system

Abstract

Provide a system. A means for collecting work data of each member; means for calculating the fatigue level of a member based on the work data; A means for proposing paid vacation dates to members whose fatigue level exceeds a certain standard; means for reallocating tasks to other members based on the proposed vacation dates; means for sending notifications of said paid vacation dates and reassigned tasks; 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] In today's workplace, it is often difficult for busy employees to take time off. In particular, considering the progress of teams and projects, employees find it difficult to take time off voluntarily, resulting in accumulated fatigue, reduced work efficiency, and increased health risks. Existing leave management systems lack the mechanisms to properly assess individual levels of fatigue and automatically suggest leave, making it difficult to balance employee health management with work efficiency. [Means for solving the problem]

[0005] The present invention provides a system including a means for collecting work data for each team member, a means for calculating the fatigue level of each team member based on the work data, a means for proposing paid vacation dates to a team member whose fatigue level exceeds a certain standard, a means for reallocating tasks to other team members based on the proposed paid vacation dates, and a means for sending notifications of the paid vacation dates and reallocated tasks. This system makes it possible to appropriately evaluate the fatigue level of employees and automatically suggest vacations as needed, thereby improving the work efficiency of the entire team while maintaining the health of employees.

[0006] Understood. Below are definitions of important terms included in the scope of the patent claim for the "automatic paid vacation acquisition system."

[0007] ---

[0008] "Work data" refers to data such as work hours, number of tasks, and progress status that employees enter during their work hours.

[0009] "Fatigue level" is a score or indicator that indicates the degree of fatigue calculated based on an employee's work data.

[0010] The term "standard" refers to a certain numerical value or judgment standard used when assessing fatigue levels.

[0011] "Paid leave dates" means the specific dates proposed for an employee to take paid leave.

[0012] "Suggest" means recommending that an employee take time off and suggesting a date for it.

[0013] "Task reassignment" refers to the redistribution of the work of an employee taking leave to other team members.

[0014] "Means for sending notifications" refers to the communication methods and capabilities used to notify employees and their managers of specific information (e.g., vacation suggestions or task reassignments).

[0015] A "system" refers to a collection of devices and programs that work together to achieve a specific purpose (in this case, proposing paid vacation and coordinating work).

[0016] An "algorithm" refers to a set of multiple calculation procedures or rules used to calculate fatigue levels.

[0017] --- [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0026] [First embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0039] This invention relates to an "automatic paid vacation acquisition system" that automatically suggests paid vacation to employees whose fatigue level exceeds a certain level and adjusts work within the team at that time. This system consists of a server and a user terminal, and operates as follows.

[0040] System Overview

[0041] The system collects each member's work data and calculates their fatigue level based on this data. If the fatigue level exceeds a certain threshold, the system will suggest paid leave for the member and reassign tasks to other members. Finally, it will send notifications of the leave schedule and reassigned tasks to the relevant members.

[0042] Program processing

[0043] Data entry on user terminals

[0044] Users input their work start time, end time, progress, and task completion level into the terminal. This data is sent to the server in real time. For example, if user A works for 12 hours every day from Monday to Friday and inputs that data into the terminal, the information is sent to the server.

[0045] Data collection and analysis on the server

[0046] The server collects work data sent from user devices in real time and stores it in a database. The server calculates the user's fatigue level based on the accumulated work data. The fatigue level is calculated using an algorithm that takes into account the work hours, number of tasks, progress, and whether or not the user has taken a vacation over the past week. For example, in the case of User A, a fatigue score of 80 is calculated based on work data for 60 hours per week.

[0047] Paid leave proposals and schedule adjustments

[0048] The server determines that User A's fatigue score exceeds the management standard value (for example, 70), so the system will suggest that he take paid leave on the following Friday. The server checks the schedule of the entire team and the progress of the project, and selects the optimal leave date.

[0049] Task reassignment

[0050] The server retrieves the task list of the user who has been decided to take a vacation and reassigns the tasks to other members. During the reassignment, the server reevaluates the working status of each member and adjusts the tasks so that they are distributed fairly and efficiently. For example, user A's tasks are reassigned to members B and C.

[0051] Notification and confirmation

[0052] The server notifies the user and their supervisor of the final vacation schedule and details of the reassigned tasks. The user checks the notification on their terminal and, if necessary, adjusts the vacation schedule or requests approval from their supervisor. This operation ensures smooth vacation planning.

[0053] Specific examples

[0054] As a concrete example of a case where User A has a high level of fatigue, User A works 60 hours a week, and the server calculates a fatigue score of 80 based on the accumulated data. The server then proposes paid vacation for the following Friday and reassigns the tasks to Members B and C. A notification is sent to User A and the team, and their supervisor approves, allowing for smooth vacation acquisition and work adjustments.

[0055] In this way, the "automated paid leave acquisition system" supports employee fatigue management and efficient business operations.

[0056] The processing flow will be explained below.

[0057] Step 1:

[0058] Users input their work start time, end time, progress, and task completion level into the terminal. This data is sent to the server in real time. For example, if User A works for 12 hours every day from Monday to Friday and inputs that data into the terminal, the information is sent to the server.

[0059] Step 2:

[0060] The server receives the task data sent from the user terminal. The received data is stored in a database. This data includes the task time, number of tasks, progress, etc.

[0061] Step 3:

[0062] The server analyzes the work data stored in the database and calculates each user's fatigue level. The fatigue level is calculated using an algorithm that takes into account the work hours, number of tasks, progress, and whether or not the user has taken a vacation in the past week. For example, a fatigue score of 80 is calculated for user A based on his work data of 60 hours per week.

[0063] Step 4:

[0064] The server evaluates each user's fatigue score and determines that the user needs a vacation if it exceeds a certain threshold (e.g., 70). The fatigue score is periodically recalculated to reflect the latest status.

[0065] Step 5:

[0066] The server will suggest paid vacation dates to users whose fatigue score exceeds a certain threshold. The server will check the team's overall schedule and project progress to select the optimal vacation date. For example, it will suggest paid vacation for User A on the following Friday.

[0067] Step 6:

[0068] The server retrieves the task list of the user who has been decided to take a vacation from the database and reassigns it to other members. When reassigning, it reevaluates the availability status of each member and adjusts the allocation so that the tasks are distributed fairly and efficiently. For example, user A's tasks are reassigned to members B and C.

[0069] Step 7:

[0070] The server notifies the new assignee of the reassigned task details via email or chat tool. Members B and C then check the notification on their respective devices.

[0071] Step 8:

[0072] The server notifies the user and their supervisor of the vacation date and the details of the reassigned tasks. A notification is displayed on the user's device, and a confirmation and approval request is sent to the supervisor's device.

[0073] Step 9:

[0074] The user checks the notification on their own device and adjusts their vacation schedule if necessary. The supervisor also checks the approval request on their device and approves or modifies it as appropriate.

[0075] Step 10:

[0076] The server updates the database with the final vacation and task reassignment information, and the process is complete. The entire system is ready to start collecting data and assessing fatigue again.

[0077] Through these steps, the "Automatic Paid Leave Acquisition System" properly manages employee fatigue levels and automatically suggests vacation time, thereby supporting efficient business operations and maintaining employee health.

[0078] Example 1

[0079] 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."

[0080] Managing employee fatigue levels is extremely important, but traditional methods make it difficult to accurately assess employee fatigue levels, making it difficult to suggest time off at the right time. Furthermore, rescheduling work when employees take time off is often done manually, which requires additional effort and time.

[0081] 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.

[0082] In this invention, the server includes means for users to input work start times, end times, progress, and task completion levels, means for collecting the input data in real time and storing it in a database, means for calculating users' fatigue levels based on the data stored in the database, means for proposing paid vacation dates to users whose fatigue levels exceed a certain standard, means for reallocating tasks to other members based on the proposed paid vacation dates, and means for notifying details of the paid vacation dates and reallocated tasks. This allows for real-time management of employee fatigue levels, making it possible to propose vacations and readjust work at appropriate times.

[0083] A "user" is a person who accesses the system and inputs work data.

[0084] "Work start time" refers to the time when the user starts work.

[0085] "End time" refers to the time when the user finishes their work.

[0086] "Progress" refers to the degree of completion or progress of the tasks for which a user is responsible.

[0087] The "degree of task completion" is an index that indicates the degree to which the task assigned to the user has been completed.

[0088] A "terminal" refers to an information input device such as a computer or mobile device used by a user.

[0089] A "database" is a structured collection of information that a server uses to store and manage data collected by the server.

[0090] A "server" is a computer system that receives, stores, and analyzes data sent from a user terminal.

[0091] "Fatigue level" is a numerical representation of the user's workload and level of fatigue.

[0092] "Real-time" means that data is processed and transmitted the moment it is generated.

[0093] "Paid leave" refers to paid leave granted to employees by their employer.

[0094] "Dates" refer to the specific dates and times when paid leave will be taken.

[0095] "Reassignment" refers to the act of allocating the work of a user who is on vacation to another member.

[0096] "Notification" refers to the means by which the system communicates information to users and their superiors about paid leave and reassignment matters.

[0097] The present invention relates to a system for managing employee fatigue levels and appropriately proposing paid vacations. This system uses a server and user terminals to collect work data, calculate fatigue levels, suggest paid vacations, reassign tasks, and notify users.

[0098] Users input their work start time, finish time, progress, and task completion status into a terminal. The terminal used by the user is an information input device such as a computer or mobile device. The data entered by the user is sent to the server in real time.

[0099] The server receives the task data sent from the user terminal and stores it in a database. Specifically, the server can use a distributed database such as Apache Cassandra. The server calculates each user's fatigue level based on the data stored in the database. This fatigue level calculation uses an algorithm that takes into account the work hours, number of tasks, progress, and whether or not the user has taken a vacation over the past week.

[0100] The server suggests paid vacation dates if the user's fatigue score exceeds a certain standard. Specifically, if the user's fatigue score exceeds, for example, 70, the server suggests taking paid vacation on the following Friday. Based on this suggestion, the server uses a project management tool (e.g., Microsoft® Project) to check the schedule and project progress of the entire team and reassign tasks to other members.

[0101] When reassigning tasks, the server reevaluates each member's availability and adjusts the distribution so that tasks are distributed fairly and efficiently. As a result of the reassignment, all relevant members are notified of their vacation and new task schedules. Notifications are sent via a mail server (e.g., Postfix, Microsoft Exchange Server) or a real-time communication tool (e.g., Slack, MICROSOFT TEAMS (registered trademark)). Users can check the notifications on their devices and, if necessary, rearrange their vacation schedules or seek approval from their superiors.

[0102] As a concrete example, if User A works 12 hours a day from Monday to Friday, he or she enters that data into the terminal, and the server calculates a fatigue score of 80 based on that data. The server then proposes paid leave for the following Friday and reassigns User A's tasks to Members B and C. A notification is then sent to User A and his or her supervisor, who then approves the leave. This system enables employee fatigue management and efficient business operations.

[0103] Examples of prompt statements

[0104] "User A worked 12 hours a day from Monday to Friday and entered that data into a terminal. The server analyzed the data and calculated User A's fatigue score as 80. Because this score exceeds the reference value, the server suggested that User A take paid leave on the following Friday and reassigned the task to another member. The system notified all relevant members, and the supervisor approved the leave. Please explain the detailed process of this system."

[0105] In this way, the "automated paid leave acquisition system" enables employee fatigue management and efficient business operations.

[0106] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0107] Step 1:

[0108] Users input their work start time, end time, progress, and task completion status into the terminal. The input data is sent to the server in real time. For example, if a user works for 8 hours on Monday, the start time, end time, and progress status are entered.

[0109] Inputs: Start time, end time, progress, and task completion rate

[0110] Output: Working data sent to the server

[0111] Step 2:

[0112] The server receives the work data sent from the user terminal in real time and stores it in a database. The data is stored using a distributed database such as Apache Cassandra. The server analyzes the received data and stores it while maintaining consistency.

[0113] Input: Work data sent from the user's terminal

[0114] Output: Working data stored in a database

[0115] Step 3:

[0116] The server calculates the user's fatigue level based on the data stored in the database, using an algorithm that takes into account the amount of time worked in the past week, the number of tasks, progress, and whether or not the user has taken a vacation. For example, a Python script is used to perform the analysis and calculate the fatigue score.

[0117] Input: Work data from the past week stored in the database

[0118] Output: Calculated fatigue score

[0119] Step 4:

[0120] If the user's fatigue score exceeds a certain standard (e.g., 70), the server suggests a paid vacation date. For example, it suggests a vacation date on Friday of the following week. Specifically, it adjusts the schedule using a project management tool.

[0121] Input: Calculated fatigue score

[0122] Output: Proposed vacation dates

[0123] Step 5:

[0124] The server reassigns tasks to other members based on the proposed paid vacation dates. It reevaluates each member's availability based on Use Case Diagrams, Gantt Charts, etc., and allocates tasks efficiently. For example, user A's tasks are reassigned to members B and C.

[0125] Input: Proposed vacation dates

[0126] Output: Reassigned tasks

[0127] Step 6:

[0128] The server notifies the user and their manager of the vacation date and the details of the reassigned tasks using a mail server or real-time communication tool, such as Postfix or Slack.

[0129] Input: Paid time off dates, reassigned task details

[0130] Output: Notification sent

[0131] Specifically, the user inputs work data into the device, which is then sent to the server, where it is saved, analyzed, and the fatigue level is calculated. If the fatigue level exceeds the standard, the server suggests the next appropriate day off, reallocates work, and notifies relevant parties. This series of processes enables user fatigue management and efficient work operations.

[0132] (Application example 1)

[0133] 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."

[0134] Employee fatigue management is an important issue for labor efficiency and safety, but manual monitoring and management is time-consuming and difficult to respond to in real time. Proposing paid leave and reassigning tasks based on fatigue levels is also cumbersome. Therefore, there is a need for a system that can calculate employee fatigue levels in real time, suggest appropriate leave, and smoothly reorganize work. This problem is particularly pronounced in workplaces where workers, such as factory operators, are under heavy strain and require automatic task reassignment.

[0135] 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.

[0136] In this invention, the server includes a means for collecting work data of each member, a means for calculating the fatigue level of each member based on the work data, a means for proposing paid vacation dates for members whose fatigue level exceeds a certain standard, a means for reallocating tasks to other members or automated machines based on the proposed paid vacation dates, a means for sending notifications of the paid vacation dates and reallocated tasks, and a means including an algorithm for collecting user work data in real time and calculating fatigue levels. This allows for real-time monitoring of employee fatigue levels, appropriate paid vacation suggestions, and efficient work reorganization.

[0137] "Members" are users whose activity data is collected by the system, and are individuals or automated entities responsible for certain tasks.

[0138] "Work data" is data containing information about work performed by members, including work time, number of tasks, and progress.

[0139] "Fatigue level" is an index calculated based on the work data of a member, and is a value that indicates the degree of fatigue of the member.

[0140] The "certain standard" is a fatigue threshold set by the system, and if this value is exceeded, paid leave will be suggested.

[0141] "Paid vacation dates" are vacation dates proposed to members whose fatigue levels exceed a certain threshold.

[0142] "Task reassignment" refers to the act of redistributing presence tasks to other members or automated machines based on the proposed paid vacation dates.

[0143] "Notification" means information sent by the system to members and administrators, including details about vacation dates and reassigned tasks.

[0144] An "algorithm" is a calculation procedure for solving a specific problem, in this case a method for calculating fatigue levels based on work data.

[0145] This invention is a "smart fatigue management system" that collects work data from operators working in factories and calculates their fatigue levels in real time. If an operator's fatigue level exceeds a certain standard, the system automatically suggests paid leave and reallocates work. Specifically, it consists of a server and a user terminal.

[0146] Hardware and software used

[0147] Hardware: Smartphones, PCs, Tablets

[0148] Software: Python 3.x

[0149] Detailed explanation of data processing and calculation

[0150] Data entry on user terminals

[0151] The user (operator) inputs the start time, end time, and task progress into the terminal. This data is sent to the server in real time. For example, if an operator works for 12 hours, that information is updated in real time to the server.

[0152] Data collection and analysis on the server

[0153] The server collects work data sent from user devices in real time and stores it in a database. The server calculates the operator's fatigue level based on the accumulated work data. To calculate fatigue, an algorithm is used that takes into account the work hours, number of tasks, progress, and whether or not the operator has taken a vacation in the past week. For example, in the case of Operator A, the fatigue score is calculated from the accumulated work hours over the past week.

[0154] Paid leave proposals and schedule adjustments

[0155] The server then suggests paid vacation dates for operators whose fatigue scores exceed a certain threshold, and then checks the schedules and project progress of the entire team to select the optimal vacation dates.

[0156] Task reassignment

[0157] The server reallocates the tasks of the operator who is scheduled to take a vacation to other members or automated machines. When reallocating, it reevaluates the availability of each member and adjusts the allocation to distribute tasks fairly or efficiently. For example, Operator A's tasks are reallocated to two other operators, Operator B and Operator C.

[0158] Notification and confirmation

[0159] The server notifies the operator and their manager of the final vacation schedule and details of the reassigned tasks. The operator checks the notification on their terminal and, if necessary, adjusts the vacation schedule or seeks approval from the manager. This ensures smooth vacation planning.

[0160] Specific examples

[0161] If Operator A works 60 hours in a week, the server calculates a fatigue score based on the accumulated data, and since it exceeds the standard value, it proposes paid leave for the following Friday and reassigns tasks to Members B and C. A notification is sent to Operator A and the team, and the manager approves, allowing for smooth vacation acquisition and work adjustments.

[0162] Prompt Sentence Examples

[0163] Create an application that calculates the fatigue level of a factory robot operator when the specified working hours are exceeded, and suggests taking paid leave for the next day if the fatigue level exceeds the threshold. Also, explain the structure of the program that automatically reassigns tasks and notifies the operator when this happens.

[0164] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0165] Step 1: Data entry and collection

[0166] Users (operators) input their work start time, end time, and task progress status into their terminals. The input data is sent to the server in real time. The input data includes the work start time, end time, work content, and progress status. The server receives this data and saves it in a database. This accumulates the work data of each operator.

[0167] Step 2: Calculate fatigue

[0168] The server calculates fatigue levels based on the collected work data using an algorithm that includes the work hours, number of tasks, progress, and whether or not a vacation was taken over the past week. Specifically, the server extracts each piece of work data, adds up the work hours, and calculates a fatigue score taking into account the number of tasks and progress. For example, if the total work hours for the week is 60 hours, a fatigue score is calculated.

[0169] Step 3: Propose paid time off

[0170] If the calculated fatigue score exceeds a certain threshold (for example, 70), the server will suggest a paid vacation date for the relevant operator. The server checks the overall schedule and project progress and automatically selects the optimal vacation date. For example, if the fatigue score exceeds the threshold at 80, the server will suggest the next Friday as a paid vacation date.

[0171] Step 4: Reassign tasks

[0172] The server obtains the list of tasks for the operator who has been decided to take paid leave and reallocates the tasks to other operators or automated machines. When reallocating tasks, the server reevaluates the operating status of each operator and adjusts the allocation to ensure fair and efficient task distribution. For example, if Operator A's tasks are reallocated to Operator B and Operator C, the server will reallocate the tasks taking into account the operating status of each operator.

[0173] Step 5: Notification

[0174] The server notifies the operator and his / her manager of the determined paid vacation date and details of the reassigned tasks. The notification includes the vacation date, details of the reassigned tasks, and a list of the reassigned operators. The notification is sent to the terminal, allowing the operator to confirm their vacation and, if necessary, request approval.

[0175] Through these steps, the system can efficiently manage operator fatigue, allowing them to take time off and adjust their work schedules at the right time.

[0176] 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.

[0177] This invention relates to an "automated paid vacation acquisition system" that automatically suggests paid vacation to employees whose fatigue level exceeds a certain standard, adjusts work within the team at that time, and accurately calculates fatigue levels by taking into account the user's emotional data. This system is composed of a server, a user terminal, and an emotion engine, and operates as follows.

[0178] System Overview

[0179] The system collects each member's work data and emotional data, calculates their fatigue level based on this, and if the fatigue level exceeds a certain threshold, suggests that the member take paid leave and reassigns tasks to other members. Finally, it sends notifications of the leave schedule and reassigned tasks to the relevant members.

[0180] Program processing

[0181] Data entry on user terminals

[0182] Users input their work start time, end time, progress, and task completion level into the terminal. This data is sent to the server in real time. For example, if user A works for 12 hours every day from Monday to Friday and inputs that data into the terminal, the information is sent to the server.

[0183] Emotion Engine Data Acquisition

[0184] The emotion engine collects information such as the user's facial expressions, voice tone, and text input, and classifies them into emotion categories such as stress, joy, anger, sadness, etc. This emotion data is also sent to the server in real time.

