System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving a task instruction input by a superior; means for analyzing the received task instruction; means for assigning a task to an appropriate subordinate based on the analyzed task instruction; means for managing a progressing state of the task reported from the subordinate; means for evaluating the task completed by the subordinate and reporting a result of the evaluation to the superior; and means for changing a type of a AI manager according to a selection of the subordinate.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Many companies and organizations are experiencing low psychological safety among young employees, with an increasing number of employees suffering from social anxiety disorder. This situation has a negative impact on workplace productivity and employee mental health, causing a decline in the organization's overall performance. Another problem is that direct intervention by human supervisors creates pressure and stress for employees, leading to a decline in psychological safety. To solve these issues, a system is needed that improves psychological safety and provides an environment where subordinates can work with peace of mind. [Means for solving the problem]

[0005] To address this issue, the present invention provides a system that includes a means for receiving task instructions entered by a superior, a means for analyzing the received task instructions using natural language processing, a means for assigning tasks to appropriate subordinates based on the analyzed task instructions, a means for managing task progress reported by subordinates and providing feedback on the progress, a means for evaluating tasks completed by subordinates and reporting the evaluation results to a superior, and a means for changing the AI ​​boss type based on the subordinate's selection. This system eliminates direct intervention by superiors and enables objective and fair task management and evaluation. Furthermore, by increasing the psychological safety of subordinates, productivity and mental health throughout the workplace can be improved.

[0006] A "superior" is someone in a position to give instructions to employees about work and tasks in a company or organization.

[0007] "Task instructions" are specific instructions from superiors to subordinates regarding work content and goals.

[0008] "Natural language processing" is a technology that allows computers to understand, interpret, and process human language.

[0009] "Analysis" is the process of breaking down received information or data and understanding its meaning and content.

[0010] "Task assignment" refers to giving specific tasks or duties to subordinates based on their skills and current work situation.

[0011] "Progress" refers to the state or degree of progress that indicates whether a task or work is progressing as planned.

[0012] "Feedback" means providing information such as evaluation, criticism, and advice regarding the process and results of a subordinate's work in relation to established standards and expectations.

[0013] "Task evaluation" is the objective evaluation of the quality and results of the work or tasks completed by subordinates.

[0014] "Means to change boss type" is a function that allows subordinates to change the leadership style and behavior of their AI boss according to their wishes.

[0015] "Psychological safety" refers to an environment in which people can freely express their opinions and thoughts within their work or organization, and can concentrate on their work without feeling any interpersonal risk. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] The present invention is a system that improves psychological safety in the workplace by building an AI system that takes on the role of middle managers and eliminates direct intervention by superiors. Specific embodiments for implementing the present invention are described below.

[0038] Receiving and analyzing task instructions

[0039] The server receives task instructions from a superior. These task instructions have specific content, such as "Please create a new market analysis report by next week." The server receives these instructions and analyzes them using a natural language processing engine. The analyzed task instructions are broken down into the work content, "Create a market analysis report," and the deadline, "By next week." The results of this analysis are saved in the server's database.

[0040] Task assignment

[0041] The server then assigns the analyzed tasks to the appropriate subordinates. It retrieves the subordinates' skill sets and current task status from the database and runs an algorithm to identify the most suitable subordinate. Once the most suitable subordinate is identified, the task is assigned and the results are saved in the database. At the same time, a notification of the new task is sent to the subordinate's device.

[0042] Task progress management and feedback

[0043] The terminal (subordinate device) reports the progress of the task to the server. For example, it inputs "Progress on task ID 1234 is 50%." The server receives the progress report and manages the progress based on it. The server uses a progress evaluation algorithm to compare the reported progress with the deadline, and generates feedback as needed, such as "At this rate, you may not meet the deadline." This feedback is sent to the subordinate device.

[0044] Evaluation and Reporting

[0045] When a user (subordinate) completes a task, he / she reports the result to the server. For example, he / she might type, "I have completed a market analysis report." The server receives this report and evaluates the task. The server uses an evaluation algorithm to evaluate the quality and outcome of the task, and generates a report to report the evaluation results to the superior. The report is then sent from the server to the superior's terminal.

[0046] Change boss type

[0047] The user (subordinate) can change the type of AI boss from the settings screen. For example, if a subordinate thinks, "I want to change my leadership style," he or she clicks the "Change boss type" option from the settings menu. The device sends this change request to the server. The server receives this request and sets the new boss type in the database. The subordinate's task management algorithm is updated based on the new boss type.

[0048] Specific examples

[0049] Example of receiving and analyzing task instructions

[0050] A superior gives instructions to the AI ​​system: "Conduct market research for a new product and submit a report within two weeks."

[0051] The server analyzes: The task is "conduct market research" and the deadline is "submit the report within two weeks" and records this in the database.

[0052] Examples of task progress management and feedback

[0053] Subordinate reports progress: Enters "Task ID 5678 is 30% complete."

[0054] The server evaluates and provides feedback: "Progress is behind schedule. Do you need help?" feedback is generated and sent to the subordinate's device.

[0055] These processes enable the AI ​​boss to effectively manage tasks and provide appropriate feedback when necessary while ensuring psychological safety for subordinates, thereby improving organizational productivity and workplace mental health.

[0056] The processing flow will be explained below.

[0057] Step 1:

[0058] A task instruction is input from the superior's terminal and sent to the AI ​​system. For example, "Please create a new market analysis report by next week."

[0059] Step 2:

[0060] The server receives task instructions sent by superiors and stores them in a task instruction database.

[0061] Step 3:

[0062] The server passes the received task instructions to a natural language processing engine, which analyzes the task content and deadline. For example, it extracts "Create a market analysis report" and "By next week."

[0063] Step 4:

[0064] The server stores the analysis results in a database and retrieves the subordinates' skill sets and current task status from the database.

[0065] Step 5:

[0066] The server runs an algorithm to identify the most suitable subordinate based on the acquired skill set and task situation.

[0067] Step 6:

[0068] The server assigns tasks to the identified subordinates and stores the results in a database.

[0069] Step 7:

[0070] The terminal (subordinate device) receives a notification of a new task, e.g., a specified task and deadline.

[0071] Step 8:

[0072] The terminal periodically reports the progress of the task to the server. Example: "Progress of task ID 1234 is 50%."

[0073] Step 9:

[0074] The server records the received progress reports in a database and uses a progress evaluation algorithm to compare the report status with deadlines.

[0075] Step 10:

[0076] If the progress is not as planned, the server uses a feedback generation engine to generate appropriate feedback, e.g., "At this rate, you may not meet the deadline."

[0077] Step 11:

[0078] The terminal (subordinate device) receives the feedback message from the server as a notification.

[0079] Step 12:

[0080] After completing a task, the user (subordinate) reports the results of the work to the server. For example, the user might write, "I have completed the market analysis report."

[0081] Step 13:

[0082] The server receives the reports and uses a rating algorithm to rate the quality and success of the tasks.

[0083] Step 14:

[0084] The server generates a report based on the evaluation results and sends it to the superior's terminal, which includes the evaluation results and a link to the completed market analysis report.

[0085] Step 15:

[0086] Users (subordinates) can change the type of AI boss from the settings screen. For example, they can think, "Let's try changing the leadership style," and select an option.

[0087] Step 16:

[0088] The terminal transmits the selection of the new boss type to the server.

[0089] Step 17:

[0090] The server receives the new boss type setting, updates the database, and changes the task management algorithm of subordinates.

[0091] Example 1

[0092] 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."

[0093] In a typical workplace, direct instructions and feedback from superiors can cause subordinates to feel psychological stress. In such situations, there are concerns about a decline in productivity and a worsening work environment. The purpose of this invention is to solve these problems and increase psychological safety.

[0094] 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.

[0095] In this invention, the server includes means for receiving task instructions entered by a superior, means for analyzing the received task instructions using natural language processing, means for assigning tasks to appropriate subordinates based on the analyzed task instructions, means for acquiring the subordinates' skill sets and current task statuses and identifying the most suitable subordinates, means for sending notifications of new tasks to the subordinates, means for managing task progress reported by the subordinates and providing feedback on the progress, means for evaluating tasks completed by the subordinates and reporting the evaluation results to the superior, and means for changing the type of AI boss according to the subordinates' selection. This enables effective task management without causing psychological stress to subordinates, improving workplace productivity and mental health.

[0096] "Superior" refers to a manager or leader who has the authority to give instructions within an organization.

[0097] "Task instructions" refers to specific instructions given by a superior to a subordinate, such as the work content, goals, deadlines, etc.

[0098] "Natural language processing" is a technology that allows computers to understand and analyze human language.

[0099] "Analysis" refers to the act of breaking down received task instructions and extracting information such as work content and deadlines.

[0100] A "subordinate" is someone who performs tasks under the direction of a superior within an organization.

[0101] A "skill set" refers to the totality of a subordinate's abilities and expertise.

[0102] "Task status" refers to the progress and completion status of the work that a subordinate is currently responsible for.

[0103] "Progress" refers to the degree of progress and completion of tasks assigned to subordinates.

[0104] "Feedback" refers to evaluation and advice provided on progress and work content.

[0105] "Evaluation" is the process of judging the quality or success of a completed task.

[0106] "AI boss type" refers to the leadership style and teaching methods simulated by the AI.

[0107] "Notifications" refer to messages or alerts that communicate information about task assignments and progress to subordinates.

[0108] A "database" is a system that efficiently stores and manages digital information.

[0109] An "algorithm" refers to a computational procedure or process for solving a particular problem.

[0110] "Machine learning" is a technology that allows computers to learn by themselves using empirical data and improve their performance.

[0111] This invention is a system that improves psychological safety in the workplace by constructing an AI system that takes on the role of middle managers and eliminates direct intervention by superiors. As an implementation form of this system, we will explain how the server, terminals, and users participate in this system and play their respective roles.

[0112] Server Roles

[0113] The server receives task instructions from superiors and analyzes them using a natural language processing engine. This system preferably uses natural language processing engines such as Google NLP API or IBM Watson. The analyzed task instructions are broken down into work content and deadlines and stored in a database (MySQL, PostgreSQL, etc.). The server then retrieves the subordinates' skill sets and current task status from the database and identifies the most suitable subordinates using machine learning algorithms (e.g., Random Forest, SVM).

[0114] As subordinates progress with assigned tasks, the server receives progress reports and evaluates the progress using an evaluation algorithm (e.g., linear regression model). Feedback is generated as needed and sent to the subordinate's device. Furthermore, when the subordinate completes the task, the server evaluates the results and generates a report based on the evaluation results to be sent to the superior's device.

[0115] Device Role

[0116] The terminals are devices primarily used by subordinates and superiors. The subordinate's terminal receives notifications of new tasks sent from the server and provides an interface for reporting progress to the server. The superior's terminal receives task analysis results and progress evaluation reports. It also provides an interface for subordinates to send requests from the settings screen if they want to change the type of AI boss.

[0117] User Roles

[0118] The user (subordinate) reports the progress of assigned tasks to the server from their device and proceeds with the work while checking feedback as needed. When the task is completed, the user reports the completion and sends the results to the server. In addition, the user can change the type of AI boss from the settings screen.

[0119] As a concrete example, if a superior inputs a task instruction into an AI system, such as "Conduct market research for a new product and submit a report within two weeks," the server will analyze the instruction using natural language processing and record the task content and deadline in a database. If a subordinate reports progress as "Task ID 5678 is 30% complete," the server will evaluate the progress, generate feedback such as "Progress is behind schedule. Do you need assistance?" and send it to the subordinate's device.

[0120] In this way, the AI ​​system of the present invention aims to improve workplace productivity and mental health by effectively managing tasks while ensuring the psychological safety of subordinates.

[0121] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0122] Step 1: Receiving task instructions

[0123] The server receives task instructions from superiors. The input is a natural language task instruction such as "I want you to create a new market analysis report by next week." The server receives this input and passes it on to the next step.

[0124] Step 2: Parsing task instructions

[0125] The server analyzes the received task instructions using a natural language processing engine (e.g., Google NLP API, IBM Watson). The input is the task instruction received in step 1, which is analyzed and broken down into the work content ("Create a market analysis report") and deadline ("By next week"). The output is the analyzed task content and deadline, which are stored in a database.

[0126] Step 3: Obtain information about your subordinates

[0127] The server retrieves the subordinate's skill set and current task status from the database. The input is the subordinate's information stored in the database, including data on the skill set and progress status. The output is the retrieved subordinate's information, which is passed to the next task assignment step.

[0128] Step 4: Assign tasks

[0129] The server uses a machine learning algorithm (e.g., Random Forest, SVM) to identify the most suitable subordinate based on the acquired information about the subordinates. The input is the analyzed task content and deadline, and the acquired information about the subordinates. The algorithm identifies which subordinate to assign the task to. The output is the task assignment result, and the information about the task assigned to the specific subordinate is saved in a database.

[0130] Step 5: Sending task notifications

[0131] The server sends a notification of a new task to the subordinate's terminal. The input is the result of task assignment, which is information about which task has been assigned to which subordinate. The output is the task notification message sent to the subordinate's terminal. The notification includes the task content and deadline.

[0132] Step 6: Enter progress reports

[0133] The user (subordinate) inputs the progress status of a task from a terminal. For example, "Progress of task ID 1234 is 50%." The input is a progress report, indicating the progress of each task and how much progress has been made. The output is progress information, which is sent to the server.

[0134] Step 7: Record your progress

[0135] The server stores the received progress reports in a database. The input is the progress information sent in step 6. The output is the progress information recorded in the database, which is passed to the next progress evaluation step.

[0136] Step 8: Evaluate progress and generate feedback

[0137] The server evaluates the progress information using a progress evaluation algorithm (e.g., a linear regression model). The input is the progress information and the task deadlines recorded in a dictionary. The algorithm compares the progress with the deadlines and generates feedback, if necessary, such as "At this rate, you may not meet the deadline." The output is the generated feedback message, which is passed to the next feedback sending step.

[0138] Step 9: Submit your feedback

[0139] The server sends the generated feedback to the terminal (subordinate's device). The input is the evaluated progress information and the generated feedback message. The output is the feedback message sent to the subordinate's terminal. The subordinate checks this and adjusts the work pace as necessary.

[0140] Step 10: Reporting Task Completion

[0141] When a user (subordinate) completes a task, he / she reports the result to the server. The input is a task completion report such as "I have completed the market analysis report." The output is the report data of the completed task, which is sent to the server.

[0142] Step 11: Evaluation and Report Generation

[0143] The server receives the task completion reports and evaluates the quality and performance of the tasks using an evaluation algorithm (e.g., a rule-based evaluation model). The input is the completion report data. The algorithm generates an evaluation score, which is then used to generate a report to be reported to superiors. The output is the generated evaluation report.

[0144] Step 12: Submit the report

[0145] The server sends the evaluation report to the superior's terminal. The input is the generated evaluation report. The output is the evaluation report sent to the superior's terminal. The superior checks it and provides necessary feedback or additional instructions.

[0146] Step 13: Request a change of manager type

[0147] The user (subordinate) selects the "Change supervisor type" option from the settings screen, selects a new supervisor type, and submits it. The input is a request to change supervisor type. The output is the request data, which is sent to the server.

[0148] Step 14: Update Manager Type

[0149] The server receives the boss type change request and updates the database settings. The input is the boss type change request. The output is the new boss type setting stored in the database, and the task management algorithm is updated.

[0150] (Application example 1)

[0151] 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."

[0152] In today's factory work environment, efficient task management and appropriate feedback to workers are difficult. Line workers, in particular, need to receive real-time instructions and manage progress, which can lead to stress and mistakes. There is a need for a system that minimizes direct intervention from superiors, ensures psychological safety for workers, and improves factory productivity.

[0153] 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.

[0154] In this invention, the server includes means for receiving task instructions entered by a superior, means for analyzing the received task instructions using natural language processing, means for assigning tasks to appropriate subordinates based on the analyzed task instructions, means for managing task progress reported by subordinates and providing feedback on the progress, means for evaluating tasks completed by subordinates and reporting the evaluation results to a superior, means for changing the AI ​​boss type according to the subordinate's selection, means for installing the AI ​​system in terminals used for work in the factory, means for visually presenting work content in real time via devices worn by line workers in the factory, and means for workers to report progress by voice input. This makes it easier for workers to understand task content in real time, enabling efficient progress management and appropriate feedback.

[0155] A "server" is a central processing unit that receives task instructions from superiors, analyzes them, and assigns tasks and manages their progress.

[0156] "Task instructions" are documents or orders that show specific work instructions given by superiors to subordinates.

[0157] "Natural language processing" is the technology for understanding, analyzing, and generating human language.

[0158] A "subordinate" is an employee who performs specific tasks under the direction of a superior.

[0159] "Progress" is a report that shows how much of a task a subordinate has completed.

[0160] "Feedback" is advice or notification from superiors to subordinates based on progress.

[0161] "Evaluation" means judging the quality and success of tasks completed by subordinates.

[0162] "AI Boss" is a system that uses artificial intelligence to take on the role of middle management.

[0163] "Terminal" refers to a device used by a worker, including, for example, smart glasses and a head-mounted display.

[0164] "Devices" refers to hardware such as smart glasses and head-mounted displays worn by line workers.

[0165] "Voice input" is an input method in which workers communicate information to the system by speaking.

[0166] "Real-time" refers to a state in which data transmission, reception, and processing are carried out immediately without delay.

[0167] The present invention is a system for receiving task instructions entered by superiors, assigning appropriate tasks to subordinates, managing progress, and providing feedback. The system is intended to be used by line workers in factories wearing smart glasses.

[0168] The server receives task instructions from superiors and analyzes them using natural language processing. The analyzed task instructions are stored in a database within the server. The server then retrieves the subordinates' skill sets and current task status from the database and assigns the tasks to the appropriate subordinates. The assigned tasks are then notified to the subordinates' smart glasses.

