learning algorithm to select the most suitable employee, formats the selection results, and notifies the employee's communication device.

[0793] Data processing and calculation

[0794] Analyze employee information: Map each employee's skill set to a numerical value and feed it into machine learning algorithms.

[0795] Analyze job information: Map required skills to a numerical value and compare it with employee skill sets to find the best match.

[0796] Sending the selection results: Using the push notification API, the selection results are notified to the employee's communication device.

[0797] As a specific example, if Employee A's skill set "project management, programming, data analysis" and Task 1 "data analysis" are entered, the server will match the skill set with the task and determine that Employee A is the best person for this task. The result will be notified to Employee A's smartphone.

[0798] Generative AI model prompt example

[0799] Create an application that automatically selects the best workers to improve work efficiency. Enter employee skill sets and work task information, and the system will select the best employee for each task. The output will be displayed in a list format, including the selected workers and the task names.

[0800] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0801] Step 1:

[0802] A user uses a tablet or smart glasses to enter employee information, including the employee's name, department, and skill set, which is then converted into JSON format.

[0803] Step 2:

[0804] The entered JSON data is sent from the terminal to the server, which receives it and stores it in a database for analysis. When stored in the database, each employee's information is saved as a separate record.

[0805] Step 3:

[0806] The user again uses a tablet or smart glasses to input task information, including the task name, required skills, and deadline, which is then converted to JSON format.

[0807] Step 4:

[0808] The JSON data of the job content information is sent from the terminal to the server. The server receives this data and stores it in a database. Information about each job content is saved as an individual record.

[0809] Step 5:

[0810] The server retrieves employee information and job information from the database. It uses a machine learning algorithm (K-nearest neighbor method) to process and calculate the data to select the best employee for each task. Specifically, it maps the employee's skill set and the skills required for the task to a numerical value, and generates a dataset for analysis.

[0811] Step 6:

[0812] The machine learning model selects the best employee, and the matching results between the selected employee and the task are formatted in JSON format and presented to the user. This generates a list of the best people to assign each task.

[0813] Step 7:

[0814] The server sends a push notification to not only the user but also the employee's communication device (smartphone or smart glasses). The notification system API is used to deliver the selection results to the employee's device.

[0815] Step 8:

[0816] Employees receive notifications via their communication devices and check the details of their assigned tasks. The notifications include the task name, required skills, deadline, and the name of the person in charge. They can immediately begin working on the assigned task.

[0817] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0818] The present invention combines a system that automatically assigns optimal work tasks based on the skill sets of employees with an emotion engine that recognizes the emotions of users. Specific embodiments will be described below.

[0819] Enter employee information and skill sets

[0820] User operations

[0821] The user (administrator) enters employee information (such as name, department, skill set, etc.) in a dedicated input form. For example, the information for Employee A is entered as "Name: Employee A, Department: IT, Skills: Project management, Programming, Data analysis."

[0822] Receiving and storing data

[0823] Terminal handling

[0824] The terminal converts the employee information obtained from the input form into JSON format and sends it to the server.

[0825] Server Processing

[0826] The server receives the JSON data sent from the terminal and stores it in the database. For example, the information of employee A is saved in the corresponding table in the database.

[0827] Receive and store information about business tasks

[0828] User operations

[0829] The user enters the details of the work task (task name, required skills, deadline, etc.) into the system. For example, the user enters "Task name: Data analysis, Required skills: Data analysis, Deadline: End of October 2023."

[0830] Terminal handling

[0831] The device collects data on business tasks, converts it into JSON format, and sends it to the server.

[0832] Server Processing

[0833] The server receives the business task information and stores it in a database, for example, the information of a data analysis task is stored in a corresponding table in the database.

[0834] Skill set analysis and personnel selection

[0835] Server Processing

[0836] The server retrieves employee and task information from the database and uses machine learning or rule-based algorithms to select the best employee for each task. For example, it may determine that Employee A, with his data analysis skills, is best suited for the data analysis task.

[0837] Introducing the Emotion Engine

[0838] Emotion Engine Functions

[0839] The emotion engine monitors the emotional state of employees in real time and provides that information to analytical tools. The emotion engine evaluates the emotional state of employees, for example, through facial expression recognition and voice analysis.

[0840] Collaboration between the server and emotion engine

[0841] The server stores the emotional information obtained from the emotion engine in a database. For example, information such as "Employee A's current emotional state: high stress level" is saved.

[0842] Adjustment based on emotional state

[0843] The server analyzes the emotional and skill information stored in the database and reflects this information in task allocation. For example, if employee A has a high stress level, the server will adjust the allocation to avoid high-stress tasks.

[0844] Generate and display a contact list

[0845] Server Processing

[0846] The server generates a list of optimal agents that reflects the emotional information and sends it to the terminal in a data format such as JSON.

[0847] Terminal handling

[0848] The terminal analyzes the list of people in charge received from the server and displays it on the user interface. For example, it displays "Employee B has been assigned to the data analysis task."

[0849] Specific examples

[0850] Specific examples are shown below.

[0851] input

[0852] Employee A: Skills (project management, programming, data analysis), Emotional state (high stress level)

[0853] Employee B: Skills (system design, programming, security), emotional state (low stress level)

[0854] Employee C: Skills (Writing, Data Analysis, Marketing), Emotional State (Medium Stress Level)

[0855] Business tasks

[0856] Task 1: Data Analysis

[0857] Task 2: System Development

[0858] Task 3: Report writing

[0859] Output

[0860] Task 1 (Data Analysis): Employee C

[0861] Task 2 (System Development): Employee B

[0862] Task 3 (Report Writing): Employee C

[0863] In this way, by combining emotion engines, optimal task allocation can be achieved while taking into consideration the emotional state of employees, enabling even more efficient business operations.

[0864] The processing flow will be explained below.

[0865] Step 1:

[0866] The user enters employee information (such as name, department, skill set, etc.) using a dedicated input form.

[0867] For example, the user enters "Employee A, Department: IT, Skills: Project management, Programming, Data analysis."

[0868] Step 2:

[0869] The terminal takes the employee information entered by the user and converts it into JSON format or another appropriate data format.

[0870] Step 3:

[0871] The terminal transmits the converted data to the server.

[0872] Step 4:

[0873] The server parses the JSON data received from the device and breaks down the employee information into individual fields.

[0874] The server stores the parsed data in a database.

[0875] For example, store the information of "Employee A" in a corresponding table in the database.

[0876] Step 5:

[0877] The user enters details of the business task (task name, required skills, deadline, etc.) using a dedicated input form.

[0878] For example, enter "Task name: Data analysis, Required skills: Data analysis, Deadline: End of October 2023."

[0879] Step 6:

[0880] The device acquires the business task information entered by the user and converts it into JSON format or another appropriate data format.

[0881] Step 7:

[0882] The terminal transmits the converted data to the server.

[0883] Step 8:

[0884] The server analyzes the JSON data received from the terminal and breaks down the business task information into individual fields.

[0885] The server stores the parsed data in a database.

[0886] For example, the information of the "data analysis task" is stored in a corresponding table in the database.

[0887] Step 9:

[0888] The server retrieves employee information and work task information from the database.

[0889] Step 10:

[0890] The server requests the emotion engine for the emotion information of the current employee.

[0891] Step 11:

[0892] The emotion engine monitors the emotional state of employees in real time and sends that information to the server.

[0893] For example, information such as "Employee A's current emotional state: stress level is high" is sent.

[0894] Step 12:

[0895] The server stores the emotion information obtained from the emotion engine in a database.

[0896] Step 13:

[0897] The server uses machine learning or rule-based algorithms to select the best employee for each task based on the employee's skill set and emotional state.

[0898] For example, you might determine that "Employee A, who has data analysis skills, would be well suited to the data analysis task, but because he has a high stress level, Employee C would be the best choice instead."

[0899] Step 14:

[0900] The server formats the selection results into a list and sends it to the terminal in JSON format or other appropriate data format.

[0901] Step 15:

[0902] The terminal analyzes the list of personnel received from the server and displays it on the user interface.

[0903] For example, it might say, "Taking into consideration stress levels, employee C has been assigned the data analysis task."

[0904] In this way, the system of the present invention automates the allocation of optimal work tasks based on the employee's skill set and emotional state throughout each processing step, thereby achieving efficient business operations and reducing the psychological burden on employees.

[0905] Example 2

[0906] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0907] While conventional task allocation systems can assign tasks based on employees' skill sets, they are unable to take into account the emotional state of employees, which can lead to issues such as employees with high stress levels being assigned more demanding tasks, making it difficult to achieve efficient business operations.

[0908] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a user input means for inputting employee information, a server means for receiving the input employee information and storing it in a database, a user input means for inputting work task information, a server means for receiving the input work task information and storing it in the database, an analysis means for analyzing the employee information and work task information stored in the database and selecting the optimal employee for each task, an emotion engine means for monitoring the emotional states of employees in real time and providing that information, an analysis means for adjusting task assignments based on the emotional information, and an output means for presenting the selection results to the user. This enables optimal work task assignments that take into account not only the employee's skill set but also their emotional state.

[0909] "Employee information" refers to information such as an employee's name, department, and skill set.

[0910] "User input means" refers to an interface through which a user inputs employee information and business task information.

[0911] "Server means" refers to a server having the function of receiving input data and storing it in a database.

[0912] "Business task information" refers to information about the details of a business task (task name, required skills, deadline, etc.).

[0913] "Database" refers to a data management system for storing employee information and business task information.

[0914] "Analysis means" refers to the function for analyzing the information stored in the database and determining the best employee for each task.

[0915] The "emotion engine means" refers to a function for monitoring the emotional state of employees in real time and providing that information to the server.

[0916] "Analysis means" refers to the function for adjusting task allocation by reflecting emotional information.

[0917] "Output means" refers to a function for presenting the results of task allocation to the user.

[0918] The present invention relates to a system that manages employee information and work task information, and assigns optimal work tasks to employees by taking into consideration their skill sets and emotional states. The system efficiently and effectively assigns tasks by using a user input means, a server means, an analysis means, an emotion engine means, an analysis means, and an output means.

[0919] Hardware and software used

[0920] Hardware: Web server, client terminal (PC, tablet, etc.)

[0921] software:

[0922] Database: MySQL

[0923] Programming language: Python

[0924] Machine learning library: Scikit-Learn

[0925] Sentiment analysis engine: Microsoft Azure Face API, Google Cloud Speech-to-Text

[0926] Frontend: HTML, CSS, JavaScript

[0927] System configuration

[0928] Enter employee information and skill sets

[0929] The user (administrator) uses a dedicated input form to enter employee information (name, department, skill set). For example, for employee A, enter "Name: Employee A, Department: IT, Skills: Project management, Programming, Data analysis." Then, the user clicks the "Submit" button to complete the input.

[0930] Receiving and storing data

[0931] The terminal receives the employee information entered by the user and converts it into JSON format. The converted data is sent to the server, which then receives the data and stores it in the database. For example, the information of employee A is saved in the corresponding table in the database.

[0932] Entering and storing business task information

[0933] The user accesses the system's task input form and enters the details of the work task (task name, required skills, deadline, etc.). For example, the user enters "Task name: Data analysis, required skills: Data analysis, deadline: End of October 2023." The user then clicks the "Submit" button.

[0934] The device collects data on business tasks, converts it into JSON format, and sends it to the server, which receives the data and stores it in a database.

[0935] Skill set analysis and personnel selection

[0936] The server retrieves employee and task information from the database and uses machine learning or rule-based algorithms to select the best employee for each task. For example, it may determine that an employee with data analysis skills is best suited for a data analysis task.

[0937] Introducing the Emotion Engine

[0938] The emotion engine monitors the emotional state of employees in real time and provides emotional information to the server. The emotion engine recognizes the employee's facial expressions and voice to evaluate their emotional state.

[0939] The server stores the emotion information provided by the emotion engine in a database. For example, information such as "Employee A's current emotional state: high stress level" is saved.

[0940] Adjustment based on emotional state

[0941] The server analyzes the emotional and skill information stored in the database in an integrated manner and reflects this in task allocation. For example, if employee A has a high stress level, it will adjust the allocation so that high-stress tasks are not assigned to him.

[0942] Generate and display a contact list

[0943] The server generates a list of optimal agents that reflects the emotional information and sends it to the device in a data format such as JSON. The device analyzes the list of agents received from the server and displays it on the user interface. For example, it displays "Employee B has been assigned to the data analysis task."

[0944] Specific examples

[0945] Specific examples are shown below.

[0946] Employee A: Skills (project management, programming, data analysis), Emotional state (high stress level)

[0947] Employee B: Skills (system design, programming, security), emotional state (low stress level)

[0948] Employee C: Skills (Writing, Data Analysis, Marketing), Emotional State (Medium Stress Level)

[0949] Business tasks

[0950] Task 1: Data Analysis

[0951] Task 2: System Development

[0952] Task 3: Report writing

[0953] Output

[0954] Task 1 (Data Analysis): Employee C

[0955] Task 2 (System Development): Employee B

[0956] Task 3 (Report Writing): Employee C

[0957] Prompt Sentence Examples

[0958] "Assign the most appropriate work tasks based on employee information and their skill set. Also, consider the employee's current emotional state (high, low, medium stress level) and adjust accordingly. For example, assign Task 1 (data analysis) to Employee C (data analysis skills, medium stress level) rather than Employee A (data analysis skills, high stress level)."

[0959] In this way, by combining the emotion engine, the system can achieve optimal task allocation that takes into account the emotional state of employees, enabling more efficient business operations.

[0960] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0961] Step 1:

[0962] User operations

[0963] The user (administrator) accesses a dedicated input form, where they enter the employee's name, department, and skill set. For example, the information for employee A is entered as "Name: Employee A, Department: IT, Skills: Project management, Programming, Data analysis." After entering the information, the user clicks the "Submit" button.