[0185] Data collection and analysis on the server

[0186] The server receives the work data and emotion data sent from the user device and emotion engine and stores them in a database. The server calculates the user's fatigue level based on the accumulated data. The fatigue level is calculated using an algorithm that takes into account the work hours, number of tasks, progress, whether or not the user has taken a vacation, and emotion data from the past week. For example, in the case of User A, the fatigue level score is calculated based on the work data and emotion data for 60 hours per week.

[0187] Paid leave proposals and schedule adjustments

[0188] The server determines that User A's fatigue score exceeds the management standard value (for example, 70), so the system will suggest that he take paid leave on the following Friday. The server checks the schedule of the entire team and the progress of the project, and selects the optimal leave date.

[0189] Task reassignment

[0190] The server retrieves the task list of the user who has been decided to take a vacation from the database and reassigns the tasks to other members. When reassigning, it reevaluates the availability status of each member and adjusts the distribution of tasks so that they are fair and efficient. For example, user A's tasks are reassigned to members B and C.

[0191] Notification and confirmation

[0192] The server notifies the new assignee of the reassigned task details via email or chat tool. Members B and C then check the notification on their respective devices.

[0193] Notification and approval of leave proposals

[0194] The server notifies the user and their supervisor of the vacation date and the details of the reassigned tasks. A notification is displayed on the user's device, and a confirmation and approval request is sent to the supervisor's device.

[0195] User and supervisor review and approval

[0196] The user checks the notification on their own device and adjusts their vacation schedule if necessary. The supervisor also checks the approval request on their device and approves or modifies it as appropriate.

[0197] Database Update

[0198] The server updates the database with the final vacation and task reassignment information, and the process is complete. The entire system is ready to start collecting data and assessing fatigue again.

[0199] Specific examples

[0200] As a concrete example of when User A's fatigue level is high, User A works 60 hours a week and the emotion engine recognizes that he or she is under high stress. When the server calculates the fatigue level score based on the accumulated data, the score reaches 80. The server then proposes paid leave for the following Friday and reassigns the tasks to Members B and C. A notification is sent to User A and the team, and their supervisor approves, allowing for smooth vacation acquisition and work adjustments.

[0201] In this way, the "Automatic Paid Leave Acquisition System" properly manages employee fatigue and emotional data and automatically suggests taking time off, thereby supporting efficient business operations and maintaining employee health.

[0202] The processing flow will be explained below.

[0203] Step 1:

[0204] Users input their work start time, end time, progress, and task completion status into their terminal. This data is sent to the server in real time. For example, if User A inputs 12 hours of work time and multiple completed tasks at the end of a day's work, the information is sent to the server immediately.

[0205] Step 2:

[0206] The emotion engine analyzes emotion data by capturing the user's facial expressions with a webcam, collecting voice tones with a microphone, and monitoring the user's text input. For example, if a user inputs keywords such as "tired" or "troubled" in an email or chat, the emotion engine will recognize this as stress.

[0207] Step 3:

[0208] The emotion engine sends collected emotion data to the server in real time. For example, if user A feels stressed during a meeting, it analyzes his facial expressions and tone of voice, and based on this, it recognizes the user as in a high-stress state and sends the data to the server.

[0209] Step 4:

[0210] The server receives task data and emotion data sent from the user device and emotion engine, and stores them in a database. The stored data includes task time, number of tasks, progress, and recognized emotion data.

[0211] Step 5:

[0212] The server analyzes the work data and emotional data stored in the database to calculate each user's fatigue level. The algorithm used to calculate fatigue levels takes into account the past week's work hours, number of tasks, progress, whether or not the user has taken a vacation, and emotional data. For example, a fatigue score of 80 is calculated for User A based on his or her 60-hour work week and high stress level.

[0213] Step 6:

[0214] The server evaluates the calculated fatigue score, and if it exceeds a reference value (for example, 70), it determines that the user needs a vacation. The server then proposes paid vacation to User A for the following Friday.

[0215] Step 7:

[0216] The server retrieves the task list of the user who has been decided to take a vacation from the database and reallocates the tasks to other members. When reallocating, it reevaluates each member's operating status and emotional data and adjusts the allocation so that the tasks are distributed fairly and efficiently. For example, user A's tasks are reallocated to members B and C.

[0217] Step 8:

[0218] The server notifies the new assignee of the reassigned task details via email or chat tool. Members B and C then check the notification on their respective devices.

[0219] Step 9:

[0220] The server notifies the user and their supervisor of the vacation date and the details of the reassigned tasks. A notification is displayed on the user's device, and a confirmation and approval request is sent to the supervisor's device.

[0221] Step 10:

[0222] The user can check the notification on their own device and adjust their vacation schedule if necessary, while the supervisor can check the approval request on their device and approve or modify it as appropriate.

[0223] Step 11:

[0224] The server updates the database with the final vacation and task reassignment information, and the process is complete. The entire system is ready to start collecting data and assessing fatigue again.

[0225] Through these steps, the "Automatic Paid Leave Acquisition System" properly manages employees' work data and emotional data, accurately calculates fatigue levels, suggests vacation time, and adjusts work schedules, thereby supporting efficient business operations and maintaining employee health.

[0226] Example 2

[0227] 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."

[0228] Properly managing employee fatigue levels and simultaneously achieving efficient business operations and maintaining employee health is a key challenge for many organizations. Conventional systems rely solely on work data to assess fatigue levels, making it difficult to consider employees' emotions and stress levels. This makes it difficult to grasp an employee's true fatigue level and make appropriate leave recommendations. Furthermore, the processes of task reassignment and notification must be done manually, which contributes to reduced organizational efficiency.

[0229] 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 means for collecting work data and emotion data of each member, means for accurately calculating the fatigue level of each member based on the work data and emotion data, means for automatically proposing paid vacation dates for a member whose fatigue level exceeds a certain standard, means for reassigning tasks to other members based on the proposed paid vacation dates, and means for sending notifications of the paid vacation dates and reassigned tasks to each member and their supervisor. This enables highly accurate fatigue level assessment that takes into account not only the employee's working time and task progress but also their emotion data, thereby automatically proposing appropriate vacations and efficiently reassigning tasks.

[0230] "Work data" refers to information such as the start time, end time, number of tasks, and progress of work performed by each member.

[0231] "Emotional data" refers to information about emotions collected from each member's facial expressions, voice tone, text input, etc., and is classified into emotional categories such as stress, joy, anger, and sadness.

[0232] "Fatigue level" refers to a score or index that indicates a member's fatigue state based on each member's work data and emotional data.

[0233] "Paid vacation dates" refers to the dates and periods for each member's paid vacation that the system automatically suggests.

[0234] "Reassigned tasks" refers to the work or tasks that are redistributed to other members when a highly fatigued member takes paid leave.

[0235] "Notification" means a communication sent by the system to each Member and their Manager containing the vacation proposal, details of the reassigned tasks, and any other necessary information.

[0236] "Server" refers to a central processing unit that collects, stores, and analyzes work data and emotional data, calculates fatigue levels, suggests vacations, reassigns tasks, and sends notifications.

[0237] "Algorithm" refers to the calculation procedures and rules for calculating a member's fatigue level with high accuracy based on work data and emotional data.

[0238] This invention relates to an "automated paid vacation acquisition system" that automatically suggests paid vacation to team members whose fatigue level exceeds a certain standard, adjusts work within the team, and calculates fatigue levels with high accuracy by taking into account the user's emotional data. This system is composed of a server, a user terminal, and an emotion engine, and operates as follows.

[0239] System Overview

[0240] The system collects each member's work data and emotional data, calculates their fatigue level based on this, and if the fatigue level exceeds a certain threshold, suggests that the member take paid leave and reassigns tasks to other members. Finally, it sends notifications of the leave schedule and reassigned tasks to the relevant members.

[0241] Hardware and software used

[0242] Server: A central processing unit that collects, stores, analyzes, and sends notifications.

[0243] User terminal: A device (e.g., PC, smartphone) for each member to input task data and emotion data.

[0244] Emotion Engine: Software and hardware that uses a webcam, microphone, and natural language processing algorithms to collect and classify emotion data.

[0245] Details of data processing and calculation

[0246] 1. Data entry on the user's device

[0247] Users input their work start time, end time, progress, and task completion status into the terminal, and this data is sent to the server in real time.

[0248] 2. Emotion Engine Data Acquisition

[0249] The emotion engine captures the user's facial expressions with a webcam, analyzes their voice tone with a microphone, and analyzes their text input with a natural language processing algorithm, all of which transmits the emotion data to a server in real time.

[0250] 3. Data collection and analysis on the server

[0251] The server receives the work data and emotion data sent from the user device and emotion engine, and stores them in a database. Based on the accumulated data, the server calculates the user's fatigue level using an algorithm that takes into account the work hours, number of tasks, progress, whether or not the user has taken a vacation, and emotion data from the past week.

[0252] 4. Proposing and scheduling paid leave

[0253] The server automatically suggests optimal vacation days for users whose calculated fatigue score exceeds a threshold, taking into account the overall team schedule and project progress.

[0254] 5. Reassignment of Work

[0255] The server retrieves the task list of the user who has been decided to take a vacation from the database and reassigns the tasks to other members. It evaluates the working status of each member and adjusts the distribution of tasks so that they are fair and efficient.

[0256] 6. Sending Notifications

[0257] The server notifies each member and their manager of the details of the reassigned tasks and the vacation proposal via email or chat tool.

[0258] Specific examples

[0259] As a concrete example of a case where User A has a high level of fatigue, User A works 60 hours a week and the emotion engine recognizes that he or she is under high stress. When the server calculates the fatigue score based on this data, the score reaches 80, and the server proposes paid leave for the following Friday. The server reassigns the task to Members B and C, a notification is sent to all members, and the supervisor approves the leave, allowing for smooth leave acquisition and work adjustments.

[0260] Prompt Sentence Examples

[0261] "If User A's fatigue level is high, please explain what kind of vacation suggestions and work adjustments will be made."

[0262] "Can you tell me more about what the emotion engine does and how it obtains data?"

[0263] The above is a specific embodiment for carrying out the invention.

[0264] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0265] System program processing steps

[0266] Step 1: Enter data on the user's device

[0267] Input: The user inputs their work start time, end time, progress, and degree of completion of the task into the terminal.

[0268] Data processing and calculation: The user terminal formats the input data and sends it to the server in real time. The input data is also temporarily stored in local storage.

[0269] Output: The terminal sends the formatted work data to the server and displays an input confirmation message to the user.

[0270] Specific operation: When a user enters a start time of work as 9:00 on Monday and an end time of work as 21:00, the data is sent to the server.

[0271] Step 2: Obtaining Data for the Emotion Engine

[0272] Input: The emotion engine collects data from the user's facial expressions, voice tone, and text input.

[0273] Data processing and calculation: The emotion engine analyzes the collected data and classifies it into emotion categories (e.g., stress, joy, anger, sadness) using natural language processing and image recognition algorithms.

[0274] Output: The classified emotion data is sent to the server.

[0275] Specific operation: The emotion engine analyzes the sentence "Today was a very stressful day" entered by the user and classifies it as stress. The result is sent to the server.

[0276] Step 3: Collecting and storing data on the server

[0277] Input: Task data and emotion data sent from the user terminal and emotion engine.

[0278] Data processing and calculation: The server stores the received data in a database. It also performs data integrity and validation.

[0279] Output: Task and emotion data stored in the server database are available for the next analysis step.

[0280] Specific operation: The server stores the work time data and emotion data of user A in a database.

[0281] Step 4: Calculate fatigue level

[0282] Input: Task data and emotion data stored on the server.

[0283] Data processing and calculation: The server inputs the past week's work hours, number of tasks, progress, whether or not vacation was taken, and emotional data into an algorithm to calculate a fatigue score.

[0284] Output: A fatigue score is calculated and used in the next vacation suggestion step.

[0285] Specific operation: A calculation is performed based on user A's work data and emotion data for one week, and the fatigue score is calculated as 80.

[0286] Step 5: Propose and schedule paid time off

[0287] Inputs: Fatigue score and overall team schedule and project progress.

[0288] Data processing and calculation: If the fatigue score exceeds a certain level (e.g., 70), the server will suggest a paid vacation date. The optimal vacation date will be selected taking into account the schedule of the entire team.

[0289] Output: A vacation proposal notification is generated and used in the next task reassignment step.

[0290] Specific operation: The server suggests to user A that he take paid vacation on the following Friday and generates a notification of this.

[0291] Step 6: Reassign tasks

[0292] Input: A list of tasks for users who have been confirmed as on vacation, and the availability data of each member.

[0293] Data processing and calculation: The server reassigns the tasks of the user who has been determined to be on vacation to other members fairly and efficiently. It evaluates the working status of each member and distributes them optimally.

[0294] Output: Details of the reassigned task are generated and used in the next notification step.

[0295] Specific behavior: User A's task is reassigned to members B and C.

[0296] Step 7: Sending notifications

[0297] Inputs: Reassigned task details and leave proposal notification.

[0298] Data processing and calculation: The server generates notifications containing details of the reassigned tasks and vacation proposals and sends them to each member and their manager via email or chat tool.

[0299] Output: Each member and their manager will receive a notification.

[0300] Specific behavior: The server sends a notification to Member B, Member C, and their superiors with details of the reassigned tasks and the vacation proposal.

[0301] The above is an explanation of the specific processing steps and their operations.

[0302] (Application example 2)

[0303] 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."

[0304] Conventional fatigue management systems calculated fatigue levels based solely on the work data of members, but this had the problem of making it difficult to accurately grasp the actual fatigue state of members. Furthermore, when fatigue levels increased, task redistribution often concentrated the burden on a few members without taking into account the impact on other members. Furthermore, in factories and other workplaces, work adjustments that take into account the timing of robot maintenance were necessary, but there was no system that could do this automatically.

[0305] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting each member's work data and emotion data, means for calculating the member's fatigue level based on the work data and emotion data, means for proposing paid vacation dates to a member whose fatigue level exceeds a certain threshold, means for reallocating tasks to other members based on the proposed paid vacation dates, means for sending notifications of the paid vacation dates and reallocated tasks, and means for automatically reallocating robot maintenance tasks to other engineers when the member's fatigue level exceeds a threshold. This enables highly accurate fatigue assessment that takes into account the member's actual emotional state, and by appropriately distributing the workload to other members, efficient and fair task allocation is possible. Furthermore, automatically adjusting the robot maintenance schedule can improve work efficiency within the factory.

[0306] "Members" refer to individuals, primarily employees and engineers, who provide work data and sentiment data.

[0307] "Task data" refers to data that includes information such as the start time, end time, progress, and degree of completion of a task.

[0308] "Emotion data" refers to emotional information such as stress, joy, anger, sadness, etc. obtained from the user's facial expression, voice tone, and character input.

[0309] "Fatigue level" is an index that indicates the degree of fatigue calculated based on the member's work data and emotional data.

[0310] "Paid leave" refers to a period of leave from work that is automatically offered to a member if they meet certain criteria.

[0311] "Task reassignment" refers to the process of reassigning tasks from a member whose fatigue level has exceeded a standard value to another member.

[0312] "Notification" is a means of communication to inform members and their superiors of proposed vacation dates and details of reassigned tasks.

[0313] "Data collection means" refers to a method or device for acquiring task data and emotion data from members.

[0314] "Data analysis means" refers to a method or device for calculating fatigue levels based on collected task data and emotion data.

[0315] "Proposal means" refers to a method or device for proposing paid vacation dates to members when their fatigue level exceeds a certain standard.

[0316] "Robot maintenance tasks" refer to work performed to maintain and repair robots in a factory.

[0317] The "reference value" refers to the threshold at which paid leave is suggested when fatigue levels are above a certain level.

[0318] This invention is an "automated paid vacation acquisition system" that collects work data and emotional data of members, calculates their fatigue level based on the data, and automatically suggests paid vacation if the fatigue level exceeds a certain threshold. This system is composed of a server, a user terminal, and an emotion engine.

[0319] System Overview

[0320] The system collects each member's work data and emotional data, calculates their fatigue level based on this, and if their fatigue level exceeds a certain threshold, suggests that they take paid leave. It also reassigns tasks to other members and automatically adjusts robot maintenance tasks. Finally, it sends notifications of vacation dates and reassigned tasks to the relevant members.

[0321] Server Program

[0322] The server receives each member's work data and emotional data and stores them in a database. The server uses data processing libraries (e.g., numpy, pandas) to analyze the collected data and calculate fatigue levels. Fatigue assessment uses an algorithm that takes into account the past week's work hours, number of tasks, progress, whether or not a member has taken vacation, and emotional data. If the fatigue score exceeds a threshold, the server suggests the member take paid vacation and reassigns tasks.

[0323] User terminal

[0324] The user terminal provides an interface for each member to input work data. Users input data such as the start time, end time, progress, and degree of task completion. In addition, emotional data (e.g., facial expressions, voice tone, and text input) is also collected through the interface provided by the emotion engine.

[0325] Emotion Engine

[0326] The emotion engine analyzes information such as members' facial expressions, voice tone, and text input, and classifies them into emotion categories such as stress, joy, anger, and sadness. This emotion data is sent to the server in real time. For example, services such as Emotion API are used for emotion analysis.

[0327] Specific examples

[0328] For example, suppose Engineer A works 60 consecutive hours a week and the emotion engine detects high stress levels. The server calculates the fatigue level based on this data and finds that the fatigue score exceeds the threshold. The server then suggests that the engineer take paid leave on Wednesday of the following week. The server then reassigns the tasks to Engineers B and C, sends notifications, and automatically assigns robot maintenance tasks to the other engineers.

[0329] Prompt Sentence Examples

[0330] "Please help me design a system that calculates an engineer's fatigue level based on their work data and emotional data, and suggests paid vacation time. Engineer A works 60 hours a week. Taking this into consideration, if his fatigue level is determined to be high, we need to suggest appropriate vacation time and reassign tasks to other engineers."

[0331] In this way, the "automated paid leave acquisition system" properly manages members' work data and emotional data, performs highly precise fatigue assessments, and automatically suggests leave, thereby achieving efficient business operations and maintaining members' health.

[0332] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0333] Step 1:

[0334] Data entry on user terminal:

[0335] Users use a smartphone or tablet to input their work start time, end time, progress, and degree of task completion. Emotional data is also input at the same time. For example, the user might provide information such as "Start time: 9:00, End time: 18:00, Task progress: 80%, Emotion: High stress." This data is sent to the server in real time. The output is the work data and emotional data sent to the server.

[0336] Step 2:

[0337] Emotion Engine Data Acquisition:

[0338] The emotion engine collects emotional data from the user's facial expressions, voice tone, and text input, and classifies it into emotional categories such as stress, joy, anger, and sadness. Image data of facial expressions, voice data, and text input data are provided as input. Specifically, analysis is performed using the Emotion API. Emotional data such as stress level and joy level is sent to the server as output.

[0339] Step 3:

[0340] Data collection and analysis on the server:

[0341] The server receives work data and emotion data sent from the user device and emotion engine and stores them in a database. The received data is used as input. Using a data processing library (e.g., numpy, pandas), fatigue levels are calculated based on this data. The past week's work hours, number of tasks, progress, whether or not someone has taken a vacation, and emotion data are weighted, and an overall fatigue score is calculated using an algorithm. The output is a fatigue score for each member.

[0342] Step 4:

[0343] Paid Time Off Proposals and Scheduling:

[0344] The server proposes paid leave to members whose fatigue score exceeds a certain threshold. The fatigue score is used as input. If the fatigue score exceeds, for example, 70, the server proposes paid leave on the most appropriate day of the following week. As output, a vacation proposal notification is generated for the relevant member.

[0345] Step 5:

[0346] Task reassignment:

[0347] The server retrieves the task list of the user who has decided to take a vacation from the database and reassigns the tasks to other members. The user's task data is used as input. When reassigning, adjustments are made to ensure that tasks are distributed efficiently, taking into account each member's current operating status. New task assignment data is generated as output.

[0348] Step 6:

[0349] Notification and confirmation:

[0350] The server notifies the new assignee of the reassigned task details. This notification is sent via email or chat tool. The new task assignment data is used as input. The notification is sent to each member as output.

[0351] Step 7:

[0352] Notification and Approval of Leave Proposals:

[0353] The server notifies the user and their supervisor of the vacation date and the details of the reassigned tasks. The server uses the vacation proposal notification data as input. As output, the notification is sent to the user and supervisor.

[0354] Step 8:

[0355] User and manager review and approval:

[0356] The user checks the notification on their own device and adjusts the vacation schedule if necessary. The supervisor also checks the approval request on their device and approves or modifies it as appropriate. The inputs are the vacation proposal notification and the task reassignment notification. The output is the confirmation and approval data.