[0169] The smart glasses, which serve as terminals, visually display the work content to subordinates in real time. Workers use the smart glasses to check specific instructions and progress as they go about their work. Progress reports are made via voice input. For example, a report such as "Progress on task ID 5678 is 30%." The server receives the progress reports and uses a progress evaluation algorithm to compare the task progress with the deadline. If necessary, it generates feedback such as "Progress is behind schedule. Do you need help?" and sends it to the subordinate's terminal.

[0170] When the task is completed, the subordinate reports through the smart glasses, "I have completed the market analysis report." The server receives this report and evaluates the task. The evaluation results are then reported to the superior in the form of a report generated by the AI ​​boss. This allows the superior to check the subordinate's performance and provide appropriate feedback.

[0171] Subordinates can also change the type of their AI boss from the settings screen. This change request is sent to the server, and the new AI boss type is set in the database. The subordinate's task management algorithm is updated based on the new boss type.

[0172] This system configuration improves work efficiency in the factory and also ensures psychological safety for workers. As a concrete example, the server can receive, analyze, and assign the following task instructions to subordinates:

[0173] "Try out the new line work procedure and let us know the results within a week."

[0174] "Please inspect the product and report the results within two days."

[0175] This allows for timely feedback on progress, enabling efficient task management.

[0176] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0177] Step 1:

[0178] The server receives task instructions from superiors. The input task instructions are written in natural language and contain specific content such as "Please create a new market analysis report by next week." The server receives this task instruction and stores it in a database.

[0179] Step 2:

[0180] The server analyzes the received task instructions using a natural language processing engine (e.g., Spacy). It receives the task instructions as input and generates the analyzed task content and deadline information as output. It breaks them down into specific elements such as "Create a market analysis report" and "By next week." The analysis results are stored in a database.

[0181] Step 3:

[0182] The server assigns tasks to the most suitable subordinates based on the analyzed task instructions. The subordinates' skill sets and current task status are retrieved from the database and input into the algorithm. The algorithm evaluates the skill sets and task status, selects the most suitable subordinates, and outputs the assigned tasks. The subordinates' devices are notified of the assigned tasks.

[0183] Step 4:

[0184] The subordinate's device (smart glasses) receives the task notification sent from the server and visually presents it to the worker. The device displays task details and deadline information in an easy-to-read format for the worker. Once the worker confirms the information, the task is ready to begin.

[0185] Step 5:

[0186] The user (subordinate) reports the progress of the work to the server through the terminal. The progress is reported using the voice input function, and the user inputs "The progress of task ID 5678 is 30%." The server receives the report and stores it in the database.

[0187] Step 6:

[0188] The server manages progress based on the received progress status. Using a progress evaluation algorithm, it compares the reported progress with the deadline and generates feedback. For example, feedback such as "Progress is behind schedule. Do you need help?" is generated and sent to the subordinate's device.

[0189] Step 7:

[0190] When a user (subordinate) completes a task, he / she reports it to the server via a terminal. For example, he / she may type, "I have completed the market analysis report." The server receives this report and evaluates the quality and success of the task using an evaluation algorithm.

[0191] Step 8:

[0192] The server generates a report for the superior based on the evaluation results. The server retrieves the evaluation results from the database, compiles them into a report, and sends it to the superior's terminal. The superior can check this report to understand the performance of his subordinates.

[0193] Step 9:

[0194] Users (subordinates) can change the type of their AI boss from the settings screen. They input a change request on their device and send it to the server. The server receives this request and sets the new boss type in the database. When the setting is changed, the task management algorithm is updated based on the new boss type.

[0195] 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.

[0196] The present invention builds an AI system that takes on the role of middle managers, eliminating direct intervention by superiors to improve psychological safety in the workplace, and also incorporates an emotion engine to recognize the user's emotions and provide appropriate feedback and task management based on those emotions. Specific embodiments for implementing the present invention are described below.

[0197] Receiving and analyzing task instructions

[0198] The server receives task instructions from a superior. These task instructions have specific content, such as "Please create a new market analysis report by next week." The server receives these instructions and analyzes them using a natural language processing engine. The analyzed task instructions are broken down into the work content, "Create a market analysis report," and the deadline, "By next week." The results of this analysis are saved in the server's database.

[0199] Task assignment

[0200] The server then assigns the analyzed tasks to the appropriate subordinates. It retrieves the subordinates' skill sets and current task status from the database and runs an algorithm to identify the most suitable subordinate. Once the most suitable subordinate is identified, the task is assigned and the results are saved in the database. At the same time, a notification of the new task is sent to the subordinate's device.

[0201] Task progress management and feedback

[0202] The terminal (subordinate device) reports the task progress to the server. For example, input "Progress on task ID 1234 is 50%." The server receives the progress report and manages progress based on it. The server uses a progress evaluation algorithm to compare the reported progress with the deadline, and generates feedback as needed, such as "At this rate, you may not meet the deadline." This feedback is sent to the subordinate device.

[0203] Feedback using an emotion engine

[0204] The server is equipped with an emotion engine that obtains emotional data from subordinates' input, facial expressions, tone of voice, etc. The server uses this emotional data to adjust the feedback method. For example, if the server recognizes that a subordinate is feeling stressed, it can provide gentle feedback such as, "You seem to be behind on progress. Do you need any support?" This allows subordinates to work with greater peace of mind.

[0205] Evaluation and Reporting

[0206] After completing a task, the user (subordinate) reports the results to the server. For example, the user might type, "I have completed a market analysis report." The server receives this report and uses an evaluation algorithm to evaluate the quality and success of the task. Based on this evaluation, the server generates a report to be sent to the superior's terminal and sends it from the server to the superior's terminal.

[0207] Change boss type

[0208] The user (subordinate) can change the type of AI boss from the settings screen. For example, if a subordinate thinks, "I want to change my leadership style," he or she clicks the "Change boss type" option from the settings menu. The device sends this change request to the server. The server receives this request, sets the new boss type in the database, and changes the task management algorithm.

[0209] Specific examples

[0210] Example of receiving and analyzing task instructions

[0211] A superior gives instructions to the AI ​​system: "Conduct market research for a new product and submit a report within two weeks."

[0212] The server analyzes: The task is "conduct market research" and the deadline is "submit the report within two weeks" and records this in the database.

[0213] Examples of task progress management and feedback

[0214] Subordinate reports progress: Enters "Task ID 5678 is 30% complete."

[0215] The server evaluates and provides feedback: "Progress is behind schedule. Do you need help?" feedback is generated and sent to the subordinate's device.

[0216] Examples of emotion engines

[0217] Subordinate reports progress: Enters "Task ID 5678 is 30% complete."

[0218] The server analyzes using an emotion engine: it recognizes that "your subordinate is feeling stressed" and provides gentle feedback such as "progress seems to be lagging behind. Do you need any support?"

[0219] Through these processes, the AI ​​boss can effectively manage tasks and provide appropriate feedback when necessary while ensuring psychological safety for subordinates, thereby improving organizational productivity and workplace mental health.

[0220] The processing flow will be explained below.

[0221] The present invention is a system that builds an AI system that plays the role of middle management, eliminates direct intervention by superiors, and recognizes the user's emotions by combining an emotion engine, and provides appropriate feedback and task management based on the emotions. Specific embodiments for implementing the present invention will be described below, divided into processing steps.

[0222] Receiving and analyzing task instructions

[0223] Step 1:

[0224] A task instruction is input from the superior's terminal and sent to the AI ​​system. For example, "Please create a new market analysis report by next week."

[0225] Step 2:

[0226] The server receives task instructions sent by superiors and stores them in a task instruction database.

[0227] Step 3:

[0228] The server passes the received task instructions to a natural language processing engine, which analyzes the task content and deadline. For example, it extracts "Create a market analysis report" and "By next week."

[0229] Step 4:

[0230] The server stores the analysis results in a database.

[0231] Task assignment

[0232] Step 5:

[0233] The server obtains the subordinate's skill set and current task status from the database in order to appropriately assign the analyzed task to the subordinate.

[0234] Step 6:

[0235] The server runs an algorithm to identify the most suitable subordinate based on the acquired skill set and task situation.

[0236] Step 7:

[0237] The server assigns tasks to the identified subordinates and stores the results in a database.

[0238] Step 8:

[0239] Send a notification of a new task to the terminal (subordinate device).

[0240] Task progress management and feedback

[0241] Step 9:

[0242] The device reports the task progress to the server. Example: "Task ID 1234 progress is 50%."

[0243] Step 10:

[0244] The server receives the progress reports and records them in a database.

[0245] Step 11:

[0246] The server uses a progress evaluation algorithm to compare the reported progress with the deadline.

[0247] Step 12:

[0248] If the progress is not as planned, the server uses a feedback generation engine to generate appropriate feedback, e.g., "At this rate, you may not meet the deadline."

[0249] Step 13:

[0250] The terminal (subordinate device) receives a feedback message from the server as a notification.

[0251] Feedback using an emotion engine

[0252] Step 14:

[0253] The terminal (subordinate's device) collects the subordinate's emotion data through an emotion tracking device during task progress reports and other interactions.

[0254] Step 15:

[0255] The server uses an emotion engine to analyze the subordinates' emotional data, e.g., to detect stress or anxiety from their facial expressions, tone of voice, and text input.

[0256] Step 16:

[0257] The server adjusts the feedback method based on the emotional data. For example, if a subordinate feels stressed, the server provides feedback such as, "You seem to be behind in your progress. Do you need any support?"

[0258] Evaluation and Reporting

[0259] Step 17:

[0260] After completing a task, the user (subordinate) reports the results of the work to the server. For example, the user might write, "I have completed the market analysis report."

[0261] Step 18:

[0262] The server receives the reports and uses a rating algorithm to evaluate the quality and success of the tasks.

[0263] Step 19:

[0264] The server generates a report based on the evaluation results and sends it to the superior's terminal, which includes the evaluation results and a link to the completed market analysis report.

[0265] Change boss type

[0266] Step 20:

[0267] The user (subordinate) can change the type of AI boss from the settings screen. For example, if you think, "I want to change my leadership style," click the "Change boss type" option from the settings menu.

[0268] Step 21:

[0269] The terminal transmits the selection of the new boss type to the server.

[0270] Step 22:

[0271] The server receives the new manager type setting and updates the database with the changes.

[0272] Step 23:

[0273] The server changes the task management algorithm of the subordinates based on the new boss type.

[0274] Through these processes, the AI ​​boss can effectively manage tasks and provide feedback using an emotion engine while ensuring psychological safety for subordinates, thereby improving organizational productivity and workplace mental health.

[0275] Example 2

[0276] 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."

[0277] In traditional management systems, superiors often intervene directly, which can undermine the psychological safety of subordinates. Furthermore, traditional systems do not provide feedback that takes into account the emotions of subordinates, making effective task management difficult.

[0278] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0279] In this invention, the server includes means for receiving task instructions entered by a superior, means for analyzing the received task instructions using natural language processing, means for assigning tasks to appropriate subordinates based on the analyzed task instructions, means for managing task progress reported by subordinates and providing feedback on the progress, means for collecting emotional data on subordinates and adjusting the feedback method based on the data, means for evaluating tasks completed by subordinates and reporting the evaluation results to the superior, and means for changing the type of AI boss according to the subordinate's selection. This enables effective task management and appropriate feedback while ensuring psychological safety for subordinates.

[0280] A "superior" is a person in an organization who commands and orders subordinates.

[0281] "Task instructions" are instructions given by a superior to a subordinate that specify specific work content and deadlines.

[0282] "Natural language processing" is a technology that analyzes and understands human language on a computer.

[0283] A "subordinate" is someone who performs work under the command and order of a superior.

[0284] "Task assignment" refers to assigning a specific task to an appropriate person from among multiple subordinates.

[0285] "Progress" is information indicating the degree of completion of the tasks assigned to a subordinate.

[0286] "Feedback" refers to evaluation and advice on progress and results, with the aim of improving and supporting work.

[0287] "Emotion data" is data that indicates the emotional state of a subordinate, as determined from facial expressions, tone of voice, etc.

[0288] "Evaluation" is the process of making judgments based on the quality and results of completed tasks.

[0289] "Boss type" refers to the type of leadership style and management method that an AI boss possesses.

[0290] This invention aims to improve psychological safety in the workplace by reducing direct intervention by superiors using an artificial intelligence (AI) system that plays the role of middle managers. The system recognizes users' emotions through an emotion engine and provides appropriate feedback and task management based on that information.

[0291] Receiving and analyzing task instructions

[0292] The server receives specific task instructions from its superior, which are sent as HTTP requests to API endpoints.

[0293] For example, a user might receive an instruction such as "Create a new market analysis report by next week." The instruction is parsed using the Google Cloud Natural Language API and broken down into tasks and deadlines. The analysis results are then stored in a MySQL database.

[0294] Task assignment

[0295] The server retrieves the subordinates' skill sets and task status from a MySQL database, executes a custom algorithm written in Python, and assigns tasks to the most suitable subordinates. The assignment results are stored in the database, and new task notifications are sent to the subordinates' devices via a push notification service.

[0296] Task progress management and feedback

[0297] The terminal (subordinate device) reports its progress to the server. For example, it inputs and sends "Task ID 1234 is 50% complete." The server receives the progress report and evaluates it using a progress evaluation algorithm. If necessary, it generates feedback such as "At this rate, you may not meet the deadline," and sends it to the subordinate device.

[0298] Feedback using an emotion engine

[0299] The server uses the Affectiva SDK to collect emotional data from subordinates' input, facial expressions, and tone of voice. Based on this emotional data, the server adjusts the feedback provided to the subordinate's device, providing gentle feedback such as, "You seem to be behind schedule. Do you need any help?"

[0300] Evaluation and Reporting

[0301] After completing the task, the user (subordinate) reports to the server, "I have completed the market analysis report." The server receives the report and performs task evaluation using a Python script. Based on the evaluation results, a report to be reported to the superior is generated and sent to the superior's terminal in HTML or PDF format.

[0302] Change boss type

[0303] The user (subordinate) requests a change of supervisor type from the settings screen. The subordinate's device sends this change request to the server. The server receives the request and updates the database using an SQL UPDATE statement to change the supervisor type.

[0304] Specific examples

[0305] Example of receiving and analyzing task instructions

[0306] A superior gives instructions to the AI ​​system: "Conduct market research for a new product and submit a report within two weeks."

[0307] The server analyzes: The task "conduct market research" and the deadline "submit report within two weeks" are analyzed and recorded in a database.

[0308] Examples of task progress management and feedback

[0309] Subordinate reports progress: Enters "Task ID 5678 is 30% complete."

[0310] The server evaluates and provides feedback: "Progress is behind schedule. Do you need help?" feedback is generated and sent to the subordinate's device.

[0311] Examples of emotion engines

[0312] Subordinate reports progress: Enters "Task ID 5678 is 30% complete."

[0313] The server analyzes using an emotion engine: it recognizes that "your subordinate is feeling stressed" and provides gentle feedback such as "progress seems to be lagging behind. Do you need any support?"

[0314] Example of input prompt for generative AI model

[0315] "We've received a request to create a new market analysis report by next week. Please parse this request using a natural language processing engine to extract the task and deadline."

[0316] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0317] Step 1:

[0318] The server receives a task instruction from a superior saying, "I want you to create a new market analysis report by next week."

[0319] Specific operation: A task instruction is sent as an HTTP request to an API endpoint.

[0320] Input: Task instructions from superiors.

[0321] Output: An HTTP request containing the task instructions.

[0322] Step 2:

[0323] The server parses the received task instructions using the Google Cloud Natural Language API.

[0324] Specific operation: Send an API request and receive the analysis results as a response.

[0325] Input: An HTTP request containing task instructions.

[0326] Output: Job description: "Create a market analysis report" with deadline "by next week."

[0327] Step 3:

[0328] The server stores the analysis results in a MySQL database.

[0329] Specific operation: Inserts data into the database using the SQL INSERT statement.

[0330] Input: "Create market analysis report" and analysis results "by next week".

[0331] Output: Task instructions and deadlines recorded in a database.

[0332] Step 4:

[0333] The server retrieves the subordinate's skill set and current task status from the database.

[0334] What it does: Uses a SQL SELECT statement to query for the information you need.

[0335] Input: Subordinate ID information.

[0336] Output: Skill set and task status data for each subordinate.

[0337] Step 5:

[0338] The server runs a custom algorithm written in Python to assign tasks to the best subordinates.

[0339] Specific behavior: The algorithm evaluates subordinates' skill sets and task situations and selects the most suitable subordinate.

[0340] Inputs: Subordinate skill set, current task status, task instructions.

[0341] Output: Information about the subordinates who have been assigned the task.

[0342] Step 6:

[0343] The server stores the task assignment results in a database and sends new task notifications to the subordinate terminals.

[0344] Specific operation: Inserts data into the database using an SQL INSERT statement and sends a notification to the subordinate's device via the push notification service.

[0345] Input: Task assignment results.

[0346] Output: Task assignment results stored in the database and notifications sent to subordinates' devices.

[0347] Step 7:

[0348] The terminals (subordinate devices) report their progress to the server.

[0349] Specific operation: The subordinate enters the progress status and sends it to the server as an HTTP POST request.

[0350] Input: A progress report such as "Task ID 1234 is 50% complete."

[0351] Output: Progress report sent to the server.

[0352] Step 8:

[0353] The server receives the progress reports and evaluates them using a progress evaluation algorithm.

[0354] Specific actions: Compare progress with deadlines and generate evaluation results.

[0355] Input: Progress report from subordinate.

[0356] Output: Feedback such as "At this rate, you may not meet the deadline."

[0357] Step 9:

[0358] The server generates feedback as needed and sends it to the subordinate terminals.

[0359] Specific actions: Feedback is automatically generated based on progress evaluation results and sent to subordinates via push notification or email.