[0964] Input: Employee information (name, department, skill set)

[0965] Output: Employee information passed to the terminal

[0966] Specific operation: When the user fills in the form and presses the "Submit" button, the data is passed to the terminal.

[0967] Step 2:

[0968] Terminal handling

[0969] The terminal acquires employee information entered by the user, converts the acquired data into JSON format, and sends the converted JSON data to the server.

[0970] Input: Employee information (name, department, skill set)

[0971] Output: JSON data sent to the server

[0972] Specific operation: The terminal obtains employee information from the input form, converts it into JSON format, and sends it to the server.

[0973] Step 3:

[0974] Server Processing

[0975] The server receives the JSON data sent from the terminal and stores it in the database. For example, the information of employee A is saved in the corresponding table in the database.

[0976] Input: Employee data in JSON format

[0977] Output: Employee information stored in a database

[0978] Specific operation: The server receives the JSON data and stores it in the database.

[0979] Step 4:

[0980] User operations

[0981] The user accesses the system's task input form and enters the details of the work task (task name, required skills, deadline, etc.). For example, they might enter "Task name: Data analysis, required skills: Data analysis, deadline: End of October 2023." After entering the information, the user clicks the "Submit" button.

[0982] Input: Work task information (task name, required skills, deadline)

[0983] Output: Task information passed to the terminal

[0984] Specific operation: When the user enters work task information into the form and presses the "Submit" button, the data is passed to the terminal.

[0985] Step 5:

[0986] Terminal handling

[0987] The device collects data on business tasks, converts it into JSON format, and sends it to the server.

[0988] Input: Work task information (task name, required skills, deadline)

[0989] Output: JSON data sent to the server

[0990] Specific operation: The terminal acquires business task information, converts it into JSON format, and sends it to the server.

[0991] Step 6:

[0992] Server Processing

[0993] The server receives the business task information sent from the terminal and stores it in a database. For example, information about a data analysis task is saved in a corresponding table in the database.

[0994] Input: JSON formatted business task information

[0995] Output: Business task information stored in a database

[0996] Specific operation: The server receives the JSON data and stores the business task information in a database.

[0997] Step 7:

[0998] Server Processing

[0999] The server retrieves employee and task information from a database and uses machine learning or rule-based algorithms to select the best employee for each task.

[1000] Input: Employee information and work task information stored in a database

[1001] Output: Optimal employee-task pairs

[1002] Specific operation: The server analyzes the information and selects the most suitable employee.

[1003] Step 8:

[1004] Emotion engine processing

[1005] The emotion engine monitors the emotional state of employees in real time and provides that information to the server. The emotion engine recognizes the employee's facial expressions and voice to evaluate their emotional state.

[1006] Input: Employee facial expressions and voice data

[1007] Output: Employee sentiment information

[1008] Specific operation: The emotion engine analyzes facial expressions and voice to generate emotional information.

[1009] Step 9:

[1010] Collaboration between the server and emotion engine

[1011] The server stores the emotion information provided by the emotion engine in a database. For example, information such as "Employee A's current emotional state: high stress level" is saved.

[1012] Input: Employee emotional information

[1013] Output: Emotion information stored in a database

[1014] Specific operation: The server receives the emotion information and stores it in a database.

[1015] Step 10:

[1016] Server Processing

[1017] The server analyzes the emotional and skill information stored in the database and adjusts task allocation accordingly. For example, if employee A has a high stress level, it will avoid assigning high-stress tasks to him.

[1018] Input: Emotion information and skill information stored in the database

[1019] Output: Adjusted task assignments

[1020] Specific operation: The server comprehensively analyzes the information and adjusts task allocation.

[1021] Step 11:

[1022] Server Processing

[1023] The server generates a list of optimal agents that reflects the emotional information and sends it to the terminal in a data format such as JSON.

[1024] Input: Adjusted task assignment information

[1025] Output: A list of contacts sent to the terminal

[1026] Specific operation: The server generates a list of personnel and sends it to the terminal.

[1027] Step 12:

[1028] Terminal handling

[1029] The terminal analyzes the list of people in charge received from the server and displays it on the user interface. For example, it displays "Employee B has been assigned to the data analysis task."

[1030] Input: List of agents received from the server

[1031] Output: A list of agents displayed in the user interface

[1032] Specific behavior: The device parses and displays the list.

[1033] (Application example 2)

[1034] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1035] Conventional work task allocation systems based on employee skill sets were unable to consider the emotional state of employees, and tasks were assigned without regard for employee stress or motivation. This resulted in lower employee productivity and satisfaction, and was unable to contribute to work efficiency. This problem was particularly pronounced in high-stress environments such as logistics centers.

[1036] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1037] In this invention, the server includes user input means for inputting employee information, server means for receiving the input employee information and storing it in a database, user input means for inputting work task information, server means for receiving the input work task information and storing it in the database, analysis means for analyzing the employee information and work task information stored in the database and selecting the employee most suitable for each task, emotion engine means for detecting the emotional state of employees and storing the emotion information in the database, adjustment means for integrating the emotion information and the analysis results and optimizing task allocation, and output means for presenting the selection results to the user. This enables optimal task allocation taking into account the skill sets and emotional states of employees.

[1038] The "user input means for inputting employee information" is a means for providing an interface for the manager to input employee information.

[1039] The "server means" is a server device that has the function of receiving input information and storing it in a database.

[1040] The "user input means for inputting business task information" is a means for providing an interface for the administrator to input business task information.

[1041] The "analysis means" is a means having a function for analyzing employee information and business task information stored in the database and selecting the most suitable employee for each task.

[1042] The "emotion engine means" is a means having a function for detecting the emotional state of an employee and storing the information in a database.

[1043] The "adjustment means" is a means that has the function of integrating emotional information and analysis results to optimize task allocation.

[1044] The "output means" is a means for providing an interface for presenting the selection results to the user.

[1045] The present invention relates to an optimal work task allocation system that takes into account the skill sets and emotional states of employees in a logistics center. This system is implemented using a server, terminals, and an emotion engine. Specific embodiments are described below.

[1046] Enter employee information and skill sets

[1047] First, the administrator (user) uses a dedicated input interface to input employee information (such as name, skill set, and emotional state). For example, information about employee A is entered as "Name: Employee A, Skills: Programming, Data Analysis, Emotional State: High Stress Level."

[1048] Receiving and storing data

[1049] The terminal receives the entered employee information, converts it to JSON format, and sends it to the server. The server receives this information and stores it in a database. For example, the information of "Employee A" is saved in a database table.

[1050] Entering and storing business task information

[1051] Next, the administrator enters the details of the work task (task name, required skills, deadline, etc.) into the system. For example, "Task name: Data analysis, required skills: Data analysis, deadline: End of October 2023." The terminal converts this data into JSON format and sends it to the server, which then stores the information in a database.

[1052] Analysis and selection of personnel based on skill sets and emotional states

[1053] The server retrieves employee information and work task information stored in the database and uses machine learning or rule-based algorithms to select the employee best suited for each task. The server also takes into account the employee's emotional state, as provided by the emotion engine. For example, it determines that "Employee C" is best suited for "Task 1 (Data Analysis)" and "Employee B" is best suited for "Task 2 (System Development)."

[1054] Emotion Engine Functions

[1055] The emotion engine monitors employees' emotional states in real time and provides that information to the server. The emotion engine evaluates the employee's emotional state, for example, through facial expression recognition or voice analysis. The server stores this emotional information in a database and reflects it in task allocation. For example, if the emotional state of "Employee A" is evaluated as "high stress level," it will avoid assigning tasks that cause high stress.

[1056] Generate and view a contact list

[1057] Finally, the server generates a list of optimal agents that reflects the emotional information and sends it to the device in JSON format. The device then analyzes the received list and displays it on the user interface. For example, it displays that "Employee C" is assigned to "Task 1 (Data Analysis)."

[1058] Specific examples

[1059] Employee Information:

[1060] Employee A: Skills (programming, data analysis), emotional state (high stress level)

[1061] Employee B: Skills (system design, programming), emotional state (low stress level)

[1062] Employee C: Skills (Writing, Data Analysis, Marketing), Emotional State (Medium Stress Level)

[1063] Business tasks:

[1064] Task 1: Data Analysis

[1065] Task 2: System Development

[1066] Task 3: Report writing

[1067] Allocation results:

[1068] Task 1 (Data Analysis): Employee C

[1069] Task 2 (System Development): Employee B

[1070] Task 3 (Report Writing): Employee C

[1071] Example prompt sentence:

[1072] "Enter employee information (name, skill set, emotional state) and work tasks (task name, required skills) and generate code for a system that optimally assigns tasks based on the employee's emotional state."

[1073] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1074] Step 1:

[1075] The user uses a dedicated input interface to input employee information, including the employee's name, department, skill set, current emotional state, etc. This input information is converted into JSON format and sent to the terminal.

[1076] Input: Employee information (name, department, skill set, emotional state)

[1077] Output: Employee information in JSON format

[1078] Step 2:

[1079] The terminal sends the JSON-formatted employee information entered by the user to the server, and also correctly formats each employee entry and sends it as an HTTP request to the appropriate server endpoint.

[1080] Input: Employee information in JSON format

[1081] Output: HTTP request to the server

[1082] Step 3:

[1083] The server receives the employee information sent from the terminal and saves it in the database. Specifically, it parses the JSON format employee information and stores it in the corresponding table in the database.

[1084] Input: HTTP request from the terminal (employee information in JSON format)

[1085] Output: Employee information stored in the database

[1086] Step 4:

[1087] The user inputs task information using a dedicated input interface, including the task name, required skills, deadline, etc. This input information is converted into JSON format and sent to the device.

[1088] Input: Work task information (task name, required skills, deadline)

[1089] Output: JSON formatted business task information

[1090] Step 5:

[1091] The device sends the JSON-formatted work task information entered by the user to the server, and then correctly formats each piece of work task information and sends it as an HTTP request to the appropriate server endpoint.

[1092] Input: JSON formatted business task information

[1093] Output: HTTP request to the server

[1094] Step 6:

[1095] The server receives the business task information sent from the terminal and saves it in the database. Specifically, it parses the business task information in JSON format and stores it in the corresponding table in the database.

[1096] Input: HTTP request from the device (JSON formatted business task information)

[1097] Output: Business task information stored in the database

[1098] Step 7:

[1099] The server retrieves employee and task information stored in the database and analyzes it using analytical tools. Taking into account the employee's skill set and emotional state, the server selects the employee best suited for each task. It uses machine learning algorithms to find the optimal combination.

[1100] Input: Employee and task information in the database

[1101] Output: Optimal task assignment information

[1102] Step 8:

[1103] The emotion engine monitors the emotional state of employees in real time and sends the data to the server. The emotion engine uses facial expression recognition and voice analysis to evaluate the emotional state of employees in real time and sends the results in JSON format.

[1104] Input: Real-time employee emotion data (facial expressions, voice data)

[1105] Output: Emotion information in JSON format

[1106] Step 9:

[1107] The server stores the emotional information obtained from the emotion engine in a database, integrates this information with employee information, and adjusts task assignments based on the employee's emotional state.

[1108] Input: JSON format emotion information from the emotion engine

[1109] Output: Employee information and sentiment information integrated into a database

[1110] Step 10:

[1111] The server generates a list of optimal agents that reflects the emotional information and sends it to the device in JSON format. It then performs a final check of the analyzed information and sends it back to the device as a task assignment list.

[1112] Input: Parsed employee information and work task information

[1113] Output: HTTP request to the device (optimal task allocation information)

[1114] Step 11:

[1115] The terminal analyzes the list of people in charge received from the server and displays it on the user interface, specifically displaying the name of the employee assigned to each task and the task name.

[1116] Input: HTTP request from the server (optimal task allocation information)

[1117] Output: Optimal task assignment list on the user interface

[1118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1119] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1120] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1121] [Third embodiment]

[1122] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1123] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1125] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1126] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1130] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1132] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1133] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1134] The present invention is a system for automatically assigning optimal work tasks based on the skill sets of employees. Specific embodiments will be described below.

[1135] Enter employee information and skill sets

[1136] 1. User Action:

[1137] The user (administrator) enters employee information (such as name, department, skill set, etc.) in a dedicated input form. For example, the information for Employee A is entered as "Name: Employee A, Department: IT, Skills: Project management, Programming, Data analysis."

[1138] Receiving and storing data

[1139] 2. Terminal processing:

[1140] The terminal converts the employee information obtained from the input form into JSON format and sends the data to the server.

[1141] 3. Server processing:

[1142] The server receives the JSON data sent from the terminal and stores it in the database. For example, the information of employee A is saved in the corresponding table in the database.

[1143] Receive and store information about business tasks

[1144] 4. User Action:

[1145] The user enters the details of the work task (task name, required skills, deadline, etc.) into the system. For example, the user enters "Task name: Data analysis, Required skills: Data analysis, Deadline: End of October 2023."

[1146] 5. Terminal processing:

[1147] The device collects business task data, converts it into JSON format, and sends it to the server.

[1148] 6. Server Processing:

[1149] The server receives the business task information and stores it in a database, for example, the information of a data analysis task is stored in a corresponding table in the database.

[1150] Skill set analysis and personnel selection

[1151] 7. Server Processing:

[1152] The server retrieves employee and task information from the database and uses machine learning or rule-based algorithms to select the best employee for each task. For example, it may determine that Employee A, with his data analysis skills, is best suited for the data analysis task.

[1153] 8. Server Processing:

[1154] The selection results are formatted into a list and sent to the terminal in a data format such as JSON.

[1155] Generate and display a contact list

[1156] 9. Terminal Processing:

[1157] The terminal analyzes the list of people in charge received from the server and displays it on the user interface. For example, it displays "Employee A has been assigned to the data analysis task."

[1158] Specific examples

[1159] Specific examples are shown below.