[0357] Step 9:

[0358] Database update:

[0359] The server updates the database with the final leave and task reassignment decisions. The input is the confirmation and approval data. The output is the updated leave and task reassignment data. The whole system is ready to repeat the data collection and fatigue assessment again.

[0360] 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.

[0361] 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.

[0362] 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.

[0363] [Second embodiment]

[0364] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0365] 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.

[0366] 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).

[0367] 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.

[0368] 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.

[0369] 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).

[0370] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0371] 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.

[0372] 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.

[0373] 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.

[0374] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0375] 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."

[0376] This invention relates to an "automatic paid vacation acquisition system" that automatically suggests paid vacation to employees whose fatigue level exceeds a certain level and adjusts work within the team at that time. This system consists of a server and a user terminal, and operates as follows.

[0377] System Overview

[0378] The system collects each member's work data and calculates their fatigue level based on this data. If the fatigue level exceeds a certain threshold, the system will suggest paid leave for the member and reassign tasks to other members. Finally, it will send notifications of the leave schedule and reassigned tasks to the relevant members.

[0379] Program processing

[0380] Data entry on user terminals

[0381] Users input their work start time, end time, progress, and task completion level into the terminal. This data is sent to the server in real time. For example, if user A works for 12 hours every day from Monday to Friday and inputs that data into the terminal, the information is sent to the server.

[0382] Data collection and analysis on the server

[0383] The server collects work data sent from user devices in real time and stores it in a database. The server calculates the user's fatigue level based on the accumulated work data. The fatigue level is calculated using an algorithm that takes into account the work hours, number of tasks, progress, and whether or not the user has taken a vacation over the past week. For example, in the case of User A, a fatigue score of 80 is calculated based on work data for 60 hours per week.

[0384] Paid leave proposals and schedule adjustments

[0385] The server determines that User A's fatigue score exceeds the management standard value (for example, 70), so the system will suggest that he take paid leave on the following Friday. The server checks the schedule of the entire team and the progress of the project, and selects the optimal leave date.

[0386] Task reassignment

[0387] The server retrieves the task list of the user who has been decided to take a vacation and reassigns the tasks to other members. During the reassignment, the server reevaluates the working status of each member and adjusts the tasks so that they are distributed fairly and efficiently. For example, user A's tasks are reassigned to members B and C.

[0388] Notification and confirmation

[0389] The server notifies the user and their supervisor of the final vacation schedule and details of the reassigned tasks. The user checks the notification on their terminal and, if necessary, adjusts the vacation schedule or requests approval from their supervisor. This operation ensures smooth vacation planning.

[0390] Specific examples

[0391] As a concrete example of a case where User A has a high level of fatigue, User A works 60 hours a week, and the server calculates a fatigue score of 80 based on the accumulated data. The server then proposes paid vacation for the following Friday and reassigns the tasks to Members B and C. A notification is sent to User A and the team, and their supervisor approves, allowing for smooth vacation acquisition and work adjustments.

[0392] In this way, the "automated paid leave acquisition system" supports employee fatigue management and efficient business operations.

[0393] The processing flow will be explained below.

[0394] Step 1:

[0395] Users input their work start time, end time, progress, and task completion level into the terminal. This data is sent to the server in real time. For example, if User A works for 12 hours every day from Monday to Friday and inputs that data into the terminal, the information is sent to the server.

[0396] Step 2:

[0397] The server receives the task data sent from the user terminal. The received data is stored in a database. This data includes the task time, number of tasks, progress, etc.

[0398] Step 3:

[0399] The server analyzes the work data stored in the database and calculates each user's fatigue level. The fatigue level is calculated using an algorithm that takes into account the work hours, number of tasks, progress, and whether or not the user has taken a vacation in the past week. For example, a fatigue score of 80 is calculated for user A based on his work data of 60 hours per week.

[0400] Step 4:

[0401] The server evaluates each user's fatigue score and determines that the user needs a vacation if it exceeds a certain threshold (e.g., 70). The fatigue score is periodically recalculated to reflect the latest status.

[0402] Step 5:

[0403] The server will suggest paid vacation dates to users whose fatigue score exceeds a certain threshold. The server will check the team's overall schedule and project progress to select the optimal vacation date. For example, it will suggest paid vacation for User A on the following Friday.

[0404] Step 6:

[0405] The server retrieves the task list of the user who has been decided to take a vacation from the database and reassigns it to other members. When reassigning, it reevaluates the availability status of each member and adjusts the allocation so that the tasks are distributed fairly and efficiently. For example, user A's tasks are reassigned to members B and C.

[0406] Step 7:

[0407] The server notifies the new assignee of the reassigned task details via email or chat tool. Members B and C then check the notification on their respective devices.

[0408] Step 8:

[0409] The server notifies the user and their supervisor of the vacation date and the details of the reassigned tasks. A notification is displayed on the user's device, and a confirmation and approval request is sent to the supervisor's device.

[0410] Step 9:

[0411] The user checks the notification on their own device and adjusts their vacation schedule if necessary. The supervisor also checks the approval request on their device and approves or modifies it as appropriate.

[0412] Step 10:

[0413] The server updates the database with the final vacation and task reassignment information, and the process is complete. The entire system is ready to start collecting data and assessing fatigue again.

[0414] Through these steps, the "Automatic Paid Leave Acquisition System" properly manages employee fatigue levels and automatically suggests vacation time, thereby supporting efficient business operations and maintaining employee health.

[0415] Example 1

[0416] 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."

[0417] Managing employee fatigue levels is extremely important, but traditional methods make it difficult to accurately assess employee fatigue levels, making it difficult to suggest time off at the right time. Furthermore, rescheduling work when employees take time off is often done manually, which requires additional effort and time.

[0418] 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.

[0419] In this invention, the server includes means for users to input work start times, end times, progress, and task completion levels, means for collecting the input data in real time and storing it in a database, means for calculating users' fatigue levels based on the data stored in the database, means for proposing paid vacation dates to users whose fatigue levels exceed a certain standard, means for reallocating tasks to other members based on the proposed paid vacation dates, and means for notifying details of the paid vacation dates and reallocated tasks. This allows for real-time management of employee fatigue levels, making it possible to propose vacations and readjust work at appropriate times.

[0420] A "user" is a person who accesses the system and inputs work data.

[0421] "Work start time" refers to the time when the user starts work.

[0422] "End time" refers to the time when the user finishes their work.

[0423] "Progress" refers to the degree of completion or progress of the tasks for which a user is responsible.

[0424] The "degree of task completion" is an index that indicates the degree to which the task assigned to the user has been completed.

[0425] A "terminal" refers to an information input device such as a computer or mobile device used by a user.

[0426] A "database" is a structured collection of information that a server uses to store and manage data collected by the server.

[0427] A "server" is a computer system that receives, stores, and analyzes data sent from a user terminal.

[0428] "Fatigue level" is a numerical representation of the user's workload and level of fatigue.

[0429] "Real-time" means that data is processed and transmitted the moment it is generated.

[0430] "Paid leave" refers to paid leave granted to employees by their employer.

[0431] "Dates" refer to the specific dates and times when paid leave will be taken.

[0432] "Reassignment" refers to the act of allocating the work of a user who is on vacation to another member.

[0433] "Notification" refers to the means by which the system communicates information to users and their superiors about paid leave and reassignment matters.

[0434] The present invention relates to a system for managing employee fatigue levels and appropriately proposing paid vacations. This system uses a server and user terminals to collect work data, calculate fatigue levels, suggest paid vacations, reassign tasks, and notify users.

[0435] Users input their work start time, finish time, progress, and task completion status into a terminal. The terminal used by the user is an information input device such as a computer or mobile device. The data entered by the user is sent to the server in real time.

[0436] The server receives the work data sent from the user terminal and stores it in a database. The server can use a distributed database such as Apache Cassandra. The server calculates each user's fatigue level based on the data stored in the database. This fatigue level calculation uses an algorithm that takes into account the work hours, number of tasks, progress, and whether or not the user has taken a vacation over the past week.

[0437] The server suggests paid vacation dates if the user's fatigue score exceeds a certain standard. Specifically, if the user's fatigue score exceeds 70, for example, it suggests taking paid vacation on the following Friday. Based on this suggestion, the server uses a project management tool (e.g., Microsoft Project) to check the schedule and project progress of the entire team and reassign tasks to other members.

[0438] When reassigning tasks, the server reevaluates each member's availability and adjusts the distribution so that tasks are distributed fairly and efficiently. As a result of the reassignment, all relevant members are notified of their vacation and new task schedules. Notifications are sent via a mail server (e.g., Postfix, Microsoft Exchange Server) or a real-time communication tool (e.g., Slack, Microsoft Teams). Users can check the notifications on their devices and, if necessary, rearrange their vacation schedules or seek approval from their superiors.

[0439] As a concrete example, if User A works 12 hours a day from Monday to Friday, he or she enters that data into the terminal, and the server calculates a fatigue score of 80 based on that data. The server then proposes paid leave for the following Friday and reassigns User A's tasks to Members B and C. A notification is then sent to User A and his or her supervisor, who then approves the leave. This system enables employee fatigue management and efficient business operations.

[0440] Examples of prompt statements

[0441] "User A worked 12 hours a day from Monday to Friday and entered that data into a terminal. The server analyzed the data and calculated User A's fatigue score as 80. Because this score exceeds the reference value, the server suggested that User A take paid leave on the following Friday and reassigned the task to another member. The system notified all relevant members, and the supervisor approved the leave. Please explain the detailed process of this system."

[0442] In this way, the "automated paid leave acquisition system" enables employee fatigue management and efficient business operations.

[0443] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0444] Step 1:

[0445] Users input their work start time, end time, progress, and task completion status into the terminal. The input data is sent to the server in real time. For example, if a user works for 8 hours on Monday, the start time, end time, and progress status are entered.

[0446] Inputs: Start time, end time, progress, and task completion rate

[0447] Output: Working data sent to the server

[0448] Step 2:

[0449] The server receives the work data sent from the user terminal in real time and stores it in a database. The data is stored using a distributed database such as Apache Cassandra. The server analyzes the received data and stores it while maintaining consistency.

[0450] Input: Work data sent from the user's terminal

[0451] Output: Working data stored in a database

[0452] Step 3:

[0453] The server calculates the user's fatigue level based on the data stored in the database, using an algorithm that takes into account the amount of time worked in the past week, the number of tasks, progress, and whether or not the user has taken a vacation. For example, a Python script is used to perform the analysis and calculate the fatigue score.

[0454] Input: Work data from the past week stored in the database

[0455] Output: Calculated fatigue score

[0456] Step 4:

[0457] If the user's fatigue score exceeds a certain standard (e.g., 70), the server suggests a paid vacation date. For example, it suggests a vacation date on Friday of the following week. Specifically, it adjusts the schedule using a project management tool.

[0458] Input: Calculated fatigue score

[0459] Output: Proposed vacation dates

[0460] Step 5:

[0461] The server reassigns tasks to other members based on the proposed paid vacation dates. It reevaluates each member's availability based on Use Case Diagrams, Gantt Charts, etc., and allocates tasks efficiently. For example, user A's tasks are reassigned to members B and C.

[0462] Input: Proposed vacation dates

[0463] Output: Reassigned tasks

[0464] Step 6:

[0465] The server notifies the user and their manager of the vacation date and the details of the reassigned tasks using a mail server or real-time communication tool, such as Postfix or Slack.

[0466] Input: Paid time off dates, reassigned task details

[0467] Output: Notification sent

[0468] Specifically, the user inputs work data into the device, which is then sent to the server, where it is saved, analyzed, and the fatigue level is calculated. If the fatigue level exceeds the standard, the server suggests the next appropriate day off, reallocates work, and notifies relevant parties. This series of processes enables user fatigue management and efficient work operations.

[0469] (Application example 1)

[0470] 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."

[0471] Employee fatigue management is an important issue for labor efficiency and safety, but manual monitoring and management is time-consuming and difficult to respond to in real time. Proposing paid leave and reassigning tasks based on fatigue levels is also cumbersome. Therefore, there is a need for a system that can calculate employee fatigue levels in real time, suggest appropriate leave, and smoothly reorganize work. This problem is particularly pronounced in workplaces where workers, such as factory operators, are under heavy strain and require automatic task reassignment.

[0472] 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.

[0473] In this invention, the server includes a means for collecting work data of each member, a means for calculating the fatigue level of each member based on the work data, a means for proposing paid vacation dates for members whose fatigue level exceeds a certain standard, a means for reallocating tasks to other members or automated machines based on the proposed paid vacation dates, a means for sending notifications of the paid vacation dates and reallocated tasks, and a means including an algorithm for collecting user work data in real time and calculating fatigue levels. This allows for real-time monitoring of employee fatigue levels, appropriate paid vacation suggestions, and efficient work reorganization.

[0474] "Members" are users whose activity data is collected by the system, and are individuals or automated entities responsible for certain tasks.

[0475] "Work data" is data containing information about work performed by members, including work time, number of tasks, and progress.

[0476] "Fatigue level" is an index calculated based on the work data of a member, and is a value that indicates the degree of fatigue of the member.

[0477] The "certain standard" is a fatigue threshold set by the system, and if this value is exceeded, paid leave will be suggested.

[0478] "Paid vacation dates" are vacation dates proposed to members whose fatigue levels exceed a certain threshold.

[0479] "Task reassignment" refers to the act of redistributing presence tasks to other members or automated machines based on the proposed paid vacation dates.

[0480] "Notification" means information sent by the system to members and administrators, including details about vacation dates and reassigned tasks.

[0481] An "algorithm" is a calculation procedure for solving a specific problem, in this case a method for calculating fatigue levels based on work data.

[0482] This invention is a "smart fatigue management system" that collects work data from operators working in factories and calculates their fatigue levels in real time. If an operator's fatigue level exceeds a certain standard, the system automatically suggests paid leave and reallocates work. Specifically, it consists of a server and a user terminal.

[0483] Hardware and software used

[0484] Hardware: Smartphones, PCs, Tablets

[0485] Software: Python 3.x

[0486] Detailed explanation of data processing and calculation

[0487] Data entry on user terminals

[0488] The user (operator) inputs the start time, end time, and task progress into the terminal. This data is sent to the server in real time. For example, if an operator works for 12 hours, that information is updated in real time to the server.

[0489] Data collection and analysis on the server

[0490] The server collects work data sent from user devices in real time and stores it in a database. The server calculates the operator's fatigue level based on the accumulated work data. To calculate fatigue, an algorithm is used that takes into account the work hours, number of tasks, progress, and whether or not the operator has taken a vacation in the past week. For example, in the case of Operator A, the fatigue score is calculated from the accumulated work hours over the past week.

[0491] Paid leave proposals and schedule adjustments

[0492] The server then suggests paid vacation dates for operators whose fatigue scores exceed a certain threshold, and then checks the schedules and project progress of the entire team to select the optimal vacation dates.

[0493] Task reassignment

[0494] The server reallocates the tasks of the operator who is scheduled to take a vacation to other members or automated machines. When reallocating, it reevaluates the availability of each member and adjusts the allocation to distribute tasks fairly or efficiently. For example, Operator A's tasks are reallocated to two other operators, Operator B and Operator C.

[0495] Notification and confirmation

[0496] The server notifies the operator and their manager of the final vacation schedule and details of the reassigned tasks. The operator checks the notification on their terminal and, if necessary, adjusts the vacation schedule or seeks approval from the manager. This ensures smooth vacation planning.

[0497] Specific examples

[0498] If Operator A works 60 hours in a week, the server calculates a fatigue score based on the accumulated data, and since it exceeds the standard value, it proposes paid leave for the following Friday and reassigns tasks to Members B and C. A notification is sent to Operator A and the team, and the manager approves, allowing for smooth vacation acquisition and work adjustments.

[0499] Prompt Sentence Examples

[0500] Create an application that calculates the fatigue level of a factory robot operator when the specified working hours are exceeded, and suggests taking paid leave for the next day if the fatigue level exceeds the threshold. Also, explain the structure of the program that automatically reassigns tasks and notifies the operator when this happens.

[0501] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0502] Step 1: Data entry and collection

[0503] Users (operators) input their work start time, end time, and task progress status into their terminals. The input data is sent to the server in real time. The input data includes the work start time, end time, work content, and progress status. The server receives this data and saves it in a database. This accumulates the work data of each operator.

[0504] Step 2: Calculate fatigue

[0505] The server calculates fatigue levels based on the collected work data using an algorithm that includes the work hours, number of tasks, progress, and whether or not a vacation was taken over the past week. Specifically, the server extracts each piece of work data, adds up the work hours, and calculates a fatigue score taking into account the number of tasks and progress. For example, if the total work hours for the week is 60 hours, a fatigue score is calculated.

[0506] Step 3: Propose paid time off

[0507] If the calculated fatigue score exceeds a certain threshold (for example, 70), the server will suggest a paid vacation date for the relevant operator. The server checks the overall schedule and project progress and automatically selects the optimal vacation date. For example, if the fatigue score exceeds the threshold at 80, the server will suggest the next Friday as a paid vacation date.

[0508] Step 4: Reassign tasks

[0509] The server obtains the list of tasks for the operator who has been decided to take paid leave and reallocates the tasks to other operators or automated machines. When reallocating tasks, the server reevaluates the operating status of each operator and adjusts the allocation to ensure fair and efficient task distribution. For example, if Operator A's tasks are reallocated to Operator B and Operator C, the server will reallocate the tasks taking into account the operating status of each operator.

[0510] Step 5: Notification

[0511] The server notifies the operator and his / her manager of the determined paid vacation date and details of the reassigned tasks. The notification includes the vacation date, details of the reassigned tasks, and a list of the reassigned operators. The notification is sent to the terminal, allowing the operator to confirm their vacation and, if necessary, request approval.

[0512] Through these steps, the system can efficiently manage operator fatigue, allowing them to take time off and adjust their work schedules at the right time.

[0513] 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.

[0514] This invention relates to an "automated paid vacation acquisition system" that automatically suggests paid vacation to employees whose fatigue level exceeds a certain standard, adjusts work within the team at that time, and accurately calculates fatigue levels by taking into account the user's emotional data. This system is composed of a server, a user terminal, and an emotion engine, and operates as follows.

[0515] System Overview

[0516] The system collects each member's work data and emotional data, calculates their fatigue level based on this, and if the fatigue level exceeds a certain threshold, suggests that the member take paid leave and reassigns tasks to other members. Finally, it sends notifications of the leave schedule and reassigned tasks to the relevant members.

[0517] Program processing

[0518] Data entry on user terminals

[0519] Users input their work start time, end time, progress, and task completion level into the terminal. This data is sent to the server in real time. For example, if user A works for 12 hours every day from Monday to Friday and inputs that data into the terminal, the information is sent to the server.

[0520] Emotion Engine Data Acquisition

[0521] The emotion engine collects information such as the user's facial expressions, voice tone, and text input, and classifies them into emotion categories such as stress, joy, anger, sadness, etc. This emotion data is also sent to the server in real time.

[0522] Data collection and analysis on the server

[0523] The server receives the work data and emotion data sent from the user device and emotion engine and stores them in a database. The server calculates the user's fatigue level based on the accumulated data. The fatigue level is calculated using an algorithm that takes into account the work hours, number of tasks, progress, whether or not the user has taken a vacation, and emotion data from the past week. For example, in the case of User A, the fatigue level score is calculated based on the work data and emotion data for 60 hours per week.

[0524] Paid leave proposals and schedule adjustments

[0525] The server determines that User A's fatigue score exceeds the management standard value (for example, 70), so the system will suggest that he take paid leave on the following Friday. The server checks the schedule of the entire team and the progress of the project, and selects the optimal leave date.

[0526] Task reassignment

[0527] The server retrieves the task list of the user who has been decided to take a vacation from the database and reassigns the tasks to other members. When reassigning, it reevaluates the availability status of each member and adjusts the distribution of tasks so that they are fair and efficient. For example, user A's tasks are reassigned to members B and C.

[0528] Notification and confirmation

[0529] The server notifies the new assignee of the reassigned task details via email or chat tool. Members B and C then check the notification on their respective devices.

[0530] Notification and approval of leave proposals

[0531] The server notifies the user and their supervisor of the vacation date and the details of the reassigned tasks. A notification is displayed on the user's device, and a confirmation and approval request is sent to the supervisor's device.

[0532] User and supervisor review and approval

[0533] The user checks the notification on their own device and adjusts their vacation schedule if necessary. The supervisor also checks the approval request on their device and approves or modifies it as appropriate.

[0534] Database Update

[0535] The server updates the database with the final vacation and task reassignment information, and the process is complete. The entire system is ready to start collecting data and assessing fatigue again.