[0360] Input: Progress assessment results.

[0361] Output: Feedback sent to subordinate devices.

[0362] Step 10:

[0363] The server uses the Affectiva SDK to collect emotional data from subordinates' inputs, facial expressions, and tone of voice.

[0364] Specific operation: Data is acquired from subordinate devices via the camera and microphone, and analyzed using the SDK.

[0365] Input: Subordinates' facial expressions and tone of voice.

[0366] Output: Parsed emotion data.

[0367] Step 11:

[0368] The server adjusts the feedback content based on the emotional data and sends it to the subordinate's device.

[0369] Specific action: Analyze emotional data and generate appropriate feedback content.

[0370] Input: Emotion data.

[0371] Output: Gentle feedback such as, "You seem to be making slow progress, do you need any help?"

[0372] Step 12:

[0373] After completing the task, the user (subordinate) reports to the server, "I have completed the market analysis report."

[0374] Specific operation: Enter a task completion report and send it to the server as an HTTP POST request.

[0375] Input: Task completion report.

[0376] Output: Task completion report sent to the server.

[0377] Step 13:

[0378] The server evaluates the report and evaluates the task quality and performance.

[0379] Specific operation: A Python script is used to analyze the report content and generate a rating score.

[0380] Input: Task completion report.

[0381] Output: Evaluation score.

[0382] Step 14:

[0383] The server generates a report to report to the superior based on the evaluation results and transmits it to the superior's terminal.

[0384] Specific Actions: Based on the assessment results, a report is generated in HTML or PDF format and sent via email or internal messaging system.

[0385] Input: Rating score.

[0386] Output: Report sent to superior's terminal.

[0387] Step 15:

[0388] The user (subordinate) requests a change of superior type from the settings screen.

[0389] Specific actions: Select the Change Manager Type option and submit a change request.

[0390] Input: Supervisor type change request.

[0391] Output: The change request sent to the server.

[0392] Step 16:

[0393] The terminal sends a change request to the server.

[0394] Specific operation: A change request is sent to the server as an HTTP POST request.

[0395] Input: Supervisor type change request.

[0396] Output: The change request sent to the server.

[0397] Step 17:

[0398] The server receives the request and updates the database with the new boss type.

[0399] Specific Actions: Update the database using a SQL UPDATE statement to apply the task management algorithm based on the new supervisor type.

[0400] Input: Supervisor type change request.

[0401] Output: The manager type information updated in the database.

[0402] (Application example 2)

[0403] 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."

[0404] The problem to be solved by this invention is to eliminate direct intervention by superiors, improve psychological safety in the workplace, and provide appropriate task management and feedback. In particular, the object is to reduce stress for subordinates and support efficient work performance by recognizing subordinates' emotions in real time and adjusting feedback methods based on those emotions.

[0405] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0406] In this invention, the server includes means for receiving task instructions entered by a superior, means for analyzing the received task instructions using natural language processing, means for assigning tasks to appropriate subordinates based on the analyzed task instructions, means for managing task progress reported by subordinates and providing feedback on the progress, means for evaluating tasks completed by subordinates and reporting the evaluation results to the superior, means for using an emotion recognition engine that recognizes the emotions of subordinates and adjusts the feedback content based on the emotions, and means for changing the type of AI boss according to the selection of the subordinate. This enables flexible feedback and progress management according to the emotions of subordinates.

[0407] "Task instructions" refer to specific work content and deadlines instructed by a superior to a subordinate.

[0408] "Natural language processing" is a technology that uses computers to analyze, understand, and generate human language (natural language).

[0409] "Task progress" refers to the status that indicates how much a subordinate has performed on a given task and how close they are to completing it.

[0410] "Feedback" refers to the guidance and evaluation provided to subordinates by superiors or systems based on task progress.

[0411] An "emotion recognition engine" is a technology that analyzes a subordinate's emotional state from their input, facial expressions, tone of voice, etc.

[0412] A "generative AI model" is an artificial intelligence model that has been trained to generate results for a specific task.

[0413] A "skill set" is a collection of knowledge, abilities, experience, etc. that a subordinate possesses that are relevant to a specific job.

[0414] "AI Boss" is a management system with artificial intelligence that assigns tasks and provides feedback to subordinates.

[0415] "Task management" is the process of monitoring task progress and providing adjustments and feedback.

[0416] "Psychological safety" refers to a state in the workplace where members feel safe to express their opinions and feelings.

[0417] "Progress management" is the process of making sure that tasks are progressing as planned and making adjustments as necessary.

[0418] An "evaluation algorithm" is a calculation method for quantitatively or qualitatively evaluating the work performance of subordinates upon task completion.

[0419] The present invention is a system that builds an AI system that takes on the role of middle managers, improves psychological safety in the workplace by eliminating direct intervention by superiors, and recognizes the user's emotions by combining it with an emotion recognition engine, and provides appropriate feedback and task management based on the emotions. Specific embodiments for implementing the present invention are described in detail below.

[0420] Receiving and analyzing task instructions

[0421] The server receives task instructions input by a superior. For example, the superior's input may be specific, such as "Please create a new market analysis report by next week." The server receives this instruction and analyzes it using a natural language processing engine. The analyzed task instruction is broken down into the work content, "Create a market analysis report," and the deadline, "By next week," and the analysis results are saved in the server's database.

[0422] Task assignment

[0423] The server assigns the analyzed tasks to subordinates. It retrieves the subordinates' skill sets and current task status from the database and runs an algorithm to identify the most suitable subordinate. Once the most suitable subordinate is identified, the task is assigned to that subordinate and the result is saved in the database. At the same time, a notification of the new task is sent to the subordinate's device.

[0424] Task progress management and feedback

[0425] The subordinate's device reports the task progress to the server. For example, "Progress on task ID 1234 is 50%." The server receives the progress report and manages the progress based on it. The server uses a progress evaluation algorithm to compare the reported progress with the deadline, and generates feedback such as "At this rate, you may not meet the deadline." This feedback is sent to the subordinate's device.

[0426] Feedback using an emotion recognition engine

[0427] The server is equipped with an emotion recognition engine that obtains emotional data from subordinates' input, facial expressions, tone of voice, etc. The server uses this emotional data to adjust the feedback method. For example, if the server recognizes that a subordinate is feeling stressed, it can provide gentle feedback such as, "You seem to be behind on progress. Do you need any support?"

[0428] Evaluation and Reporting

[0429] After completing the task, the subordinate reports the results to the server, entering "I have completed the market analysis report." The server receives this report and uses an evaluation algorithm to evaluate the quality and results of the task. Based on this evaluation result, a report is generated for reporting to the superior, and sent from the server to the superior's terminal.

[0430] Change boss type

[0431] Subordinates can change the type of their AI boss from the settings screen. For example, if a subordinate decides to "change their leadership style," they can click the "Change boss type" option from the settings menu. The device then sends this change request to the server. The server receives this request, sets the new boss type in the database, and changes the task management algorithm.

[0432] Specific examples

[0433] Example of receiving and analyzing task instructions

[0434] A superior gives instructions to the AI ​​system: "Conduct market research for a new product and submit a report within two weeks."

[0435] The server analyzes: The task is "conduct market research" and the deadline is "submit the report within two weeks" and records this in the database.

[0436] Examples of task progress management and feedback

[0437] Subordinate reports progress: Enters "Task ID 5678 is 30% complete."

[0438] The server evaluates and provides feedback: "Progress is behind schedule. Do you need help?" feedback is generated and sent to the subordinate's device.

[0439] Example of an emotion recognition engine

[0440] Subordinate reports progress: Enters "Task ID 5678 is 30% complete."

[0441] The server analyzes using an emotion recognition engine: it recognizes that "your subordinate is feeling stressed" and provides gentle feedback such as "You seem to be making slow progress. Do you need any support?"

[0442] Through these processes, the AI ​​boss can effectively manage tasks and provide appropriate feedback when necessary while ensuring psychological safety for subordinates, thereby improving organizational productivity and workplace mental health.

[0443] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0444] Step 1:

[0445] The server receives task instructions entered by superiors.

[0446] Specifically, when a superior inputs an instruction into the system such as "Please create a new market analysis report by next week," the server receives this task instruction as a string of characters.

[0447] Input: Task instructions from superiors

[0448] Output: Raw task instruction data (string format)

[0449] Step 2:

[0450] The server analyzes the received task instructions using a natural language processing engine.

[0451] Specifically, the server breaks down the task instructions into components such as "create a market analysis report" and "by next week," and stores this information in a database.

[0452] Input: Raw task instruction data

[0453] Output: Parsed task details and deadlines

[0454] Step 3:

[0455] The server assigns tasks to subordinates based on the parsed task instructions.

[0456] Specifically, the server retrieves the subordinates' skill sets and current task status from the database, identifies the most suitable subordinate, and assigns the task to that subordinate.

[0457] Input: Analyzed task content, subordinate skill set, current task status

[0458] Output: Task assignment notification sent to subordinate's device

[0459] Step 4:

[0460] The subordinate terminals report the progress of the tasks to the server.

[0461] Specifically, the subordinate enters the task progress as "Progress of task ID 1234 is 50%" and sends this progress data to the server.

[0462] Input: Progress report data from subordinates

[0463] Output: Progress data stored on the server

[0464] Step 5:

[0465] The server manages the progress based on the progress reports and generates feedback as needed.

[0466] Specifically, the server uses a progress evaluation algorithm to compare the reported progress with the deadline, generates feedback such as "At this rate, you may not meet the deadline," and sends it to the subordinate's device.

[0467] Input: Progress report data

[0468] Output: Feedback message

[0469] Step 6:

[0470] The server recognizes the emotions of the subordinates using an emotion recognition engine and adjusts the feedback content.

[0471] Specifically, it extracts emotional data from the text of progress reports and the input of subordinates, and if it recognizes emotions such as "feeling stressed," it generates gentle feedback such as "You seem to be making slow progress. Do you need any support?"

[0472] Input: Progress report text data

[0473] Output: Emotion-based regulatory feedback

[0474] Step 7:

[0475] After completing a task, the subordinate reports the result to the server.

[0476] Specifically, the subordinate enters "Market analysis report completed" and sends the task completion report to the server.

[0477] Input: Task completion report data

[0478] Output: Task completion report saved on the server

[0479] Step 8:

[0480] The server receives the task completion reports and evaluates the quality and success of the tasks using an evaluation algorithm.

[0481] Specifically, the task results are evaluated quantitatively or qualitatively using an evaluation algorithm, and a report is generated to report the evaluation results to superiors.

[0482] Input: Task completion report data

[0483] Output: Evaluation report for superiors

[0484] Step 9:

[0485] Subordinates can change the type of AI boss they have from the settings screen.

[0486] Specifically, the subordinate selects the "Change Supervisor Type" option from the settings menu and sends a change request to the server. The server receives this request, sets the new supervisor type in the database, and changes the task management algorithm.

[0487] Input: Request from subordinate to change supervisor type

[0488] Output: New manager type set in the database

[0489] Example prompt sentence:

[0490] "Progress of task ID xxxx is yy%"

[0491] "The user's progress report text is sent to the emotion engine for emotion analysis."

[0492] "Market analysis report completed."

[0493] 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.

[0494] 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.

[0495] 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.

[0496] [Second embodiment]

[0497] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0498] 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.

[0499] 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).

[0500] 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.

[0501] 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.

[0502] 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).

[0503] 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.

[0504] 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.

[0505] 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.

[0506] 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.

[0507] 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.

[0508] 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."

[0509] The present invention is a system that improves psychological safety in the workplace by building an AI system that takes on the role of middle managers and eliminates direct intervention by superiors. Specific embodiments for implementing the present invention are described below.

[0510] Receiving and analyzing task instructions

[0511] The server receives task instructions from a superior. These task instructions have specific content, such as "Please create a new market analysis report by next week." The server receives these instructions and analyzes them using a natural language processing engine. The analyzed task instructions are broken down into the work content, "Create a market analysis report," and the deadline, "By next week." The results of this analysis are saved in the server's database.

[0512] Task assignment

[0513] The server then assigns the analyzed tasks to the appropriate subordinates. It retrieves the subordinates' skill sets and current task status from the database and runs an algorithm to identify the most suitable subordinate. Once the most suitable subordinate is identified, the task is assigned and the results are saved in the database. At the same time, a notification of the new task is sent to the subordinate's device.

[0514] Task progress management and feedback

[0515] The terminal (subordinate device) reports the progress of the task to the server. For example, it inputs "Progress on task ID 1234 is 50%." The server receives the progress report and manages the progress based on it. The server uses a progress evaluation algorithm to compare the reported progress with the deadline, and generates feedback as needed, such as "At this rate, you may not meet the deadline." This feedback is sent to the subordinate device.

[0516] Evaluation and Reporting

[0517] When a user (subordinate) completes a task, he / she reports the result to the server. For example, he / she might type, "I have completed a market analysis report." The server receives this report and evaluates the task. The server uses an evaluation algorithm to evaluate the quality and outcome of the task, and generates a report to report the evaluation results to the superior. The report is then sent from the server to the superior's terminal.

[0518] Change boss type

[0519] The user (subordinate) can change the type of AI boss from the settings screen. For example, if a subordinate thinks, "I want to change my leadership style," he or she clicks the "Change boss type" option from the settings menu. The device sends this change request to the server. The server receives this request and sets the new boss type in the database. The subordinate's task management algorithm is updated based on the new boss type.

[0520] Specific examples

[0521] Example of receiving and analyzing task instructions

[0522] A superior gives instructions to the AI ​​system: "Conduct market research for a new product and submit a report within two weeks."

[0523] The server analyzes: The task is "conduct market research" and the deadline is "submit the report within two weeks" and records this in the database.

[0524] Examples of task progress management and feedback

[0525] Subordinate reports progress: Enters "Task ID 5678 is 30% complete."

[0526] The server evaluates and provides feedback: "Progress is behind schedule. Do you need help?" feedback is generated and sent to the subordinate's device.

[0527] These processes enable the AI ​​boss to effectively manage tasks and provide appropriate feedback when necessary while ensuring psychological safety for subordinates, thereby improving organizational productivity and workplace mental health.

[0528] The processing flow will be explained below.

[0529] Step 1:

[0530] A task instruction is input from the superior's terminal and sent to the AI ​​system. For example, "Please create a new market analysis report by next week."

[0531] Step 2:

[0532] The server receives task instructions sent by superiors and stores them in a task instruction database.

[0533] Step 3:

[0534] The server passes the received task instructions to a natural language processing engine, which analyzes the task content and deadline. For example, it extracts "Create a market analysis report" and "By next week."

[0535] Step 4:

[0536] The server stores the analysis results in a database and retrieves the subordinates' skill sets and current task status from the database.

[0537] Step 5:

[0538] The server runs an algorithm to identify the most suitable subordinate based on the acquired skill set and task situation.

[0539] Step 6:

[0540] The server assigns tasks to the identified subordinates and stores the results in a database.

[0541] Step 7:

[0542] The terminal (subordinate device) receives a notification of a new task, e.g., a specified task and deadline.

[0543] Step 8:

[0544] The terminal periodically reports the progress of the task to the server. Example: "Progress of task ID 1234 is 50%."

[0545] Step 9:

[0546] The server records the received progress reports in a database and uses a progress evaluation algorithm to compare the report status with deadlines.

[0547] Step 10:

[0548] If the progress is not as planned, the server uses a feedback generation engine to generate appropriate feedback, e.g., "At this rate, you may not meet the deadline."

[0549] Step 11:

[0550] The terminal (subordinate device) receives the feedback message from the server as a notification.

[0551] Step 12:

[0552] After completing a task, the user (subordinate) reports the results of the work to the server. For example, the user might write, "I have completed the market analysis report."

[0553] Step 13:

[0554] The server receives the reports and uses a rating algorithm to rate the quality and success of the tasks.

[0555] Step 14:

[0556] The server generates a report based on the evaluation results and sends it to the superior's terminal, which includes the evaluation results and a link to the completed market analysis report.

[0557] Step 15:

[0558] Users (subordinates) can change the type of AI boss from the settings screen. For example, they can think, "Let's try changing the leadership style," and select an option.

[0559] Step 16:

[0560] The terminal transmits the selection of the new boss type to the server.

[0561] Step 17:

[0562] The server receives the new boss type setting, updates the database, and changes the task management algorithm of subordinates.

[0563] Example 1

[0564] 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."

[0565] In a typical workplace, direct instructions and feedback from superiors can cause subordinates to feel psychological stress. In such situations, there are concerns about a decline in productivity and a worsening work environment. The purpose of this invention is to solve these problems and increase psychological safety.

[0566] 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.

[0567] In this invention, the server includes means for receiving task instructions entered by a superior, means for analyzing the received task instructions using natural language processing, means for assigning tasks to appropriate subordinates based on the analyzed task instructions, means for acquiring the subordinates' skill sets and current task statuses and identifying the most suitable subordinates, means for sending notifications of new tasks to the subordinates, means for managing task progress reported by the subordinates and providing feedback on the progress, means for evaluating tasks completed by the subordinates and reporting the evaluation results to the superior, and means for changing the type of AI boss according to the subordinates' selection. This enables effective task management without causing psychological stress to subordinates, improving workplace productivity and mental health.

[0568] "Superior" refers to a manager or leader who has the authority to give instructions within an organization.

[0569] "Task instructions" refers to specific instructions given by a superior to a subordinate, such as the work content, goals, deadlines, etc.

[0570] "Natural language processing" is a technology that allows computers to understand and analyze human language.

[0571] "Analysis" refers to the act of breaking down received task instructions and extracting information such as work content and deadlines.

[0572] A "subordinate" is someone who performs tasks under the direction of a superior within an organization.

[0573] A "skill set" refers to the totality of a subordinate's abilities and expertise.