[1160] input

[1161] Employee A: Skills (project management, programming, data analysis)

[1162] Employee B: Skills (system design, programming, security)

[1163] Employee C: Skills (writing, data analysis, marketing)

[1164] Business tasks

[1165] Task 1: Data Analysis

[1166] Task 2: System Development

[1167] Task 3: Report writing

[1168] Output

[1169] Task 1 (Data Analysis): Employee A

[1170] Task 2 (System Development): Employee B

[1171] Task 3 (Report Writing): Employee C

[1172] In this way, work tasks can be automatically assigned based on employees' skill sets, resulting in increased work efficiency.

[1173] The processing flow will be explained below.

[1174] Step 1:

[1175] The user enters employee information (such as name, department, skill set, etc.) using a dedicated input form.

[1176] For example, the user enters "Employee A, Department: IT, Skills: Project management, Programming, Data analysis."

[1177] Step 2:

[1178] The terminal takes the employee information entered by the user and converts it into JSON format or another appropriate data format.

[1179] Step 3:

[1180] The terminal transmits the converted data to the server.

[1181] Step 4:

[1182] The server parses the JSON data received from the device and breaks down the employee information into individual fields.

[1183] The server stores the parsed data in a database.

[1184] For example, store the information of "Employee A" in a corresponding table in the database.

[1185] Step 5:

[1186] The user enters details of the business task (task name, required skills, deadline, etc.) using a dedicated input form.

[1187] For example, enter "Task name: Data analysis, Required skills: Data analysis, Deadline: End of October 2023."

[1188] Step 6:

[1189] The device acquires the business task information entered by the user and converts it into JSON format or another appropriate data format.

[1190] Step 7:

[1191] The terminal transmits the converted data to the server.

[1192] Step 8:

[1193] The server analyzes the JSON data received from the terminal and breaks down the business task information into individual fields.

[1194] The server stores the parsed data in a database.

[1195] For example, the information of the "data analysis task" is stored in a corresponding table in the database.

[1196] Step 9:

[1197] The server retrieves employee information and work task information from the database.

[1198] Step 10:

[1199] The server uses machine learning or rule-based algorithms to select the best employee for each task.

[1200] For example, you might determine that "Employee A, who has data analysis skills, is best suited for the data analysis task."

[1201] Step 11:

[1202] The server formats the selection results into a list and sends it to the terminal in JSON format or other appropriate data format.

[1203] Step 12:

[1204] The terminal analyzes the list of personnel received from the server.

[1205] Step 13:

[1206] The terminal displays the analyzed list of personnel on the user interface.

[1207] For example, it might say, "Employee A has been assigned the data analysis task."

[1208] In this way, the system of the present invention automates the allocation of optimal business tasks based on the skill sets of employees through each processing step, thereby realizing efficient business operations.

[1209] Example 1

[1210] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1211] The process of selecting the most suitable work tasks based on employees' skill sets has traditionally been done manually, requiring time and effort. It also poses challenges in ensuring fair allocation of personnel and improving work efficiency. Furthermore, in large organizations, it is difficult to centrally manage employee skill information and work task information and quickly select the most suitable personnel.

[1212] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1213] In this invention, the server includes user input means for inputting employee information, terminal means for receiving the input employee information, converting it into a data format and sending it to the server, server means for storing the received employee information in a database, user input means for inputting work task information, terminal means for receiving the input work task information, converting it into a data format and sending it to the server, server means for storing the received work task information in the database, analysis means for acquiring the employee information and work task information stored in the database and selecting the most suitable employee for each task, and output means for displaying the selection results on a user interface. This makes it possible to quickly and efficiently assign work tasks to the most suitable employees.

[1214] "Employee information" is data that includes basic attribute information and skill sets about employees.

[1215] A "user input means" is a device or interface through which a user inputs information, including, for example, web forms and input fields.

[1216] "Terminal means" refers to a device or function for collecting employee information and business task information, converting it into an appropriate format, and transmitting it to a server.

[1217] The "server means" is a device or system for processing information received from the terminal means and storing it in a database.

[1218] "Business task information" is data that includes details of a specific business task (such as task name, required skills, deadline, etc.).

[1219] A "data format" is a way of representing data in a particular format, such as JSON or XML.

[1220] A "database" is a system for efficiently storing, managing, and searching data, and examples include MySQL and PostgreSQL.

[1221] "Analysis means" refers to devices and algorithms used to retrieve and analyze information in a database.

[1222] "Output means" refers to a device or interface for displaying the analysis results to the user.

[1223] A "machine learning algorithm" is an algorithm that learns patterns and rules based on data and makes predictions and classifications.

[1224] The present invention is a system for automatically assigning work tasks based on the skill sets of employees. This system is operated by combining users, terminals, and a server.

[1225] Enter employee information and skill sets

[1226] The user enters employee information using a dedicated input form provided on the web browser. The information entered includes name, department, skill set, etc. For example, for employee A, the user enters "Name: Employee A, Department: IT, Skills: Project management, Programming, Data analysis."

[1227] Receiving and storing data

[1228] The terminal receives the employee information entered by the user and converts it into JSON format. The converted data is sent to the server as an HTTP POST request. The server receives this request, parses the JSON data, and stores the employee information in a database (for example, MySQL or PostgreSQL).

[1229] Receive and store information about business tasks

[1230] The user enters the details of the work task using a similarly provided input form. For example, they enter "Task name: Data analysis, Required skills: Data analysis, Deadline: End of October 2023." The device converts this work task information into JSON format and sends it to the server. The server analyzes the received JSON data and stores the work task information in a database.

[1231] Skill set analysis and personnel selection

[1232] The server retrieves employee and task information from the database using SQL SELECT statements. Based on the retrieved information, it applies machine learning or rule-based algorithms to select the best employee for each task. For example, it uses the Python scikit-learn library to determine that Employee A, with his data analysis skills, is best suited for a data analysis task.

[1233] Generate and display a contact list

[1234] The server formats the selection results into a list and converts it into a data format such as JSON. This data is sent to the terminal as an HTTP response. The terminal analyzes the list of people in charge received from the server and displays it in the user interface. For example, the browser displays "Employee A has been assigned to the data analysis task."

[1235] Specific examples

[1236] input

[1237] Employee A: Skills (project management, programming, data analysis)

[1238] Employee B: Skills (system design, programming, security)

[1239] Employee C: Skills (writing, data analysis, marketing)

[1240] Business tasks

[1241] Task 1: Data Analysis

[1242] Task 2: System Development

[1243] Task 3: Report writing

[1244] Prompt Sentence Examples

[1245] You can use the prompt text as follows:

[1246] Employee Information:

[1247] Name: Employee A

[1248] Department: IT

[1249] Skills: Project Management, Programming, Data Analysis

[1250] Business tasks:

[1251] Task Name: Data Analysis

[1252] Required skills: Data analysis

[1253] Deadline: End of October 2023

[1254] Using the information above, explain how to assign tasks to the appropriate employees.

[1255] This prompt can be fed into an AI model to generate instructions on how to assign the task to the best employee.

[1256] As such, the system streamlines the allocation of complex work tasks and makes the most of employee skill sets.

[1257] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1258] Step 1:

[1259] Entering employee information

[1260] Users access a dedicated input form on a web browser and enter employee information.

[1261] Specifically, the user fills in form fields with details such as the employee's name, department, and skill set.

[1262] Example: "Name: Employee A, Department: IT, Skills: Project management, programming, data analysis"

[1263] Input: Employee information entered by the user.

[1264] Output: Employee information entered into the input form.

[1265] Step 2:

[1266] Receiving employee information

[1267] The terminal receives the data entered by the user into the input form.

[1268] Converts input data into JSON format.

[1269] Input: Employee information entered by the user.

[1270] Output: Employee information converted to JSON format.

[1271] Step 3:

[1272] Transmitting and storing employee information

[1273] The device sends the converted JSON format data to the server as an HTTP POST request.

[1274] The server receives the HTTP POST request and parses the JSON data.

[1275] The server stores the parsed employee information in a database, for example, the employee information is inserted into the "Employees" table.

[1276] Input: Employee information in JSON format.

[1277] Output: Employee information stored in the database.

[1278] Step 4:

[1279] Entering work task information

[1280] The user opens a separate input form in a web browser and enters the details of the work task.

[1281] For example, enter "Task name: Data analysis, Required skills: Data analysis, Deadline: End of October 2023."

[1282] Input: Work task information entered by the user.

[1283] Output: Business task information entered in the input form.

[1284] Step 5:

[1285] Receiving business task information

[1286] The terminal receives the business task information entered by the user in the input form.

[1287] Converts input data into JSON format.

[1288] Input: Work task information entered by the user.

[1289] Output: Business task information converted to JSON format.

[1290] Step 6:

[1291] Sending and storing business task information

[1292] The device sends the converted JSON format data to the server as an HTTP POST request.

[1293] The server receives the HTTP POST request and parses the JSON data.

[1294] The server stores the analyzed business task information in a database. For example, the business task information is inserted into a "Tasks" table.

[1295] Input: Business task information in JSON format.

[1296] Output: Business task information stored in a database.

[1297] Step 7:

[1298] Obtaining employee and task information

[1299] The server retrieves employee and task information from the database using SQL SELECT statements.

[1300] The obtained information is passed to an analysis means.

[1301] Input: Employee and work task information stored in a database.

[1302] Output: Employee information and work task information captured for analysis.

[1303] Step 8:

[1304] Analysis and selection of personnel

[1305] The server analyzes the acquired information using an analysis means (machine learning algorithm or rule-based algorithm).

[1306] The analytical method matches employee skill sets with business task requirements and selects the most suitable employee.

[1307] For example, employee A, who has data analysis skills, is assigned to the data analysis task.

[1308] Input: Captured employee information and work task information.

[1309] Output: Selection of the best employee for each task.

[1310] Step 9:

[1311] Sending and displaying the selection results

[1312] The server formats the selection results into a list and converts them into JSON format.

[1313] The server sends this data to the terminal as an HTTP response.

[1314] The terminal analyzes the received list of people in charge and displays the results on the user interface, for example, "Employee A has been assigned to the data analysis task."

[1315] Input: JSON data of the selection result.

[1316] Output: The list of agents displayed in the user interface.

[1317] (Application example 1)

[1318] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1319] Although systems already exist that can efficiently assign optimal work tasks to employees based on their skill sets, there is a lack of a way to quickly notify employees of the results of the selection process. This means that employees are unable to immediately know about the assigned tasks and take action, resulting in a decline in work efficiency in factories and offices.

[1320] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1321] In this invention, the server includes user input means for inputting employee information, server means for receiving the input employee information and storing it in a database, user input means for inputting job content information, server means for receiving the input job content information and storing it in a database, analysis means for analyzing the employee information and job content information stored in the database and selecting the employee most suitable for each job, output means for presenting the selection results to the user, and communication means for notifying the employee's communication terminal of the selection results, thereby making it possible to promptly notify the employee of the selection results.

[1322] "Employee information" includes information such as the employee's name, department, and skill set.

[1323] The "user input means" is an interface that allows employees or managers to input employee information and job content information.

[1324] The "server means" is a server having a function for receiving input data and storing it in a database as necessary.

[1325] "Work content information" is information such as the name of the work task, the required skills, and the deadline.

[1326] The "analysis means" is a system that has the function of analyzing employee information and job content information stored in the database and selecting the most suitable employee for each job.

[1327] The "output means" is an interface for presenting the analysis results and selection results to the user.

[1328] "Communication means" refers to a system that has the function of notifying the selection results to the employee's communication terminal.

[1329] A "machine learning algorithm" is an algorithm that learns patterns from data and makes predictions and classifications.

[1330] The present invention is a system that automatically selects the most suitable employee based on the employee's skill set and job content information in order to improve work efficiency in factories and offices, and notifies the results to the employee's communication terminal. A specific implementation method is shown below.

[1331] System configuration

[1332] Hardware

[1333] User terminal: A device (e.g., tablet, smart glasses) for inputting employee information and job content information.

[1334] Server: A server system for receiving, storing, and analyzing data.

[1335] Communication terminal: Employee device (e.g., smartphone, smart glasses) for receiving the selection results.

[1336] software

[1337] Input form: An input interface for employee information and job content information installed on the user's terminal.

[1338] Receiving and storing data: A server-side script that converts the received data into JSON format and stores it in a database.

[1339] Machine learning algorithm: Python's scikit-learn library is used to analyze employee information and job description information and select the most suitable employees.

[1340] Notification system: An API for pushing the selection results to employees' communication devices.

[1341] Program processing explanation

[1342] The server receives employee information and job details sent from the user's device in JSON format and stores the data in a database. It then uses a machine learning algorithm to select the most suitable employee, formats the selection results, and notifies the employee's communication device.

[1343] Data processing and calculation

[1344] Analyze employee information: Map each employee's skill set to a numerical value and feed it into machine learning algorithms.

[1345] Analyze job information: Map required skills to a numerical value and compare it with employee skill sets to find the best match.

[1346] Sending the selection results: Using the push notification API, the selection results are notified to the employee's communication device.

[1347] As a specific example, if Employee A's skill set "project management, programming, data analysis" and Task 1 "data analysis" are entered, the server will match the skill set with the task and determine that Employee A is the best person for this task. The result will be notified to Employee A's smartphone.

[1348] Generative AI model prompt example

[1349] Create an application that automatically selects the best workers to improve work efficiency. Enter employee skill sets and work task information, and the system will select the best employee for each task. The output will be displayed in a list format, including the selected workers and the task names.

[1350] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1351] Step 1:

[1352] A user uses a tablet or smart glasses to enter employee information, including the employee's name, department, and skill set, which is then converted into JSON format.

[1353] Step 2:

[1354] The entered JSON data is sent from the terminal to the server, which receives it and stores it in a database for analysis. When stored in the database, each employee's information is saved as a separate record.

[1355] Step 3:

[1356] The user again uses a tablet or smart glasses to input task information, including the task name, required skills, and deadline, which is then converted to JSON format.