[0536] Specific examples

[0537] As a concrete example of when User A's fatigue level is high, User A works 60 hours a week and the emotion engine recognizes that he or she is under high stress. When the server calculates the fatigue level score based on the accumulated data, the score reaches 80. The server then proposes paid leave for the following Friday and reassigns the tasks to Members B and C. A notification is sent to User A and the team, and their supervisor approves, allowing for smooth vacation acquisition and work adjustments.

[0538] In this way, the "Automatic Paid Leave Acquisition System" properly manages employee fatigue and emotional data and automatically suggests taking time off, thereby supporting efficient business operations and maintaining employee health.

[0539] The processing flow will be explained below.

[0540] Step 1:

[0541] Users input their work start time, end time, progress, and task completion status into their terminal. This data is sent to the server in real time. For example, if User A inputs 12 hours of work time and multiple completed tasks at the end of a day's work, the information is sent to the server immediately.

[0542] Step 2:

[0543] The emotion engine analyzes emotion data by capturing the user's facial expressions with a webcam, collecting voice tones with a microphone, and monitoring the user's text input. For example, if a user inputs keywords such as "tired" or "troubled" in an email or chat, the emotion engine will recognize this as stress.

[0544] Step 3:

[0545] The emotion engine sends collected emotion data to the server in real time. For example, if user A feels stressed during a meeting, it analyzes his facial expressions and tone of voice, and based on this, it recognizes the user as in a high-stress state and sends the data to the server.

[0546] Step 4:

[0547] The server receives task data and emotion data sent from the user device and emotion engine, and stores them in a database. The stored data includes task time, number of tasks, progress, and recognized emotion data.

[0548] Step 5:

[0549] The server analyzes the work data and emotional data stored in the database to calculate each user's fatigue level. The algorithm used to calculate fatigue levels takes into account the past week's work hours, number of tasks, progress, whether or not the user has taken a vacation, and emotional data. For example, a fatigue score of 80 is calculated for User A based on his or her 60-hour work week and high stress level.

[0550] Step 6:

[0551] The server evaluates the calculated fatigue score, and if it exceeds a reference value (for example, 70), it determines that the user needs a vacation. The server then proposes paid vacation to User A for the following Friday.

[0552] Step 7:

[0553] The server retrieves the task list of the user who has been decided to take a vacation from the database and reallocates the tasks to other members. When reallocating, it reevaluates each member's operating status and emotional data and adjusts the allocation so that the tasks are distributed fairly and efficiently. For example, user A's tasks are reallocated to members B and C.

[0554] Step 8:

[0555] The server notifies the new assignee of the reassigned task details via email or chat tool. Members B and C then check the notification on their respective devices.

[0556] Step 9:

[0557] The server notifies the user and their supervisor of the vacation date and the details of the reassigned tasks. A notification is displayed on the user's device, and a confirmation and approval request is sent to the supervisor's device.

[0558] Step 10:

[0559] The user can check the notification on their own device and adjust their vacation schedule if necessary, while the supervisor can check the approval request on their device and approve or modify it as appropriate.

[0560] Step 11:

[0561] The server updates the database with the final vacation and task reassignment information, and the process is complete. The entire system is ready to start collecting data and assessing fatigue again.

[0562] Through these steps, the "Automatic Paid Leave Acquisition System" properly manages employees' work data and emotional data, accurately calculates fatigue levels, suggests vacation time, and adjusts work schedules, thereby supporting efficient business operations and maintaining employee health.

[0563] Example 2

[0564] 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."

[0565] Properly managing employee fatigue levels and simultaneously achieving efficient business operations and maintaining employee health is a key challenge for many organizations. Conventional systems rely solely on work data to assess fatigue levels, making it difficult to consider employees' emotions and stress levels. This makes it difficult to grasp an employee's true fatigue level and make appropriate leave recommendations. Furthermore, the processes of task reassignment and notification must be done manually, which contributes to reduced organizational efficiency.

[0566] 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 means for collecting work data and emotion data of each member, means for accurately calculating the fatigue level of each member based on the work data and emotion data, means for automatically proposing paid vacation dates for a member whose fatigue level exceeds a certain standard, means for reassigning tasks to other members based on the proposed paid vacation dates, and means for sending notifications of the paid vacation dates and reassigned tasks to each member and their supervisor. This enables highly accurate fatigue level assessment that takes into account not only the employee's working time and task progress but also their emotion data, thereby automatically proposing appropriate vacations and efficiently reassigning tasks.

[0567] "Work data" refers to information such as the start time, end time, number of tasks, and progress of work performed by each member.

[0568] "Emotional data" refers to information about emotions collected from each member's facial expressions, voice tone, text input, etc., and is classified into emotional categories such as stress, joy, anger, and sadness.

[0569] "Fatigue level" refers to a score or index that indicates a member's fatigue state based on each member's work data and emotional data.

[0570] "Paid vacation dates" refers to the dates and periods for each member's paid vacation that the system automatically suggests.

[0571] "Reassigned tasks" refers to the work or tasks that are redistributed to other members when a highly fatigued member takes paid leave.

[0572] "Notification" means a communication sent by the system to each Member and their Manager containing the vacation proposal, details of the reassigned tasks, and any other necessary information.

[0573] "Server" refers to a central processing unit that collects, stores, and analyzes work data and emotional data, calculates fatigue levels, suggests vacations, reassigns tasks, and sends notifications.

[0574] "Algorithm" refers to the calculation procedures and rules for calculating a member's fatigue level with high accuracy based on work data and emotional data.

[0575] This invention relates to an "automated paid vacation acquisition system" that automatically suggests paid vacation to team members whose fatigue level exceeds a certain standard, adjusts work within the team, and calculates fatigue levels with high accuracy by taking into account the user's emotional data. This system is composed of a server, a user terminal, and an emotion engine, and operates as follows.

[0576] System Overview

[0577] The system collects each member's work data and emotional data, calculates their fatigue level based on this, and if the fatigue level exceeds a certain threshold, suggests that the member take paid leave and reassigns tasks to other members. Finally, it sends notifications of the leave schedule and reassigned tasks to the relevant members.

[0578] Hardware and software used

[0579] Server: A central processing unit that collects, stores, analyzes, and sends notifications.

[0580] User terminal: A device (e.g., PC, smartphone) for each member to input task data and emotion data.

[0581] Emotion Engine: Software and hardware that uses a webcam, microphone, and natural language processing algorithms to collect and classify emotion data.

[0582] Details of data processing and calculation

[0583] 1. Data entry on the user's device

[0584] Users input their work start time, end time, progress, and task completion status into the terminal, and this data is sent to the server in real time.

[0585] 2. Emotion Engine Data Acquisition

[0586] The emotion engine captures the user's facial expressions with a webcam, analyzes their voice tone with a microphone, and analyzes their text input with a natural language processing algorithm, all of which transmits the emotion data to a server in real time.

[0587] 3. Data collection and analysis on the server

[0588] The server receives the work data and emotion data sent from the user device and emotion engine, and stores them in a database. Based on the accumulated data, the server calculates the user's fatigue level using an algorithm that takes into account the work hours, number of tasks, progress, whether or not the user has taken a vacation, and emotion data from the past week.

[0589] 4. Proposing and scheduling paid leave

[0590] The server automatically suggests optimal vacation days for users whose calculated fatigue score exceeds a threshold, taking into account the overall team schedule and project progress.

[0591] 5. Reassignment of Work

[0592] The server retrieves the task list of the user who has been decided to take a vacation from the database and reassigns the tasks to other members. It evaluates the working status of each member and adjusts the distribution of tasks so that they are fair and efficient.

[0593] 6. Sending Notifications

[0594] The server notifies each member and their manager of the details of the reassigned tasks and the vacation proposal via email or chat tool.

[0595] Specific examples

[0596] As a concrete example of a case where User A has a high level of fatigue, User A works 60 hours a week and the emotion engine recognizes that he or she is under high stress. When the server calculates the fatigue score based on this data, the score reaches 80, and the server proposes paid leave for the following Friday. The server reassigns the task to Members B and C, a notification is sent to all members, and the supervisor approves the leave, allowing for smooth leave acquisition and work adjustments.

[0597] Prompt Sentence Examples

[0598] "If User A's fatigue level is high, please explain what kind of vacation suggestions and work adjustments will be made."

[0599] "Can you tell me more about what the emotion engine does and how it obtains data?"

[0600] The above is a specific embodiment for carrying out the invention.

[0601] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0602] System program processing steps

[0603] Step 1: Enter data on the user's device

[0604] Input: The user inputs their work start time, end time, progress, and degree of completion of the task into the terminal.

[0605] Data processing and calculation: The user terminal formats the input data and sends it to the server in real time. The input data is also temporarily stored in local storage.

[0606] Output: The terminal sends the formatted work data to the server and displays an input confirmation message to the user.

[0607] Specific operation: When a user enters a start time of work as 9:00 on Monday and an end time of work as 21:00, the data is sent to the server.

[0608] Step 2: Obtaining Data for the Emotion Engine

[0609] Input: The emotion engine collects data from the user's facial expressions, voice tone, and text input.

[0610] Data processing and calculation: The emotion engine analyzes the collected data and classifies it into emotion categories (e.g., stress, joy, anger, sadness) using natural language processing and image recognition algorithms.

[0611] Output: The classified emotion data is sent to the server.

[0612] Specific operation: The emotion engine analyzes the sentence "Today was a very stressful day" entered by the user and classifies it as stress. The result is sent to the server.

[0613] Step 3: Collecting and storing data on the server

[0614] Input: Task data and emotion data sent from the user terminal and emotion engine.

[0615] Data processing and calculation: The server stores the received data in a database. It also performs data integrity and validation.

[0616] Output: Task and emotion data stored in the server database are available for the next analysis step.

[0617] Specific operation: The server stores the work time data and emotion data of user A in a database.

[0618] Step 4: Calculate fatigue level

[0619] Input: Task data and emotion data stored on the server.

[0620] Data processing and calculation: The server inputs the past week's work hours, number of tasks, progress, whether or not vacation was taken, and emotional data into an algorithm to calculate a fatigue score.

[0621] Output: A fatigue score is calculated and used in the next vacation suggestion step.

[0622] Specific operation: A calculation is performed based on user A's work data and emotion data for one week, and the fatigue score is calculated as 80.

[0623] Step 5: Propose and schedule paid time off

[0624] Inputs: Fatigue score and overall team schedule and project progress.

[0625] Data processing and calculation: If the fatigue score exceeds a certain level (e.g., 70), the server will suggest a paid vacation date. The optimal vacation date will be selected taking into account the schedule of the entire team.

[0626] Output: A vacation proposal notification is generated and used in the next task reassignment step.

[0627] Specific operation: The server suggests to user A that he take paid vacation on the following Friday and generates a notification of this.

[0628] Step 6: Reassign tasks

[0629] Input: A list of tasks for users who have been confirmed as on vacation, and the availability data of each member.

[0630] Data processing and calculation: The server reassigns the tasks of the user who has been determined to be on vacation to other members fairly and efficiently. It evaluates the working status of each member and distributes them optimally.

[0631] Output: Details of the reassigned task are generated and used in the next notification step.

[0632] Specific behavior: User A's task is reassigned to members B and C.

[0633] Step 7: Sending notifications

[0634] Inputs: Reassigned task details and leave proposal notification.

[0635] Data processing and calculation: The server generates notifications containing details of the reassigned tasks and vacation proposals and sends them to each member and their manager via email or chat tool.

[0636] Output: Each member and their manager will receive a notification.

[0637] Specific behavior: The server sends a notification to Member B, Member C, and their superiors with details of the reassigned tasks and the vacation proposal.

[0638] The above is an explanation of the specific processing steps and their operations.

[0639] (Application example 2)

[0640] 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."

[0641] Conventional fatigue management systems calculated fatigue levels based solely on the work data of members, but this had the problem of making it difficult to accurately grasp the actual fatigue state of members. Furthermore, when fatigue levels increased, task redistribution often concentrated the burden on a few members without taking into account the impact on other members. Furthermore, in factories and other workplaces, work adjustments that take into account the timing of robot maintenance were necessary, but there was no system that could do this automatically.

[0642] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting each member's work data and emotion data, means for calculating the member's fatigue level based on the work data and emotion data, means for proposing paid vacation dates to a member whose fatigue level exceeds a certain threshold, means for reallocating tasks to other members based on the proposed paid vacation dates, means for sending notifications of the paid vacation dates and reallocated tasks, and means for automatically reallocating robot maintenance tasks to other engineers when the member's fatigue level exceeds a threshold. This enables highly accurate fatigue assessment that takes into account the member's actual emotional state, and by appropriately distributing the workload to other members, efficient and fair task allocation is possible. Furthermore, automatically adjusting the robot maintenance schedule can improve work efficiency within the factory.

[0643] "Members" refer to individuals, primarily employees and engineers, who provide work data and sentiment data.

[0644] "Task data" refers to data that includes information such as the start time, end time, progress, and degree of completion of a task.

[0645] "Emotion data" refers to emotional information such as stress, joy, anger, sadness, etc. obtained from the user's facial expression, voice tone, and character input.

[0646] "Fatigue level" is an index that indicates the degree of fatigue calculated based on the member's work data and emotional data.

[0647] "Paid leave" refers to a period of leave from work that is automatically offered to a member if they meet certain criteria.

[0648] "Task reassignment" refers to the process of reassigning tasks from a member whose fatigue level has exceeded a standard value to another member.

[0649] "Notification" is a means of communication to inform members and their superiors of proposed vacation dates and details of reassigned tasks.

[0650] "Data collection means" refers to a method or device for acquiring task data and emotion data from members.

[0651] "Data analysis means" refers to a method or device for calculating fatigue levels based on collected task data and emotion data.

[0652] "Proposal means" refers to a method or device for proposing paid vacation dates to members when their fatigue level exceeds a certain standard.

[0653] "Robot maintenance tasks" refer to work performed to maintain and repair robots in a factory.

[0654] The "reference value" refers to the threshold at which paid leave is suggested when fatigue levels are above a certain level.

[0655] This invention is an "automated paid vacation acquisition system" that collects work data and emotional data of members, calculates their fatigue level based on the data, and automatically suggests paid vacation if the fatigue level exceeds a certain threshold. This system is composed of a server, a user terminal, and an emotion engine.

[0656] System Overview

[0657] The system collects each member's work data and emotional data, calculates their fatigue level based on this, and if their fatigue level exceeds a certain threshold, suggests that they take paid leave. It also reassigns tasks to other members and automatically adjusts robot maintenance tasks. Finally, it sends notifications of vacation dates and reassigned tasks to the relevant members.

[0658] Server Program

[0659] The server receives each member's work data and emotional data and stores them in a database. The server uses data processing libraries (e.g., numpy, pandas) to analyze the collected data and calculate fatigue levels. Fatigue assessment uses an algorithm that takes into account the past week's work hours, number of tasks, progress, whether or not a member has taken vacation, and emotional data. If the fatigue score exceeds a threshold, the server suggests the member take paid vacation and reassigns tasks.

[0660] User terminal

[0661] The user terminal provides an interface for each member to input work data. Users input data such as the start time, end time, progress, and degree of task completion. In addition, emotional data (e.g., facial expressions, voice tone, and text input) is also collected through the interface provided by the emotion engine.

[0662] Emotion Engine

[0663] The emotion engine analyzes information such as members' facial expressions, voice tone, and text input, and classifies them into emotion categories such as stress, joy, anger, and sadness. This emotion data is sent to the server in real time. For example, services such as Emotion API are used for emotion analysis.

[0664] Specific examples

[0665] For example, suppose Engineer A works 60 consecutive hours a week and the emotion engine detects high stress levels. The server calculates the fatigue level based on this data and finds that the fatigue score exceeds the threshold. The server then suggests that the engineer take paid leave on Wednesday of the following week. The server then reassigns the tasks to Engineers B and C, sends notifications, and automatically assigns robot maintenance tasks to the other engineers.

[0666] Prompt Sentence Examples

[0667] "Please help me design a system that calculates an engineer's fatigue level based on their work data and emotional data, and suggests paid vacation time. Engineer A works 60 hours a week. Taking this into consideration, if his fatigue level is determined to be high, we need to suggest appropriate vacation time and reassign tasks to other engineers."

[0668] In this way, the "automated paid leave acquisition system" properly manages members' work data and emotional data, performs highly precise fatigue assessments, and automatically suggests leave, thereby achieving efficient business operations and maintaining members' health.

[0669] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0670] Step 1:

[0671] Data entry on user terminal:

[0672] Users use a smartphone or tablet to input their work start time, end time, progress, and degree of task completion. Emotional data is also input at the same time. For example, the user might provide information such as "Start time: 9:00, End time: 18:00, Task progress: 80%, Emotion: High stress." This data is sent to the server in real time. The output is the work data and emotional data sent to the server.

[0673] Step 2:

[0674] Emotion Engine Data Acquisition:

[0675] The emotion engine collects emotional data from the user's facial expressions, voice tone, and text input, and classifies it into emotional categories such as stress, joy, anger, and sadness. Image data of facial expressions, voice data, and text input data are provided as input. Specifically, analysis is performed using the Emotion API. Emotional data such as stress level and joy level is sent to the server as output.

[0676] Step 3:

[0677] Data collection and analysis on the server:

[0678] The server receives work data and emotion data sent from the user device and emotion engine and stores them in a database. The received data is used as input. Using a data processing library (e.g., numpy, pandas), fatigue levels are calculated based on this data. The past week's work hours, number of tasks, progress, whether or not someone has taken a vacation, and emotion data are weighted, and an overall fatigue score is calculated using an algorithm. The output is a fatigue score for each member.

[0679] Step 4:

[0680] Paid Time Off Proposals and Scheduling:

[0681] The server proposes paid leave to members whose fatigue score exceeds a certain threshold. The fatigue score is used as input. If the fatigue score exceeds, for example, 70, the server proposes paid leave on the most appropriate day of the following week. As output, a vacation proposal notification is generated for the relevant member.

[0682] Step 5:

[0683] Task reassignment:

[0684] The server retrieves the task list of the user who has decided to take a vacation from the database and reassigns the tasks to other members. The user's task data is used as input. When reassigning, adjustments are made to ensure that tasks are distributed efficiently, taking into account each member's current operating status. New task assignment data is generated as output.

[0685] Step 6:

[0686] Notification and confirmation:

[0687] The server notifies the new assignee of the reassigned task details. This notification is sent via email or chat tool. The new task assignment data is used as input. The notification is sent to each member as output.

[0688] Step 7:

[0689] Notification and Approval of Leave Proposals:

[0690] The server notifies the user and their supervisor of the vacation date and the details of the reassigned tasks. The server uses the vacation proposal notification data as input. As output, the notification is sent to the user and supervisor.

[0691] Step 8:

[0692] User and manager review and approval:

[0693] The user checks the notification on their own device and adjusts the vacation schedule if necessary. The supervisor also checks the approval request on their device and approves or modifies it as appropriate. The inputs are the vacation proposal notification and the task reassignment notification. The output is the confirmation and approval data.

[0694] Step 9:

[0695] Database update:

[0696] The server updates the database with the final leave and task reassignment decisions. The input is the confirmation and approval data. The output is the updated leave and task reassignment data. The whole system is ready to repeat the data collection and fatigue assessment again.

[0697] 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.

[0698] 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.

[0699] 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.

[0700] [Third embodiment]

[0701] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0702] 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.

[0703] 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).

[0704] 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.

[0705] 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.

[0706] 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).

[0707] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0708] 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.

[0709] 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.

[0710] 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.

[0711] 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.

[0712] 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."

[0713] This invention relates to an "automatic paid vacation acquisition system" that automatically suggests paid vacation to employees whose fatigue level exceeds a certain level and adjusts work within the team at that time. This system consists of a server and a user terminal, and operates as follows.

[0714] System Overview

[0715] The system collects each member's work data and calculates their fatigue level based on this data. If the fatigue level exceeds a certain threshold, the system will suggest paid leave for the member and reassign tasks to other members. Finally, it will send notifications of the leave schedule and reassigned tasks to the relevant members.

[0716] Program processing

[0717] Data entry on user terminals

[0718] Users input their work start time, end time, progress, and task completion level into the terminal. This data is sent to the server in real time. For example, if user A works for 12 hours every day from Monday to Friday and inputs that data into the terminal, the information is sent to the server.

[0719] Data collection and analysis on the server

[0720] The server collects work data sent from user devices in real time and stores it in a database. The server calculates the user's fatigue level based on the accumulated work data. The fatigue level is calculated using an algorithm that takes into account the work hours, number of tasks, progress, and whether or not the user has taken a vacation over the past week. For example, in the case of User A, a fatigue score of 80 is calculated based on work data for 60 hours per week.

[0721] Paid leave proposals and schedule adjustments

[0722] The server determines that User A's fatigue score exceeds the management standard value (for example, 70), so the system will suggest that he take paid leave on the following Friday. The server checks the schedule of the entire team and the progress of the project, and selects the optimal leave date.