[0574] "Task status" refers to the progress and completion status of the work that a subordinate is currently responsible for.

[0575] "Progress" refers to the degree of progress and completion of tasks assigned to subordinates.

[0576] "Feedback" refers to evaluation and advice provided on progress and work content.

[0577] "Evaluation" is the process of judging the quality or success of a completed task.

[0578] "AI boss type" refers to the leadership style and teaching methods simulated by the AI.

[0579] "Notifications" refer to messages or alerts that communicate information about task assignments and progress to subordinates.

[0580] A "database" is a system that efficiently stores and manages digital information.

[0581] An "algorithm" refers to a computational procedure or process for solving a particular problem.

[0582] "Machine learning" is a technology that allows computers to learn by themselves using empirical data and improve their performance.

[0583] This invention is a system that improves psychological safety in the workplace by constructing an AI system that takes on the role of middle managers and eliminates direct intervention by superiors. As an implementation form of this system, we will explain how the server, terminals, and users participate in this system and play their respective roles.

[0584] Server Roles

[0585] The server receives task instructions from superiors and analyzes them using a natural language processing engine. This system preferably uses natural language processing engines such as Google NLP API or IBM Watson. The analyzed task instructions are broken down into work content and deadlines and stored in a database (MySQL, PostgreSQL, etc.). The server then retrieves the subordinates' skill sets and current task status from the database and identifies the most suitable subordinates using machine learning algorithms (e.g., Random Forest, SVM).

[0586] As subordinates progress with assigned tasks, the server receives progress reports and evaluates the progress using an evaluation algorithm (e.g., linear regression model). Feedback is generated as needed and sent to the subordinate's device. Furthermore, when the subordinate completes the task, the server evaluates the results and generates a report based on the evaluation results to be sent to the superior's device.

[0587] Device Role

[0588] The terminals are devices primarily used by subordinates and superiors. The subordinate's terminal receives notifications of new tasks sent from the server and provides an interface for reporting progress to the server. The superior's terminal receives task analysis results and progress evaluation reports. It also provides an interface for subordinates to send requests from the settings screen if they want to change the type of AI boss.

[0589] User Roles

[0590] The user (subordinate) reports the progress of assigned tasks to the server from their device and proceeds with the work while checking feedback as needed. When the task is completed, the user reports the completion and sends the results to the server. In addition, the user can change the type of AI boss from the settings screen.

[0591] As a concrete example, if a superior inputs a task instruction into an AI system, such as "Conduct market research for a new product and submit a report within two weeks," the server will analyze the instruction using natural language processing and record the task content and deadline in a database. If a subordinate reports progress as "Task ID 5678 is 30% complete," the server will evaluate the progress, generate feedback such as "Progress is behind schedule. Do you need assistance?" and send it to the subordinate's device.

[0592] In this way, the AI ​​system of the present invention aims to improve workplace productivity and mental health by effectively managing tasks while ensuring the psychological safety of subordinates.

[0593] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0594] Step 1: Receiving task instructions

[0595] The server receives task instructions from superiors. The input is a natural language task instruction such as "I want you to create a new market analysis report by next week." The server receives this input and passes it on to the next step.

[0596] Step 2: Parsing task instructions

[0597] The server analyzes the received task instructions using a natural language processing engine (e.g., Google NLP API, IBM Watson). The input is the task instruction received in step 1, which is analyzed and broken down into the work content ("Create a market analysis report") and deadline ("By next week"). The output is the analyzed task content and deadline, which are stored in a database.

[0598] Step 3: Obtain information about your subordinates

[0599] The server retrieves the subordinate's skill set and current task status from the database. The input is the subordinate's information stored in the database, including data on the skill set and progress status. The output is the retrieved subordinate's information, which is passed to the next task assignment step.

[0600] Step 4: Assign tasks

[0601] The server uses a machine learning algorithm (e.g., Random Forest, SVM) to identify the most suitable subordinate based on the acquired information about the subordinates. The input is the analyzed task content and deadline, and the acquired information about the subordinates. The algorithm identifies which subordinate to assign the task to. The output is the task assignment result, and the information about the task assigned to the specific subordinate is saved in a database.

[0602] Step 5: Sending task notifications

[0603] The server sends a notification of a new task to the subordinate's terminal. The input is the result of task assignment, which is information about which task has been assigned to which subordinate. The output is the task notification message sent to the subordinate's terminal. The notification includes the task content and deadline.

[0604] Step 6: Enter progress reports

[0605] The user (subordinate) inputs the progress status of a task from a terminal. For example, "Progress of task ID 1234 is 50%." The input is a progress report, indicating the progress of each task and how much progress has been made. The output is progress information, which is sent to the server.

[0606] Step 7: Record your progress

[0607] The server stores the received progress reports in a database. The input is the progress information sent in step 6. The output is the progress information recorded in the database, which is passed to the next progress evaluation step.

[0608] Step 8: Evaluate progress and generate feedback

[0609] The server evaluates the progress information using a progress evaluation algorithm (e.g., a linear regression model). The input is the progress information and the task deadlines recorded in a dictionary. The algorithm compares the progress with the deadlines and generates feedback, if necessary, such as "At this rate, you may not meet the deadline." The output is the generated feedback message, which is passed to the next feedback sending step.

[0610] Step 9: Submit your feedback

[0611] The server sends the generated feedback to the terminal (subordinate's device). The input is the evaluated progress information and the generated feedback message. The output is the feedback message sent to the subordinate's terminal. The subordinate checks this and adjusts the work pace as necessary.

[0612] Step 10: Reporting Task Completion

[0613] When a user (subordinate) completes a task, he / she reports the result to the server. The input is a task completion report such as "I have completed the market analysis report." The output is the report data of the completed task, which is sent to the server.

[0614] Step 11: Evaluation and Report Generation

[0615] The server receives the task completion reports and evaluates the quality and performance of the tasks using an evaluation algorithm (e.g., a rule-based evaluation model). The input is the completion report data. The algorithm generates an evaluation score, which is then used to generate a report to be reported to superiors. The output is the generated evaluation report.

[0616] Step 12: Submit the report

[0617] The server sends the evaluation report to the superior's terminal. The input is the generated evaluation report. The output is the evaluation report sent to the superior's terminal. The superior checks it and provides necessary feedback or additional instructions.

[0618] Step 13: Request a change of manager type

[0619] The user (subordinate) selects the "Change supervisor type" option from the settings screen, selects a new supervisor type, and submits it. The input is a request to change supervisor type. The output is the request data, which is sent to the server.

[0620] Step 14: Update Manager Type

[0621] The server receives the boss type change request and updates the database settings. The input is the boss type change request. The output is the new boss type setting stored in the database, and the task management algorithm is updated.

[0622] (Application example 1)

[0623] 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."

[0624] In today's factory work environment, efficient task management and appropriate feedback to workers are difficult. Line workers, in particular, need to receive real-time instructions and manage progress, which can lead to stress and mistakes. There is a need for a system that minimizes direct intervention from superiors, ensures psychological safety for workers, and improves factory productivity.

[0625] 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.

[0626] In this invention, the server includes means for receiving task instructions entered by a superior, means for analyzing the received task instructions using natural language processing, means for assigning tasks to appropriate subordinates based on the analyzed task instructions, means for managing task progress reported by subordinates and providing feedback on the progress, means for evaluating tasks completed by subordinates and reporting the evaluation results to a superior, means for changing the AI ​​boss type according to the subordinate's selection, means for installing the AI ​​system in terminals used for work in the factory, means for visually presenting work content in real time via devices worn by line workers in the factory, and means for workers to report progress by voice input. This makes it easier for workers to understand task content in real time, enabling efficient progress management and appropriate feedback.

[0627] A "server" is a central processing unit that receives task instructions from superiors, analyzes them, and assigns tasks and manages their progress.

[0628] "Task instructions" are documents or orders that show specific work instructions given by superiors to subordinates.

[0629] "Natural language processing" is the technology for understanding, analyzing, and generating human language.

[0630] A "subordinate" is an employee who performs specific tasks under the direction of a superior.

[0631] "Progress" is a report that shows how much of a task a subordinate has completed.

[0632] "Feedback" is advice or notification from superiors to subordinates based on progress.

[0633] "Evaluation" means judging the quality and success of tasks completed by subordinates.

[0634] "AI Boss" is a system that uses artificial intelligence to take on the role of middle management.

[0635] "Terminal" refers to a device used by a worker, including, for example, smart glasses and a head-mounted display.

[0636] "Devices" refers to hardware such as smart glasses and head-mounted displays worn by line workers.

[0637] "Voice input" is an input method in which workers communicate information to the system by speaking.

[0638] "Real-time" refers to a state in which data transmission, reception, and processing are carried out immediately without delay.

[0639] The present invention is a system for receiving task instructions entered by superiors, assigning appropriate tasks to subordinates, managing progress, and providing feedback. The system is intended to be used by line workers in factories wearing smart glasses.

[0640] The server receives task instructions from superiors and analyzes them using natural language processing. The analyzed task instructions are stored in a database within the server. The server then retrieves the subordinates' skill sets and current task status from the database and assigns the tasks to the appropriate subordinates. The assigned tasks are then notified to the subordinates' smart glasses.

[0641] The smart glasses, which serve as terminals, visually display the work content to subordinates in real time. Workers use the smart glasses to check specific instructions and progress as they go about their work. Progress reports are made via voice input. For example, a report such as "Progress on task ID 5678 is 30%." The server receives the progress reports and uses a progress evaluation algorithm to compare the task progress with the deadline. If necessary, it generates feedback such as "Progress is behind schedule. Do you need help?" and sends it to the subordinate's terminal.

[0642] When the task is completed, the subordinate reports through the smart glasses, "I have completed the market analysis report." The server receives this report and evaluates the task. The evaluation results are then reported to the superior in the form of a report generated by the AI ​​boss. This allows the superior to check the subordinate's performance and provide appropriate feedback.

[0643] Subordinates can also change the type of their AI boss from the settings screen. This change request is sent to the server, and the new AI boss type is set in the database. The subordinate's task management algorithm is updated based on the new boss type.

[0644] This system configuration improves work efficiency in the factory and also ensures psychological safety for workers. As a concrete example, the server can receive, analyze, and assign the following task instructions to subordinates:

[0645] "Try out the new line work procedure and let us know the results within a week."

[0646] "Please inspect the product and report the results within two days."

[0647] This allows for timely feedback on progress, enabling efficient task management.

[0648] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0649] Step 1:

[0650] The server receives task instructions from superiors. The input task instructions are written in natural language and contain specific content such as "Please create a new market analysis report by next week." The server receives this task instruction and stores it in a database.

[0651] Step 2:

[0652] The server analyzes the received task instructions using a natural language processing engine (e.g., Spacy). It receives the task instructions as input and generates the analyzed task content and deadline information as output. It breaks them down into specific elements such as "Create a market analysis report" and "By next week." The analysis results are stored in a database.

[0653] Step 3:

[0654] The server assigns tasks to the most suitable subordinates based on the analyzed task instructions. The subordinates' skill sets and current task status are retrieved from the database and input into the algorithm. The algorithm evaluates the skill sets and task status, selects the most suitable subordinates, and outputs the assigned tasks. The subordinates' devices are notified of the assigned tasks.

[0655] Step 4:

[0656] The subordinate's device (smart glasses) receives the task notification sent from the server and visually presents it to the worker. The device displays task details and deadline information in an easy-to-read format for the worker. Once the worker confirms the information, the task is ready to begin.

[0657] Step 5:

[0658] The user (subordinate) reports the progress of the work to the server through the terminal. The progress is reported using the voice input function, and the user inputs "The progress of task ID 5678 is 30%." The server receives the report and stores it in the database.

[0659] Step 6:

[0660] The server manages progress based on the received progress status. Using a progress evaluation algorithm, it compares the reported progress with the deadline and generates feedback. For example, feedback such as "Progress is behind schedule. Do you need help?" is generated and sent to the subordinate's device.

[0661] Step 7:

[0662] When a user (subordinate) completes a task, he / she reports it to the server via a terminal. For example, he / she may type, "I have completed the market analysis report." The server receives this report and evaluates the quality and success of the task using an evaluation algorithm.

[0663] Step 8:

[0664] The server generates a report for the superior based on the evaluation results. The server retrieves the evaluation results from the database, compiles them into a report, and sends it to the superior's terminal. The superior can check this report to understand the performance of his subordinates.

[0665] Step 9:

[0666] Users (subordinates) can change the type of their AI boss from the settings screen. They input a change request on their device and send it to the server. The server receives this request and sets the new boss type in the database. When the setting is changed, the task management algorithm is updated based on the new boss type.

[0667] 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.

[0668] The present invention builds an AI system that takes on the role of middle managers, eliminating direct intervention by superiors to improve psychological safety in the workplace, and also incorporates an emotion engine to recognize the user's emotions and provide appropriate feedback and task management based on those emotions. Specific embodiments for implementing the present invention are described below.

[0669] Receiving and analyzing task instructions

[0670] The server receives task instructions from a superior. These task instructions have specific content, such as "Please create a new market analysis report by next week." The server receives these instructions and analyzes them using a natural language processing engine. The analyzed task instructions are broken down into the work content, "Create a market analysis report," and the deadline, "By next week." The results of this analysis are saved in the server's database.

[0671] Task assignment

[0672] The server then assigns the analyzed tasks to the appropriate subordinates. It retrieves the subordinates' skill sets and current task status from the database and runs an algorithm to identify the most suitable subordinate. Once the most suitable subordinate is identified, the task is assigned and the results are saved in the database. At the same time, a notification of the new task is sent to the subordinate's device.

[0673] Task progress management and feedback

[0674] The terminal (subordinate device) reports the task progress to the server. For example, input "Progress on task ID 1234 is 50%." The server receives the progress report and manages progress based on it. The server uses a progress evaluation algorithm to compare the reported progress with the deadline, and generates feedback as needed, such as "At this rate, you may not meet the deadline." This feedback is sent to the subordinate device.

[0675] Feedback using an emotion engine

[0676] The server is equipped with an emotion engine that obtains emotional data from subordinates' input, facial expressions, tone of voice, etc. The server uses this emotional data to adjust the feedback method. For example, if the server recognizes that a subordinate is feeling stressed, it can provide gentle feedback such as, "You seem to be behind on progress. Do you need any support?" This allows subordinates to work with greater peace of mind.

[0677] Evaluation and Reporting

[0678] After completing a task, the user (subordinate) reports the results to the server. For example, the user might type, "I have completed a market analysis report." The server receives this report and uses an evaluation algorithm to evaluate the quality and success of the task. Based on this evaluation, the server generates a report to be sent to the superior's terminal and sends it from the server to the superior's terminal.

[0679] Change boss type

[0680] The user (subordinate) can change the type of AI boss from the settings screen. For example, if a subordinate thinks, "I want to change my leadership style," he or she clicks the "Change boss type" option from the settings menu. The device sends this change request to the server. The server receives this request, sets the new boss type in the database, and changes the task management algorithm.

[0681] Specific examples

[0682] Example of receiving and analyzing task instructions

[0683] A superior gives instructions to the AI ​​system: "Conduct market research for a new product and submit a report within two weeks."

[0684] The server analyzes: The task is "conduct market research" and the deadline is "submit the report within two weeks" and records this in the database.

[0685] Examples of task progress management and feedback

[0686] Subordinate reports progress: Enters "Task ID 5678 is 30% complete."

[0687] The server evaluates and provides feedback: "Progress is behind schedule. Do you need help?" feedback is generated and sent to the subordinate's device.

[0688] Examples of emotion engines

[0689] Subordinate reports progress: Enters "Task ID 5678 is 30% complete."

[0690] The server analyzes using an emotion engine: it recognizes that "your subordinate is feeling stressed" and provides gentle feedback such as "progress seems to be lagging behind. Do you need any support?"

[0691] Through these processes, the AI ​​boss can effectively manage tasks and provide appropriate feedback when necessary while ensuring psychological safety for subordinates, thereby improving organizational productivity and workplace mental health.

[0692] The processing flow will be explained below.

[0693] The present invention is a system that builds an AI system that plays the role of middle management, eliminates direct intervention by superiors, and recognizes the user's emotions by combining an emotion engine, and provides appropriate feedback and task management based on the emotions. Specific embodiments for implementing the present invention will be described below, divided into processing steps.

[0694] Receiving and analyzing task instructions

[0695] Step 1:

[0696] A task instruction is input from the superior's terminal and sent to the AI ​​system. For example, "Please create a new market analysis report by next week."

[0697] Step 2:

[0698] The server receives task instructions sent by superiors and stores them in a task instruction database.

[0699] Step 3:

[0700] The server passes the received task instructions to a natural language processing engine, which analyzes the task content and deadline. For example, it extracts "Create a market analysis report" and "By next week."

[0701] Step 4:

[0702] The server stores the analysis results in a database.

[0703] Task assignment

[0704] Step 5:

[0705] The server obtains the subordinate's skill set and current task status from the database in order to appropriately assign the analyzed task to the subordinate.

[0706] Step 6:

[0707] The server runs an algorithm to identify the most suitable subordinate based on the acquired skill set and task situation.

[0708] Step 7:

[0709] The server assigns tasks to the identified subordinates and stores the results in a database.

[0710] Step 8:

[0711] Send a notification of a new task to the terminal (subordinate device).

[0712] Task progress management and feedback

[0713] Step 9:

[0714] The device reports the task progress to the server. Example: "Task ID 1234 progress is 50%."

[0715] Step 10:

[0716] The server receives the progress reports and records them in a database.

[0717] Step 11:

[0718] The server uses a progress evaluation algorithm to compare the reported progress with the deadline.

[0719] Step 12:

[0720] If the progress is not as planned, the server uses a feedback generation engine to generate appropriate feedback, e.g., "At this rate, you may not meet the deadline."

[0721] Step 13:

[0722] The terminal (subordinate device) receives a feedback message from the server as a notification.