[1357] Step 4:

[1358] The JSON data of the job content information is sent from the terminal to the server. The server receives this data and stores it in a database. Information about each job content is saved as an individual record.

[1359] Step 5:

[1360] The server retrieves employee information and job information from the database. It uses a machine learning algorithm (K-nearest neighbor method) to process and calculate the data to select the best employee for each task. Specifically, it maps the employee's skill set and the skills required for the task to a numerical value, and generates a dataset for analysis.

[1361] Step 6:

[1362] The machine learning model selects the best employee, and the matching results between the selected employee and the task are formatted in JSON format and presented to the user. This generates a list of the best people to assign each task.

[1363] Step 7:

[1364] The server sends a push notification to not only the user but also the employee's communication device (smartphone or smart glasses). The notification system API is used to deliver the selection results to the employee's device.

[1365] Step 8:

[1366] Employees receive notifications via their communication devices and check the details of their assigned tasks. The notifications include the task name, required skills, deadline, and the name of the person in charge. They can immediately begin working on the assigned task.

[1367] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1368] The present invention combines a system that automatically assigns optimal work tasks based on the skill sets of employees with an emotion engine that recognizes the emotions of users. Specific embodiments will be described below.

[1369] Enter employee information and skill sets

[1370] User operations

[1371] The user (administrator) enters employee information (such as name, department, skill set, etc.) in a dedicated input form. For example, the information for Employee A is entered as "Name: Employee A, Department: IT, Skills: Project management, Programming, Data analysis."

[1372] Receiving and storing data

[1373] Terminal handling

[1374] The terminal converts the employee information obtained from the input form into JSON format and sends it to the server.

[1375] Server Processing

[1376] The server receives the JSON data sent from the terminal and stores it in the database. For example, the information of employee A is saved in the corresponding table in the database.

[1377] Receive and store information about business tasks

[1378] User operations

[1379] The user enters the details of the work task (task name, required skills, deadline, etc.) into the system. For example, the user enters "Task name: Data analysis, Required skills: Data analysis, Deadline: End of October 2023."

[1380] Terminal handling

[1381] The device collects data on business tasks, converts it into JSON format, and sends it to the server.

[1382] Server Processing

[1383] The server receives the business task information and stores it in a database, for example, the information of a data analysis task is stored in a corresponding table in the database.

[1384] Skill set analysis and personnel selection

[1385] Server Processing

[1386] The server retrieves employee and task information from the database and uses machine learning or rule-based algorithms to select the best employee for each task. For example, it may determine that Employee A, with his data analysis skills, is best suited for the data analysis task.

[1387] Introducing the Emotion Engine

[1388] Emotion Engine Functions

[1389] The emotion engine monitors the emotional state of employees in real time and provides that information to analytical tools. The emotion engine evaluates the emotional state of employees, for example, through facial expression recognition and voice analysis.

[1390] Collaboration between the server and emotion engine

[1391] The server stores the emotional information obtained from the emotion engine in a database. For example, information such as "Employee A's current emotional state: high stress level" is saved.

[1392] Adjustment based on emotional state

[1393] The server analyzes the emotional and skill information stored in the database and reflects this information in task allocation. For example, if employee A has a high stress level, the server will adjust the allocation to avoid high-stress tasks.

[1394] Generate and display a contact list

[1395] Server Processing

[1396] The server generates a list of optimal agents that reflects the emotional information and sends it to the terminal in a data format such as JSON.

[1397] Terminal handling

[1398] The terminal analyzes the list of people in charge received from the server and displays it on the user interface. For example, it displays "Employee B has been assigned to the data analysis task."

[1399] Specific examples

[1400] Specific examples are shown below.

[1401] input

[1402] Employee A: Skills (project management, programming, data analysis), Emotional state (high stress level)

[1403] Employee B: Skills (system design, programming, security), emotional state (low stress level)

[1404] Employee C: Skills (Writing, Data Analysis, Marketing), Emotional State (Medium Stress Level)

[1405] Business tasks

[1406] Task 1: Data Analysis

[1407] Task 2: System Development

[1408] Task 3: Report writing

[1409] Output

[1410] Task 1 (Data Analysis): Employee C

[1411] Task 2 (System Development): Employee B

[1412] Task 3 (Report Writing): Employee C

[1413] In this way, by combining emotion engines, optimal task allocation can be achieved while taking into consideration the emotional state of employees, enabling even more efficient business operations.

[1414] The processing flow will be explained below.

[1415] Step 1:

[1416] The user enters employee information (such as name, department, skill set, etc.) using a dedicated input form.

[1417] For example, the user enters "Employee A, Department: IT, Skills: Project management, Programming, Data analysis."

[1418] Step 2:

[1419] The terminal takes the employee information entered by the user and converts it into JSON format or another appropriate data format.

[1420] Step 3:

[1421] The terminal transmits the converted data to the server.

[1422] Step 4:

[1423] The server parses the JSON data received from the device and breaks down the employee information into individual fields.

[1424] The server stores the parsed data in a database.

[1425] For example, store the information of "Employee A" in a corresponding table in the database.

[1426] Step 5:

[1427] The user enters details of the business task (task name, required skills, deadline, etc.) using a dedicated input form.

[1428] For example, enter "Task name: Data analysis, Required skills: Data analysis, Deadline: End of October 2023."

[1429] Step 6:

[1430] The device acquires the business task information entered by the user and converts it into JSON format or another appropriate data format.

[1431] Step 7:

[1432] The terminal transmits the converted data to the server.

[1433] Step 8:

[1434] The server analyzes the JSON data received from the terminal and breaks down the business task information into individual fields.

[1435] The server stores the parsed data in a database.

[1436] For example, the information of the "data analysis task" is stored in a corresponding table in the database.

[1437] Step 9:

[1438] The server retrieves employee information and work task information from the database.

[1439] Step 10:

[1440] The server requests the emotion engine for the emotion information of the current employee.

[1441] Step 11:

[1442] The emotion engine monitors the emotional state of employees in real time and sends that information to the server.

[1443] For example, information such as "Employee A's current emotional state: stress level is high" is sent.

[1444] Step 12:

[1445] The server stores the emotion information obtained from the emotion engine in a database.

[1446] Step 13:

[1447] The server uses machine learning or rule-based algorithms to select the best employee for each task based on the employee's skill set and emotional state.

[1448] For example, you might determine that "Employee A, who has data analysis skills, would be well suited to the data analysis task, but because he has a high stress level, Employee C would be the best choice instead."

[1449] Step 14:

[1450] The server formats the selection results into a list and sends it to the terminal in JSON format or other appropriate data format.

[1451] Step 15:

[1452] The terminal analyzes the list of personnel received from the server and displays it on the user interface.

[1453] For example, it might say, "Taking into consideration stress levels, employee C has been assigned the data analysis task."

[1454] In this way, the system of the present invention automates the allocation of optimal work tasks based on the employee's skill set and emotional state throughout each processing step, thereby achieving efficient business operations and reducing the psychological burden on employees.

[1455] Example 2

[1456] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1457] While conventional task allocation systems can assign tasks based on employees' skill sets, they are unable to take into account the emotional state of employees, which can lead to issues such as employees with high stress levels being assigned more demanding tasks, making it difficult to achieve efficient business operations.

[1458] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a user input means for inputting employee information, a server means for receiving the input employee information and storing it in a database, a user input means for inputting work task information, a server means for receiving the input work task information and storing it in the database, an analysis means for analyzing the employee information and work task information stored in the database and selecting the optimal employee for each task, an emotion engine means for monitoring the emotional states of employees in real time and providing that information, an analysis means for adjusting task assignments based on the emotional information, and an output means for presenting the selection results to the user. This enables optimal work task assignments that take into account not only the employee's skill set but also their emotional state.

[1459] "Employee information" refers to information such as an employee's name, department, and skill set.

[1460] "User input means" refers to an interface through which a user inputs employee information and business task information.

[1461] "Server means" refers to a server having the function of receiving input data and storing it in a database.

[1462] "Business task information" refers to information about the details of a business task (task name, required skills, deadline, etc.).

[1463] "Database" refers to a data management system for storing employee information and business task information.

[1464] "Analysis means" refers to the function for analyzing the information stored in the database and determining the best employee for each task.

[1465] The "emotion engine means" refers to a function for monitoring the emotional state of employees in real time and providing that information to the server.

[1466] "Analysis means" refers to the function for adjusting task allocation by reflecting emotional information.

[1467] "Output means" refers to a function for presenting the results of task allocation to the user.

[1468] The present invention relates to a system that manages employee information and work task information, and assigns optimal work tasks to employees by taking into consideration their skill sets and emotional states. The system efficiently and effectively assigns tasks by using a user input means, a server means, an analysis means, an emotion engine means, an analysis means, and an output means.

[1469] Hardware and software used

[1470] Hardware: Web server, client terminal (PC, tablet, etc.)

[1471] software:

[1472] Database: MySQL

[1473] Programming language: Python

[1474] Machine learning library: Scikit-Learn

[1475] Sentiment analysis engine: Microsoft Azure Face API, Google Cloud Speech-to-Text

[1476] Frontend: HTML, CSS, JavaScript

[1477] System configuration

[1478] Enter employee information and skill sets

[1479] The user (administrator) uses a dedicated input form to enter employee information (name, department, skill set). For example, for employee A, enter "Name: Employee A, Department: IT, Skills: Project management, Programming, Data analysis." Then, the user clicks the "Submit" button to complete the input.

[1480] Receiving and storing data

[1481] The terminal receives the employee information entered by the user and converts it into JSON format. The converted data is sent to the server, which then receives the data and stores it in the database. For example, the information of employee A is saved in the corresponding table in the database.

[1482] Entering and storing business task information

[1483] The user accesses the system's task input form and enters the details of the work task (task name, required skills, deadline, etc.). For example, the user enters "Task name: Data analysis, required skills: Data analysis, deadline: End of October 2023." The user then clicks the "Submit" button.

[1484] The device collects data on business tasks, converts it into JSON format, and sends it to the server, which receives the data and stores it in a database.

[1485] Skill set analysis and personnel selection

[1486] The server retrieves employee and task information from the database and uses machine learning or rule-based algorithms to select the best employee for each task. For example, it may determine that an employee with data analysis skills is best suited for a data analysis task.

[1487] Introducing the Emotion Engine

[1488] The emotion engine monitors the emotional state of employees in real time and provides emotional information to the server. The emotion engine recognizes the employee's facial expressions and voice to evaluate their emotional state.

[1489] The server stores the emotion information provided by the emotion engine in a database. For example, information such as "Employee A's current emotional state: high stress level" is saved.

[1490] Adjustment based on emotional state

[1491] The server analyzes the emotional and skill information stored in the database in an integrated manner and reflects this in task allocation. For example, if employee A has a high stress level, it will adjust the allocation so that high-stress tasks are not assigned to him.

[1492] Generate and display a contact list

[1493] The server generates a list of optimal agents that reflects the emotional information and sends it to the device in a data format such as JSON. The device analyzes the list of agents received from the server and displays it on the user interface. For example, it displays "Employee B has been assigned to the data analysis task."

[1494] Specific examples

[1495] Specific examples are shown below.

[1496] Employee A: Skills (project management, programming, data analysis), Emotional state (high stress level)

[1497] Employee B: Skills (system design, programming, security), emotional state (low stress level)

[1498] Employee C: Skills (Writing, Data Analysis, Marketing), Emotional State (Medium Stress Level)

[1499] Business tasks

[1500] Task 1: Data Analysis

[1501] Task 2: System Development

[1502] Task 3: Report writing

[1503] Output

[1504] Task 1 (Data Analysis): Employee C

[1505] Task 2 (System Development): Employee B

[1506] Task 3 (Report Writing): Employee C

[1507] Prompt Sentence Examples

[1508] "Assign the most appropriate work tasks based on employee information and their skill set. Also, consider the employee's current emotional state (high, low, medium stress level) and adjust accordingly. For example, assign Task 1 (data analysis) to Employee C (data analysis skills, medium stress level) rather than Employee A (data analysis skills, high stress level)."

[1509] In this way, by combining the emotion engine, the system can achieve optimal task allocation that takes into account the emotional state of employees, enabling more efficient business operations.

[1510] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1511] Step 1:

[1512] User operations

[1513] The user (administrator) accesses a dedicated input form, where they enter the employee's name, department, and skill set. For example, the information for employee A is entered as "Name: Employee A, Department: IT, Skills: Project management, Programming, Data analysis." After entering the information, the user clicks the "Submit" button.

[1514] Input: Employee information (name, department, skill set)

[1515] Output: Employee information passed to the terminal

[1516] Specific operation: When the user fills in the form and presses the "Submit" button, the data is passed to the terminal.

[1517] Step 2:

[1518] Terminal handling

[1519] The terminal acquires employee information entered by the user, converts the acquired data into JSON format, and sends the converted JSON data to the server.

[1520] Input: Employee information (name, department, skill set)

[1521] Output: JSON data sent to the server

[1522] Specific operation: The terminal obtains employee information from the input form, converts it into JSON format, and sends it to the server.

[1523] Step 3:

[1524] Server Processing

[1525] The server receives the JSON data sent from the terminal and stores it in the database. For example, the information of employee A is saved in the corresponding table in the database.

[1526] Input: Employee data in JSON format

[1527] Output: Employee information stored in a database

[1528] Specific operation: The server receives the JSON data and stores it in the database.

[1529] Step 4:

[1530] User operations

[1531] The user accesses the system's task input form and enters the details of the work task (task name, required skills, deadline, etc.). For example, they might enter "Task name: Data analysis, required skills: Data analysis, deadline: End of October 2023." After entering the information, the user clicks the "Submit" button.

[1532] Input: Work task information (task name, required skills, deadline)

[1533] Output: Task information passed to the terminal

[1534] Specific operation: When the user enters work task information into the form and presses the "Submit" button, the data is passed to the terminal.