[0723] Task reassignment

[0724] The server retrieves the task list of the user who has been decided to take a vacation and reassigns the tasks to other members. During the reassignment, the server reevaluates the working status of each member and adjusts the tasks so that they are distributed fairly and efficiently. For example, user A's tasks are reassigned to members B and C.

[0725] Notification and confirmation

[0726] The server notifies the user and their supervisor of the final vacation schedule and details of the reassigned tasks. The user checks the notification on their terminal and, if necessary, adjusts the vacation schedule or requests approval from their supervisor. This operation ensures smooth vacation planning.

[0727] Specific examples

[0728] As a concrete example of a case where User A has a high level of fatigue, User A works 60 hours a week, and the server calculates a fatigue score of 80 based on the accumulated data. The server then proposes paid vacation for the following Friday and reassigns the tasks to Members B and C. A notification is sent to User A and the team, and their supervisor approves, allowing for smooth vacation acquisition and work adjustments.

[0729] In this way, the "automated paid leave acquisition system" supports employee fatigue management and efficient business operations.

[0730] The processing flow will be explained below.

[0731] Step 1:

[0732] Users input their work start time, end time, progress, and task completion level into the terminal. This data is sent to the server in real time. For example, if User A works for 12 hours every day from Monday to Friday and inputs that data into the terminal, the information is sent to the server.

[0733] Step 2:

[0734] The server receives the task data sent from the user terminal. The received data is stored in a database. This data includes the task time, number of tasks, progress, etc.

[0735] Step 3:

[0736] The server analyzes the work data stored in the database and calculates each user's fatigue level. The fatigue level is calculated using an algorithm that takes into account the work hours, number of tasks, progress, and whether or not the user has taken a vacation in the past week. For example, a fatigue score of 80 is calculated for user A based on his work data of 60 hours per week.

[0737] Step 4:

[0738] The server evaluates each user's fatigue score and determines that the user needs a vacation if it exceeds a certain threshold (e.g., 70). The fatigue score is periodically recalculated to reflect the latest status.

[0739] Step 5:

[0740] The server will suggest paid vacation dates to users whose fatigue score exceeds a certain threshold. The server will check the team's overall schedule and project progress to select the optimal vacation date. For example, it will suggest paid vacation for User A on the following Friday.

[0741] Step 6:

[0742] The server retrieves the task list of the user who has been decided to take a vacation from the database and reassigns it to other members. When reassigning, it reevaluates the availability status of each member and adjusts the allocation so that the tasks are distributed fairly and efficiently. For example, user A's tasks are reassigned to members B and C.

[0743] Step 7:

[0744] The server notifies the new assignee of the reassigned task details via email or chat tool. Members B and C then check the notification on their respective devices.

[0745] Step 8:

[0746] The server notifies the user and their supervisor of the vacation date and the details of the reassigned tasks. A notification is displayed on the user's device, and a confirmation and approval request is sent to the supervisor's device.

[0747] Step 9:

[0748] The user checks the notification on their own device and adjusts their vacation schedule if necessary. The supervisor also checks the approval request on their device and approves or modifies it as appropriate.

[0749] Step 10:

[0750] The server updates the database with the final vacation and task reassignment information, and the process is complete. The entire system is ready to start collecting data and assessing fatigue again.

[0751] Through these steps, the "Automatic Paid Leave Acquisition System" properly manages employee fatigue levels and automatically suggests vacation time, thereby supporting efficient business operations and maintaining employee health.

[0752] Example 1

[0753] 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."

[0754] Managing employee fatigue levels is extremely important, but traditional methods make it difficult to accurately assess employee fatigue levels, making it difficult to suggest time off at the right time. Furthermore, rescheduling work when employees take time off is often done manually, which requires additional effort and time.

[0755] 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.

[0756] In this invention, the server includes means for users to input work start times, end times, progress, and task completion levels, means for collecting the input data in real time and storing it in a database, means for calculating users' fatigue levels based on the data stored in the database, means for proposing paid vacation dates to users whose fatigue levels exceed a certain standard, means for reallocating tasks to other members based on the proposed paid vacation dates, and means for notifying details of the paid vacation dates and reallocated tasks. This allows for real-time management of employee fatigue levels, making it possible to propose vacations and readjust work at appropriate times.

[0757] A "user" is a person who accesses the system and inputs work data.

[0758] "Work start time" refers to the time when the user starts work.

[0759] "End time" refers to the time when the user finishes their work.

[0760] "Progress" refers to the degree of completion or progress of the tasks for which a user is responsible.

[0761] The "degree of task completion" is an index that indicates the degree to which the task assigned to the user has been completed.

[0762] A "terminal" refers to an information input device such as a computer or mobile device used by a user.

[0763] A "database" is a structured collection of information that a server uses to store and manage data collected by the server.

[0764] A "server" is a computer system that receives, stores, and analyzes data sent from a user terminal.

[0765] "Fatigue level" is a numerical representation of the user's workload and level of fatigue.

[0766] "Real-time" means that data is processed and transmitted the moment it is generated.

[0767] "Paid leave" refers to paid leave granted to employees by their employer.

[0768] "Dates" refer to the specific dates and times when paid leave will be taken.

[0769] "Reassignment" refers to the act of allocating the work of a user who is on vacation to another member.

[0770] "Notification" refers to the means by which the system communicates information to users and their superiors about paid leave and reassignment matters.

[0771] The present invention relates to a system for managing employee fatigue levels and appropriately proposing paid vacations. This system uses a server and user terminals to collect work data, calculate fatigue levels, suggest paid vacations, reassign tasks, and notify users.

[0772] Users input their work start time, finish time, progress, and task completion status into a terminal. The terminal used by the user is an information input device such as a computer or mobile device. The data entered by the user is sent to the server in real time.

[0773] The server receives the work data sent from the user terminal and stores it in a database. The server can use a distributed database such as Apache Cassandra. The server calculates each user's fatigue level based on the data stored in the database. This fatigue level calculation uses an algorithm that takes into account the work hours, number of tasks, progress, and whether or not the user has taken a vacation over the past week.

[0774] The server suggests paid vacation dates if the user's fatigue score exceeds a certain standard. Specifically, if the user's fatigue score exceeds 70, for example, it suggests taking paid vacation on the following Friday. Based on this suggestion, the server uses a project management tool (e.g., Microsoft Project) to check the schedule and project progress of the entire team and reassign tasks to other members.

[0775] When reassigning tasks, the server reevaluates each member's availability and adjusts the distribution so that tasks are distributed fairly and efficiently. As a result of the reassignment, all relevant members are notified of their vacation and new task schedules. Notifications are sent via a mail server (e.g., Postfix, Microsoft Exchange Server) or a real-time communication tool (e.g., Slack, Microsoft Teams). Users can check the notifications on their devices and, if necessary, rearrange their vacation schedules or seek approval from their superiors.

[0776] As a concrete example, if User A works 12 hours a day from Monday to Friday, he or she enters that data into the terminal, and the server calculates a fatigue score of 80 based on that data. The server then proposes paid leave for the following Friday and reassigns User A's tasks to Members B and C. A notification is then sent to User A and his or her supervisor, who then approves the leave. This system enables employee fatigue management and efficient business operations.

[0777] Examples of prompt statements

[0778] "User A worked 12 hours a day from Monday to Friday and entered that data into a terminal. The server analyzed the data and calculated User A's fatigue score as 80. Because this score exceeds the reference value, the server suggested that User A take paid leave on the following Friday and reassigned the task to another member. The system notified all relevant members, and the supervisor approved the leave. Please explain the detailed process of this system."

[0779] In this way, the "automated paid leave acquisition system" enables employee fatigue management and efficient business operations.

[0780] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0781] Step 1:

[0782] Users input their work start time, end time, progress, and task completion status into the terminal. The input data is sent to the server in real time. For example, if a user works for 8 hours on Monday, the start time, end time, and progress status are entered.

[0783] Inputs: Start time, end time, progress, and task completion rate

[0784] Output: Working data sent to the server

[0785] Step 2:

[0786] The server receives the work data sent from the user terminal in real time and stores it in a database. The data is stored using a distributed database such as Apache Cassandra. The server analyzes the received data and stores it while maintaining consistency.

[0787] Input: Work data sent from the user's terminal

[0788] Output: Working data stored in a database

[0789] Step 3:

[0790] The server calculates the user's fatigue level based on the data stored in the database, using an algorithm that takes into account the amount of time worked in the past week, the number of tasks, progress, and whether or not the user has taken a vacation. For example, a Python script is used to perform the analysis and calculate the fatigue score.

[0791] Input: Work data from the past week stored in the database

[0792] Output: Calculated fatigue score

[0793] Step 4:

[0794] If the user's fatigue score exceeds a certain standard (e.g., 70), the server suggests a paid vacation date. For example, it suggests a vacation date on Friday of the following week. Specifically, it adjusts the schedule using a project management tool.

[0795] Input: Calculated fatigue score

[0796] Output: Proposed vacation dates

[0797] Step 5:

[0798] The server reassigns tasks to other members based on the proposed paid vacation dates. It reevaluates each member's availability based on Use Case Diagrams, Gantt Charts, etc., and allocates tasks efficiently. For example, user A's tasks are reassigned to members B and C.

[0799] Input: Proposed vacation dates

[0800] Output: Reassigned tasks

[0801] Step 6:

[0802] The server notifies the user and their manager of the vacation date and the details of the reassigned tasks using a mail server or real-time communication tool, such as Postfix or Slack.

[0803] Input: Paid time off dates, reassigned task details

[0804] Output: Notification sent

[0805] Specifically, the user inputs work data into the device, which is then sent to the server, where it is saved, analyzed, and the fatigue level is calculated. If the fatigue level exceeds the standard, the server suggests the next appropriate day off, reallocates work, and notifies relevant parties. This series of processes enables user fatigue management and efficient work operations.

[0806] (Application example 1)

[0807] 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."

[0808] Employee fatigue management is an important issue for labor efficiency and safety, but manual monitoring and management is time-consuming and difficult to respond to in real time. Proposing paid leave and reassigning tasks based on fatigue levels is also cumbersome. Therefore, there is a need for a system that can calculate employee fatigue levels in real time, suggest appropriate leave, and smoothly reorganize work. This problem is particularly pronounced in workplaces where workers, such as factory operators, are under heavy strain and require automatic task reassignment.

[0809] 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.

[0810] In this invention, the server includes a means for collecting work data of each member, a means for calculating the fatigue level of each member based on the work data, a means for proposing paid vacation dates for members whose fatigue level exceeds a certain standard, a means for reallocating tasks to other members or automated machines based on the proposed paid vacation dates, a means for sending notifications of the paid vacation dates and reallocated tasks, and a means including an algorithm for collecting user work data in real time and calculating fatigue levels. This allows for real-time monitoring of employee fatigue levels, appropriate paid vacation suggestions, and efficient work reorganization.

[0811] "Members" are users whose activity data is collected by the system, and are individuals or automated entities responsible for certain tasks.

[0812] "Work data" is data containing information about work performed by members, including work time, number of tasks, and progress.

[0813] "Fatigue level" is an index calculated based on the work data of a member, and is a value that indicates the degree of fatigue of the member.

[0814] The "certain standard" is a fatigue threshold set by the system, and if this value is exceeded, paid leave will be suggested.

[0815] "Paid vacation dates" are vacation dates proposed to members whose fatigue levels exceed a certain threshold.

[0816] "Task reassignment" refers to the act of redistributing presence tasks to other members or automated machines based on the proposed paid vacation dates.

[0817] "Notification" means information sent by the system to members and administrators, including details about vacation dates and reassigned tasks.

[0818] An "algorithm" is a calculation procedure for solving a specific problem, in this case a method for calculating fatigue levels based on work data.

[0819] This invention is a "smart fatigue management system" that collects work data from operators working in factories and calculates their fatigue levels in real time. If an operator's fatigue level exceeds a certain standard, the system automatically suggests paid leave and reallocates work. Specifically, it consists of a server and a user terminal.

[0820] Hardware and software used

[0821] Hardware: Smartphones, PCs, Tablets

[0822] Software: Python 3.x

[0823] Detailed explanation of data processing and calculation

[0824] Data entry on user terminals

[0825] The user (operator) inputs the start time, end time, and task progress into the terminal. This data is sent to the server in real time. For example, if an operator works for 12 hours, that information is updated in real time to the server.

[0826] Data collection and analysis on the server

[0827] The server collects work data sent from user devices in real time and stores it in a database. The server calculates the operator's fatigue level based on the accumulated work data. To calculate fatigue, an algorithm is used that takes into account the work hours, number of tasks, progress, and whether or not the operator has taken a vacation in the past week. For example, in the case of Operator A, the fatigue score is calculated from the accumulated work hours over the past week.

[0828] Paid leave proposals and schedule adjustments

[0829] The server then suggests paid vacation dates for operators whose fatigue scores exceed a certain threshold, and then checks the schedules and project progress of the entire team to select the optimal vacation dates.

[0830] Task reassignment

[0831] The server reallocates the tasks of the operator who is scheduled to take a vacation to other members or automated machines. When reallocating, it reevaluates the availability of each member and adjusts the allocation to distribute tasks fairly or efficiently. For example, Operator A's tasks are reallocated to two other operators, Operator B and Operator C.

[0832] Notification and confirmation

[0833] The server notifies the operator and their manager of the final vacation schedule and details of the reassigned tasks. The operator checks the notification on their terminal and, if necessary, adjusts the vacation schedule or seeks approval from the manager. This ensures smooth vacation planning.

[0834] Specific examples

[0835] If Operator A works 60 hours in a week, the server calculates a fatigue score based on the accumulated data, and since it exceeds the standard value, it proposes paid leave for the following Friday and reassigns tasks to Members B and C. A notification is sent to Operator A and the team, and the manager approves, allowing for smooth vacation acquisition and work adjustments.

[0836] Prompt Sentence Examples

[0837] Create an application that calculates the fatigue level of a factory robot operator when the specified working hours are exceeded, and suggests taking paid leave for the next day if the fatigue level exceeds the threshold. Also, explain the structure of the program that automatically reassigns tasks and notifies the operator when this happens.

[0838] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0839] Step 1: Data entry and collection

[0840] Users (operators) input their work start time, end time, and task progress status into their terminals. The input data is sent to the server in real time. The input data includes the work start time, end time, work content, and progress status. The server receives this data and saves it in a database. This accumulates the work data of each operator.

[0841] Step 2: Calculate fatigue

[0842] The server calculates fatigue levels based on the collected work data using an algorithm that includes the work hours, number of tasks, progress, and whether or not a vacation was taken over the past week. Specifically, the server extracts each piece of work data, adds up the work hours, and calculates a fatigue score taking into account the number of tasks and progress. For example, if the total work hours for the week is 60 hours, a fatigue score is calculated.

[0843] Step 3: Propose paid time off

[0844] If the calculated fatigue score exceeds a certain threshold (for example, 70), the server will suggest a paid vacation date for the relevant operator. The server checks the overall schedule and project progress and automatically selects the optimal vacation date. For example, if the fatigue score exceeds the threshold at 80, the server will suggest the next Friday as a paid vacation date.

[0845] Step 4: Reassign tasks

[0846] The server obtains the list of tasks for the operator who has been decided to take paid leave and reallocates the tasks to other operators or automated machines. When reallocating tasks, the server reevaluates the operating status of each operator and adjusts the allocation to ensure fair and efficient task distribution. For example, if Operator A's tasks are reallocated to Operator B and Operator C, the server will reallocate the tasks taking into account the operating status of each operator.

[0847] Step 5: Notification

[0848] The server notifies the operator and his / her manager of the determined paid vacation date and details of the reassigned tasks. The notification includes the vacation date, details of the reassigned tasks, and a list of the reassigned operators. The notification is sent to the terminal, allowing the operator to confirm their vacation and, if necessary, request approval.

[0849] Through these steps, the system can efficiently manage operator fatigue, allowing them to take time off and adjust their work schedules at the right time.

[0850] 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.

[0851] This invention relates to an "automated paid vacation acquisition system" that automatically suggests paid vacation to employees whose fatigue level exceeds a certain standard, adjusts work within the team at that time, and accurately calculates fatigue levels by taking into account the user's emotional data. This system is composed of a server, a user terminal, and an emotion engine, and operates as follows.

[0852] System Overview

[0853] The system collects each member's work data and emotional data, calculates their fatigue level based on this, and if the fatigue level exceeds a certain threshold, suggests that the member take paid leave and reassigns tasks to other members. Finally, it sends notifications of the leave schedule and reassigned tasks to the relevant members.

[0854] Program processing

[0855] Data entry on user terminals

[0856] Users input their work start time, end time, progress, and task completion level into the terminal. This data is sent to the server in real time. For example, if user A works for 12 hours every day from Monday to Friday and inputs that data into the terminal, the information is sent to the server.

[0857] Emotion Engine Data Acquisition

[0858] The emotion engine collects information such as the user's facial expressions, voice tone, and text input, and classifies them into emotion categories such as stress, joy, anger, sadness, etc. This emotion data is also sent to the server in real time.

[0859] Data collection and analysis on the server

[0860] The server receives the work data and emotion data sent from the user device and emotion engine and stores them in a database. The server calculates the user's fatigue level based on the accumulated data. The fatigue level is calculated using an algorithm that takes into account the work hours, number of tasks, progress, whether or not the user has taken a vacation, and emotion data from the past week. For example, in the case of User A, the fatigue level score is calculated based on the work data and emotion data for 60 hours per week.

[0861] Paid leave proposals and schedule adjustments

[0862] The server determines that User A's fatigue score exceeds the management standard value (for example, 70), so the system will suggest that he take paid leave on the following Friday. The server checks the schedule of the entire team and the progress of the project, and selects the optimal leave date.

[0863] Task reassignment

[0864] The server retrieves the task list of the user who has been decided to take a vacation from the database and reassigns the tasks to other members. When reassigning, it reevaluates the availability status of each member and adjusts the distribution of tasks so that they are fair and efficient. For example, user A's tasks are reassigned to members B and C.

[0865] Notification and confirmation

[0866] The server notifies the new assignee of the reassigned task details via email or chat tool. Members B and C then check the notification on their respective devices.

[0867] Notification and approval of leave proposals

[0868] The server notifies the user and their supervisor of the vacation date and the details of the reassigned tasks. A notification is displayed on the user's device, and a confirmation and approval request is sent to the supervisor's device.

[0869] User and supervisor review and approval

[0870] The user checks the notification on their own device and adjusts their vacation schedule if necessary. The supervisor also checks the approval request on their device and approves or modifies it as appropriate.

[0871] Database Update

[0872] The server updates the database with the final vacation and task reassignment information, and the process is complete. The entire system is ready to start collecting data and assessing fatigue again.

[0873] Specific examples

[0874] As a concrete example of when User A's fatigue level is high, User A works 60 hours a week and the emotion engine recognizes that he or she is under high stress. When the server calculates the fatigue level score based on the accumulated data, the score reaches 80. The server then proposes paid leave for the following Friday and reassigns the tasks to Members B and C. A notification is sent to User A and the team, and their supervisor approves, allowing for smooth vacation acquisition and work adjustments.

[0875] In this way, the "Automatic Paid Leave Acquisition System" properly manages employee fatigue and emotional data and automatically suggests taking time off, thereby supporting efficient business operations and maintaining employee health.

[0876] The processing flow will be explained below.

[0877] Step 1:

[0878] Users input their work start time, end time, progress, and task completion status into their terminal. This data is sent to the server in real time. For example, if User A inputs 12 hours of work time and multiple completed tasks at the end of a day's work, the information is sent to the server immediately.

[0879] Step 2:

[0880] The emotion engine analyzes emotion data by capturing the user's facial expressions with a webcam, collecting voice tones with a microphone, and monitoring the user's text input. For example, if a user inputs keywords such as "tired" or "troubled" in an email or chat, the emotion engine will recognize this as stress.

[0881] Step 3:

[0882] The emotion engine sends collected emotion data to the server in real time. For example, if user A feels stressed during a meeting, it analyzes his facial expressions and tone of voice, and based on this, it recognizes the user as in a high-stress state and sends the data to the server.

[0883] Step 4:

[0884] The server receives task data and emotion data sent from the user device and emotion engine, and stores them in a database. The stored data includes task time, number of tasks, progress, and recognized emotion data.

[0885] Step 5:

[0886] The server analyzes the work data and emotional data stored in the database to calculate each user's fatigue level. The algorithm used to calculate fatigue levels takes into account the past week's work hours, number of tasks, progress, whether or not the user has taken a vacation, and emotional data. For example, a fatigue score of 80 is calculated for User A based on his or her 60-hour work week and high stress level.