[0723] Feedback using an emotion engine

[0724] Step 14:

[0725] The terminal (subordinate's device) collects the subordinate's emotion data through an emotion tracking device during task progress reports and other interactions.

[0726] Step 15:

[0727] The server uses an emotion engine to analyze the subordinates' emotional data, e.g., to detect stress or anxiety from their facial expressions, tone of voice, and text input.

[0728] Step 16:

[0729] The server adjusts the feedback method based on the emotional data. For example, if a subordinate feels stressed, the server provides feedback such as, "You seem to be behind in your progress. Do you need any support?"

[0730] Evaluation and Reporting

[0731] Step 17:

[0732] After completing a task, the user (subordinate) reports the results of the work to the server. For example, the user might write, "I have completed the market analysis report."

[0733] Step 18:

[0734] The server receives the reports and uses a rating algorithm to evaluate the quality and success of the tasks.

[0735] Step 19:

[0736] The server generates a report based on the evaluation results and sends it to the superior's terminal, which includes the evaluation results and a link to the completed market analysis report.

[0737] Change boss type

[0738] Step 20:

[0739] The user (subordinate) can change the type of AI boss from the settings screen. For example, if you think, "I want to change my leadership style," click the "Change boss type" option from the settings menu.

[0740] Step 21:

[0741] The terminal transmits the selection of the new boss type to the server.

[0742] Step 22:

[0743] The server receives the new manager type setting and updates the database with the changes.

[0744] Step 23:

[0745] The server changes the task management algorithm of the subordinates based on the new boss type.

[0746] Through these processes, the AI ​​boss can effectively manage tasks and provide feedback using an emotion engine while ensuring psychological safety for subordinates, thereby improving organizational productivity and workplace mental health.

[0747] Example 2

[0748] 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."

[0749] In traditional management systems, superiors often intervene directly, which can undermine the psychological safety of subordinates. Furthermore, traditional systems do not provide feedback that takes into account the emotions of subordinates, making effective task management difficult.

[0750] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0751] In this invention, the server includes means for receiving task instructions entered by a superior, means for analyzing the received task instructions using natural language processing, means for assigning tasks to appropriate subordinates based on the analyzed task instructions, means for managing task progress reported by subordinates and providing feedback on the progress, means for collecting emotional data on subordinates and adjusting the feedback method based on the data, means for evaluating tasks completed by subordinates and reporting the evaluation results to the superior, and means for changing the type of AI boss according to the subordinate's selection. This enables effective task management and appropriate feedback while ensuring psychological safety for subordinates.

[0752] A "superior" is a person in an organization who commands and orders subordinates.

[0753] "Task instructions" are instructions given by a superior to a subordinate that specify specific work content and deadlines.

[0754] "Natural language processing" is a technology that analyzes and understands human language on a computer.

[0755] A "subordinate" is someone who performs work under the command and order of a superior.

[0756] "Task assignment" refers to assigning a specific task to an appropriate person from among multiple subordinates.

[0757] "Progress" is information indicating the degree of completion of the tasks assigned to a subordinate.

[0758] "Feedback" refers to evaluation and advice on progress and results, with the aim of improving and supporting work.

[0759] "Emotion data" is data that indicates the emotional state of a subordinate, as determined from facial expressions, tone of voice, etc.

[0760] "Evaluation" is the process of making judgments based on the quality and results of completed tasks.

[0761] "Boss type" refers to the type of leadership style and management method that an AI boss possesses.

[0762] This invention aims to improve psychological safety in the workplace by reducing direct intervention by superiors using an artificial intelligence (AI) system that plays the role of middle managers. The system recognizes users' emotions through an emotion engine and provides appropriate feedback and task management based on that information.

[0763] Receiving and analyzing task instructions

[0764] The server receives specific task instructions from its superior, which are sent as HTTP requests to API endpoints.

[0765] For example, a user might receive an instruction such as "Create a new market analysis report by next week." The instruction is parsed using the Google Cloud Natural Language API and broken down into tasks and deadlines. The analysis results are then stored in a MySQL database.

[0766] Task assignment

[0767] The server retrieves the subordinates' skill sets and task status from a MySQL database, executes a custom algorithm written in Python, and assigns tasks to the most suitable subordinates. The assignment results are stored in the database, and new task notifications are sent to the subordinates' devices via a push notification service.

[0768] Task progress management and feedback

[0769] The terminal (subordinate device) reports its progress to the server. For example, it inputs and sends "Task ID 1234 is 50% complete." The server receives the progress report and evaluates it using a progress evaluation algorithm. If necessary, it generates feedback such as "At this rate, you may not meet the deadline," and sends it to the subordinate device.

[0770] Feedback using an emotion engine

[0771] The server uses the Affectiva SDK to collect emotional data from subordinates' input, facial expressions, and tone of voice. Based on this emotional data, the server adjusts the feedback provided to the subordinate's device, providing gentle feedback such as, "You seem to be behind schedule. Do you need any help?"

[0772] Evaluation and Reporting

[0773] After completing the task, the user (subordinate) reports to the server, "I have completed the market analysis report." The server receives the report and performs task evaluation using a Python script. Based on the evaluation results, a report to be reported to the superior is generated and sent to the superior's terminal in HTML or PDF format.

[0774] Change boss type

[0775] The user (subordinate) requests a change of supervisor type from the settings screen. The subordinate's device sends this change request to the server. The server receives the request and updates the database using an SQL UPDATE statement to change the supervisor type.

[0776] Specific examples

[0777] Example of receiving and analyzing task instructions

[0778] A superior gives instructions to the AI ​​system: "Conduct market research for a new product and submit a report within two weeks."

[0779] The server analyzes: The task "conduct market research" and the deadline "submit report within two weeks" are analyzed and recorded in a database.

[0780] Examples of task progress management and feedback

[0781] Subordinate reports progress: Enters "Task ID 5678 is 30% complete."

[0782] The server evaluates and provides feedback: "Progress is behind schedule. Do you need help?" feedback is generated and sent to the subordinate's device.

[0783] Examples of emotion engines

[0784] Subordinate reports progress: Enters "Task ID 5678 is 30% complete."

[0785] The server analyzes using an emotion engine: it recognizes that "your subordinate is feeling stressed" and provides gentle feedback such as "progress seems to be lagging behind. Do you need any support?"

[0786] Example of input prompt for generative AI model

[0787] "We've received a request to create a new market analysis report by next week. Please parse this request using a natural language processing engine to extract the task and deadline."

[0788] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0789] Step 1:

[0790] The server receives a task instruction from a superior saying, "I want you to create a new market analysis report by next week."

[0791] Specific operation: A task instruction is sent as an HTTP request to an API endpoint.

[0792] Input: Task instructions from superiors.

[0793] Output: An HTTP request containing the task instructions.

[0794] Step 2:

[0795] The server parses the received task instructions using the Google Cloud Natural Language API.

[0796] Specific operation: Send an API request and receive the analysis results as a response.

[0797] Input: An HTTP request containing task instructions.

[0798] Output: Job description: "Create a market analysis report" with deadline "by next week."

[0799] Step 3:

[0800] The server stores the analysis results in a MySQL database.

[0801] Specific operation: Inserts data into the database using the SQL INSERT statement.

[0802] Input: "Create market analysis report" and analysis results "by next week".

[0803] Output: Task instructions and deadlines recorded in a database.

[0804] Step 4:

[0805] The server retrieves the subordinate's skill set and current task status from the database.

[0806] What it does: Uses a SQL SELECT statement to query for the information you need.

[0807] Input: Subordinate ID information.

[0808] Output: Skill set and task status data for each subordinate.

[0809] Step 5:

[0810] The server runs a custom algorithm written in Python to assign tasks to the best subordinates.

[0811] Specific behavior: The algorithm evaluates subordinates' skill sets and task situations and selects the most suitable subordinate.

[0812] Inputs: Subordinate skill set, current task status, task instructions.

[0813] Output: Information about the subordinates who have been assigned the task.

[0814] Step 6:

[0815] The server stores the task assignment results in a database and sends new task notifications to the subordinate terminals.

[0816] Specific operation: Inserts data into the database using an SQL INSERT statement and sends a notification to the subordinate's device via the push notification service.

[0817] Input: Task assignment results.

[0818] Output: Task assignment results stored in the database and notifications sent to subordinates' devices.

[0819] Step 7:

[0820] The terminals (subordinate devices) report their progress to the server.

[0821] Specific operation: The subordinate enters the progress status and sends it to the server as an HTTP POST request.

[0822] Input: A progress report such as "Task ID 1234 is 50% complete."

[0823] Output: Progress report sent to the server.

[0824] Step 8:

[0825] The server receives the progress reports and evaluates them using a progress evaluation algorithm.

[0826] Specific actions: Compare progress with deadlines and generate evaluation results.

[0827] Input: Progress report from subordinate.

[0828] Output: Feedback such as "At this rate, you may not meet the deadline."

[0829] Step 9:

[0830] The server generates feedback as needed and sends it to the subordinate terminals.

[0831] Specific actions: Feedback is automatically generated based on progress evaluation results and sent to subordinates via push notification or email.

[0832] Input: Progress assessment results.

[0833] Output: Feedback sent to subordinate devices.

[0834] Step 10:

[0835] The server uses the Affectiva SDK to collect emotional data from subordinates' inputs, facial expressions, and tone of voice.

[0836] Specific operation: Data is acquired from subordinate devices via the camera and microphone, and analyzed using the SDK.

[0837] Input: Subordinates' facial expressions and tone of voice.

[0838] Output: Parsed emotion data.

[0839] Step 11:

[0840] The server adjusts the feedback content based on the emotional data and sends it to the subordinate's device.

[0841] Specific action: Analyze emotional data and generate appropriate feedback content.

[0842] Input: Emotion data.

[0843] Output: Gentle feedback such as, "You seem to be making slow progress, do you need any help?"

[0844] Step 12:

[0845] After completing the task, the user (subordinate) reports to the server, "I have completed the market analysis report."

[0846] Specific operation: Enter a task completion report and send it to the server as an HTTP POST request.

[0847] Input: Task completion report.

[0848] Output: Task completion report sent to the server.

[0849] Step 13:

[0850] The server evaluates the report and evaluates the task quality and performance.

[0851] Specific operation: A Python script is used to analyze the report content and generate a rating score.

[0852] Input: Task completion report.

[0853] Output: Evaluation score.

[0854] Step 14:

[0855] The server generates a report to report to the superior based on the evaluation results and transmits it to the superior's terminal.

[0856] Specific Actions: Based on the assessment results, a report is generated in HTML or PDF format and sent via email or internal messaging system.

[0857] Input: Rating score.

[0858] Output: Report sent to superior's terminal.

[0859] Step 15:

[0860] The user (subordinate) requests a change of superior type from the settings screen.

[0861] Specific actions: Select the Change Manager Type option and submit a change request.

[0862] Input: Supervisor type change request.

[0863] Output: The change request sent to the server.

[0864] Step 16:

[0865] The terminal sends a change request to the server.

[0866] Specific operation: A change request is sent to the server as an HTTP POST request.

[0867] Input: Supervisor type change request.

[0868] Output: The change request sent to the server.

[0869] Step 17:

[0870] The server receives the request and updates the database with the new boss type.

[0871] Specific Actions: Update the database using a SQL UPDATE statement to apply the task management algorithm based on the new supervisor type.

[0872] Input: Supervisor type change request.

[0873] Output: The manager type information updated in the database.

[0874] (Application example 2)

[0875] 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."

[0876] The problem to be solved by this invention is to eliminate direct intervention by superiors, improve psychological safety in the workplace, and provide appropriate task management and feedback. In particular, the object is to reduce stress for subordinates and support efficient work performance by recognizing subordinates' emotions in real time and adjusting feedback methods based on those emotions.

[0877] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0878] In this invention, the server includes means for receiving task instructions entered by a superior, means for analyzing the received task instructions using natural language processing, means for assigning tasks to appropriate subordinates based on the analyzed task instructions, means for managing task progress reported by subordinates and providing feedback on the progress, means for evaluating tasks completed by subordinates and reporting the evaluation results to the superior, means for using an emotion recognition engine that recognizes the emotions of subordinates and adjusts the feedback content based on the emotions, and means for changing the type of AI boss according to the selection of the subordinate. This enables flexible feedback and progress management according to the emotions of subordinates.

[0879] "Task instructions" refer to specific work content and deadlines instructed by a superior to a subordinate.

[0880] "Natural language processing" is a technology that uses computers to analyze, understand, and generate human language (natural language).

[0881] "Task progress" refers to the status that indicates how much a subordinate has performed on a given task and how close they are to completing it.

[0882] "Feedback" refers to the guidance and evaluation provided to subordinates by superiors or systems based on task progress.

[0883] An "emotion recognition engine" is a technology that analyzes a subordinate's emotional state from their input, facial expressions, tone of voice, etc.

[0884] A "generative AI model" is an artificial intelligence model that has been trained to generate results for a specific task.

[0885] A "skill set" is a collection of knowledge, abilities, experience, etc. that a subordinate possesses that are relevant to a specific job.

[0886] "AI Boss" is a management system with artificial intelligence that assigns tasks and provides feedback to subordinates.

[0887] "Task management" is the process of monitoring task progress and providing adjustments and feedback.

[0888] "Psychological safety" refers to a state in the workplace where members feel safe to express their opinions and feelings.

[0889] "Progress management" is the process of making sure that tasks are progressing as planned and making adjustments as necessary.

[0890] An "evaluation algorithm" is a calculation method for quantitatively or qualitatively evaluating the work performance of subordinates upon task completion.

[0891] The present invention is a system that builds an AI system that takes on the role of middle managers, improves psychological safety in the workplace by eliminating direct intervention by superiors, and recognizes the user's emotions by combining it with an emotion recognition engine, and provides appropriate feedback and task management based on the emotions. Specific embodiments for implementing the present invention are described in detail below.

[0892] Receiving and analyzing task instructions

[0893] The server receives task instructions input by a superior. For example, the superior's input may be specific, such as "Please create a new market analysis report by next week." The server receives this instruction and analyzes it using a natural language processing engine. The analyzed task instruction is broken down into the work content, "Create a market analysis report," and the deadline, "By next week," and the analysis results are saved in the server's database.

[0894] Task assignment

[0895] The server assigns the analyzed tasks to subordinates. It retrieves the subordinates' skill sets and current task status from the database and runs an algorithm to identify the most suitable subordinate. Once the most suitable subordinate is identified, the task is assigned to that subordinate and the result is saved in the database. At the same time, a notification of the new task is sent to the subordinate's device.

[0896] Task progress management and feedback

[0897] The subordinate's device reports the task progress to the server. For example, "Progress on task ID 1234 is 50%." The server receives the progress report and manages the progress based on it. The server uses a progress evaluation algorithm to compare the reported progress with the deadline, and generates feedback such as "At this rate, you may not meet the deadline." This feedback is sent to the subordinate's device.

[0898] Feedback using an emotion recognition engine

[0899] The server is equipped with an emotion recognition engine that obtains emotional data from subordinates' input, facial expressions, tone of voice, etc. The server uses this emotional data to adjust the feedback method. For example, if the server recognizes that a subordinate is feeling stressed, it can provide gentle feedback such as, "You seem to be behind on progress. Do you need any support?"

[0900] Evaluation and Reporting

[0901] After completing the task, the subordinate reports the results to the server, entering "I have completed the market analysis report." The server receives this report and uses an evaluation algorithm to evaluate the quality and results of the task. Based on this evaluation result, a report is generated for reporting to the superior, and sent from the server to the superior's terminal.

[0902] Change boss type

[0903] Subordinates can change the type of their AI boss from the settings screen. For example, if a subordinate decides to "change their leadership style," they can click the "Change boss type" option from the settings menu. The device then sends this change request to the server. The server receives this request, sets the new boss type in the database, and changes the task management algorithm.

[0904] Specific examples

[0905] Example of receiving and analyzing task instructions

[0906] A superior gives instructions to the AI ​​system: "Conduct market research for a new product and submit a report within two weeks."

[0907] The server analyzes: The task is "conduct market research" and the deadline is "submit the report within two weeks" and records this in the database.

[0908] Examples of task progress management and feedback

[0909] Subordinate reports progress: Enters "Task ID 5678 is 30% complete."

[0910] The server evaluates and provides feedback: "Progress is behind schedule. Do you need help?" feedback is generated and sent to the subordinate's device.

[0911] Example of an emotion recognition engine

[0912] Subordinate reports progress: Enters "Task ID 5678 is 30% complete."

[0913] The server analyzes using an emotion recognition engine: it recognizes that "your subordinate is feeling stressed" and provides gentle feedback such as "You seem to be making slow progress. Do you need any support?"

[0914] Through these processes, the AI ​​boss can effectively manage tasks and provide appropriate feedback when necessary while ensuring psychological safety for subordinates, thereby improving organizational productivity and workplace mental health.

[0915] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0916] Step 1:

[0917] The server receives task instructions entered by superiors.

[0918] Specifically, when a superior inputs an instruction into the system such as "Please create a new market analysis report by next week," the server receives this task instruction as a string of characters.

[0919] Input: Task instructions from superiors

[0920] Output: Raw task instruction data (string format)

[0921] Step 2:

[0922] The server analyzes the received task instructions using a natural language processing engine.

[0923] Specifically, the server breaks down the task instructions into components such as "create a market analysis report" and "by next week," and stores this information in a database.

[0924] Input: Raw task instruction data

[0925] Output: Parsed task details and deadlines

[0926] Step 3:

[0927] The server assigns tasks to subordinates based on the parsed task instructions.

[0928] Specifically, the server retrieves the subordinates' skill sets and current task status from the database, identifies the most suitable subordinate, and assigns the task to that subordinate.