[1535] Step 5:

[1536] Terminal handling

[1537] The device collects data on business tasks, converts it into JSON format, and sends it to the server.

[1538] Input: Work task information (task name, required skills, deadline)

[1539] Output: JSON data sent to the server

[1540] Specific operation: The terminal acquires business task information, converts it into JSON format, and sends it to the server.

[1541] Step 6:

[1542] Server Processing

[1543] The server receives the business task information sent from the terminal and stores it in a database. For example, information about a data analysis task is saved in a corresponding table in the database.

[1544] Input: JSON formatted business task information

[1545] Output: Business task information stored in a database

[1546] Specific operation: The server receives the JSON data and stores the business task information in a database.

[1547] Step 7:

[1548] Server Processing

[1549] The server retrieves employee and task information from a database and uses machine learning or rule-based algorithms to select the best employee for each task.

[1550] Input: Employee information and work task information stored in a database

[1551] Output: Optimal employee-task pairs

[1552] Specific operation: The server analyzes the information and selects the most suitable employee.

[1553] Step 8:

[1554] Emotion engine processing

[1555] The emotion engine monitors the emotional state of employees in real time and provides that information to the server. The emotion engine recognizes the employee's facial expressions and voice to evaluate their emotional state.

[1556] Input: Employee facial expressions and voice data

[1557] Output: Employee sentiment information

[1558] Specific operation: The emotion engine analyzes facial expressions and voice to generate emotional information.

[1559] Step 9:

[1560] Collaboration between the server and emotion engine

[1561] The server stores the emotion information provided by the emotion engine in a database. For example, information such as "Employee A's current emotional state: high stress level" is saved.

[1562] Input: Employee emotional information

[1563] Output: Emotion information stored in a database

[1564] Specific operation: The server receives the emotion information and stores it in a database.

[1565] Step 10:

[1566] Server Processing

[1567] The server analyzes the emotional and skill information stored in the database and adjusts task allocation accordingly. For example, if employee A has a high stress level, it will avoid assigning high-stress tasks to him.

[1568] Input: Emotion information and skill information stored in the database

[1569] Output: Adjusted task assignments

[1570] Specific operation: The server comprehensively analyzes the information and adjusts task allocation.

[1571] Step 11:

[1572] Server Processing

[1573] The server generates a list of optimal agents that reflects the emotional information and sends it to the terminal in a data format such as JSON.

[1574] Input: Adjusted task assignment information

[1575] Output: A list of contacts sent to the terminal

[1576] Specific operation: The server generates a list of personnel and sends it to the terminal.

[1577] Step 12:

[1578] Terminal handling

[1579] The terminal analyzes the list of people in charge received from the server and displays it on the user interface. For example, it displays "Employee B has been assigned to the data analysis task."

[1580] Input: List of agents received from the server

[1581] Output: A list of agents displayed in the user interface

[1582] Specific behavior: The device parses and displays the list.

[1583] (Application example 2)

[1584] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1585] Conventional work task allocation systems based on employee skill sets were unable to consider the emotional state of employees, and tasks were assigned without regard for employee stress or motivation. This resulted in lower employee productivity and satisfaction, and was unable to contribute to work efficiency. This problem was particularly pronounced in high-stress environments such as logistics centers.

[1586] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1587] In this invention, the server includes user input means for inputting employee information, server means for receiving the input employee information and storing it in a database, user input means for inputting work task information, server means for receiving the input work task information and storing it in the database, analysis means for analyzing the employee information and work task information stored in the database and selecting the employee most suitable for each task, emotion engine means for detecting the emotional state of employees and storing the emotion information in the database, adjustment means for integrating the emotion information and the analysis results and optimizing task allocation, and output means for presenting the selection results to the user. This enables optimal task allocation taking into account the skill sets and emotional states of employees.

[1588] The "user input means for inputting employee information" is a means for providing an interface for the manager to input employee information.

[1589] The "server means" is a server device that has the function of receiving input information and storing it in a database.

[1590] The "user input means for inputting business task information" is a means for providing an interface for the administrator to input business task information.

[1591] The "analysis means" is a means having a function for analyzing employee information and business task information stored in the database and selecting the most suitable employee for each task.

[1592] The "emotion engine means" is a means having a function for detecting the emotional state of an employee and storing the information in a database.

[1593] The "adjustment means" is a means that has the function of integrating emotional information and analysis results to optimize task allocation.

[1594] The "output means" is a means for providing an interface for presenting the selection results to the user.

[1595] The present invention relates to an optimal work task allocation system that takes into account the skill sets and emotional states of employees in a logistics center. This system is implemented using a server, terminals, and an emotion engine. Specific embodiments are described below.

[1596] Enter employee information and skill sets

[1597] First, the administrator (user) uses a dedicated input interface to input employee information (such as name, skill set, and emotional state). For example, information about employee A is entered as "Name: Employee A, Skills: Programming, Data Analysis, Emotional State: High Stress Level."

[1598] Receiving and storing data

[1599] The terminal receives the entered employee information, converts it to JSON format, and sends it to the server. The server receives this information and stores it in a database. For example, the information of "Employee A" is saved in a database table.

[1600] Entering and storing business task information

[1601] Next, the administrator enters the details of the work task (task name, required skills, deadline, etc.) into the system. For example, "Task name: Data analysis, required skills: Data analysis, deadline: End of October 2023." The terminal converts this data into JSON format and sends it to the server, which then stores the information in a database.

[1602] Analysis and selection of personnel based on skill sets and emotional states

[1603] The server retrieves employee information and work task information stored in the database and uses machine learning or rule-based algorithms to select the employee best suited for each task. The server also takes into account the employee's emotional state, as provided by the emotion engine. For example, it determines that "Employee C" is best suited for "Task 1 (Data Analysis)" and "Employee B" is best suited for "Task 2 (System Development)."

[1604] Emotion Engine Functions

[1605] The emotion engine monitors employees' emotional states in real time and provides that information to the server. The emotion engine evaluates the employee's emotional state, for example, through facial expression recognition or voice analysis. The server stores this emotional information in a database and reflects it in task allocation. For example, if the emotional state of "Employee A" is evaluated as "high stress level," it will avoid assigning tasks that cause high stress.

[1606] Generate and view a contact list

[1607] Finally, the server generates a list of optimal agents that reflects the emotional information and sends it to the device in JSON format. The device then analyzes the received list and displays it on the user interface. For example, it displays that "Employee C" is assigned to "Task 1 (Data Analysis)."

[1608] Specific examples

[1609] Employee Information:

[1610] Employee A: Skills (programming, data analysis), emotional state (high stress level)

[1611] Employee B: Skills (system design, programming), emotional state (low stress level)

[1612] Employee C: Skills (Writing, Data Analysis, Marketing), Emotional State (Medium Stress Level)

[1613] Business tasks:

[1614] Task 1: Data Analysis

[1615] Task 2: System Development

[1616] Task 3: Report writing

[1617] Allocation results:

[1618] Task 1 (Data Analysis): Employee C

[1619] Task 2 (System Development): Employee B

[1620] Task 3 (Report Writing): Employee C

[1621] Example prompt sentence:

[1622] "Enter employee information (name, skill set, emotional state) and work tasks (task name, required skills) and generate code for a system that optimally assigns tasks based on the employee's emotional state."

[1623] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1624] Step 1:

[1625] The user uses a dedicated input interface to input employee information, including the employee's name, department, skill set, current emotional state, etc. This input information is converted into JSON format and sent to the terminal.

[1626] Input: Employee information (name, department, skill set, emotional state)

[1627] Output: Employee information in JSON format

[1628] Step 2:

[1629] The terminal sends the JSON-formatted employee information entered by the user to the server, and also correctly formats each employee entry and sends it as an HTTP request to the appropriate server endpoint.

[1630] Input: Employee information in JSON format

[1631] Output: HTTP request to the server

[1632] Step 3:

[1633] The server receives the employee information sent from the terminal and saves it in the database. Specifically, it parses the JSON format employee information and stores it in the corresponding table in the database.

[1634] Input: HTTP request from the terminal (employee information in JSON format)

[1635] Output: Employee information stored in the database

[1636] Step 4:

[1637] The user inputs task information using a dedicated input interface, including the task name, required skills, deadline, etc. This input information is converted into JSON format and sent to the device.

[1638] Input: Work task information (task name, required skills, deadline)

[1639] Output: JSON formatted business task information

[1640] Step 5:

[1641] The device sends the JSON-formatted work task information entered by the user to the server, and then correctly formats each piece of work task information and sends it as an HTTP request to the appropriate server endpoint.

[1642] Input: JSON formatted business task information

[1643] Output: HTTP request to the server

[1644] Step 6:

[1645] The server receives the business task information sent from the terminal and saves it in the database. Specifically, it parses the business task information in JSON format and stores it in the corresponding table in the database.

[1646] Input: HTTP request from the device (JSON formatted business task information)

[1647] Output: Business task information stored in the database

[1648] Step 7:

[1649] The server retrieves employee and task information stored in the database and analyzes it using analytical tools. Taking into account the employee's skill set and emotional state, the server selects the employee best suited for each task. It uses machine learning algorithms to find the optimal combination.

[1650] Input: Employee and task information in the database

[1651] Output: Optimal task assignment information

[1652] Step 8:

[1653] The emotion engine monitors the emotional state of employees in real time and sends the data to the server. The emotion engine uses facial expression recognition and voice analysis to evaluate the emotional state of employees in real time and sends the results in JSON format.

[1654] Input: Real-time employee emotion data (facial expressions, voice data)

[1655] Output: Emotion information in JSON format

[1656] Step 9:

[1657] The server stores the emotional information obtained from the emotion engine in a database, integrates this information with employee information, and adjusts task assignments based on the employee's emotional state.

[1658] Input: JSON format emotion information from the emotion engine

[1659] Output: Employee information and sentiment information integrated into a database

[1660] Step 10:

[1661] The server generates a list of optimal agents that reflects the emotional information and sends it to the device in JSON format. It then performs a final check of the analyzed information and sends it back to the device as a task assignment list.

[1662] Input: Parsed employee information and work task information

[1663] Output: HTTP request to the device (optimal task allocation information)

[1664] Step 11:

[1665] The terminal analyzes the list of people in charge received from the server and displays it on the user interface, specifically displaying the name of the employee assigned to each task and the task name.

[1666] Input: HTTP request from the server (optimal task allocation information)

[1667] Output: Optimal task assignment list on the user interface

[1668] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1669] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1670] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1671] [Fourth embodiment]

[1672] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1673] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1674] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1675] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1676] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1677] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1678] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1679] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1680] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1681] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1682] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1683] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1684] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1685] The present invention is a system for automatically assigning optimal work tasks based on the skill sets of employees. Specific embodiments will be described below.

[1686] Enter employee information and skill sets

[1687] 1. User Action:

[1688] The user (administrator) enters employee information (such as name, department, skill set, etc.) in a dedicated input form. For example, the information for Employee A is entered as "Name: Employee A, Department: IT, Skills: Project management, Programming, Data analysis."

[1689] Receiving and storing data

[1690] 2. Terminal processing:

[1691] The terminal converts the employee information obtained from the input form into JSON format and sends the data to the server.

[1692] 3. Server processing:

[1693] The server receives the JSON data sent from the terminal and stores it in the database. For example, the information of employee A is saved in the corresponding table in the database.

[1694] Receive and store information about business tasks

[1695] 4. User Action:

[1696] The user enters the details of the work task (task name, required skills, deadline, etc.) into the system. For example, the user enters "Task name: Data analysis, Required skills: Data analysis, Deadline: End of October 2023."

[1697] 5. Terminal processing:

[1698] The device collects business task data, converts it into JSON format, and sends it to the server.

[1699] 6. Server Processing:

[1700] The server receives the business task information and stores it in a database, for example, the information of a data analysis task is stored in a corresponding table in the database.

[1701] Skill set analysis and personnel selection

[1702] 7. Server Processing:

[1703] The server retrieves employee and task information from the database and uses machine learning or rule-based algorithms to select the best employee for each task. For example, it may determine that Employee A, with his data analysis skills, is best suited for the data analysis task.

[1704] 8. Server Processing:

[1705] The selection results are formatted into a list and sent to the terminal in a data format such as JSON.

[1706] Generate and display a contact list

[1707] 9. Terminal Processing:

[1708] The terminal analyzes the list of people in charge received from the server and displays it on the user interface. For example, it displays "Employee A has been assigned to the data analysis task."

[1709] Specific examples

[1710] Specific examples are shown below.

[1711] input

[1712] Employee A: Skills (project management, programming, data analysis)

[1713] Employee B: Skills (system design, programming, security)

[1714] Employee C: Skills (writing, data analysis, marketing)

[1715] Business tasks

[1716] Task 1: Data Analysis

[1717] Task 2: System Development

[1718] Task 3: Report writing

[1719] Output

[1720] Task 1 (Data Analysis): Employee A

[1721] Task 2 (System Development): Employee B

[1722] Task 3 (Report Writing): Employee C

[1723] In this way, work tasks can be automatically assigned based on employees' skill sets, resulting in increased work efficiency.

[1724] The processing flow will be explained below.

[1725] Step 1:

[1726] The user enters employee information (such as name, department, skill set, etc.) using a dedicated input form.

[1727] For example, the user enters "Employee A, Department: IT, Skills: Project management, Programming, Data analysis."

[1728] Step 2:

[1729] The terminal takes the employee information entered by the user and converts it into JSON format or another appropriate data format.

[1730] Step 3:

[1731] The terminal transmits the converted data to the server.

[1732] Step 4:

[1733] The server parses the JSON data received from the device and breaks down the employee information into individual fields.

[1734] The server stores the parsed data in a database.

[1735] For example, store the information of "Employee A" in a corresponding table in the database.

[1736] Step 5:

[1737] The user enters details of the business task (task name, required skills, deadline, etc.) using a dedicated input form.