[0887] Step 6:

[0888] The server evaluates the calculated fatigue score, and if it exceeds a reference value (for example, 70), it determines that the user needs a vacation. The server then proposes paid vacation to User A for the following Friday.

[0889] Step 7:

[0890] The server retrieves the task list of the user who has been decided to take a vacation from the database and reallocates the tasks to other members. When reallocating, it reevaluates each member's operating status and emotional data and adjusts the allocation so that the tasks are distributed fairly and efficiently. For example, user A's tasks are reallocated to members B and C.

[0891] Step 8:

[0892] The server notifies the new assignee of the reassigned task details via email or chat tool. Members B and C then check the notification on their respective devices.

[0893] Step 9:

[0894] The server notifies the user and their supervisor of the vacation date and the details of the reassigned tasks. A notification is displayed on the user's device, and a confirmation and approval request is sent to the supervisor's device.

[0895] Step 10:

[0896] The user can check the notification on their own device and adjust their vacation schedule if necessary, while the supervisor can check the approval request on their device and approve or modify it as appropriate.

[0897] Step 11:

[0898] The server updates the database with the final vacation and task reassignment information, and the process is complete. The entire system is ready to start collecting data and assessing fatigue again.

[0899] Through these steps, the "Automatic Paid Leave Acquisition System" properly manages employees' work data and emotional data, accurately calculates fatigue levels, suggests vacation time, and adjusts work schedules, thereby supporting efficient business operations and maintaining employee health.

[0900] Example 2

[0901] 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."

[0902] Properly managing employee fatigue levels and simultaneously achieving efficient business operations and maintaining employee health is a key challenge for many organizations. Conventional systems rely solely on work data to assess fatigue levels, making it difficult to consider employees' emotions and stress levels. This makes it difficult to grasp an employee's true fatigue level and make appropriate leave recommendations. Furthermore, the processes of task reassignment and notification must be done manually, which contributes to reduced organizational efficiency.

[0903] 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 means for collecting work data and emotion data of each member, means for accurately calculating the fatigue level of each member based on the work data and emotion data, means for automatically proposing paid vacation dates for a member whose fatigue level exceeds a certain standard, means for reassigning tasks to other members based on the proposed paid vacation dates, and means for sending notifications of the paid vacation dates and reassigned tasks to each member and their supervisor. This enables highly accurate fatigue level assessment that takes into account not only the employee's working time and task progress but also their emotion data, thereby automatically proposing appropriate vacations and efficiently reassigning tasks.

[0904] "Work data" refers to information such as the start time, end time, number of tasks, and progress of work performed by each member.

[0905] "Emotional data" refers to information about emotions collected from each member's facial expressions, voice tone, text input, etc., and is classified into emotional categories such as stress, joy, anger, and sadness.

[0906] "Fatigue level" refers to a score or index that indicates a member's fatigue state based on each member's work data and emotional data.

[0907] "Paid vacation dates" refers to the dates and periods for each member's paid vacation that the system automatically suggests.

[0908] "Reassigned tasks" refers to the work or tasks that are redistributed to other members when a highly fatigued member takes paid leave.

[0909] "Notification" means a communication sent by the system to each Member and their Manager containing the vacation proposal, details of the reassigned tasks, and any other necessary information.

[0910] "Server" refers to a central processing unit that collects, stores, and analyzes work data and emotional data, calculates fatigue levels, suggests vacations, reassigns tasks, and sends notifications.

[0911] "Algorithm" refers to the calculation procedures and rules for calculating a member's fatigue level with high accuracy based on work data and emotional data.

[0912] This invention relates to an "automated paid vacation acquisition system" that automatically suggests paid vacation to team members whose fatigue level exceeds a certain standard, adjusts work within the team, and calculates fatigue levels with high accuracy by taking into account the user's emotional data. This system is composed of a server, a user terminal, and an emotion engine, and operates as follows.

[0913] System Overview

[0914] The system collects each member's work data and emotional data, calculates their fatigue level based on this, and if the fatigue level exceeds a certain threshold, suggests that the member take paid leave and reassigns tasks to other members. Finally, it sends notifications of the leave schedule and reassigned tasks to the relevant members.

[0915] Hardware and software used

[0916] Server: A central processing unit that collects, stores, analyzes, and sends notifications.

[0917] User terminal: A device (e.g., PC, smartphone) for each member to input task data and emotion data.

[0918] Emotion Engine: Software and hardware that uses a webcam, microphone, and natural language processing algorithms to collect and classify emotion data.

[0919] Details of data processing and calculation

[0920] 1. Data entry on the user's device

[0921] Users input their work start time, end time, progress, and task completion status into the terminal, and this data is sent to the server in real time.

[0922] 2. Emotion Engine Data Acquisition

[0923] The emotion engine captures the user's facial expressions with a webcam, analyzes their voice tone with a microphone, and analyzes their text input with a natural language processing algorithm, all of which transmits the emotion data to a server in real time.

[0924] 3. Data collection and analysis on the server

[0925] The server receives the work data and emotion data sent from the user device and emotion engine, and stores them in a database. Based on the accumulated data, the server calculates the user's fatigue level using an algorithm that takes into account the work hours, number of tasks, progress, whether or not the user has taken a vacation, and emotion data from the past week.

[0926] 4. Proposing and scheduling paid leave

[0927] The server automatically suggests optimal vacation days for users whose calculated fatigue score exceeds a threshold, taking into account the overall team schedule and project progress.

[0928] 5. Reassignment of Work

[0929] The server retrieves the task list of the user who has been decided to take a vacation from the database and reassigns the tasks to other members. It evaluates the working status of each member and adjusts the distribution of tasks so that they are fair and efficient.

[0930] 6. Sending Notifications

[0931] The server notifies each member and their manager of the details of the reassigned tasks and the vacation proposal via email or chat tool.

[0932] Specific examples

[0933] As a concrete example of a case where User A has a high level of fatigue, User A works 60 hours a week and the emotion engine recognizes that he or she is under high stress. When the server calculates the fatigue score based on this data, the score reaches 80, and the server proposes paid leave for the following Friday. The server reassigns the task to Members B and C, a notification is sent to all members, and the supervisor approves the leave, allowing for smooth leave acquisition and work adjustments.

[0934] Prompt Sentence Examples

[0935] "If User A's fatigue level is high, please explain what kind of vacation suggestions and work adjustments will be made."

[0936] "Can you tell me more about what the emotion engine does and how it obtains data?"

[0937] The above is a specific embodiment for carrying out the invention.

[0938] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0939] System program processing steps

[0940] Step 1: Enter data on the user's device

[0941] Input: The user inputs their work start time, end time, progress, and degree of completion of the task into the terminal.

[0942] Data processing and calculation: The user terminal formats the input data and sends it to the server in real time. The input data is also temporarily stored in local storage.

[0943] Output: The terminal sends the formatted work data to the server and displays an input confirmation message to the user.

[0944] Specific operation: When a user enters a start time of work as 9:00 on Monday and an end time of work as 21:00, the data is sent to the server.

[0945] Step 2: Obtaining Data for the Emotion Engine

[0946] Input: The emotion engine collects data from the user's facial expressions, voice tone, and text input.

[0947] Data processing and calculation: The emotion engine analyzes the collected data and classifies it into emotion categories (e.g., stress, joy, anger, sadness) using natural language processing and image recognition algorithms.

[0948] Output: The classified emotion data is sent to the server.

[0949] Specific operation: The emotion engine analyzes the sentence "Today was a very stressful day" entered by the user and classifies it as stress. The result is sent to the server.

[0950] Step 3: Collecting and storing data on the server

[0951] Input: Task data and emotion data sent from the user terminal and emotion engine.

[0952] Data processing and calculation: The server stores the received data in a database. It also performs data integrity and validation.

[0953] Output: Task and emotion data stored in the server database are available for the next analysis step.

[0954] Specific operation: The server stores the work time data and emotion data of user A in a database.

[0955] Step 4: Calculate fatigue level

[0956] Input: Task data and emotion data stored on the server.

[0957] Data processing and calculation: The server inputs the past week's work hours, number of tasks, progress, whether or not vacation was taken, and emotional data into an algorithm to calculate a fatigue score.

[0958] Output: A fatigue score is calculated and used in the next vacation suggestion step.

[0959] Specific operation: A calculation is performed based on user A's work data and emotion data for one week, and the fatigue score is calculated as 80.

[0960] Step 5: Propose and schedule paid time off

[0961] Inputs: Fatigue score and overall team schedule and project progress.

[0962] Data processing and calculation: If the fatigue score exceeds a certain level (e.g., 70), the server will suggest a paid vacation date. The optimal vacation date will be selected taking into account the schedule of the entire team.

[0963] Output: A vacation proposal notification is generated and used in the next task reassignment step.

[0964] Specific operation: The server suggests to user A that he take paid vacation on the following Friday and generates a notification of this.

[0965] Step 6: Reassign tasks

[0966] Input: A list of tasks for users who have been confirmed as on vacation, and the availability data of each member.

[0967] Data processing and calculation: The server reassigns the tasks of the user who has been determined to be on vacation to other members fairly and efficiently. It evaluates the working status of each member and distributes them optimally.

[0968] Output: Details of the reassigned task are generated and used in the next notification step.

[0969] Specific behavior: User A's task is reassigned to members B and C.

[0970] Step 7: Sending notifications

[0971] Inputs: Reassigned task details and leave proposal notification.

[0972] Data processing and calculation: The server generates notifications containing details of the reassigned tasks and vacation proposals and sends them to each member and their manager via email or chat tool.

[0973] Output: Each member and their manager will receive a notification.

[0974] Specific behavior: The server sends a notification to Member B, Member C, and their superiors with details of the reassigned tasks and the vacation proposal.

[0975] The above is an explanation of the specific processing steps and their operations.

[0976] (Application example 2)

[0977] 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."

[0978] Conventional fatigue management systems calculated fatigue levels based solely on the work data of members, but this had the problem of making it difficult to accurately grasp the actual fatigue state of members. Furthermore, when fatigue levels increased, task redistribution often concentrated the burden on a few members without taking into account the impact on other members. Furthermore, in factories and other workplaces, work adjustments that take into account the timing of robot maintenance were necessary, but there was no system that could do this automatically.

[0979] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting each member's work data and emotion data, means for calculating the member's fatigue level based on the work data and emotion data, means for proposing paid vacation dates to a member whose fatigue level exceeds a certain threshold, means for reallocating tasks to other members based on the proposed paid vacation dates, means for sending notifications of the paid vacation dates and reallocated tasks, and means for automatically reallocating robot maintenance tasks to other engineers when the member's fatigue level exceeds a threshold. This enables highly accurate fatigue assessment that takes into account the member's actual emotional state, and by appropriately distributing the workload to other members, efficient and fair task allocation is possible. Furthermore, automatically adjusting the robot maintenance schedule can improve work efficiency within the factory.

[0980] "Members" refer to individuals, primarily employees and engineers, who provide work data and sentiment data.

[0981] "Task data" refers to data that includes information such as the start time, end time, progress, and degree of completion of a task.

[0982] "Emotion data" refers to emotional information such as stress, joy, anger, sadness, etc. obtained from the user's facial expression, voice tone, and character input.

[0983] "Fatigue level" is an index that indicates the degree of fatigue calculated based on the member's work data and emotional data.

[0984] "Paid leave" refers to a period of leave from work that is automatically offered to a member if they meet certain criteria.

[0985] "Task reassignment" refers to the process of reassigning tasks from a member whose fatigue level has exceeded a standard value to another member.

[0986] "Notification" is a means of communication to inform members and their superiors of proposed vacation dates and details of reassigned tasks.

[0987] "Data collection means" refers to a method or device for acquiring task data and emotion data from members.

[0988] "Data analysis means" refers to a method or device for calculating fatigue levels based on collected task data and emotion data.

[0989] "Proposal means" refers to a method or device for proposing paid vacation dates to members when their fatigue level exceeds a certain standard.

[0990] "Robot maintenance tasks" refer to work performed to maintain and repair robots in a factory.

[0991] The "reference value" refers to the threshold at which paid leave is suggested when fatigue levels are above a certain level.

[0992] This invention is an "automated paid vacation acquisition system" that collects work data and emotional data of members, calculates their fatigue level based on the data, and automatically suggests paid vacation if the fatigue level exceeds a certain threshold. This system is composed of a server, a user terminal, and an emotion engine.

[0993] System Overview

[0994] The system collects each member's work data and emotional data, calculates their fatigue level based on this, and if their fatigue level exceeds a certain threshold, suggests that they take paid leave. It also reassigns tasks to other members and automatically adjusts robot maintenance tasks. Finally, it sends notifications of vacation dates and reassigned tasks to the relevant members.

[0995] Server Program

[0996] The server receives each member's work data and emotional data and stores them in a database. The server uses data processing libraries (e.g., numpy, pandas) to analyze the collected data and calculate fatigue levels. Fatigue assessment uses an algorithm that takes into account the past week's work hours, number of tasks, progress, whether or not a member has taken vacation, and emotional data. If the fatigue score exceeds a threshold, the server suggests the member take paid vacation and reassigns tasks.

[0997] User terminal

[0998] The user terminal provides an interface for each member to input work data. Users input data such as the start time, end time, progress, and degree of task completion. In addition, emotional data (e.g., facial expressions, voice tone, and text input) is also collected through the interface provided by the emotion engine.

[0999] Emotion Engine

[1000] The emotion engine analyzes information such as members' facial expressions, voice tone, and text input, and classifies them into emotion categories such as stress, joy, anger, and sadness. This emotion data is sent to the server in real time. For example, services such as Emotion API are used for emotion analysis.

[1001] Specific examples

[1002] For example, suppose Engineer A works 60 consecutive hours a week and the emotion engine detects high stress levels. The server calculates the fatigue level based on this data and finds that the fatigue score exceeds the threshold. The server then suggests that the engineer take paid leave on Wednesday of the following week. The server then reassigns the tasks to Engineers B and C, sends notifications, and automatically assigns robot maintenance tasks to the other engineers.

[1003] Prompt Sentence Examples

[1004] "Please help me design a system that calculates an engineer's fatigue level based on their work data and emotional data, and suggests paid vacation time. Engineer A works 60 hours a week. Taking this into consideration, if his fatigue level is determined to be high, we need to suggest appropriate vacation time and reassign tasks to other engineers."

[1005] In this way, the "automated paid leave acquisition system" properly manages members' work data and emotional data, performs highly precise fatigue assessments, and automatically suggests leave, thereby achieving efficient business operations and maintaining members' health.

[1006] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1007] Step 1:

[1008] Data entry on user terminal:

[1009] Users use a smartphone or tablet to input their work start time, end time, progress, and degree of task completion. Emotional data is also input at the same time. For example, the user might provide information such as "Start time: 9:00, End time: 18:00, Task progress: 80%, Emotion: High stress." This data is sent to the server in real time. The output is the work data and emotional data sent to the server.

[1010] Step 2:

[1011] Emotion Engine Data Acquisition:

[1012] The emotion engine collects emotional data from the user's facial expressions, voice tone, and text input, and classifies it into emotional categories such as stress, joy, anger, and sadness. Image data of facial expressions, voice data, and text input data are provided as input. Specifically, analysis is performed using the Emotion API. Emotional data such as stress level and joy level is sent to the server as output.

[1013] Step 3:

[1014] Data collection and analysis on the server:

[1015] The server receives work data and emotion data sent from the user device and emotion engine and stores them in a database. The received data is used as input. Using a data processing library (e.g., numpy, pandas), fatigue levels are calculated based on this data. The past week's work hours, number of tasks, progress, whether or not someone has taken a vacation, and emotion data are weighted, and an overall fatigue score is calculated using an algorithm. The output is a fatigue score for each member.

[1016] Step 4:

[1017] Paid Time Off Proposals and Scheduling:

[1018] The server proposes paid leave to members whose fatigue score exceeds a certain threshold. The fatigue score is used as input. If the fatigue score exceeds, for example, 70, the server proposes paid leave on the most appropriate day of the following week. As output, a vacation proposal notification is generated for the relevant member.

[1019] Step 5:

[1020] Task reassignment:

[1021] The server retrieves the task list of the user who has decided to take a vacation from the database and reassigns the tasks to other members. The user's task data is used as input. When reassigning, adjustments are made to ensure that tasks are distributed efficiently, taking into account each member's current operating status. New task assignment data is generated as output.

[1022] Step 6:

[1023] Notification and confirmation:

[1024] The server notifies the new assignee of the reassigned task details. This notification is sent via email or chat tool. The new task assignment data is used as input. The notification is sent to each member as output.

[1025] Step 7:

[1026] Notification and Approval of Leave Proposals:

[1027] The server notifies the user and their supervisor of the vacation date and the details of the reassigned tasks. The server uses the vacation proposal notification data as input. As output, the notification is sent to the user and supervisor.

[1028] Step 8:

[1029] User and manager review and approval:

[1030] The user checks the notification on their own device and adjusts the vacation schedule if necessary. The supervisor also checks the approval request on their device and approves or modifies it as appropriate. The inputs are the vacation proposal notification and the task reassignment notification. The output is the confirmation and approval data.

[1031] Step 9:

[1032] Database update:

[1033] The server updates the database with the final leave and task reassignment decisions. The input is the confirmation and approval data. The output is the updated leave and task reassignment data. The whole system is ready to repeat the data collection and fatigue assessment again.

[1034] 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.

[1035] 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.

[1036] 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.

[1037] [Fourth embodiment]

[1038] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1039] 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.

[1040] 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).

[1041] 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.

[1042] 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.

[1043] 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).

[1044] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1045] 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.

[1046] 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.

[1047] 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.

[1048] 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.

[1049] 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.

[1050] 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."

[1051] This invention relates to an "automatic paid vacation acquisition system" that automatically suggests paid vacation to employees whose fatigue level exceeds a certain level and adjusts work within the team at that time. This system consists of a server and a user terminal, and operates as follows.

[1052] System Overview

[1053] The system collects each member's work data and calculates their fatigue level based on this data. If the fatigue level exceeds a certain threshold, the system will suggest paid leave for the member and reassign tasks to other members. Finally, it will send notifications of the leave schedule and reassigned tasks to the relevant members.

[1054] Program processing

[1055] Data entry on user terminals

[1056] Users input their work start time, end time, progress, and task completion level into the terminal. This data is sent to the server in real time. For example, if user A works for 12 hours every day from Monday to Friday and inputs that data into the terminal, the information is sent to the server.

[1057] Data collection and analysis on the server

[1058] The server collects work data sent from user devices in real time and stores it in a database. The server calculates the user's fatigue level based on the accumulated work data. The fatigue level is calculated using an algorithm that takes into account the work hours, number of tasks, progress, and whether or not the user has taken a vacation over the past week. For example, in the case of User A, a fatigue score of 80 is calculated based on work data for 60 hours per week.

[1059] Paid leave proposals and schedule adjustments

[1060] The server determines that User A's fatigue score exceeds the management standard value (for example, 70), so the system will suggest that he take paid leave on the following Friday. The server checks the schedule of the entire team and the progress of the project, and selects the optimal leave date.

[1061] Task reassignment

[1062] The server retrieves the task list of the user who has been decided to take a vacation and reassigns the tasks to other members. During the reassignment, the server reevaluates the working status of each member and adjusts the tasks so that they are distributed fairly and efficiently. For example, user A's tasks are reassigned to members B and C.

[1063] Notification and confirmation

[1064] The server notifies the user and their supervisor of the final vacation schedule and details of the reassigned tasks. The user checks the notification on their terminal and, if necessary, adjusts the vacation schedule or requests approval from their supervisor. This operation ensures smooth vacation planning.

[1065] Specific examples

[1066] As a concrete example of a case where User A has a high level of fatigue, User A works 60 hours a week, and the server calculates a fatigue score of 80 based on the accumulated data. The server then proposes paid vacation for the following Friday and reassigns the tasks to Members B and C. A notification is sent to User A and the team, and their supervisor approves, allowing for smooth vacation acquisition and work adjustments.

[1067] In this way, the "automated paid leave acquisition system" supports employee fatigue management and efficient business operations.

[1068] The processing flow will be explained below.

[1069] Step 1:

[1070] Users input their work start time, end time, progress, and task completion level into the terminal. This data is sent to the server in real time. For example, if User A works for 12 hours every day from Monday to Friday and inputs that data into the terminal, the information is sent to the server.

[1071] Step 2:

[1072] The server receives the task data sent from the user terminal. The received data is stored in a database. This data includes the task time, number of tasks, progress, etc.

[1073] Step 3:

[1074] The server analyzes the work data stored in the database and calculates each user's fatigue level. The fatigue level is calculated using an algorithm that takes into account the work hours, number of tasks, progress, and whether or not the user has taken a vacation in the past week. For example, a fatigue score of 80 is calculated for user A based on his work data of 60 hours per week.