[0929] Input: Analyzed task content, subordinate skill set, current task status

[0930] Output: Task assignment notification sent to subordinate's device

[0931] Step 4:

[0932] The subordinate terminals report the progress of the tasks to the server.

[0933] Specifically, the subordinate enters the task progress as "Progress of task ID 1234 is 50%" and sends this progress data to the server.

[0934] Input: Progress report data from subordinates

[0935] Output: Progress data stored on the server

[0936] Step 5:

[0937] The server manages the progress based on the progress reports and generates feedback as needed.

[0938] Specifically, the server uses a progress evaluation algorithm to compare the reported progress with the deadline, generates feedback such as "At this rate, you may not meet the deadline," and sends it to the subordinate's device.

[0939] Input: Progress report data

[0940] Output: Feedback message

[0941] Step 6:

[0942] The server recognizes the emotions of the subordinates using an emotion recognition engine and adjusts the feedback content.

[0943] Specifically, it extracts emotional data from the text of progress reports and the input of subordinates, and if it recognizes emotions such as "feeling stressed," it generates gentle feedback such as "You seem to be making slow progress. Do you need any support?"

[0944] Input: Progress report text data

[0945] Output: Emotion-based regulatory feedback

[0946] Step 7:

[0947] After completing a task, the subordinate reports the result to the server.

[0948] Specifically, the subordinate enters "Market analysis report completed" and sends the task completion report to the server.

[0949] Input: Task completion report data

[0950] Output: Task completion report saved on the server

[0951] Step 8:

[0952] The server receives the task completion reports and evaluates the quality and success of the tasks using an evaluation algorithm.

[0953] Specifically, the task results are evaluated quantitatively or qualitatively using an evaluation algorithm, and a report is generated to report the evaluation results to superiors.

[0954] Input: Task completion report data

[0955] Output: Evaluation report for superiors

[0956] Step 9:

[0957] Subordinates can change the type of AI boss they have from the settings screen.

[0958] Specifically, the subordinate selects the "Change Supervisor Type" option from the settings menu and sends a change request to the server. The server receives this request, sets the new supervisor type in the database, and changes the task management algorithm.

[0959] Input: Request from subordinate to change supervisor type

[0960] Output: New manager type set in the database

[0961] Example prompt sentence:

[0962] "Progress of task ID xxxx is yy%"

[0963] "The user's progress report text is sent to the emotion engine for emotion analysis."

[0964] "Market analysis report completed."

[0965] 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.

[0966] 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.

[0967] 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.

[0968] [Third embodiment]

[0969] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0970] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0971] 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).

[0972] 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.

[0973] 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.

[0974] 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).

[0975] 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.

[0976] 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.

[0977] 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.

[0978] 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.

[0979] 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.

[0980] 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."

[0981] The present invention is a system that improves psychological safety in the workplace by building an AI system that takes on the role of middle managers and eliminates direct intervention by superiors. Specific embodiments for implementing the present invention are described below.

[0982] Receiving and analyzing task instructions

[0983] The server receives task instructions from a superior. These task instructions have specific content, such as "Please create a new market analysis report by next week." The server receives these instructions and analyzes them using a natural language processing engine. The analyzed task instructions are broken down into the work content, "Create a market analysis report," and the deadline, "By next week." The results of this analysis are saved in the server's database.

[0984] Task assignment

[0985] The server then assigns the analyzed tasks to the appropriate subordinates. It retrieves the subordinates' skill sets and current task status from the database and runs an algorithm to identify the most suitable subordinate. Once the most suitable subordinate is identified, the task is assigned and the results are saved in the database. At the same time, a notification of the new task is sent to the subordinate's device.

[0986] Task progress management and feedback

[0987] The terminal (subordinate device) reports the progress of the task to the server. For example, it inputs "Progress on task ID 1234 is 50%." The server receives the progress report and manages the progress based on it. The server uses a progress evaluation algorithm to compare the reported progress with the deadline, and generates feedback as needed, such as "At this rate, you may not meet the deadline." This feedback is sent to the subordinate device.

[0988] Evaluation and Reporting

[0989] When a user (subordinate) completes a task, he / she reports the result to the server. For example, he / she might type, "I have completed a market analysis report." The server receives this report and evaluates the task. The server uses an evaluation algorithm to evaluate the quality and outcome of the task, and generates a report to report the evaluation results to the superior. The report is then sent from the server to the superior's terminal.

[0990] Change boss type

[0991] The user (subordinate) can change the type of AI boss from the settings screen. For example, if a subordinate thinks, "I want to change my leadership style," he or she clicks the "Change boss type" option from the settings menu. The device sends this change request to the server. The server receives this request and sets the new boss type in the database. The subordinate's task management algorithm is updated based on the new boss type.

[0992] Specific examples

[0993] Example of receiving and analyzing task instructions

[0994] A superior gives instructions to the AI ​​system: "Conduct market research for a new product and submit a report within two weeks."

[0995] The server analyzes: The task is "conduct market research" and the deadline is "submit the report within two weeks" and records this in the database.

[0996] Examples of task progress management and feedback

[0997] Subordinate reports progress: Enters "Task ID 5678 is 30% complete."

[0998] The server evaluates and provides feedback: "Progress is behind schedule. Do you need help?" feedback is generated and sent to the subordinate's device.

[0999] These processes enable the AI ​​boss to effectively manage tasks and provide appropriate feedback when necessary while ensuring psychological safety for subordinates, thereby improving organizational productivity and workplace mental health.

[1000] The processing flow will be explained below.

[1001] Step 1:

[1002] A task instruction is input from the superior's terminal and sent to the AI ​​system. For example, "Please create a new market analysis report by next week."

[1003] Step 2:

[1004] The server receives task instructions sent by superiors and stores them in a task instruction database.

[1005] Step 3:

[1006] The server passes the received task instructions to a natural language processing engine, which analyzes the task content and deadline. For example, it extracts "Create a market analysis report" and "By next week."

[1007] Step 4:

[1008] The server stores the analysis results in a database and retrieves the subordinates' skill sets and current task status from the database.

[1009] Step 5:

[1010] The server runs an algorithm to identify the most suitable subordinate based on the acquired skill set and task situation.

[1011] Step 6:

[1012] The server assigns tasks to the identified subordinates and stores the results in a database.

[1013] Step 7:

[1014] The terminal (subordinate device) receives a notification of a new task, e.g., a specified task and deadline.

[1015] Step 8:

[1016] The terminal periodically reports the progress of the task to the server. Example: "Progress of task ID 1234 is 50%."

[1017] Step 9:

[1018] The server records the received progress reports in a database and uses a progress evaluation algorithm to compare the report status with deadlines.

[1019] Step 10:

[1020] If the progress is not as planned, the server uses a feedback generation engine to generate appropriate feedback, e.g., "At this rate, you may not meet the deadline."

[1021] Step 11:

[1022] The terminal (subordinate device) receives the feedback message from the server as a notification.

[1023] Step 12:

[1024] After completing a task, the user (subordinate) reports the results of the work to the server. For example, the user might write, "I have completed the market analysis report."

[1025] Step 13:

[1026] The server receives the reports and uses a rating algorithm to rate the quality and success of the tasks.

[1027] Step 14:

[1028] The server generates a report based on the evaluation results and sends it to the superior's terminal, which includes the evaluation results and a link to the completed market analysis report.

[1029] Step 15:

[1030] Users (subordinates) can change the type of AI boss from the settings screen. For example, they can think, "Let's try changing the leadership style," and select an option.

[1031] Step 16:

[1032] The terminal transmits the selection of the new boss type to the server.

[1033] Step 17:

[1034] The server receives the new boss type setting, updates the database, and changes the task management algorithm of subordinates.

[1035] Example 1

[1036] 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."

[1037] In a typical workplace, direct instructions and feedback from superiors can cause subordinates to feel psychological stress. In such situations, there are concerns about a decline in productivity and a worsening work environment. The purpose of this invention is to solve these problems and increase psychological safety.

[1038] 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.

[1039] In this invention, the server includes means for receiving task instructions entered by a superior, means for analyzing the received task instructions using natural language processing, means for assigning tasks to appropriate subordinates based on the analyzed task instructions, means for acquiring the subordinates' skill sets and current task statuses and identifying the most suitable subordinates, means for sending notifications of new tasks to the subordinates, means for managing task progress reported by the subordinates and providing feedback on the progress, means for evaluating tasks completed by the subordinates and reporting the evaluation results to the superior, and means for changing the type of AI boss according to the subordinates' selection. This enables effective task management without causing psychological stress to subordinates, improving workplace productivity and mental health.

[1040] "Superior" refers to a manager or leader who has the authority to give instructions within an organization.

[1041] "Task instructions" refers to specific instructions given by a superior to a subordinate, such as the work content, goals, deadlines, etc.

[1042] "Natural language processing" is a technology that allows computers to understand and analyze human language.

[1043] "Analysis" refers to the act of breaking down received task instructions and extracting information such as work content and deadlines.

[1044] A "subordinate" is someone who performs tasks under the direction of a superior within an organization.

[1045] A "skill set" refers to the totality of a subordinate's abilities and expertise.

[1046] "Task status" refers to the progress and completion status of the work that a subordinate is currently responsible for.

[1047] "Progress" refers to the degree of progress and completion of tasks assigned to subordinates.

[1048] "Feedback" refers to evaluation and advice provided on progress and work content.

[1049] "Evaluation" is the process of judging the quality or success of a completed task.

[1050] "AI boss type" refers to the leadership style and teaching methods simulated by the AI.

[1051] "Notifications" refer to messages or alerts that communicate information about task assignments and progress to subordinates.

[1052] A "database" is a system that efficiently stores and manages digital information.

[1053] An "algorithm" refers to a computational procedure or process for solving a particular problem.

[1054] "Machine learning" is a technology that allows computers to learn by themselves using empirical data and improve their performance.

[1055] This invention is a system that improves psychological safety in the workplace by constructing an AI system that takes on the role of middle managers and eliminates direct intervention by superiors. As an implementation form of this system, we will explain how the server, terminals, and users participate in this system and play their respective roles.

[1056] Server Roles

[1057] The server receives task instructions from superiors and analyzes them using a natural language processing engine. This system preferably uses natural language processing engines such as Google NLP API or IBM Watson. The analyzed task instructions are broken down into work content and deadlines and stored in a database (MySQL, PostgreSQL, etc.). The server then retrieves the subordinates' skill sets and current task status from the database and identifies the most suitable subordinates using machine learning algorithms (e.g., Random Forest, SVM).

[1058] As subordinates progress with assigned tasks, the server receives progress reports and evaluates the progress using an evaluation algorithm (e.g., linear regression model). Feedback is generated as needed and sent to the subordinate's device. Furthermore, when the subordinate completes the task, the server evaluates the results and generates a report based on the evaluation results to be sent to the superior's device.

[1059] Device Role

[1060] The terminals are devices primarily used by subordinates and superiors. The subordinate's terminal receives notifications of new tasks sent from the server and provides an interface for reporting progress to the server. The superior's terminal receives task analysis results and progress evaluation reports. It also provides an interface for subordinates to send requests from the settings screen if they want to change the type of AI boss.

[1061] User Roles

[1062] The user (subordinate) reports the progress of assigned tasks to the server from their device and proceeds with the work while checking feedback as needed. When the task is completed, the user reports the completion and sends the results to the server. In addition, the user can change the type of AI boss from the settings screen.

[1063] As a concrete example, if a superior inputs a task instruction into an AI system, such as "Conduct market research for a new product and submit a report within two weeks," the server will analyze the instruction using natural language processing and record the task content and deadline in a database. If a subordinate reports progress as "Task ID 5678 is 30% complete," the server will evaluate the progress, generate feedback such as "Progress is behind schedule. Do you need assistance?" and send it to the subordinate's device.

[1064] In this way, the AI ​​system of the present invention aims to improve workplace productivity and mental health by effectively managing tasks while ensuring the psychological safety of subordinates.

[1065] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1066] Step 1: Receiving task instructions

[1067] The server receives task instructions from superiors. The input is a natural language task instruction such as "I want you to create a new market analysis report by next week." The server receives this input and passes it on to the next step.

[1068] Step 2: Parsing task instructions

[1069] The server analyzes the received task instructions using a natural language processing engine (e.g., Google NLP API, IBM Watson). The input is the task instruction received in step 1, which is analyzed and broken down into the work content ("Create a market analysis report") and deadline ("By next week"). The output is the analyzed task content and deadline, which are stored in a database.

[1070] Step 3: Obtain information about your subordinates

[1071] The server retrieves the subordinate's skill set and current task status from the database. The input is the subordinate's information stored in the database, including data on the skill set and progress status. The output is the retrieved subordinate's information, which is passed to the next task assignment step.

[1072] Step 4: Assign tasks

[1073] The server uses a machine learning algorithm (e.g., Random Forest, SVM) to identify the most suitable subordinate based on the acquired information about the subordinates. The input is the analyzed task content and deadline, and the acquired information about the subordinates. The algorithm identifies which subordinate to assign the task to. The output is the task assignment result, and the information about the task assigned to the specific subordinate is saved in a database.

[1074] Step 5: Sending task notifications

[1075] The server sends a notification of a new task to the subordinate's terminal. The input is the result of task assignment, which is information about which task has been assigned to which subordinate. The output is the task notification message sent to the subordinate's terminal. The notification includes the task content and deadline.

[1076] Step 6: Enter progress reports

[1077] The user (subordinate) inputs the progress status of a task from a terminal. For example, "Progress of task ID 1234 is 50%." The input is a progress report, indicating the progress of each task and how much progress has been made. The output is progress information, which is sent to the server.

[1078] Step 7: Record your progress

[1079] The server stores the received progress reports in a database. The input is the progress information sent in step 6. The output is the progress information recorded in the database, which is passed to the next progress evaluation step.

[1080] Step 8: Evaluate progress and generate feedback

[1081] The server evaluates the progress information using a progress evaluation algorithm (e.g., a linear regression model). The input is the progress information and the task deadlines recorded in a dictionary. The algorithm compares the progress with the deadlines and generates feedback, if necessary, such as "At this rate, you may not meet the deadline." The output is the generated feedback message, which is passed to the next feedback sending step.

[1082] Step 9: Submit your feedback

[1083] The server sends the generated feedback to the terminal (subordinate's device). The input is the evaluated progress information and the generated feedback message. The output is the feedback message sent to the subordinate's terminal. The subordinate checks this and adjusts the work pace as necessary.

[1084] Step 10: Reporting Task Completion

[1085] When a user (subordinate) completes a task, he / she reports the result to the server. The input is a task completion report such as "I have completed the market analysis report." The output is the report data of the completed task, which is sent to the server.

[1086] Step 11: Evaluation and Report Generation

[1087] The server receives the task completion reports and evaluates the quality and performance of the tasks using an evaluation algorithm (e.g., a rule-based evaluation model). The input is the completion report data. The algorithm generates an evaluation score, which is then used to generate a report to be reported to superiors. The output is the generated evaluation report.

[1088] Step 12: Submit the report

[1089] The server sends the evaluation report to the superior's terminal. The input is the generated evaluation report. The output is the evaluation report sent to the superior's terminal. The superior checks it and provides necessary feedback or additional instructions.

[1090] Step 13: Request a change of manager type

[1091] The user (subordinate) selects the "Change supervisor type" option from the settings screen, selects a new supervisor type, and submits it. The input is a request to change supervisor type. The output is the request data, which is sent to the server.

[1092] Step 14: Update Manager Type

[1093] The server receives the boss type change request and updates the database settings. The input is the boss type change request. The output is the new boss type setting stored in the database, and the task management algorithm is updated.

[1094] (Application example 1)

[1095] 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."

[1096] In today's factory work environment, efficient task management and appropriate feedback to workers are difficult. Line workers, in particular, need to receive real-time instructions and manage progress, which can lead to stress and mistakes. There is a need for a system that minimizes direct intervention from superiors, ensures psychological safety for workers, and improves factory productivity.

[1097] 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.

[1098] In this invention, the server includes means for receiving task instructions entered by a superior, means for analyzing the received task instructions using natural language processing, means for assigning tasks to appropriate subordinates based on the analyzed task instructions, means for managing task progress reported by subordinates and providing feedback on the progress, means for evaluating tasks completed by subordinates and reporting the evaluation results to a superior, means for changing the AI ​​boss type according to the subordinate's selection, means for installing the AI ​​system in terminals used for work in the factory, means for visually presenting work content in real time via devices worn by line workers in the factory, and means for workers to report progress by voice input. This makes it easier for workers to understand task content in real time, enabling efficient progress management and appropriate feedback.

[1099] A "server" is a central processing unit that receives task instructions from superiors, analyzes them, and assigns tasks and manages their progress.

[1100] "Task instructions" are documents or orders that show specific work instructions given by superiors to subordinates.

[1101] "Natural language processing" is the technology for understanding, analyzing, and generating human language.

[1102] A "subordinate" is an employee who performs specific tasks under the direction of a superior.

[1103] "Progress" is a report that shows how much of a task a subordinate has completed.

[1104] "Feedback" is advice or notification from superiors to subordinates based on progress.

[1105] "Evaluation" means judging the quality and success of tasks completed by subordinates.

[1106] "AI Boss" is a system that uses artificial intelligence to take on the role of middle management.

[1107] "Terminal" refers to a device used by a worker, including, for example, smart glasses and a head-mounted display.

[1108] "Devices" refers to hardware such as smart glasses and head-mounted displays worn by line workers.

[1109] "Voice input" is an input method in which workers communicate information to the system by speaking.

[1110] "Real-time" refers to a state in which data transmission, reception, and processing are carried out immediately without delay.

[1111] The present invention is a system for receiving task instructions entered by superiors, assigning appropriate tasks to subordinates, managing progress, and providing feedback. The system is intended to be used by line workers in factories wearing smart glasses.