[1738] For example, enter "Task name: Data analysis, Required skills: Data analysis, Deadline: End of October 2023."

[1739] Step 6:

[1740] The device acquires the business task information entered by the user and converts it into JSON format or another appropriate data format.

[1741] Step 7:

[1742] The terminal transmits the converted data to the server.

[1743] Step 8:

[1744] The server analyzes the JSON data received from the terminal and breaks down the business task information into individual fields.

[1745] The server stores the parsed data in a database.

[1746] For example, the information of the "data analysis task" is stored in a corresponding table in the database.

[1747] Step 9:

[1748] The server retrieves employee information and work task information from the database.

[1749] Step 10:

[1750] The server uses machine learning or rule-based algorithms to select the best employee for each task.

[1751] For example, you might determine that "Employee A, who has data analysis skills, is best suited for the data analysis task."

[1752] Step 11:

[1753] The server formats the selection results into a list and sends it to the terminal in JSON format or other appropriate data format.

[1754] Step 12:

[1755] The terminal analyzes the list of personnel received from the server.

[1756] Step 13:

[1757] The terminal displays the analyzed list of personnel on the user interface.

[1758] For example, it might say, "Employee A has been assigned the data analysis task."

[1759] In this way, the system of the present invention automates the allocation of optimal business tasks based on the skill sets of employees through each processing step, thereby realizing efficient business operations.

[1760] Example 1

[1761] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1762] The process of selecting the most suitable work tasks based on employees' skill sets has traditionally been done manually, requiring time and effort. It also poses challenges in ensuring fair allocation of personnel and improving work efficiency. Furthermore, in large organizations, it is difficult to centrally manage employee skill information and work task information and quickly select the most suitable personnel.

[1763] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1764] In this invention, the server includes user input means for inputting employee information, terminal means for receiving the input employee information, converting it into a data format and sending it to the server, server means for storing the received employee information in a database, user input means for inputting work task information, terminal means for receiving the input work task information, converting it into a data format and sending it to the server, server means for storing the received work task information in the database, analysis means for acquiring the employee information and work task information stored in the database and selecting the most suitable employee for each task, and output means for displaying the selection results on a user interface. This makes it possible to quickly and efficiently assign work tasks to the most suitable employees.

[1765] "Employee information" is data that includes basic attribute information and skill sets about employees.

[1766] A "user input means" is a device or interface through which a user inputs information, including, for example, web forms and input fields.

[1767] "Terminal means" refers to a device or function for collecting employee information and business task information, converting it into an appropriate format, and transmitting it to a server.

[1768] The "server means" is a device or system for processing information received from the terminal means and storing it in a database.

[1769] "Business task information" is data that includes details of a specific business task (such as task name, required skills, deadline, etc.).

[1770] A "data format" is a way of representing data in a particular format, such as JSON or XML.

[1771] A "database" is a system for efficiently storing, managing, and searching data, and examples include MySQL and PostgreSQL.

[1772] "Analysis means" refers to devices and algorithms used to retrieve and analyze information in a database.

[1773] "Output means" refers to a device or interface for displaying the analysis results to the user.

[1774] A "machine learning algorithm" is an algorithm that learns patterns and rules based on data and makes predictions and classifications.

[1775] The present invention is a system for automatically assigning work tasks based on the skill sets of employees. This system is operated by combining users, terminals, and a server.

[1776] Enter employee information and skill sets

[1777] The user enters employee information using a dedicated input form provided on the web browser. The information entered includes name, department, skill set, etc. For example, for employee A, the user enters "Name: Employee A, Department: IT, Skills: Project management, Programming, Data analysis."

[1778] Receiving and storing data

[1779] The terminal receives the employee information entered by the user and converts it into JSON format. The converted data is sent to the server as an HTTP POST request. The server receives this request, parses the JSON data, and stores the employee information in a database (for example, MySQL or PostgreSQL).

[1780] Receive and store information about business tasks

[1781] The user enters the details of the work task using a similarly provided input form. For example, they enter "Task name: Data analysis, Required skills: Data analysis, Deadline: End of October 2023." The device converts this work task information into JSON format and sends it to the server. The server analyzes the received JSON data and stores the work task information in a database.

[1782] Skill set analysis and personnel selection

[1783] The server retrieves employee and task information from the database using SQL SELECT statements. Based on the retrieved information, it applies machine learning or rule-based algorithms to select the best employee for each task. For example, it uses the Python scikit-learn library to determine that Employee A, with his data analysis skills, is best suited for a data analysis task.

[1784] Generate and display a contact list

[1785] The server formats the selection results into a list and converts it into a data format such as JSON. This data is sent to the terminal as an HTTP response. The terminal analyzes the list of people in charge received from the server and displays it in the user interface. For example, the browser displays "Employee A has been assigned to the data analysis task."

[1786] Specific examples

[1787] input

[1788] Employee A: Skills (project management, programming, data analysis)

[1789] Employee B: Skills (system design, programming, security)

[1790] Employee C: Skills (writing, data analysis, marketing)

[1791] Business tasks

[1792] Task 1: Data Analysis

[1793] Task 2: System Development

[1794] Task 3: Report writing

[1795] Prompt Sentence Examples

[1796] You can use the prompt text as follows:

[1797] Employee Information:

[1798] Name: Employee A

[1799] Department: IT

[1800] Skills: Project Management, Programming, Data Analysis

[1801] Business tasks:

[1802] Task Name: Data Analysis

[1803] Required skills: Data analysis

[1804] Deadline: End of October 2023

[1805] Using the information above, explain how to assign tasks to the appropriate employees.

[1806] This prompt can be fed into an AI model to generate instructions on how to assign the task to the best employee.

[1807] As such, the system streamlines the allocation of complex work tasks and makes the most of employee skill sets.

[1808] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1809] Step 1:

[1810] Entering employee information

[1811] Users access a dedicated input form on a web browser and enter employee information.

[1812] Specifically, the user fills in form fields with details such as the employee's name, department, and skill set.

[1813] Example: "Name: Employee A, Department: IT, Skills: Project management, programming, data analysis"

[1814] Input: Employee information entered by the user.

[1815] Output: Employee information entered into the input form.

[1816] Step 2:

[1817] Receiving employee information

[1818] The terminal receives the data entered by the user into the input form.

[1819] Converts input data into JSON format.

[1820] Input: Employee information entered by the user.

[1821] Output: Employee information converted to JSON format.

[1822] Step 3:

[1823] Transmitting and storing employee information

[1824] The device sends the converted JSON format data to the server as an HTTP POST request.

[1825] The server receives the HTTP POST request and parses the JSON data.

[1826] The server stores the parsed employee information in a database, for example, the employee information is inserted into the "Employees" table.

[1827] Input: Employee information in JSON format.

[1828] Output: Employee information stored in the database.

[1829] Step 4:

[1830] Entering work task information

[1831] The user opens a separate input form in a web browser and enters the details of the work task.

[1832] For example, enter "Task name: Data analysis, Required skills: Data analysis, Deadline: End of October 2023."

[1833] Input: Work task information entered by the user.

[1834] Output: Business task information entered in the input form.

[1835] Step 5:

[1836] Receiving business task information

[1837] The terminal receives the business task information entered by the user in the input form.

[1838] Converts input data into JSON format.

[1839] Input: Work task information entered by the user.

[1840] Output: Business task information converted to JSON format.

[1841] Step 6:

[1842] Sending and storing business task information

[1843] The device sends the converted JSON format data to the server as an HTTP POST request.

[1844] The server receives the HTTP POST request and parses the JSON data.

[1845] The server stores the analyzed business task information in a database. For example, the business task information is inserted into a "Tasks" table.

[1846] Input: Business task information in JSON format.

[1847] Output: Business task information stored in a database.

[1848] Step 7:

[1849] Obtaining employee and task information

[1850] The server retrieves employee and task information from the database using SQL SELECT statements.

[1851] The obtained information is passed to an analysis means.

[1852] Input: Employee and work task information stored in a database.

[1853] Output: Employee information and work task information captured for analysis.

[1854] Step 8:

[1855] Analysis and selection of personnel

[1856] The server analyzes the acquired information using an analysis means (machine learning algorithm or rule-based algorithm).

[1857] The analytical method matches employee skill sets with business task requirements and selects the most suitable employee.

[1858] For example, employee A, who has data analysis skills, is assigned to the data analysis task.

[1859] Input: Captured employee information and work task information.

[1860] Output: Selection of the best employee for each task.

[1861] Step 9:

[1862] Sending and displaying the selection results

[1863] The server formats the selection results into a list and converts them into JSON format.

[1864] The server sends this data to the terminal as an HTTP response.

[1865] The terminal analyzes the received list of people in charge and displays the results on the user interface, for example, "Employee A has been assigned to the data analysis task."

[1866] Input: JSON data of the selection result.

[1867] Output: The list of agents displayed in the user interface.

[1868] (Application example 1)

[1869] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1870] Although systems already exist that can efficiently assign optimal work tasks to employees based on their skill sets, there is a lack of a way to quickly notify employees of the results of the selection process. This means that employees are unable to immediately know about the assigned tasks and take action, resulting in a decline in work efficiency in factories and offices.

[1871] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1872] In this invention, the server includes user input means for inputting employee information, server means for receiving the input employee information and storing it in a database, user input means for inputting job content information, server means for receiving the input job content information and storing it in a database, analysis means for analyzing the employee information and job content information stored in the database and selecting the employee most suitable for each job, output means for presenting the selection results to the user, and communication means for notifying the employee's communication terminal of the selection results, thereby making it possible to promptly notify the employee of the selection results.

[1873] "Employee information" includes information such as the employee's name, department, and skill set.

[1874] The "user input means" is an interface that allows employees or managers to input employee information and job content information.

[1875] The "server means" is a server having a function for receiving input data and storing it in a database as necessary.

[1876] "Work content information" is information such as the name of the work task, the required skills, and the deadline.

[1877] The "analysis means" is a system that has the function of analyzing employee information and job content information stored in the database and selecting the most suitable employee for each job.

[1878] The "output means" is an interface for presenting the analysis results and selection results to the user.

[1879] "Communication means" refers to a system that has the function of notifying the selection results to the employee's communication terminal.

[1880] A "machine learning algorithm" is an algorithm that learns patterns from data and makes predictions and classifications.

[1881] The present invention is a system that automatically selects the most suitable employee based on the employee's skill set and job content information in order to improve work efficiency in factories and offices, and notifies the results to the employee's communication terminal. A specific implementation method is shown below.

[1882] System configuration

[1883] Hardware

[1884] User terminal: A device (e.g., tablet, smart glasses) for inputting employee information and job content information.

[1885] Server: A server system for receiving, storing, and analyzing data.

[1886] Communication terminal: Employee device (e.g., smartphone, smart glasses) for receiving the selection results.

[1887] software

[1888] Input form: An input interface for employee information and job content information installed on the user's terminal.

[1889] Receiving and storing data: A server-side script that converts the received data into JSON format and stores it in a database.

[1890] Machine learning algorithm: Python's scikit-learn library is used to analyze employee information and job description information and select the most suitable employees.

[1891] Notification system: An API for pushing the selection results to employees' communication devices.

[1892] Program processing explanation

[1893] The server receives employee information and job details sent from the user's device in JSON format and stores the data in a database. It then uses a machine learning algorithm to select the most suitable employee, formats the selection results, and notifies the employee's communication device.

[1894] Data processing and calculation

[1895] Analyze employee information: Map each employee's skill set to a numerical value and feed it into machine learning algorithms.

[1896] Analyze job information: Map required skills to a numerical value and compare it with employee skill sets to find the best match.

[1897] Sending the selection results: Using the push notification API, the selection results are notified to the employee's communication device.

[1898] As a specific example, if Employee A's skill set "project management, programming, data analysis" and Task 1 "data analysis" are entered, the server will match the skill set with the task and determine that Employee A is the best person for this task. The result will be notified to Employee A's smartphone.

[1899] Generative AI model prompt example

[1900] Create an application that automatically selects the best workers to improve work efficiency. Enter employee skill sets and work task information, and the system will select the best employee for each task. The output will be displayed in a list format, including the selected workers and the task names.

[1901] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1902] Step 1:

[1903] A user uses a tablet or smart glasses to enter employee information, including the employee's name, department, and skill set, which is then converted into JSON format.

[1904] Step 2:

[1905] The entered JSON data is sent from the terminal to the server, which receives it and stores it in a database for analysis. When stored in the database, each employee's information is saved as a separate record.

[1906] Step 3:

[1907] The user again uses a tablet or smart glasses to input task information, including the task name, required skills, and deadline, which is then converted to JSON format.

[1908] Step 4:

[1909] The JSON data of the job content information is sent from the terminal to the server. The server receives this data and stores it in a database. Information about each job content is saved as an individual record.

[1910] Step 5:

[1911] The server retrieves employee information and job information from the database. It uses a machine learning algorithm (K-nearest neighbor method) to process and calculate the data to select the best employee for each task. Specifically, it maps the employee's skill set and the skills required for the task to a numerical value, and generates a dataset for analysis.

[1912] Step 6:

[1913] The machine learning model selects the best employee, and the matching results between the selected employee and the task are formatted in JSON format and presented to the user. This generates a list of the best people to assign each task.

[1914] Step 7:

[1915] The server sends a push notification to not only the user but also the employee's communication device (smartphone or smart glasses). The notification system API is used to deliver the selection results to the employee's device.

[1916] Step 8:

[1917] Employees receive notifications via their communication devices and check the details of their assigned tasks. The notifications include the task name, required skills, deadline, and the name of the person in charge. They can immediately begin working on the assigned task.

[1918] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1919] The present invention combines a system that automatically assigns optimal work tasks based on the skill sets of employees with an emotion engine that recognizes the emotions of users. Specific embodiments will be described below.

[1920] Enter employee information and skill sets

[1921] User operations

[1922] The user (administrator) enters employee information (such as name, department, skill set, etc.) in a dedicated input form. For example, the information for Employee A is entered as "Name: Employee A, Department: IT, Skills: Project management, Programming, Data analysis."