[1075] Step 4:

[1076] The server evaluates each user's fatigue score and determines that the user needs a vacation if it exceeds a certain threshold (e.g., 70). The fatigue score is periodically recalculated to reflect the latest status.

[1077] Step 5:

[1078] The server will suggest paid vacation dates to users whose fatigue score exceeds a certain threshold. The server will check the team's overall schedule and project progress to select the optimal vacation date. For example, it will suggest paid vacation for User A on the following Friday.

[1079] Step 6:

[1080] The server retrieves the task list of the user who has been decided to take a vacation from the database and reassigns it to other members. When reassigning, it reevaluates the availability status of each member and adjusts the allocation so that the tasks are distributed fairly and efficiently. For example, user A's tasks are reassigned to members B and C.

[1081] Step 7:

[1082] The server notifies the new assignee of the reassigned task details via email or chat tool. Members B and C then check the notification on their respective devices.

[1083] Step 8:

[1084] The server notifies the user and their supervisor of the vacation date and the details of the reassigned tasks. A notification is displayed on the user's device, and a confirmation and approval request is sent to the supervisor's device.

[1085] Step 9:

[1086] The user checks the notification on their own device and adjusts their vacation schedule if necessary. The supervisor also checks the approval request on their device and approves or modifies it as appropriate.

[1087] Step 10:

[1088] The server updates the database with the final vacation and task reassignment information, and the process is complete. The entire system is ready to start collecting data and assessing fatigue again.

[1089] Through these steps, the "Automatic Paid Leave Acquisition System" properly manages employee fatigue levels and automatically suggests vacation time, thereby supporting efficient business operations and maintaining employee health.

[1090] Example 1

[1091] 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."

[1092] Managing employee fatigue levels is extremely important, but traditional methods make it difficult to accurately assess employee fatigue levels, making it difficult to suggest time off at the right time. Furthermore, rescheduling work when employees take time off is often done manually, which requires additional effort and time.

[1093] 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.

[1094] In this invention, the server includes means for users to input work start times, end times, progress, and task completion levels, means for collecting the input data in real time and storing it in a database, means for calculating users' fatigue levels based on the data stored in the database, means for proposing paid vacation dates to users whose fatigue levels exceed a certain standard, means for reallocating tasks to other members based on the proposed paid vacation dates, and means for notifying details of the paid vacation dates and reallocated tasks. This allows for real-time management of employee fatigue levels, making it possible to propose vacations and readjust work at appropriate times.

[1095] A "user" is a person who accesses the system and inputs work data.

[1096] "Work start time" refers to the time when the user starts work.

[1097] "End time" refers to the time when the user finishes their work.

[1098] "Progress" refers to the degree of completion or progress of the tasks for which a user is responsible.

[1099] The "degree of task completion" is an index that indicates the degree to which the task assigned to the user has been completed.

[1100] A "terminal" refers to an information input device such as a computer or mobile device used by a user.

[1101] A "database" is a structured collection of information that a server uses to store and manage data collected by the server.

[1102] A "server" is a computer system that receives, stores, and analyzes data sent from a user terminal.

[1103] "Fatigue level" is a numerical representation of the user's workload and level of fatigue.

[1104] "Real-time" means that data is processed and transmitted the moment it is generated.

[1105] "Paid leave" refers to paid leave granted to employees by their employer.

[1106] "Dates" refer to the specific dates and times when paid leave will be taken.

[1107] "Reassignment" refers to the act of allocating the work of a user who is on vacation to another member.

[1108] "Notification" refers to the means by which the system communicates information to users and their superiors about paid leave and reassignment matters.

[1109] The present invention relates to a system for managing employee fatigue levels and appropriately proposing paid vacations. This system uses a server and user terminals to collect work data, calculate fatigue levels, suggest paid vacations, reassign tasks, and notify users.

[1110] Users input their work start time, finish time, progress, and task completion status into a terminal. The terminal used by the user is an information input device such as a computer or mobile device. The data entered by the user is sent to the server in real time.

[1111] The server receives the work data sent from the user terminal and stores it in a database. The server can use a distributed database such as Apache Cassandra. The server calculates each user's fatigue level based on the data stored in the database. This fatigue level calculation uses an algorithm that takes into account the work hours, number of tasks, progress, and whether or not the user has taken a vacation over the past week.

[1112] The server suggests paid vacation dates if the user's fatigue score exceeds a certain standard. Specifically, if the user's fatigue score exceeds 70, for example, it suggests taking paid vacation on the following Friday. Based on this suggestion, the server uses a project management tool (e.g., Microsoft Project) to check the schedule and project progress of the entire team and reassign tasks to other members.

[1113] When reassigning tasks, the server reevaluates each member's availability and adjusts the distribution so that tasks are distributed fairly and efficiently. As a result of the reassignment, all relevant members are notified of their vacation and new task schedules. Notifications are sent via a mail server (e.g., Postfix, Microsoft Exchange Server) or a real-time communication tool (e.g., Slack, Microsoft Teams). Users can check the notifications on their devices and, if necessary, rearrange their vacation schedules or seek approval from their superiors.

[1114] As a concrete example, if User A works 12 hours a day from Monday to Friday, he or she enters that data into the terminal, and the server calculates a fatigue score of 80 based on that data. The server then proposes paid leave for the following Friday and reassigns User A's tasks to Members B and C. A notification is then sent to User A and his or her supervisor, who then approves the leave. This system enables employee fatigue management and efficient business operations.

[1115] Examples of prompt statements

[1116] "User A worked 12 hours a day from Monday to Friday and entered that data into a terminal. The server analyzed the data and calculated User A's fatigue score as 80. Because this score exceeds the reference value, the server suggested that User A take paid leave on the following Friday and reassigned the task to another member. The system notified all relevant members, and the supervisor approved the leave. Please explain the detailed process of this system."

[1117] In this way, the "automated paid leave acquisition system" enables employee fatigue management and efficient business operations.

[1118] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1119] Step 1:

[1120] Users input their work start time, end time, progress, and task completion status into the terminal. The input data is sent to the server in real time. For example, if a user works for 8 hours on Monday, the start time, end time, and progress status are entered.

[1121] Inputs: Start time, end time, progress, and task completion rate

[1122] Output: Working data sent to the server

[1123] Step 2:

[1124] The server receives the work data sent from the user terminal in real time and stores it in a database. The data is stored using a distributed database such as Apache Cassandra. The server analyzes the received data and stores it while maintaining consistency.

[1125] Input: Work data sent from the user's terminal

[1126] Output: Working data stored in a database

[1127] Step 3:

[1128] The server calculates the user's fatigue level based on the data stored in the database, using an algorithm that takes into account the amount of time worked in the past week, the number of tasks, progress, and whether or not the user has taken a vacation. For example, a Python script is used to perform the analysis and calculate the fatigue score.

[1129] Input: Work data from the past week stored in the database

[1130] Output: Calculated fatigue score

[1131] Step 4:

[1132] If the user's fatigue score exceeds a certain standard (e.g., 70), the server suggests a paid vacation date. For example, it suggests a vacation date on Friday of the following week. Specifically, it adjusts the schedule using a project management tool.

[1133] Input: Calculated fatigue score

[1134] Output: Proposed vacation dates

[1135] Step 5:

[1136] The server reassigns tasks to other members based on the proposed paid vacation dates. It reevaluates each member's availability based on Use Case Diagrams, Gantt Charts, etc., and allocates tasks efficiently. For example, user A's tasks are reassigned to members B and C.

[1137] Input: Proposed vacation dates

[1138] Output: Reassigned tasks

[1139] Step 6:

[1140] The server notifies the user and their manager of the vacation date and the details of the reassigned tasks using a mail server or real-time communication tool, such as Postfix or Slack.

[1141] Input: Paid time off dates, reassigned task details

[1142] Output: Notification sent

[1143] Specifically, the user inputs work data into the device, which is then sent to the server, where it is saved, analyzed, and the fatigue level is calculated. If the fatigue level exceeds the standard, the server suggests the next appropriate day off, reallocates work, and notifies relevant parties. This series of processes enables user fatigue management and efficient work operations.

[1144] (Application example 1)

[1145] 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."

[1146] Employee fatigue management is an important issue for labor efficiency and safety, but manual monitoring and management is time-consuming and difficult to respond to in real time. Proposing paid leave and reassigning tasks based on fatigue levels is also cumbersome. Therefore, there is a need for a system that can calculate employee fatigue levels in real time, suggest appropriate leave, and smoothly reorganize work. This problem is particularly pronounced in workplaces where workers, such as factory operators, are under heavy strain and require automatic task reassignment.

[1147] 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.

[1148] In this invention, the server includes a means for collecting work data of each member, a means for calculating the fatigue level of each member based on the work data, a means for proposing paid vacation dates for members whose fatigue level exceeds a certain standard, a means for reallocating tasks to other members or automated machines based on the proposed paid vacation dates, a means for sending notifications of the paid vacation dates and reallocated tasks, and a means including an algorithm for collecting user work data in real time and calculating fatigue levels. This allows for real-time monitoring of employee fatigue levels, appropriate paid vacation suggestions, and efficient work reorganization.

[1149] "Members" are users whose activity data is collected by the system, and are individuals or automated entities responsible for certain tasks.

[1150] "Work data" is data containing information about work performed by members, including work time, number of tasks, and progress.

[1151] "Fatigue level" is an index calculated based on the work data of a member, and is a value that indicates the degree of fatigue of the member.

[1152] The "certain standard" is a fatigue threshold set by the system, and if this value is exceeded, paid leave will be suggested.

[1153] "Paid vacation dates" are vacation dates proposed to members whose fatigue levels exceed a certain threshold.

[1154] "Task reassignment" refers to the act of redistributing presence tasks to other members or automated machines based on the proposed paid vacation dates.

[1155] "Notification" means information sent by the system to members and administrators, including details about vacation dates and reassigned tasks.

[1156] An "algorithm" is a calculation procedure for solving a specific problem, in this case a method for calculating fatigue levels based on work data.

[1157] This invention is a "smart fatigue management system" that collects work data from operators working in factories and calculates their fatigue levels in real time. If an operator's fatigue level exceeds a certain standard, the system automatically suggests paid leave and reallocates work. Specifically, it consists of a server and a user terminal.

[1158] Hardware and software used

[1159] Hardware: Smartphones, PCs, Tablets

[1160] Software: Python 3.x

[1161] Detailed explanation of data processing and calculation

[1162] Data entry on user terminals

[1163] The user (operator) inputs the start time, end time, and task progress into the terminal. This data is sent to the server in real time. For example, if an operator works for 12 hours, that information is updated in real time to the server.

[1164] Data collection and analysis on the server

[1165] The server collects work data sent from user devices in real time and stores it in a database. The server calculates the operator's fatigue level based on the accumulated work data. To calculate fatigue, an algorithm is used that takes into account the work hours, number of tasks, progress, and whether or not the operator has taken a vacation in the past week. For example, in the case of Operator A, the fatigue score is calculated from the accumulated work hours over the past week.

[1166] Paid leave proposals and schedule adjustments

[1167] The server then suggests paid vacation dates for operators whose fatigue scores exceed a certain threshold, and then checks the schedules and project progress of the entire team to select the optimal vacation dates.

[1168] Task reassignment

[1169] The server reallocates the tasks of the operator who is scheduled to take a vacation to other members or automated machines. When reallocating, it reevaluates the availability of each member and adjusts the allocation to distribute tasks fairly or efficiently. For example, Operator A's tasks are reallocated to two other operators, Operator B and Operator C.

[1170] Notification and confirmation

[1171] The server notifies the operator and their manager of the final vacation schedule and details of the reassigned tasks. The operator checks the notification on their terminal and, if necessary, adjusts the vacation schedule or seeks approval from the manager. This ensures smooth vacation planning.

[1172] Specific examples

[1173] If Operator A works 60 hours in a week, the server calculates a fatigue score based on the accumulated data, and since it exceeds the standard value, it proposes paid leave for the following Friday and reassigns tasks to Members B and C. A notification is sent to Operator A and the team, and the manager approves, allowing for smooth vacation acquisition and work adjustments.

[1174] Prompt Sentence Examples

[1175] Create an application that calculates the fatigue level of a factory robot operator when the specified working hours are exceeded, and suggests taking paid leave for the next day if the fatigue level exceeds the threshold. Also, explain the structure of the program that automatically reassigns tasks and notifies the operator when this happens.

[1176] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1177] Step 1: Data entry and collection

[1178] Users (operators) input their work start time, end time, and task progress status into their terminals. The input data is sent to the server in real time. The input data includes the work start time, end time, work content, and progress status. The server receives this data and saves it in a database. This accumulates the work data of each operator.

[1179] Step 2: Calculate fatigue

[1180] The server calculates fatigue levels based on the collected work data using an algorithm that includes the work hours, number of tasks, progress, and whether or not a vacation was taken over the past week. Specifically, the server extracts each piece of work data, adds up the work hours, and calculates a fatigue score taking into account the number of tasks and progress. For example, if the total work hours for the week is 60 hours, a fatigue score is calculated.

[1181] Step 3: Propose paid time off

[1182] If the calculated fatigue score exceeds a certain threshold (for example, 70), the server will suggest a paid vacation date for the relevant operator. The server checks the overall schedule and project progress and automatically selects the optimal vacation date. For example, if the fatigue score exceeds the threshold at 80, the server will suggest the next Friday as a paid vacation date.

[1183] Step 4: Reassign tasks

[1184] The server obtains the list of tasks for the operator who has been decided to take paid leave and reallocates the tasks to other operators or automated machines. When reallocating tasks, the server reevaluates the operating status of each operator and adjusts the allocation to ensure fair and efficient task distribution. For example, if Operator A's tasks are reallocated to Operator B and Operator C, the server will reallocate the tasks taking into account the operating status of each operator.

[1185] Step 5: Notification

[1186] The server notifies the operator and his / her manager of the determined paid vacation date and details of the reassigned tasks. The notification includes the vacation date, details of the reassigned tasks, and a list of the reassigned operators. The notification is sent to the terminal, allowing the operator to confirm their vacation and, if necessary, request approval.

[1187] Through these steps, the system can efficiently manage operator fatigue, allowing them to take time off and adjust their work schedules at the right time.

[1188] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1189] This invention relates to an "automated paid vacation acquisition system" that automatically suggests paid vacation to employees whose fatigue level exceeds a certain standard, adjusts work within the team at that time, and accurately calculates fatigue levels by taking into account the user's emotional data. This system is composed of a server, a user terminal, and an emotion engine, and operates as follows.

[1190] System Overview

[1191] The system collects each member's work data and emotional data, calculates their fatigue level based on this, and if the fatigue level exceeds a certain threshold, suggests that the member take paid leave and reassigns tasks to other members. Finally, it sends notifications of the leave schedule and reassigned tasks to the relevant members.

[1192] Program processing

[1193] Data entry on user terminals

[1194] Users input their work start time, end time, progress, and task completion level into the terminal. This data is sent to the server in real time. For example, if user A works for 12 hours every day from Monday to Friday and inputs that data into the terminal, the information is sent to the server.

[1195] Emotion Engine Data Acquisition

[1196] The emotion engine collects information such as the user's facial expressions, voice tone, and text input, and classifies them into emotion categories such as stress, joy, anger, sadness, etc. This emotion data is also sent to the server in real time.

[1197] Data collection and analysis on the server

[1198] The server receives the work data and emotion data sent from the user device and emotion engine and stores them in a database. The server calculates the user's fatigue level based on the accumulated data. The fatigue level is calculated using an algorithm that takes into account the work hours, number of tasks, progress, whether or not the user has taken a vacation, and emotion data from the past week. For example, in the case of User A, the fatigue level score is calculated based on the work data and emotion data for 60 hours per week.

[1199] Paid leave proposals and schedule adjustments

[1200] The server determines that User A's fatigue score exceeds the management standard value (for example, 70), so the system will suggest that he take paid leave on the following Friday. The server checks the schedule of the entire team and the progress of the project, and selects the optimal leave date.

[1201] Task reassignment

[1202] The server retrieves the task list of the user who has been decided to take a vacation from the database and reassigns the tasks to other members. When reassigning, it reevaluates the availability status of each member and adjusts the distribution of tasks so that they are fair and efficient. For example, user A's tasks are reassigned to members B and C.

[1203] Notification and confirmation

[1204] The server notifies the new assignee of the reassigned task details via email or chat tool. Members B and C then check the notification on their respective devices.

[1205] Notification and approval of leave proposals

[1206] The server notifies the user and their supervisor of the vacation date and the details of the reassigned tasks. A notification is displayed on the user's device, and a confirmation and approval request is sent to the supervisor's device.

[1207] User and supervisor review and approval

[1208] The user checks the notification on their own device and adjusts their vacation schedule if necessary. The supervisor also checks the approval request on their device and approves or modifies it as appropriate.

[1209] Database Update

[1210] The server updates the database with the final vacation and task reassignment information, and the process is complete. The entire system is ready to start collecting data and assessing fatigue again.

[1211] Specific examples

[1212] As a concrete example of when User A's fatigue level is high, User A works 60 hours a week and the emotion engine recognizes that he or she is under high stress. When the server calculates the fatigue level score based on the accumulated data, the score reaches 80. The server then proposes paid leave for the following Friday and reassigns the tasks to Members B and C. A notification is sent to User A and the team, and their supervisor approves, allowing for smooth vacation acquisition and work adjustments.

[1213] In this way, the "Automatic Paid Leave Acquisition System" properly manages employee fatigue and emotional data and automatically suggests taking time off, thereby supporting efficient business operations and maintaining employee health.

[1214] The processing flow will be explained below.

[1215] Step 1:

[1216] Users input their work start time, end time, progress, and task completion status into their terminal. This data is sent to the server in real time. For example, if User A inputs 12 hours of work time and multiple completed tasks at the end of a day's work, the information is sent to the server immediately.

[1217] Step 2:

[1218] The emotion engine analyzes emotion data by capturing the user's facial expressions with a webcam, collecting voice tones with a microphone, and monitoring the user's text input. For example, if a user inputs keywords such as "tired" or "troubled" in an email or chat, the emotion engine will recognize this as stress.

[1219] Step 3:

[1220] The emotion engine sends collected emotion data to the server in real time. For example, if user A feels stressed during a meeting, it analyzes his facial expressions and tone of voice, and based on this, it recognizes the user as in a high-stress state and sends the data to the server.

[1221] Step 4:

[1222] The server receives task data and emotion data sent from the user device and emotion engine, and stores them in a database. The stored data includes task time, number of tasks, progress, and recognized emotion data.

[1223] Step 5:

[1224] The server analyzes the work data and emotional data stored in the database to calculate each user's fatigue level. The algorithm used to calculate fatigue levels takes into account the past week's work hours, number of tasks, progress, whether or not the user has taken a vacation, and emotional data. For example, a fatigue score of 80 is calculated for User A based on his or her 60-hour work week and high stress level.

[1225] Step 6:

[1226] The server evaluates the calculated fatigue score, and if it exceeds a reference value (for example, 70), it determines that the user needs a vacation. The server then proposes paid vacation to User A for the following Friday.

[1227] Step 7:

[1228] The server retrieves the task list of the user who has been decided to take a vacation from the database and reallocates the tasks to other members. When reallocating, it reevaluates each member's operating status and emotional data and adjusts the allocation so that the tasks are distributed fairly and efficiently. For example, user A's tasks are reallocated to members B and C.

[1229] Step 8:

[1230] The server notifies the new assignee of the reassigned task details via email or chat tool. Members B and C then check the notification on their respective devices.

[1231] Step 9:

[1232] The server notifies the user and their supervisor of the vacation date and the details of the reassigned tasks. A notification is displayed on the user's device, and a confirmation and approval request is sent to the supervisor's device.

[1233] Step 10:

[1234] The user can check the notification on their own device and adjust their vacation schedule if necessary, while the supervisor can check the approval request on their device and approve or modify it as appropriate.

[1235] Step 11:

[1236] The server updates the database with the final vacation and task reassignment information, and the process is complete. The entire system is ready to start collecting data and assessing fatigue again.

[1237] Through these steps, the "Automatic Paid Leave Acquisition System" properly manages employees' work data and emotional data, accurately calculates fatigue levels, suggests vacation time, and adjusts work schedules, thereby supporting efficient business operations and maintaining employee health.

[1238] Example 2

[1239] 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."

[1240] Properly managing employee fatigue levels and simultaneously achieving efficient business operations and maintaining employee health is a key challenge for many organizations. Conventional systems rely solely on work data to assess fatigue levels, making it difficult to consider employees' emotions and stress levels. This makes it difficult to grasp an employee's true fatigue level and make appropriate leave recommendations. Furthermore, the processes of task reassignment and notification must be done manually, which contributes to reduced organizational efficiency.