[1112] The server receives task instructions from superiors and analyzes them using natural language processing. The analyzed task instructions are stored in a database within the server. The server then retrieves the subordinates' skill sets and current task status from the database and assigns the tasks to the appropriate subordinates. The assigned tasks are then notified to the subordinates' smart glasses.

[1113] The smart glasses, which serve as terminals, visually display the work content to subordinates in real time. Workers use the smart glasses to check specific instructions and progress as they go about their work. Progress reports are made via voice input. For example, a report such as "Progress on task ID 5678 is 30%." The server receives the progress reports and uses a progress evaluation algorithm to compare the task progress with the deadline. If necessary, it generates feedback such as "Progress is behind schedule. Do you need help?" and sends it to the subordinate's terminal.

[1114] When the task is completed, the subordinate reports through the smart glasses, "I have completed the market analysis report." The server receives this report and evaluates the task. The evaluation results are then reported to the superior in the form of a report generated by the AI ​​boss. This allows the superior to check the subordinate's performance and provide appropriate feedback.

[1115] Subordinates can also change the type of their AI boss from the settings screen. This change request is sent to the server, and the new AI boss type is set in the database. The subordinate's task management algorithm is updated based on the new boss type.

[1116] This system configuration improves work efficiency in the factory and also ensures psychological safety for workers. As a concrete example, the server can receive, analyze, and assign the following task instructions to subordinates:

[1117] "Try out the new line work procedure and let us know the results within a week."

[1118] "Please inspect the product and report the results within two days."

[1119] This allows for timely feedback on progress, enabling efficient task management.

[1120] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1121] Step 1:

[1122] The server receives task instructions from superiors. The input task instructions are written in natural language and contain specific content such as "Please create a new market analysis report by next week." The server receives this task instruction and stores it in a database.

[1123] Step 2:

[1124] The server analyzes the received task instructions using a natural language processing engine (e.g., Spacy). It receives the task instructions as input and generates the analyzed task content and deadline information as output. It breaks them down into specific elements such as "Create a market analysis report" and "By next week." The analysis results are stored in a database.

[1125] Step 3:

[1126] The server assigns tasks to the most suitable subordinates based on the analyzed task instructions. The subordinates' skill sets and current task status are retrieved from the database and input into the algorithm. The algorithm evaluates the skill sets and task status, selects the most suitable subordinates, and outputs the assigned tasks. The subordinates' devices are notified of the assigned tasks.

[1127] Step 4:

[1128] The subordinate's device (smart glasses) receives the task notification sent from the server and visually presents it to the worker. The device displays task details and deadline information in an easy-to-read format for the worker. Once the worker confirms the information, the task is ready to begin.

[1129] Step 5:

[1130] The user (subordinate) reports the progress of the work to the server through the terminal. The progress is reported using the voice input function, and the user inputs "The progress of task ID 5678 is 30%." The server receives the report and stores it in the database.

[1131] Step 6:

[1132] The server manages progress based on the received progress status. Using a progress evaluation algorithm, it compares the reported progress with the deadline and generates feedback. For example, feedback such as "Progress is behind schedule. Do you need help?" is generated and sent to the subordinate's device.

[1133] Step 7:

[1134] When a user (subordinate) completes a task, he / she reports it to the server via a terminal. For example, he / she may type, "I have completed the market analysis report." The server receives this report and evaluates the quality and success of the task using an evaluation algorithm.

[1135] Step 8:

[1136] The server generates a report for the superior based on the evaluation results. The server retrieves the evaluation results from the database, compiles them into a report, and sends it to the superior's terminal. The superior can check this report to understand the performance of his subordinates.

[1137] Step 9:

[1138] Users (subordinates) can change the type of their AI boss from the settings screen. They input a change request on their device and send it to the server. The server receives this request and sets the new boss type in the database. When the setting is changed, the task management algorithm is updated based on the new boss type.

[1139] 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.

[1140] The present invention builds an AI system that takes on the role of middle managers, eliminating direct intervention by superiors to improve psychological safety in the workplace, and also incorporates an emotion engine to recognize the user's emotions and provide appropriate feedback and task management based on those emotions. Specific embodiments for implementing the present invention are described below.

[1141] Receiving and analyzing task instructions

[1142] The server receives task instructions from a superior. These task instructions have specific content, such as "Please create a new market analysis report by next week." The server receives these instructions and analyzes them using a natural language processing engine. The analyzed task instructions are broken down into the work content, "Create a market analysis report," and the deadline, "By next week." The results of this analysis are saved in the server's database.

[1143] Task assignment

[1144] The server then assigns the analyzed tasks to the appropriate subordinates. It retrieves the subordinates' skill sets and current task status from the database and runs an algorithm to identify the most suitable subordinate. Once the most suitable subordinate is identified, the task is assigned and the results are saved in the database. At the same time, a notification of the new task is sent to the subordinate's device.

[1145] Task progress management and feedback

[1146] The terminal (subordinate device) reports the task progress to the server. For example, input "Progress on task ID 1234 is 50%." The server receives the progress report and manages progress based on it. The server uses a progress evaluation algorithm to compare the reported progress with the deadline, and generates feedback as needed, such as "At this rate, you may not meet the deadline." This feedback is sent to the subordinate device.

[1147] Feedback using an emotion engine

[1148] The server is equipped with an emotion engine that obtains emotional data from subordinates' input, facial expressions, tone of voice, etc. The server uses this emotional data to adjust the feedback method. For example, if the server recognizes that a subordinate is feeling stressed, it can provide gentle feedback such as, "You seem to be behind on progress. Do you need any support?" This allows subordinates to work with greater peace of mind.

[1149] Evaluation and Reporting

[1150] After completing a task, the user (subordinate) reports the results to the server. For example, the user might type, "I have completed a market analysis report." The server receives this report and uses an evaluation algorithm to evaluate the quality and success of the task. Based on this evaluation, the server generates a report to be sent to the superior's terminal and sends it from the server to the superior's terminal.

[1151] Change boss type

[1152] The user (subordinate) can change the type of AI boss from the settings screen. For example, if a subordinate thinks, "I want to change my leadership style," he or she clicks the "Change boss type" option from the settings menu. The device sends this change request to the server. The server receives this request, sets the new boss type in the database, and changes the task management algorithm.

[1153] Specific examples

[1154] Example of receiving and analyzing task instructions

[1155] A superior gives instructions to the AI ​​system: "Conduct market research for a new product and submit a report within two weeks."

[1156] The server analyzes: The task is "conduct market research" and the deadline is "submit the report within two weeks" and records this in the database.

[1157] Examples of task progress management and feedback

[1158] Subordinate reports progress: Enters "Task ID 5678 is 30% complete."

[1159] The server evaluates and provides feedback: "Progress is behind schedule. Do you need help?" feedback is generated and sent to the subordinate's device.

[1160] Examples of emotion engines

[1161] Subordinate reports progress: Enters "Task ID 5678 is 30% complete."

[1162] The server analyzes using an emotion engine: it recognizes that "your subordinate is feeling stressed" and provides gentle feedback such as "progress seems to be lagging behind. Do you need any support?"

[1163] Through these processes, the AI ​​boss can effectively manage tasks and provide appropriate feedback when necessary while ensuring psychological safety for subordinates, thereby improving organizational productivity and workplace mental health.

[1164] The processing flow will be explained below.

[1165] The present invention is a system that builds an AI system that plays the role of middle management, eliminates direct intervention by superiors, and recognizes the user's emotions by combining an emotion engine, and provides appropriate feedback and task management based on the emotions. Specific embodiments for implementing the present invention will be described below, divided into processing steps.

[1166] Receiving and analyzing task instructions

[1167] Step 1:

[1168] A task instruction is input from the superior's terminal and sent to the AI ​​system. For example, "Please create a new market analysis report by next week."

[1169] Step 2:

[1170] The server receives task instructions sent by superiors and stores them in a task instruction database.

[1171] Step 3:

[1172] The server passes the received task instructions to a natural language processing engine, which analyzes the task content and deadline. For example, it extracts "Create a market analysis report" and "By next week."

[1173] Step 4:

[1174] The server stores the analysis results in a database.

[1175] Task assignment

[1176] Step 5:

[1177] The server obtains the subordinate's skill set and current task status from the database in order to appropriately assign the analyzed task to the subordinate.

[1178] Step 6:

[1179] The server runs an algorithm to identify the most suitable subordinate based on the acquired skill set and task situation.

[1180] Step 7:

[1181] The server assigns tasks to the identified subordinates and stores the results in a database.

[1182] Step 8:

[1183] Send a notification of a new task to the terminal (subordinate device).

[1184] Task progress management and feedback

[1185] Step 9:

[1186] The device reports the task progress to the server. Example: "Task ID 1234 progress is 50%."

[1187] Step 10:

[1188] The server receives the progress reports and records them in a database.

[1189] Step 11:

[1190] The server uses a progress evaluation algorithm to compare the reported progress with the deadline.

[1191] Step 12:

[1192] If the progress is not as planned, the server uses a feedback generation engine to generate appropriate feedback, e.g., "At this rate, you may not meet the deadline."

[1193] Step 13:

[1194] The terminal (subordinate device) receives a feedback message from the server as a notification.

[1195] Feedback using an emotion engine

[1196] Step 14:

[1197] The terminal (subordinate's device) collects the subordinate's emotion data through an emotion tracking device during task progress reports and other interactions.

[1198] Step 15:

[1199] The server uses an emotion engine to analyze the subordinates' emotional data, e.g., to detect stress or anxiety from their facial expressions, tone of voice, and text input.

[1200] Step 16:

[1201] The server adjusts the feedback method based on the emotional data. For example, if a subordinate feels stressed, the server provides feedback such as, "You seem to be behind in your progress. Do you need any support?"

[1202] Evaluation and Reporting

[1203] Step 17:

[1204] After completing a task, the user (subordinate) reports the results of the work to the server. For example, the user might write, "I have completed the market analysis report."

[1205] Step 18:

[1206] The server receives the reports and uses a rating algorithm to evaluate the quality and success of the tasks.

[1207] Step 19:

[1208] The server generates a report based on the evaluation results and sends it to the superior's terminal, which includes the evaluation results and a link to the completed market analysis report.

[1209] Change boss type

[1210] Step 20:

[1211] The user (subordinate) can change the type of AI boss from the settings screen. For example, if you think, "I want to change my leadership style," click the "Change boss type" option from the settings menu.

[1212] Step 21:

[1213] The terminal transmits the selection of the new boss type to the server.

[1214] Step 22:

[1215] The server receives the new manager type setting and updates the database with the changes.

[1216] Step 23:

[1217] The server changes the task management algorithm of the subordinates based on the new boss type.

[1218] Through these processes, the AI ​​boss can effectively manage tasks and provide feedback using an emotion engine while ensuring psychological safety for subordinates, thereby improving organizational productivity and workplace mental health.

[1219] Example 2

[1220] 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."

[1221] In traditional management systems, superiors often intervene directly, which can undermine the psychological safety of subordinates. Furthermore, traditional systems do not provide feedback that takes into account the emotions of subordinates, making effective task management difficult.

[1222] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1223] In this invention, the server includes means for receiving task instructions entered by a superior, means for analyzing the received task instructions using natural language processing, means for assigning tasks to appropriate subordinates based on the analyzed task instructions, means for managing task progress reported by subordinates and providing feedback on the progress, means for collecting emotional data on subordinates and adjusting the feedback method based on the data, means for evaluating tasks completed by subordinates and reporting the evaluation results to the superior, and means for changing the type of AI boss according to the subordinate's selection. This enables effective task management and appropriate feedback while ensuring psychological safety for subordinates.

[1224] A "superior" is a person in an organization who commands and orders subordinates.

[1225] "Task instructions" are instructions given by a superior to a subordinate that specify specific work content and deadlines.

[1226] "Natural language processing" is a technology that analyzes and understands human language on a computer.

[1227] A "subordinate" is someone who performs work under the command and order of a superior.

[1228] "Task assignment" refers to assigning a specific task to an appropriate person from among multiple subordinates.

[1229] "Progress" is information indicating the degree of completion of the tasks assigned to a subordinate.

[1230] "Feedback" refers to evaluation and advice on progress and results, with the aim of improving and supporting work.

[1231] "Emotion data" is data that indicates the emotional state of a subordinate, as determined from facial expressions, tone of voice, etc.

[1232] "Evaluation" is the process of making judgments based on the quality and results of completed tasks.

[1233] "Boss type" refers to the type of leadership style and management method that an AI boss possesses.

[1234] This invention aims to improve psychological safety in the workplace by reducing direct intervention by superiors using an artificial intelligence (AI) system that plays the role of middle managers. The system recognizes users' emotions through an emotion engine and provides appropriate feedback and task management based on that information.

[1235] Receiving and analyzing task instructions

[1236] The server receives specific task instructions from its superior, which are sent as HTTP requests to API endpoints.

[1237] For example, a user might receive an instruction such as "Create a new market analysis report by next week." The instruction is parsed using the Google Cloud Natural Language API and broken down into tasks and deadlines. The analysis results are then stored in a MySQL database.

[1238] Task assignment

[1239] The server retrieves the subordinates' skill sets and task status from a MySQL database, executes a custom algorithm written in Python, and assigns tasks to the most suitable subordinates. The assignment results are stored in the database, and new task notifications are sent to the subordinates' devices via a push notification service.

[1240] Task progress management and feedback

[1241] The terminal (subordinate device) reports its progress to the server. For example, it inputs and sends "Task ID 1234 is 50% complete." The server receives the progress report and evaluates it using a progress evaluation algorithm. If necessary, it generates feedback such as "At this rate, you may not meet the deadline," and sends it to the subordinate device.

[1242] Feedback using an emotion engine

[1243] The server uses the Affectiva SDK to collect emotional data from subordinates' input, facial expressions, and tone of voice. Based on this emotional data, the server adjusts the feedback provided to the subordinate's device, providing gentle feedback such as, "You seem to be behind schedule. Do you need any help?"

[1244] Evaluation and Reporting

[1245] After completing the task, the user (subordinate) reports to the server, "I have completed the market analysis report." The server receives the report and performs task evaluation using a Python script. Based on the evaluation results, a report to be reported to the superior is generated and sent to the superior's terminal in HTML or PDF format.

[1246] Change boss type

[1247] The user (subordinate) requests a change of supervisor type from the settings screen. The subordinate's device sends this change request to the server. The server receives the request and updates the database using an SQL UPDATE statement to change the supervisor type.

[1248] Specific examples

[1249] Example of receiving and analyzing task instructions

[1250] A superior gives instructions to the AI ​​system: "Conduct market research for a new product and submit a report within two weeks."

[1251] The server analyzes: The task "conduct market research" and the deadline "submit report within two weeks" are analyzed and recorded in a database.

[1252] Examples of task progress management and feedback

[1253] Subordinate reports progress: Enters "Task ID 5678 is 30% complete."

[1254] The server evaluates and provides feedback: "Progress is behind schedule. Do you need help?" feedback is generated and sent to the subordinate's device.

[1255] Examples of emotion engines

[1256] Subordinate reports progress: Enters "Task ID 5678 is 30% complete."

[1257] The server analyzes using an emotion engine: it recognizes that "your subordinate is feeling stressed" and provides gentle feedback such as "progress seems to be lagging behind. Do you need any support?"

[1258] Example of input prompt for generative AI model

[1259] "We've received a request to create a new market analysis report by next week. Please parse this request using a natural language processing engine to extract the task and deadline."

[1260] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1261] Step 1:

[1262] The server receives a task instruction from a superior saying, "I want you to create a new market analysis report by next week."

[1263] Specific operation: A task instruction is sent as an HTTP request to an API endpoint.

[1264] Input: Task instructions from superiors.

[1265] Output: An HTTP request containing the task instructions.

[1266] Step 2:

[1267] The server parses the received task instructions using the Google Cloud Natural Language API.

[1268] Specific operation: Send an API request and receive the analysis results as a response.

[1269] Input: An HTTP request containing task instructions.

[1270] Output: Job description: "Create a market analysis report" with deadline "by next week."

[1271] Step 3:

[1272] The server stores the analysis results in a MySQL database.

[1273] Specific operation: Inserts data into the database using the SQL INSERT statement.

[1274] Input: "Create market analysis report" and analysis results "by next week".

[1275] Output: Task instructions and deadlines recorded in a database.

[1276] Step 4:

[1277] The server retrieves the subordinate's skill set and current task status from the database.

[1278] What it does: Uses a SQL SELECT statement to query for the information you need.

[1279] Input: Subordinate ID information.

[1280] Output: Skill set and task status data for each subordinate.

[1281] Step 5:

[1282] The server runs a custom algorithm written in Python to assign tasks to the best subordinates.

[1283] Specific behavior: The algorithm evaluates subordinates' skill sets and task situations and selects the most suitable subordinate.

[1284] Inputs: Subordinate skill set, current task status, task instructions.

[1285] Output: Information about the subordinates who have been assigned the task.

[1286] Step 6:

[1287] The server stores the task assignment results in a database and sends new task notifications to the subordinate terminals.

[1288] Specific operation: Inserts data into the database using an SQL INSERT statement and sends a notification to the subordinate's device via the push notification service.

[1289] Input: Task assignment results.

[1290] Output: Task assignment results stored in the database and notifications sent to subordinates' devices.

[1291] Step 7:

[1292] The terminals (subordinate devices) report their progress to the server.

[1293] Specific operation: The subordinate enters the progress status and sends it to the server as an HTTP POST request.

[1294] Input: A progress report such as "Task ID 1234 is 50% complete."

[1295] Output: Progress report sent to the server.

[1296] Step 8:

[1297] The server receives the progress reports and evaluates them using a progress evaluation algorithm.

[1298] Specific actions: Compare progress with deadlines and generate evaluation results.

[1299] Input: Progress report from subordinate.

[1300] Output: Feedback such as "At this rate, you may not meet the deadline."