[1923] Receiving and storing data

[1924] Terminal handling

[1925] The terminal converts the employee information obtained from the input form into JSON format and sends it to the server.

[1926] Server Processing

[1927] The server receives the JSON data sent from the terminal and stores it in the database. For example, the information of employee A is saved in the corresponding table in the database.

[1928] Receive and store information about business tasks

[1929] User operations

[1930] The user enters the details of the work task (task name, required skills, deadline, etc.) into the system. For example, the user enters "Task name: Data analysis, Required skills: Data analysis, Deadline: End of October 2023."

[1931] Terminal handling

[1932] The device collects data on business tasks, converts it into JSON format, and sends it to the server.

[1933] Server Processing

[1934] The server receives the business task information and stores it in a database, for example, the information of a data analysis task is stored in a corresponding table in the database.

[1935] Skill set analysis and personnel selection

[1936] Server Processing

[1937] The server retrieves employee and task information from the database and uses machine learning or rule-based algorithms to select the best employee for each task. For example, it may determine that Employee A, with his data analysis skills, is best suited for the data analysis task.

[1938] Introducing the Emotion Engine

[1939] Emotion Engine Functions

[1940] The emotion engine monitors the emotional state of employees in real time and provides that information to analytical tools. The emotion engine evaluates the emotional state of employees, for example, through facial expression recognition and voice analysis.

[1941] Collaboration between the server and emotion engine

[1942] The server stores the emotional information obtained from the emotion engine in a database. For example, information such as "Employee A's current emotional state: high stress level" is saved.

[1943] Adjustment based on emotional state

[1944] The server analyzes the emotional and skill information stored in the database and reflects this information in task allocation. For example, if employee A has a high stress level, the server will adjust the allocation to avoid high-stress tasks.

[1945] Generate and display a contact list

[1946] Server Processing

[1947] The server generates a list of optimal agents that reflects the emotional information and sends it to the terminal in a data format such as JSON.

[1948] Terminal handling

[1949] The terminal analyzes the list of people in charge received from the server and displays it on the user interface. For example, it displays "Employee B has been assigned to the data analysis task."

[1950] Specific examples

[1951] Specific examples are shown below.

[1952] input

[1953] Employee A: Skills (project management, programming, data analysis), Emotional state (high stress level)

[1954] Employee B: Skills (system design, programming, security), emotional state (low stress level)

[1955] Employee C: Skills (Writing, Data Analysis, Marketing), Emotional State (Medium Stress Level)

[1956] Business tasks

[1957] Task 1: Data Analysis

[1958] Task 2: System Development

[1959] Task 3: Report writing

[1960] Output

[1961] Task 1 (Data Analysis): Employee C

[1962] Task 2 (System Development): Employee B

[1963] Task 3 (Report Writing): Employee C

[1964] In this way, by combining emotion engines, optimal task allocation can be achieved while taking into consideration the emotional state of employees, enabling even more efficient business operations.

[1965] The processing flow will be explained below.

[1966] Step 1:

[1967] The user enters employee information (such as name, department, skill set, etc.) using a dedicated input form.

[1968] For example, the user enters "Employee A, Department: IT, Skills: Project management, Programming, Data analysis."

[1969] Step 2:

[1970] The terminal takes the employee information entered by the user and converts it into JSON format or another appropriate data format.

[1971] Step 3:

[1972] The terminal transmits the converted data to the server.

[1973] Step 4:

[1974] The server parses the JSON data received from the device and breaks down the employee information into individual fields.

[1975] The server stores the parsed data in a database.

[1976] For example, store the information of "Employee A" in a corresponding table in the database.

[1977] Step 5:

[1978] The user enters details of the business task (task name, required skills, deadline, etc.) using a dedicated input form.

[1979] For example, enter "Task name: Data analysis, Required skills: Data analysis, Deadline: End of October 2023."

[1980] Step 6:

[1981] The device acquires the business task information entered by the user and converts it into JSON format or another appropriate data format.

[1982] Step 7:

[1983] The terminal transmits the converted data to the server.

[1984] Step 8:

[1985] The server analyzes the JSON data received from the terminal and breaks down the business task information into individual fields.

[1986] The server stores the parsed data in a database.

[1987] For example, the information of the "data analysis task" is stored in a corresponding table in the database.

[1988] Step 9:

[1989] The server retrieves employee information and work task information from the database.

[1990] Step 10:

[1991] The server requests the emotion engine for the emotion information of the current employee.

[1992] Step 11:

[1993] The emotion engine monitors the emotional state of employees in real time and sends that information to the server.

[1994] For example, information such as "Employee A's current emotional state: stress level is high" is sent.

[1995] Step 12:

[1996] The server stores the emotion information obtained from the emotion engine in a database.

[1997] Step 13:

[1998] The server uses machine learning or rule-based algorithms to select the best employee for each task based on the employee's skill set and emotional state.

[1999] For example, you might determine that "Employee A, who has data analysis skills, would be well suited to the data analysis task, but because he has a high stress level, Employee C would be the best choice instead."

[2000] Step 14:

[2001] The server formats the selection results into a list and sends it to the terminal in JSON format or other appropriate data format.

[2002] Step 15:

[2003] The terminal analyzes the list of personnel received from the server and displays it on the user interface.

[2004] For example, it might say, "Taking into consideration stress levels, employee C has been assigned the data analysis task."

[2005] In this way, the system of the present invention automates the allocation of optimal work tasks based on the employee's skill set and emotional state throughout each processing step, thereby achieving efficient business operations and reducing the psychological burden on employees.

[2006] Example 2

[2007] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2008] While conventional task allocation systems can assign tasks based on employees' skill sets, they are unable to take into account the emotional state of employees, which can lead to issues such as employees with high stress levels being assigned more demanding tasks, making it difficult to achieve efficient business operations.

[2009] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a user input means for inputting employee information, a server means for receiving the input employee information and storing it in a database, a user input means for inputting work task information, a server means for receiving the input work task information and storing it in the database, an analysis means for analyzing the employee information and work task information stored in the database and selecting the optimal employee for each task, an emotion engine means for monitoring the emotional states of employees in real time and providing that information, an analysis means for adjusting task assignments based on the emotional information, and an output means for presenting the selection results to the user. This enables optimal work task assignments that take into account not only the employee's skill set but also their emotional state.

[2010] "Employee information" refers to information such as an employee's name, department, and skill set.

[2011] "User input means" refers to an interface through which a user inputs employee information and business task information.

[2012] "Server means" refers to a server having the function of receiving input data and storing it in a database.

[2013] "Business task information" refers to information about the details of a business task (task name, required skills, deadline, etc.).

[2014] "Database" refers to a data management system for storing employee information and business task information.

[2015] "Analysis means" refers to the function for analyzing the information stored in the database and determining the best employee for each task.

[2016] The "emotion engine means" refers to a function for monitoring the emotional state of employees in real time and providing that information to the server.

[2017] "Analysis means" refers to the function for adjusting task allocation by reflecting emotional information.

[2018] "Output means" refers to a function for presenting the results of task allocation to the user.

[2019] The present invention relates to a system that manages employee information and work task information, and assigns optimal work tasks to employees by taking into consideration their skill sets and emotional states. The system efficiently and effectively assigns tasks by using a user input means, a server means, an analysis means, an emotion engine means, an analysis means, and an output means.

[2020] Hardware and software used

[2021] Hardware: Web server, client terminal (PC, tablet, etc.)

[2022] software:

[2023] Database: MySQL

[2024] Programming language: Python

[2025] Machine learning library: Scikit-Learn

[2026] Sentiment analysis engine: Microsoft Azure Face API, Google Cloud Speech-to-Text

[2027] Frontend: HTML, CSS, JavaScript

[2028] System configuration

[2029] Enter employee information and skill sets

[2030] The user (administrator) uses a dedicated input form to enter employee information (name, department, skill set). For example, for employee A, enter "Name: Employee A, Department: IT, Skills: Project management, Programming, Data analysis." Then, the user clicks the "Submit" button to complete the input.

[2031] Receiving and storing data

[2032] The terminal receives the employee information entered by the user and converts it into JSON format. The converted data is sent to the server, which then receives the data and stores it in the database. For example, the information of employee A is saved in the corresponding table in the database.

[2033] Entering and storing business task information

[2034] The user accesses the system's task input form and enters the details of the work task (task name, required skills, deadline, etc.). For example, the user enters "Task name: Data analysis, required skills: Data analysis, deadline: End of October 2023." The user then clicks the "Submit" button.

[2035] The device collects data on business tasks, converts it into JSON format, and sends it to the server, which receives the data and stores it in a database.

[2036] Skill set analysis and personnel selection

[2037] The server retrieves employee and task information from the database and uses machine learning or rule-based algorithms to select the best employee for each task. For example, it may determine that an employee with data analysis skills is best suited for a data analysis task.

[2038] Introducing the Emotion Engine

[2039] The emotion engine monitors the emotional state of employees in real time and provides emotional information to the server. The emotion engine recognizes the employee's facial expressions and voice to evaluate their emotional state.

[2040] The server stores the emotion information provided by the emotion engine in a database. For example, information such as "Employee A's current emotional state: high stress level" is saved.

[2041] Adjustment based on emotional state

[2042] The server analyzes the emotional and skill information stored in the database in an integrated manner and reflects this in task allocation. For example, if employee A has a high stress level, it will adjust the allocation so that high-stress tasks are not assigned to him.

[2043] Generate and display a contact list

[2044] The server generates a list of optimal agents that reflects the emotional information and sends it to the device in a data format such as JSON. The device analyzes the list of agents received from the server and displays it on the user interface. For example, it displays "Employee B has been assigned to the data analysis task."

[2045] Specific examples

[2046] Specific examples are shown below.

[2047] Employee A: Skills (project management, programming, data analysis), Emotional state (high stress level)

[2048] Employee B: Skills (system design, programming, security), emotional state (low stress level)

[2049] Employee C: Skills (Writing, Data Analysis, Marketing), Emotional State (Medium Stress Level)

[2050] Business tasks

[2051] Task 1: Data Analysis

[2052] Task 2: System Development

[2053] Task 3: Report writing

[2054] Output

[2055] Task 1 (Data Analysis): Employee C

[2056] Task 2 (System Development): Employee B

[2057] Task 3 (Report Writing): Employee C

[2058] Prompt Sentence Examples

[2059] "Assign the most appropriate work tasks based on employee information and their skill set. Also, consider the employee's current emotional state (high, low, medium stress level) and adjust accordingly. For example, assign Task 1 (data analysis) to Employee C (data analysis skills, medium stress level) rather than Employee A (data analysis skills, high stress level)."

[2060] In this way, by combining the emotion engine, the system can achieve optimal task allocation that takes into account the emotional state of employees, enabling more efficient business operations.

[2061] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2062] Step 1:

[2063] User operations

[2064] The user (administrator) accesses a dedicated input form, where they enter the employee's name, department, and skill set. For example, the information for employee A is entered as "Name: Employee A, Department: IT, Skills: Project management, Programming, Data analysis." After entering the information, the user clicks the "Submit" button.

[2065] Input: Employee information (name, department, skill set)

[2066] Output: Employee information passed to the terminal

[2067] Specific operation: When the user fills in the form and presses the "Submit" button, the data is passed to the terminal.

[2068] Step 2:

[2069] Terminal handling

[2070] The terminal acquires employee information entered by the user, converts the acquired data into JSON format, and sends the converted JSON data to the server.

[2071] Input: Employee information (name, department, skill set)

[2072] Output: JSON data sent to the server

[2073] Specific operation: The terminal obtains employee information from the input form, converts it into JSON format, and sends it to the server.

[2074] Step 3:

[2075] Server Processing

[2076] The server receives the JSON data sent from the terminal and stores it in the database. For example, the information of employee A is saved in the corresponding table in the database.

[2077] Input: Employee data in JSON format

[2078] Output: Employee information stored in a database

[2079] Specific operation: The server receives the JSON data and stores it in the database.

[2080] Step 4:

[2081] User operations

[2082] The user accesses the system's task input form and enters the details of the work task (task name, required skills, deadline, etc.). For example, they might enter "Task name: Data analysis, required skills: Data analysis, deadline: End of October 2023." After entering the information, the user clicks the "Submit" button.

[2083] Input: Work task information (task name, required skills, deadline)

[2084] Output: Task information passed to the terminal

[2085] Specific operation: When the user enters work task information into the form and presses the "Submit" button, the data is passed to the terminal.

[2086] Step 5:

[2087] Terminal handling

[2088] The device collects data on business tasks, converts it into JSON format, and sends it to the server.

[2089] Input: Work task information (task name, required skills, deadline)

[2090] Output: JSON data sent to the server

[2091] Specific operation: The terminal acquires business task information, converts it into JSON format, and sends it to the server.

[2092] Step 6:

[2093] Server Processing

[2094] The server receives the business task information sent from the terminal and stores it in a database. For example, information about a data analysis task is saved in a corresponding table in the database.

[2095] Input: JSON formatted business task information

[2096] Output: Business task information stored in a database

[2097] Specific operation: The server receives the JSON data and stores the business task information in a database.

[2098] Step 7:

[2099] Server Processing

[2100] The server retrieves employee and task information from a database and uses machine learning or rule-based algorithms to select the best employee for each task.

[2101] Input: Employee information and work task information stored in a database

[2102] Output: Optimal employee-task pairs

[2103] Specific operation: The server analyzes the information and selects the most suitable employee.

[2104] Step 8:

[2105] Emotion engine processing

[2106] The emotion engine monitors the emotional state of employees in real time and provides that information to the server. The emotion engine recognizes the employee's facial expressions and voice to evaluate their emotional state.

[2107] Input: Employee facial expressions and voice data

[2108] Output: Employee sentiment information

[2109] Specific operation: The emotion engine analyzes facial expressions and voice to generate emotional information.