[1241] 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 means for collecting work data and emotion data of each member, means for accurately calculating the fatigue level of each member based on the work data and emotion data, means for automatically proposing paid vacation dates for a member whose fatigue level exceeds a certain standard, means for reassigning tasks to other members based on the proposed paid vacation dates, and means for sending notifications of the paid vacation dates and reassigned tasks to each member and their supervisor. This enables highly accurate fatigue level assessment that takes into account not only the employee's working time and task progress but also their emotion data, thereby automatically proposing appropriate vacations and efficiently reassigning tasks.

[1242] "Work data" refers to information such as the start time, end time, number of tasks, and progress of work performed by each member.

[1243] "Emotional data" refers to information about emotions collected from each member's facial expressions, voice tone, text input, etc., and is classified into emotional categories such as stress, joy, anger, and sadness.

[1244] "Fatigue level" refers to a score or index that indicates a member's fatigue state based on each member's work data and emotional data.

[1245] "Paid vacation dates" refers to the dates and periods for each member's paid vacation that the system automatically suggests.

[1246] "Reassigned tasks" refers to the work or tasks that are redistributed to other members when a highly fatigued member takes paid leave.

[1247] "Notification" means a communication sent by the system to each Member and their Manager containing the vacation proposal, details of the reassigned tasks, and any other necessary information.

[1248] "Server" refers to a central processing unit that collects, stores, and analyzes work data and emotional data, calculates fatigue levels, suggests vacations, reassigns tasks, and sends notifications.

[1249] "Algorithm" refers to the calculation procedures and rules for calculating a member's fatigue level with high accuracy based on work data and emotional data.

[1250] This invention relates to an "automated paid vacation acquisition system" that automatically suggests paid vacation to team members whose fatigue level exceeds a certain standard, adjusts work within the team, and calculates fatigue levels with high accuracy by taking into account the user's emotional data. This system is composed of a server, a user terminal, and an emotion engine, and operates as follows.

[1251] System Overview

[1252] The system collects each member's work data and emotional data, calculates their fatigue level based on this, and if the fatigue level exceeds a certain threshold, suggests that the member take paid leave and reassigns tasks to other members. Finally, it sends notifications of the leave schedule and reassigned tasks to the relevant members.

[1253] Hardware and software used

[1254] Server: A central processing unit that collects, stores, analyzes, and sends notifications.

[1255] User terminal: A device (e.g., PC, smartphone) for each member to input task data and emotion data.

[1256] Emotion Engine: Software and hardware that uses a webcam, microphone, and natural language processing algorithms to collect and classify emotion data.

[1257] Details of data processing and calculation

[1258] 1. Data entry on the user's device

[1259] Users input their work start time, end time, progress, and task completion status into the terminal, and this data is sent to the server in real time.

[1260] 2. Emotion Engine Data Acquisition

[1261] The emotion engine captures the user's facial expressions with a webcam, analyzes their voice tone with a microphone, and analyzes their text input with a natural language processing algorithm, all of which transmits the emotion data to a server in real time.

[1262] 3. Data collection and analysis on the server

[1263] The server receives the work data and emotion data sent from the user device and emotion engine, and stores them in a database. Based on the accumulated data, the server calculates the user's fatigue level using an algorithm that takes into account the work hours, number of tasks, progress, whether or not the user has taken a vacation, and emotion data from the past week.

[1264] 4. Proposing and scheduling paid leave

[1265] The server automatically suggests optimal vacation days for users whose calculated fatigue score exceeds a threshold, taking into account the overall team schedule and project progress.

[1266] 5. Reassignment of Work

[1267] The server retrieves the task list of the user who has been decided to take a vacation from the database and reassigns the tasks to other members. It evaluates the working status of each member and adjusts the distribution of tasks so that they are fair and efficient.

[1268] 6. Sending Notifications

[1269] The server notifies each member and their manager of the details of the reassigned tasks and the vacation proposal via email or chat tool.

[1270] Specific examples

[1271] As a concrete example of a case where User A has a high level of fatigue, User A works 60 hours a week and the emotion engine recognizes that he or she is under high stress. When the server calculates the fatigue score based on this data, the score reaches 80, and the server proposes paid leave for the following Friday. The server reassigns the task to Members B and C, a notification is sent to all members, and the supervisor approves the leave, allowing for smooth leave acquisition and work adjustments.

[1272] Prompt Sentence Examples

[1273] "If User A's fatigue level is high, please explain what kind of vacation suggestions and work adjustments will be made."

[1274] "Can you tell me more about what the emotion engine does and how it obtains data?"

[1275] The above is a specific embodiment for carrying out the invention.

[1276] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1277] System program processing steps

[1278] Step 1: Enter data on the user's device

[1279] Input: The user inputs their work start time, end time, progress, and degree of completion of the task into the terminal.

[1280] Data processing and calculation: The user terminal formats the input data and sends it to the server in real time. The input data is also temporarily stored in local storage.

[1281] Output: The terminal sends the formatted work data to the server and displays an input confirmation message to the user.

[1282] Specific operation: When a user enters a start time of work as 9:00 on Monday and an end time of work as 21:00, the data is sent to the server.

[1283] Step 2: Obtaining Data for the Emotion Engine

[1284] Input: The emotion engine collects data from the user's facial expressions, voice tone, and text input.

[1285] Data processing and calculation: The emotion engine analyzes the collected data and classifies it into emotion categories (e.g., stress, joy, anger, sadness) using natural language processing and image recognition algorithms.

[1286] Output: The classified emotion data is sent to the server.

[1287] Specific operation: The emotion engine analyzes the sentence "Today was a very stressful day" entered by the user and classifies it as stress. The result is sent to the server.

[1288] Step 3: Collecting and storing data on the server

[1289] Input: Task data and emotion data sent from the user terminal and emotion engine.

[1290] Data processing and calculation: The server stores the received data in a database. It also performs data integrity and validation.

[1291] Output: Task and emotion data stored in the server database are available for the next analysis step.

[1292] Specific operation: The server stores the work time data and emotion data of user A in a database.

[1293] Step 4: Calculate fatigue level

[1294] Input: Task data and emotion data stored on the server.

[1295] Data processing and calculation: The server inputs the past week's work hours, number of tasks, progress, whether or not vacation was taken, and emotional data into an algorithm to calculate a fatigue score.

[1296] Output: A fatigue score is calculated and used in the next vacation suggestion step.

[1297] Specific operation: A calculation is performed based on user A's work data and emotion data for one week, and the fatigue score is calculated as 80.

[1298] Step 5: Propose and schedule paid time off

[1299] Inputs: Fatigue score and overall team schedule and project progress.

[1300] Data processing and calculation: If the fatigue score exceeds a certain level (e.g., 70), the server will suggest a paid vacation date. The optimal vacation date will be selected taking into account the schedule of the entire team.

[1301] Output: A vacation proposal notification is generated and used in the next task reassignment step.

[1302] Specific operation: The server suggests to user A that he take paid vacation on the following Friday and generates a notification of this.

[1303] Step 6: Reassign tasks

[1304] Input: A list of tasks for users who have been confirmed as on vacation, and the availability data of each member.

[1305] Data processing and calculation: The server reassigns the tasks of the user who has been determined to be on vacation to other members fairly and efficiently. It evaluates the working status of each member and distributes them optimally.

[1306] Output: Details of the reassigned task are generated and used in the next notification step.

[1307] Specific behavior: User A's task is reassigned to members B and C.

[1308] Step 7: Sending notifications

[1309] Inputs: Reassigned task details and leave proposal notification.

[1310] Data processing and calculation: The server generates notifications containing details of the reassigned tasks and vacation proposals and sends them to each member and their manager via email or chat tool.

[1311] Output: Each member and their manager will receive a notification.

[1312] Specific behavior: The server sends a notification to Member B, Member C, and their superiors with details of the reassigned tasks and the vacation proposal.

[1313] The above is an explanation of the specific processing steps and their operations.

[1314] (Application example 2)

[1315] 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."

[1316] Conventional fatigue management systems calculated fatigue levels based solely on the work data of members, but this had the problem of making it difficult to accurately grasp the actual fatigue state of members. Furthermore, when fatigue levels increased, task redistribution often concentrated the burden on a few members without taking into account the impact on other members. Furthermore, in factories and other workplaces, work adjustments that take into account the timing of robot maintenance were necessary, but there was no system that could do this automatically.

[1317] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting each member's work data and emotion data, means for calculating the member's fatigue level based on the work data and emotion data, means for proposing paid vacation dates to a member whose fatigue level exceeds a certain threshold, means for reallocating tasks to other members based on the proposed paid vacation dates, means for sending notifications of the paid vacation dates and reallocated tasks, and means for automatically reallocating robot maintenance tasks to other engineers when the member's fatigue level exceeds a threshold. This enables highly accurate fatigue assessment that takes into account the member's actual emotional state, and by appropriately distributing the workload to other members, efficient and fair task allocation is possible. Furthermore, automatically adjusting the robot maintenance schedule can improve work efficiency within the factory.

[1318] "Members" refer to individuals, primarily employees and engineers, who provide work data and sentiment data.

[1319] "Task data" refers to data that includes information such as the start time, end time, progress, and degree of completion of a task.

[1320] "Emotion data" refers to emotional information such as stress, joy, anger, sadness, etc. obtained from the user's facial expression, voice tone, and character input.

[1321] "Fatigue level" is an index that indicates the degree of fatigue calculated based on the member's work data and emotional data.

[1322] "Paid leave" refers to a period of leave from work that is automatically offered to a member if they meet certain criteria.

[1323] "Task reassignment" refers to the process of reassigning tasks from a member whose fatigue level has exceeded a standard value to another member.

[1324] "Notification" is a means of communication to inform members and their superiors of proposed vacation dates and details of reassigned tasks.

[1325] "Data collection means" refers to a method or device for acquiring task data and emotion data from members.

[1326] "Data analysis means" refers to a method or device for calculating fatigue levels based on collected task data and emotion data.

[1327] "Proposal means" refers to a method or device for proposing paid vacation dates to members when their fatigue level exceeds a certain standard.

[1328] "Robot maintenance tasks" refer to work performed to maintain and repair robots in a factory.

[1329] The "reference value" refers to the threshold at which paid leave is suggested when fatigue levels are above a certain level.

[1330] This invention is an "automated paid vacation acquisition system" that collects work data and emotional data of members, calculates their fatigue level based on the data, and automatically suggests paid vacation if the fatigue level exceeds a certain threshold. This system is composed of a server, a user terminal, and an emotion engine.

[1331] System Overview

[1332] The system collects each member's work data and emotional data, calculates their fatigue level based on this, and if their fatigue level exceeds a certain threshold, suggests that they take paid leave. It also reassigns tasks to other members and automatically adjusts robot maintenance tasks. Finally, it sends notifications of vacation dates and reassigned tasks to the relevant members.

[1333] Server Program

[1334] The server receives each member's work data and emotional data and stores them in a database. The server uses data processing libraries (e.g., numpy, pandas) to analyze the collected data and calculate fatigue levels. Fatigue assessment uses an algorithm that takes into account the past week's work hours, number of tasks, progress, whether or not a member has taken vacation, and emotional data. If the fatigue score exceeds a threshold, the server suggests the member take paid vacation and reassigns tasks.

[1335] User terminal

[1336] The user terminal provides an interface for each member to input work data. Users input data such as the start time, end time, progress, and degree of task completion. In addition, emotional data (e.g., facial expressions, voice tone, and text input) is also collected through the interface provided by the emotion engine.

[1337] Emotion Engine

[1338] The emotion engine analyzes information such as members' facial expressions, voice tone, and text input, and classifies them into emotion categories such as stress, joy, anger, and sadness. This emotion data is sent to the server in real time. For example, services such as Emotion API are used for emotion analysis.

[1339] Specific examples

[1340] For example, suppose Engineer A works 60 consecutive hours a week and the emotion engine detects high stress levels. The server calculates the fatigue level based on this data and finds that the fatigue score exceeds the threshold. The server then suggests that the engineer take paid leave on Wednesday of the following week. The server then reassigns the tasks to Engineers B and C, sends notifications, and automatically assigns robot maintenance tasks to the other engineers.

[1341] Prompt Sentence Examples

[1342] "Please help me design a system that calculates an engineer's fatigue level based on their work data and emotional data, and suggests paid vacation time. Engineer A works 60 hours a week. Taking this into consideration, if his fatigue level is determined to be high, we need to suggest appropriate vacation time and reassign tasks to other engineers."

[1343] In this way, the "automated paid leave acquisition system" properly manages members' work data and emotional data, performs highly precise fatigue assessments, and automatically suggests leave, thereby achieving efficient business operations and maintaining members' health.

[1344] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1345] Step 1:

[1346] Data entry on user terminal:

[1347] Users use a smartphone or tablet to input their work start time, end time, progress, and degree of task completion. Emotional data is also input at the same time. For example, the user might provide information such as "Start time: 9:00, End time: 18:00, Task progress: 80%, Emotion: High stress." This data is sent to the server in real time. The output is the work data and emotional data sent to the server.

[1348] Step 2:

[1349] Emotion Engine Data Acquisition:

[1350] The emotion engine collects emotional data from the user's facial expressions, voice tone, and text input, and classifies it into emotional categories such as stress, joy, anger, and sadness. Image data of facial expressions, voice data, and text input data are provided as input. Specifically, analysis is performed using the Emotion API. Emotional data such as stress level and joy level is sent to the server as output.

[1351] Step 3:

[1352] Data collection and analysis on the server:

[1353] The server receives work data and emotion data sent from the user device and emotion engine and stores them in a database. The received data is used as input. Using a data processing library (e.g., numpy, pandas), fatigue levels are calculated based on this data. The past week's work hours, number of tasks, progress, whether or not someone has taken a vacation, and emotion data are weighted, and an overall fatigue score is calculated using an algorithm. The output is a fatigue score for each member.

[1354] Step 4:

[1355] Paid Time Off Proposals and Scheduling:

[1356] The server proposes paid leave to members whose fatigue score exceeds a certain threshold. The fatigue score is used as input. If the fatigue score exceeds, for example, 70, the server proposes paid leave on the most appropriate day of the following week. As output, a vacation proposal notification is generated for the relevant member.

[1357] Step 5:

[1358] Task reassignment:

[1359] The server retrieves the task list of the user who has decided to take a vacation from the database and reassigns the tasks to other members. The user's task data is used as input. When reassigning, adjustments are made to ensure that tasks are distributed efficiently, taking into account each member's current operating status. New task assignment data is generated as output.

[1360] Step 6:

[1361] Notification and confirmation:

[1362] The server notifies the new assignee of the reassigned task details. This notification is sent via email or chat tool. The new task assignment data is used as input. The notification is sent to each member as output.

[1363] Step 7:

[1364] Notification and Approval of Leave Proposals:

[1365] The server notifies the user and their supervisor of the vacation date and the details of the reassigned tasks. The server uses the vacation proposal notification data as input. As output, the notification is sent to the user and supervisor.

[1366] Step 8:

[1367] User and manager review and approval:

[1368] The user checks the notification on their own device and adjusts the vacation schedule if necessary. The supervisor also checks the approval request on their device and approves or modifies it as appropriate. The inputs are the vacation proposal notification and the task reassignment notification. The output is the confirmation and approval data.

[1369] Step 9:

[1370] Database update:

[1371] The server updates the database with the final leave and task reassignment decisions. The input is the confirmation and approval data. The output is the updated leave and task reassignment data. The whole system is ready to repeat the data collection and fatigue assessment again.

[1372] 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.

[1373] 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.

[1374] 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.

[1375] 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.

[1376] 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.

[1377] 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.

[1378] 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).

[1379] 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.

[1380] 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."

[1381] 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.

[1382] 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).

[1383] 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.

[1384] 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.

[1385] 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.

[1386] 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.

[1387] 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.

[1388] 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.

[1389] 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.

[1390] 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.

[1391] 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.

[1392] 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.

[1393] The following is further disclosed regarding the above embodiment.

[1394] (Claim 1)

[1395] A means for collecting work data from each member;

[1396] means for calculating the fatigue level of a member based on the work data;

[1397] A means for proposing paid vacation dates to members whose fatigue level exceeds a certain standard;

[1398] means for reallocating tasks to other members based on the proposed vacation dates;

[1399] means for sending notifications of said paid vacation dates and reassigned tasks;

[1400] A system including:

[1401] (Claim 2)

[1402] The system of claim 1 , wherein the work data includes work time, number of tasks, and progress.

[1403] (Claim 3)

[1404] 2. The system of claim 1, wherein the means for calculating the fatigue level uses an algorithm that takes into account the work hours, number of tasks, progress, and whether or not the user has had a vacation in the past week.

[1405] "Example 1"

[1406] (Claim 1)

[1407] A means for the user to input the start time, end time, progress, and degree of completion of the task;

[1408] means for collecting the input data in real time and storing it in a database;

[1409] means for calculating a fatigue level of a user based on the data stored in the database;

[1410] means for suggesting paid vacation dates to a user whose fatigue level exceeds a certain standard;

[1411] means for reallocating tasks to other members based on the proposed vacation dates;

[1412] means for notifying the employee of the details of the paid leave dates and the reassigned tasks;

[1413] A system including:

[1414] (Claim 2)

[1415] The system of claim 1 , wherein the work data includes work time, task quantity, and progress status.

[1416] (Claim 3)

[1417] 2. The system according to claim 1, wherein the means for calculating the fatigue level uses an algorithm that takes into account the work time, amount of tasks, progress, and whether or not the worker has taken a vacation over a certain period of time in the past.

[1418] "Application Example 1"

[1419] (Claim 1)

[1420] A means for collecting work data from each member;

[1421] means for calculating the fatigue level of a member based on the work data;

[1422] A means for proposing paid vacation dates to members whose fatigue level exceeds a certain standard;

[1423] means for reallocating tasks to other members or automated machines based on the proposed vacation dates;

[1424] means for sending notifications of said paid vacation dates and reassigned tasks;

[1425] A means including an algorithm for collecting user work data in real time and calculating a fatigue level;

[1426] A system including:

[1427] (Claim 2)

[1428] The system of claim 1 , wherein the work data includes work time, number of tasks, and progress.

[1429] (Claim 3)

[1430] 2. The system of claim 1, wherein the means for calculating the fatigue level uses an algorithm that takes into account the work hours, number of tasks, progress, and whether or not the user has had a vacation in the past week.

[1431] "Example 2: Combining Emotion Engines"

[1432] (Claim 1)

[1433] A means for collecting work data and emotion data of each member;

[1434] means for calculating with high accuracy the fatigue level of each member based on the work data and emotion data;

[1435] A means for automatically proposing paid vacation dates to members whose fatigue level exceeds a certain standard;

[1436] means for reallocating tasks to other members based on the proposed vacation dates;

[1437] means for sending notifications of said paid vacation dates and reassigned tasks to each member and their supervisor;

[1438] A system including:

[1439] (Claim 2)

[1440] The system of claim 1 , wherein the work data includes work time, number of tasks, and progress.

[1441] (Claim 3)

[1442] 2. The system of claim 1, wherein the means for calculating the fatigue level uses an algorithm that takes into account the work hours, number of tasks, progress, whether or not the user has had a vacation, and emotional data from the past week.

[1443] "Application example 2 when combining emotion engines"

[1444] (Claim 1)

[1445] A means for collecting work data and emotion data of each member;

[1446] means for calculating a fatigue level of a member based on the work data and emotion data;

[1447] A means for proposing paid vacation dates to members whose fatigue level exceeds a certain standard;

[1448] means for reallocating tasks to other members based on the proposed vacation dates;

[1449] means for sending notifications of said paid vacation dates and reassigned tasks;

[1450] A means to automatically reallocate robot maintenance tasks to other engineers when fatigue levels exceed a threshold,

[1451] A system including:

[1452] (Claim 2)

[1453] The system of claim 1 , wherein the task data includes task time, number of tasks, progress, and emotion data.

[1454] (Claim 3)

[1455] 2. The system of claim 1, wherein the means for calculating the fatigue level uses an algorithm that takes into account the work hours, number of tasks, progress, and whether or not the user has had a vacation in the past week. [Explanation of symbols]

[1456] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for collecting work data from each member; means for calculating the fatigue level of a member based on the work data; A means for proposing paid vacation dates to members whose fatigue level exceeds a certain standard; means for reallocating tasks to other members based on the proposed vacation dates; means for sending notifications of said paid vacation dates and reassigned tasks; A system including:

2. The system according to claim 1 , wherein the work data includes work time, number of tasks, and progress status.

3. 2. The system of claim 1, wherein the means for calculating the fatigue level uses an algorithm that takes into account the number of hours worked in the past week, the number of tasks, progress, and whether or not the worker has had a vacation.

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