[2110] Step 9:

[2111] Collaboration between the server and emotion engine

[2112] The server stores the emotion information provided by the emotion engine in a database. For example, information such as "Employee A's current emotional state: high stress level" is saved.

[2113] Input: Employee emotional information

[2114] Output: Emotion information stored in a database

[2115] Specific operation: The server receives the emotion information and stores it in a database.

[2116] Step 10:

[2117] Server Processing

[2118] The server analyzes the emotional and skill information stored in the database and adjusts task allocation accordingly. For example, if employee A has a high stress level, it will avoid assigning high-stress tasks to him.

[2119] Input: Emotion information and skill information stored in the database

[2120] Output: Adjusted task assignments

[2121] Specific operation: The server comprehensively analyzes the information and adjusts task allocation.

[2122] Step 11:

[2123] Server Processing

[2124] The server generates a list of optimal agents that reflects the emotional information and sends it to the terminal in a data format such as JSON.

[2125] Input: Adjusted task assignment information

[2126] Output: A list of contacts sent to the terminal

[2127] Specific operation: The server generates a list of personnel and sends it to the terminal.

[2128] Step 12:

[2129] Terminal handling

[2130] The terminal analyzes the list of people in charge received from the server and displays it on the user interface. For example, it displays "Employee B has been assigned to the data analysis task."

[2131] Input: List of agents received from the server

[2132] Output: A list of agents displayed in the user interface

[2133] Specific behavior: The device parses and displays the list.

[2134] (Application example 2)

[2135] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2136] Conventional work task allocation systems based on employee skill sets were unable to consider the emotional state of employees, and tasks were assigned without regard for employee stress or motivation. This resulted in lower employee productivity and satisfaction, and was unable to contribute to work efficiency. This problem was particularly pronounced in high-stress environments such as logistics centers.

[2137] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2138] In this invention, the server includes user input means for inputting employee information, server means for receiving the input employee information and storing it in a database, user input means for inputting work task information, server means for receiving the input work task information and storing it in the database, analysis means for analyzing the employee information and work task information stored in the database and selecting the employee most suitable for each task, emotion engine means for detecting the emotional state of employees and storing the emotion information in the database, adjustment means for integrating the emotion information and the analysis results and optimizing task allocation, and output means for presenting the selection results to the user. This enables optimal task allocation taking into account the skill sets and emotional states of employees.

[2139] The "user input means for inputting employee information" is a means for providing an interface for the manager to input employee information.

[2140] The "server means" is a server device that has the function of receiving input information and storing it in a database.

[2141] The "user input means for inputting business task information" is a means for providing an interface for the administrator to input business task information.

[2142] The "analysis means" is a means having a function for analyzing employee information and business task information stored in the database and selecting the most suitable employee for each task.

[2143] The "emotion engine means" is a means having a function for detecting the emotional state of an employee and storing the information in a database.

[2144] The "adjustment means" is a means that has the function of integrating emotional information and analysis results to optimize task allocation.

[2145] The "output means" is a means for providing an interface for presenting the selection results to the user.

[2146] The present invention relates to an optimal work task allocation system that takes into account the skill sets and emotional states of employees in a logistics center. This system is implemented using a server, terminals, and an emotion engine. Specific embodiments are described below.

[2147] Enter employee information and skill sets

[2148] First, the administrator (user) uses a dedicated input interface to input employee information (such as name, skill set, and emotional state). For example, information about employee A is entered as "Name: Employee A, Skills: Programming, Data Analysis, Emotional State: High Stress Level."

[2149] Receiving and storing data

[2150] The terminal receives the entered employee information, converts it to JSON format, and sends it to the server. The server receives this information and stores it in a database. For example, the information of "Employee A" is saved in a database table.

[2151] Entering and storing business task information

[2152] Next, the administrator enters the details of the work task (task name, required skills, deadline, etc.) into the system. For example, "Task name: Data analysis, required skills: Data analysis, deadline: End of October 2023." The terminal converts this data into JSON format and sends it to the server, which then stores the information in a database.

[2153] Analysis and selection of personnel based on skill sets and emotional states

[2154] The server retrieves employee information and work task information stored in the database and uses machine learning or rule-based algorithms to select the employee best suited for each task. The server also takes into account the employee's emotional state, as provided by the emotion engine. For example, it determines that "Employee C" is best suited for "Task 1 (Data Analysis)" and "Employee B" is best suited for "Task 2 (System Development)."

[2155] Emotion Engine Functions

[2156] The emotion engine monitors employees' emotional states in real time and provides that information to the server. The emotion engine evaluates the employee's emotional state, for example, through facial expression recognition or voice analysis. The server stores this emotional information in a database and reflects it in task allocation. For example, if the emotional state of "Employee A" is evaluated as "high stress level," it will avoid assigning tasks that cause high stress.

[2157] Generate and view a contact list

[2158] Finally, the server generates a list of optimal agents that reflects the emotional information and sends it to the device in JSON format. The device then analyzes the received list and displays it on the user interface. For example, it displays that "Employee C" is assigned to "Task 1 (Data Analysis)."

[2159] Specific examples

[2160] Employee Information:

[2161] Employee A: Skills (programming, data analysis), emotional state (high stress level)

[2162] Employee B: Skills (system design, programming), emotional state (low stress level)

[2163] Employee C: Skills (Writing, Data Analysis, Marketing), Emotional State (Medium Stress Level)

[2164] Business tasks:

[2165] Task 1: Data Analysis

[2166] Task 2: System Development

[2167] Task 3: Report writing

[2168] Allocation results:

[2169] Task 1 (Data Analysis): Employee C

[2170] Task 2 (System Development): Employee B

[2171] Task 3 (Report Writing): Employee C

[2172] Example prompt sentence:

[2173] "Enter employee information (name, skill set, emotional state) and work tasks (task name, required skills) and generate code for a system that optimally assigns tasks based on the employee's emotional state."

[2174] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2175] Step 1:

[2176] The user uses a dedicated input interface to input employee information, including the employee's name, department, skill set, current emotional state, etc. This input information is converted into JSON format and sent to the terminal.

[2177] Input: Employee information (name, department, skill set, emotional state)

[2178] Output: Employee information in JSON format

[2179] Step 2:

[2180] The terminal sends the JSON-formatted employee information entered by the user to the server, and also correctly formats each employee entry and sends it as an HTTP request to the appropriate server endpoint.

[2181] Input: Employee information in JSON format

[2182] Output: HTTP request to the server

[2183] Step 3:

[2184] The server receives the employee information sent from the terminal and saves it in the database. Specifically, it parses the JSON format employee information and stores it in the corresponding table in the database.

[2185] Input: HTTP request from the terminal (employee information in JSON format)

[2186] Output: Employee information stored in the database

[2187] Step 4:

[2188] The user inputs task information using a dedicated input interface, including the task name, required skills, deadline, etc. This input information is converted into JSON format and sent to the device.

[2189] Input: Work task information (task name, required skills, deadline)

[2190] Output: JSON formatted business task information

[2191] Step 5:

[2192] The device sends the JSON-formatted work task information entered by the user to the server, and then correctly formats each piece of work task information and sends it as an HTTP request to the appropriate server endpoint.

[2193] Input: JSON formatted business task information

[2194] Output: HTTP request to the server

[2195] Step 6:

[2196] The server receives the business task information sent from the terminal and saves it in the database. Specifically, it parses the business task information in JSON format and stores it in the corresponding table in the database.

[2197] Input: HTTP request from the device (JSON formatted business task information)

[2198] Output: Business task information stored in the database

[2199] Step 7:

[2200] The server retrieves employee and task information stored in the database and analyzes it using analytical tools. Taking into account the employee's skill set and emotional state, the server selects the employee best suited for each task. It uses machine learning algorithms to find the optimal combination.

[2201] Input: Employee and task information in the database

[2202] Output: Optimal task assignment information

[2203] Step 8:

[2204] The emotion engine monitors the emotional state of employees in real time and sends the data to the server. The emotion engine uses facial expression recognition and voice analysis to evaluate the emotional state of employees in real time and sends the results in JSON format.

[2205] Input: Real-time employee emotion data (facial expressions, voice data)

[2206] Output: Emotion information in JSON format

[2207] Step 9:

[2208] The server stores the emotional information obtained from the emotion engine in a database, integrates this information with employee information, and adjusts task assignments based on the employee's emotional state.

[2209] Input: JSON format emotion information from the emotion engine

[2210] Output: Employee information and sentiment information integrated into a database

[2211] Step 10:

[2212] The server generates a list of optimal agents that reflects the emotional information and sends it to the device in JSON format. It then performs a final check of the analyzed information and sends it back to the device as a task assignment list.

[2213] Input: Parsed employee information and work task information

[2214] Output: HTTP request to the device (optimal task allocation information)

[2215] Step 11:

[2216] The terminal analyzes the list of people in charge received from the server and displays it on the user interface, specifically displaying the name of the employee assigned to each task and the task name.

[2217] Input: HTTP request from the server (optimal task allocation information)

[2218] Output: Optimal task assignment list on the user interface

[2219] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2220] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2221] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2222] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2223] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2224] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2225] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2226] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2227] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2228] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2229] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2230] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2231] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2232] 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.

[2233] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2234] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2235] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[2236] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[2237] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[2238] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[2239] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[2240] The following is further disclosed regarding the above embodiment.

[2241] (Claim 1)

[2242] a user input means for inputting employee information;

[2243] server means for receiving the input employee information and storing it in a database;

[2244] a user input means for inputting business task information;

[2245] a server means for receiving input business task information and storing it in a database;

[2246] An analytical means for analyzing employee information and work task information stored in the database and selecting the most suitable employee for each task;

[2247] an output means for presenting the selection results to the user;

[2248] A system including:

[2249] (Claim 2)

[2250] 10. The system of claim 1, wherein the system assigns optimal work tasks based on the employee's skill set.

[2251] (Claim 3)

[2252] 10. The system of claim 1, wherein a machine learning algorithm is used to match employees with work tasks.

[2253] "Example 1"

[2254] (Claim 1)

[2255] a user input means for inputting employee information;

[2256] terminal means for receiving input employee information, converting it into a data format, and transmitting it to a server;

[2257] server means for storing the received employee information in a database;

[2258] a user input means for inputting business task information;

[2259] a terminal means for receiving input business task information, converting it into a data format, and transmitting it to a server;

[2260] a server means for storing the received business task information in a database;

[2261] An analytical means for acquiring employee information and work task information stored in a database and selecting the most suitable employee for each task;

[2262] an output means for displaying the selection results on a user interface;

[2263] A system including:

[2264] (Claim 2)

[2265] 10. The system of claim 1, wherein the system assigns optimal work tasks based on the employee's skill set.

[2266] (Claim 3)

[2267] 10. The system of claim 1, wherein a machine learning algorithm is used to match employees with work tasks.

[2268] "Application Example 1"

[2269] (Claim 1)

[2270] a user input means for inputting employee information;

[2271] server means for receiving the input employee information and storing it in a database;

[2272] a user input means for inputting business content information;

[2273] a server means for receiving input business content information and storing it in a database;

[2274] An analytical means for analyzing employee information and job content information stored in the database and selecting the most suitable employee for each job;

[2275] output means for presenting the selection result to the user;

[2276] A communication means for notifying the employee of the selection result on their communication terminal;

[2277] A system including:

[2278] (Claim 2)

[2279] 10. The system of claim 1, wherein the system assigns optimal tasks based on the skill sets of employees.

[2280] (Claim 3)

[2281] 10. The system of claim 1, wherein a machine learning algorithm is used to match employees with jobs.

[2282] "Example 2: Combining Emotion Engines"

[2283] (Claim 1)

[2284] a user input means for inputting employee information;

[2285] server means for receiving the input employee information and storing it in a database;

[2286] a user input means for inputting business task information;

[2287] a server means for receiving input business task information and storing it in a database;

[2288] An analytical means for analyzing employee information and work task information stored in the database and selecting the most suitable employee for each task;

[2289] an emotion engine means for monitoring the employee's emotional state in real time and providing information thereon;

[2290] analytical means for adjusting task allocation based on emotional information;

[2291] an output means for presenting the selection results to the user;

[2292] A system including:

[2293] (Claim 2)

[2294] 10. The system of claim 1, wherein the system assigns optimal work tasks based on the employee's skill set and emotional state.

[2295] (Claim 3)

[2296] 10. The system of claim 1, wherein the system uses machine learning algorithms to match employees with work tasks and further considers emotional information.

[2297] "Application example 2 when combining emotion engines"

[2298] (Claim 1)

[2299] a user input means for inputting employee information;

[2300] server means for receiving the input employee information and storing it in a database;

[2301] a user input means for inputting business task information;

[2302] a server means for receiving input business task information and storing it in a database;

[2303] An analytical means for analyzing employee information and work task information stored in the database and selecting the most suitable employee for each task;

[2304] emotion engine means for detecting the employee's emotional state and storing the emotion information in a database;

[2305] a coordination measure to integrate emotional information and analysis results and optimize task allocation;

[2306] an output means for presenting the selection results to the user;

[2307] A system including:

[2308] (Claim 2)

[2309] 10. The system of claim 1, wherein the system assigns optimal work tasks based on the employee's skill set.

[2310] (Claim 3)

[2311] 10. The system of claim 1, wherein a machine learning algorithm is used to match employees with work tasks. [Explanation of symbols]

[2312] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a user input means for inputting employee information; server means for receiving the input employee information and storing it in a database; a user input means for inputting business task information; a server means for receiving input business task information and storing it in a database; An analytical means for analyzing employee information and work task information stored in the database and selecting the most suitable employee for each task; an output means for presenting the selection results to the user; A system including:

2. The system of claim 1 , wherein the system assigns optimal work tasks based on the employee's skill set.

3. The system of claim 1 , wherein a machine learning algorithm is used to match employees with work tasks.

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