[1301] Step 9:

[1302] The server generates feedback as needed and sends it to the subordinate terminals.

[1303] Specific actions: Feedback is automatically generated based on progress evaluation results and sent to subordinates via push notification or email.

[1304] Input: Progress assessment results.

[1305] Output: Feedback sent to subordinate devices.

[1306] Step 10:

[1307] The server uses the Affectiva SDK to collect emotional data from subordinates' inputs, facial expressions, and tone of voice.

[1308] Specific operation: Data is acquired from subordinate devices via the camera and microphone, and analyzed using the SDK.

[1309] Input: Subordinates' facial expressions and tone of voice.

[1310] Output: Parsed emotion data.

[1311] Step 11:

[1312] The server adjusts the feedback content based on the emotional data and sends it to the subordinate's device.

[1313] Specific action: Analyze emotional data and generate appropriate feedback content.

[1314] Input: Emotion data.

[1315] Output: Gentle feedback such as, "You seem to be making slow progress, do you need any help?"

[1316] Step 12:

[1317] After completing the task, the user (subordinate) reports to the server, "I have completed the market analysis report."

[1318] Specific operation: Enter a task completion report and send it to the server as an HTTP POST request.

[1319] Input: Task completion report.

[1320] Output: Task completion report sent to the server.

[1321] Step 13:

[1322] The server evaluates the report and evaluates the task quality and performance.

[1323] Specific operation: A Python script is used to analyze the report content and generate a rating score.

[1324] Input: Task completion report.

[1325] Output: Evaluation score.

[1326] Step 14:

[1327] The server generates a report to report to the superior based on the evaluation results and transmits it to the superior's terminal.

[1328] Specific Actions: Based on the assessment results, a report is generated in HTML or PDF format and sent via email or internal messaging system.

[1329] Input: Rating score.

[1330] Output: Report sent to superior's terminal.

[1331] Step 15:

[1332] The user (subordinate) requests a change of superior type from the settings screen.

[1333] Specific actions: Select the Change Manager Type option and submit a change request.

[1334] Input: Supervisor type change request.

[1335] Output: The change request sent to the server.

[1336] Step 16:

[1337] The terminal sends a change request to the server.

[1338] Specific operation: A change request is sent to the server as an HTTP POST request.

[1339] Input: Supervisor type change request.

[1340] Output: The change request sent to the server.

[1341] Step 17:

[1342] The server receives the request and updates the database with the new boss type.

[1343] Specific Actions: Update the database using a SQL UPDATE statement to apply the task management algorithm based on the new supervisor type.

[1344] Input: Supervisor type change request.

[1345] Output: The manager type information updated in the database.

[1346] (Application example 2)

[1347] 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."

[1348] The problem to be solved by this invention is to eliminate direct intervention by superiors, improve psychological safety in the workplace, and provide appropriate task management and feedback. In particular, the object is to reduce stress for subordinates and support efficient work performance by recognizing subordinates' emotions in real time and adjusting feedback methods based on those emotions.

[1349] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1350] In this invention, the server includes means for receiving task instructions entered by a superior, means for analyzing the received task instructions using natural language processing, means for assigning tasks to appropriate subordinates based on the analyzed task instructions, means for managing task progress reported by subordinates and providing feedback on the progress, means for evaluating tasks completed by subordinates and reporting the evaluation results to the superior, means for using an emotion recognition engine that recognizes the emotions of subordinates and adjusts the feedback content based on the emotions, and means for changing the type of AI boss according to the selection of the subordinate. This enables flexible feedback and progress management according to the emotions of subordinates.

[1351] "Task instructions" refer to specific work content and deadlines instructed by a superior to a subordinate.

[1352] "Natural language processing" is a technology that uses computers to analyze, understand, and generate human language (natural language).

[1353] "Task progress" refers to the status that indicates how much a subordinate has performed on a given task and how close they are to completing it.

[1354] "Feedback" refers to the guidance and evaluation provided to subordinates by superiors or systems based on task progress.

[1355] An "emotion recognition engine" is a technology that analyzes a subordinate's emotional state from their input, facial expressions, tone of voice, etc.

[1356] A "generative AI model" is an artificial intelligence model that has been trained to generate results for a specific task.

[1357] A "skill set" is a collection of knowledge, abilities, experience, etc. that a subordinate possesses that are relevant to a specific job.

[1358] "AI Boss" is a management system with artificial intelligence that assigns tasks and provides feedback to subordinates.

[1359] "Task management" is the process of monitoring task progress and providing adjustments and feedback.

[1360] "Psychological safety" refers to a state in the workplace where members feel safe to express their opinions and feelings.

[1361] "Progress management" is the process of making sure that tasks are progressing as planned and making adjustments as necessary.

[1362] An "evaluation algorithm" is a calculation method for quantitatively or qualitatively evaluating the work performance of subordinates upon task completion.

[1363] The present invention is a system that builds an AI system that takes on the role of middle managers, improves psychological safety in the workplace by eliminating direct intervention by superiors, and recognizes the user's emotions by combining it with an emotion recognition engine, and provides appropriate feedback and task management based on the emotions. Specific embodiments for implementing the present invention are described in detail below.

[1364] Receiving and analyzing task instructions

[1365] The server receives task instructions input by a superior. For example, the superior's input may be specific, such as "Please create a new market analysis report by next week." The server receives this instruction and analyzes it using a natural language processing engine. The analyzed task instruction is broken down into the work content, "Create a market analysis report," and the deadline, "By next week," and the analysis results are saved in the server's database.

[1366] Task assignment

[1367] The server assigns the analyzed tasks to subordinates. It retrieves the subordinates' skill sets and current task status from the database and runs an algorithm to identify the most suitable subordinate. Once the most suitable subordinate is identified, the task is assigned to that subordinate and the result is saved in the database. At the same time, a notification of the new task is sent to the subordinate's device.

[1368] Task progress management and feedback

[1369] The subordinate's device reports the task progress to the server. For example, "Progress on task ID 1234 is 50%." The server receives the progress report and manages the progress based on it. The server uses a progress evaluation algorithm to compare the reported progress with the deadline, and generates feedback such as "At this rate, you may not meet the deadline." This feedback is sent to the subordinate's device.

[1370] Feedback using an emotion recognition engine

[1371] The server is equipped with an emotion recognition engine that obtains emotional data from subordinates' input, facial expressions, tone of voice, etc. The server uses this emotional data to adjust the feedback method. For example, if the server recognizes that a subordinate is feeling stressed, it can provide gentle feedback such as, "You seem to be behind on progress. Do you need any support?"

[1372] Evaluation and Reporting

[1373] After completing the task, the subordinate reports the results to the server, entering "I have completed the market analysis report." The server receives this report and uses an evaluation algorithm to evaluate the quality and results of the task. Based on this evaluation result, a report is generated for reporting to the superior, and sent from the server to the superior's terminal.

[1374] Change boss type

[1375] Subordinates can change the type of their AI boss from the settings screen. For example, if a subordinate decides to "change their leadership style," they can click the "Change boss type" option from the settings menu. The device then sends this change request to the server. The server receives this request, sets the new boss type in the database, and changes the task management algorithm.

[1376] Specific examples

[1377] Example of receiving and analyzing task instructions

[1378] A superior gives instructions to the AI ​​system: "Conduct market research for a new product and submit a report within two weeks."

[1379] The server analyzes: The task is "conduct market research" and the deadline is "submit the report within two weeks" and records this in the database.

[1380] Examples of task progress management and feedback

[1381] Subordinate reports progress: Enters "Task ID 5678 is 30% complete."

[1382] The server evaluates and provides feedback: "Progress is behind schedule. Do you need help?" feedback is generated and sent to the subordinate's device.

[1383] Example of an emotion recognition engine

[1384] Subordinate reports progress: Enters "Task ID 5678 is 30% complete."

[1385] The server analyzes using an emotion recognition engine: it recognizes that "your subordinate is feeling stressed" and provides gentle feedback such as "You seem to be making slow progress. Do you need any support?"

[1386] Through these processes, the AI ​​boss can effectively manage tasks and provide appropriate feedback when necessary while ensuring psychological safety for subordinates, thereby improving organizational productivity and workplace mental health.

[1387] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1388] Step 1:

[1389] The server receives task instructions entered by superiors.

[1390] Specifically, when a superior inputs an instruction into the system such as "Please create a new market analysis report by next week," the server receives this task instruction as a string of characters.

[1391] Input: Task instructions from superiors

[1392] Output: Raw task instruction data (string format)

[1393] Step 2:

[1394] The server analyzes the received task instructions using a natural language processing engine.

[1395] Specifically, the server breaks down the task instructions into components such as "create a market analysis report" and "by next week," and stores this information in a database.

[1396] Input: Raw task instruction data

[1397] Output: Parsed task details and deadlines

[1398] Step 3:

[1399] The server assigns tasks to subordinates based on the parsed task instructions.

[1400] Specifically, the server retrieves the subordinates' skill sets and current task status from the database, identifies the most suitable subordinate, and assigns the task to that subordinate.

[1401] Input: Analyzed task content, subordinate skill set, current task status

[1402] Output: Task assignment notification sent to subordinate's device

[1403] Step 4:

[1404] The subordinate terminals report the progress of the tasks to the server.

[1405] Specifically, the subordinate enters the task progress as "Progress of task ID 1234 is 50%" and sends this progress data to the server.

[1406] Input: Progress report data from subordinates

[1407] Output: Progress data stored on the server

[1408] Step 5:

[1409] The server manages the progress based on the progress reports and generates feedback as needed.

[1410] Specifically, the server uses a progress evaluation algorithm to compare the reported progress with the deadline, generates feedback such as "At this rate, you may not meet the deadline," and sends it to the subordinate's device.

[1411] Input: Progress report data

[1412] Output: Feedback message

[1413] Step 6:

[1414] The server recognizes the emotions of the subordinates using an emotion recognition engine and adjusts the feedback content.

[1415] Specifically, it extracts emotional data from the text of progress reports and the input of subordinates, and if it recognizes emotions such as "feeling stressed," it generates gentle feedback such as "You seem to be making slow progress. Do you need any support?"

[1416] Input: Progress report text data

[1417] Output: Emotion-based regulatory feedback

[1418] Step 7:

[1419] After completing a task, the subordinate reports the result to the server.

[1420] Specifically, the subordinate enters "Market analysis report completed" and sends the task completion report to the server.

[1421] Input: Task completion report data

[1422] Output: Task completion report saved on the server

[1423] Step 8:

[1424] The server receives the task completion reports and evaluates the quality and success of the tasks using an evaluation algorithm.

[1425] Specifically, the task results are evaluated quantitatively or qualitatively using an evaluation algorithm, and a report is generated to report the evaluation results to superiors.

[1426] Input: Task completion report data

[1427] Output: Evaluation report for superiors

[1428] Step 9:

[1429] Subordinates can change the type of AI boss they have from the settings screen.

[1430] Specifically, the subordinate selects the "Change Supervisor Type" option from the settings menu and sends a change request to the server. The server receives this request, sets the new supervisor type in the database, and changes the task management algorithm.

[1431] Input: Request from subordinate to change supervisor type

[1432] Output: New manager type set in the database

[1433] Example prompt sentence:

[1434] "Progress of task ID xxxx is yy%"

[1435] "The user's progress report text is sent to the emotion engine for emotion analysis."

[1436] "Market analysis report completed."

[1437] 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.

[1438] 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.

[1439] 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.

[1440] [Fourth embodiment]

[1441] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1442] 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.

[1443] 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).

[1444] 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.

[1445] 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.

[1446] 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).

[1447] 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.

[1448] 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.

[1449] 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.

[1450] 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.

[1451] 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.

[1452] 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.

[1453] 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."

[1454] The present invention is a system that improves psychological safety in the workplace by building an AI system that takes on the role of middle managers and eliminates direct intervention by superiors. Specific embodiments for implementing the present invention are described below.

[1455] Receiving and analyzing task instructions

[1456] The server receives task instructions from a superior. These task instructions have specific content, such as "Please create a new market analysis report by next week." The server receives these instructions and analyzes them using a natural language processing engine. The analyzed task instructions are broken down into the work content, "Create a market analysis report," and the deadline, "By next week." The results of this analysis are saved in the server's database.

[1457] Task assignment

[1458] The server then assigns the analyzed tasks to the appropriate subordinates. It retrieves the subordinates' skill sets and current task status from the database and runs an algorithm to identify the most suitable subordinate. Once the most suitable subordinate is identified, the task is assigned and the results are saved in the database. At the same time, a notification of the new task is sent to the subordinate's device.

[1459] Task progress management and feedback

[1460] The terminal (subordinate device) reports the progress of the task to the server. For example, it inputs "Progress on task ID 1234 is 50%." The server receives the progress report and manages the progress based on it. The server uses a progress evaluation algorithm to compare the reported progress with the deadline, and generates feedback as needed, such as "At this rate, you may not meet the deadline." This feedback is sent to the subordinate device.

[1461] Evaluation and Reporting

[1462] When a user (subordinate) completes a task, he / she reports the result to the server. For example, he / she might type, "I have completed a market analysis report." The server receives this report and evaluates the task. The server uses an evaluation algorithm to evaluate the quality and outcome of the task, and generates a report to report the evaluation results to the superior. The report is then sent from the server to the superior's terminal.

[1463] Change boss type

[1464] The user (subordinate) can change the type of AI boss from the settings screen. For example, if a subordinate thinks, "I want to change my leadership style," he or she clicks the "Change boss type" option from the settings menu. The device sends this change request to the server. The server receives this request and sets the new boss type in the database. The subordinate's task management algorithm is updated based on the new boss type.

[1465] Specific examples

[1466] Example of receiving and analyzing task instructions

[1467] A superior gives instructions to the AI ​​system: "Conduct market research for a new product and submit a report within two weeks."

[1468] The server analyzes: The task is "conduct market research" and the deadline is "submit the report within two weeks" and records this in the database.

[1469] Examples of task progress management and feedback

[1470] Subordinate reports progress: Enters "Task ID 5678 is 30% complete."

[1471] The server evaluates and provides feedback: "Progress is behind schedule. Do you need help?" feedback is generated and sent to the subordinate's device.

[1472] These processes enable the AI ​​boss to effectively manage tasks and provide appropriate feedback when necessary while ensuring psychological safety for subordinates, thereby improving organizational productivity and workplace mental health.

[1473] The processing flow will be explained below.

[1474] Step 1:

[1475] A task instruction is input from the superior's terminal and sent to the AI ​​system. For example, "Please create a new market analysis report by next week."

[1476] Step 2:

[1477] The server receives task instructions sent by superiors and stores them in a task instruction database.

[1478] Step 3:

[1479] The server passes the received task instructions to a natural language processing engine, which analyzes the task content and deadline. For example, it extracts "Create a market analysis report" and "By next week."

[1480] Step 4:

[1481] The server stores the analysis results in a database and retrieves the subordinates' skill sets and current task status from the database.

[1482] Step 5:

[1483] The server runs an algorithm to identify the most suitable subordinate based on the acquired skill set and task situation.

[1484] Step 6:

[1485] The server assigns tasks to the identified subordinates and stores the results in a database.

[1486] Step 7:

[1487] The terminal (subordinate device) receives a notification of a new task, e.g., a specified task and deadline.

[1488] Step 8:

[1489] The terminal periodically reports the progress of the task to the server. Example: "Progress of task ID 1234 is 50%."

[1490] Step 9:

[1491] The server records the received progress reports in a database and uses a progress evaluation algorithm to compare the report status with deadlines.

[1492] Step 10:

[1493] If the progress is not as planned, the server uses a feedback generation engine to generate appropriate feedback, e.g., "At this rate, you may not meet the deadline."

[1494] Step 11:

[1495] The terminal (subordinate device) receives the feedback message from the server as a notification.

[1496] Step 12:

[1497] After completing a task, the user (subordinate) reports the results of the work to the server. For example, the user might write, "I have completed the market analysis report."

[1498] Step 13:

[1499] The server receives the reports and uses a rating algorithm to rate the quality and success of the tasks.

[1500] Step 14:

[1501] The server generates a report based on the evaluation results and sends it to the superior's terminal, which includes the evaluation results and a link to the completed market analysis report.

[1502] Step 15:

[1503] Users (subordinates) can change the type of AI boss from the settings screen. For example, they can think, "Let's try changing the leadership style," and select an option.

[1504] Step 16:

[1505] The terminal transmits the selection of the new boss type to the server.

[1506] Step 17:

[1507] The server receives the new boss type setting, updates the database, and changes the task management algorithm of subordinates.

[1508] Example 1

[1509] 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."

[1510] In a typical workplace, direct instructions and feedback from superiors can cause subordinates to feel psychological stress. In such situations, there are concerns about a decline in productivity and a worsening work environment. The purpose of this invention is to solve these problems and increase psychological safety.

[1511] 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.

[1512] In this invention, the server includes means for receiving task instructions entered by a superior, means for analyzing the received task instructions using natural language processing, means for assigning tasks to appropriate subordinates based on the analyzed task instructions, means for acquiring the subordinates' skill sets and current task statuses and identifying the most suitable subordinates, means for sending notifications of new tasks to the subordinates, means for managing task progress reported by the subordinates and providing feedback on the progress, means for evaluating tasks completed by the subordinates and reporting the evaluation results to the superior, and means for changing the type of AI boss according to the subordinates' selection. This enables effective task management without causing psychological stress to subordinates, improving workplace productivity and mental health.

[1513] "Superior" refers to a manager or leader who has the authority to give instructions within an organization.

[1514] "Task instructions" refers to specific instructions given by a superior to a subordinate, such as the work content, goals, deadlines, etc.

[1515] "Natural language processing" is a technology that allows computers to understand and analyze human language.

[1516] "Analysis" refers to the act of breaking down received task instructions and extracting information such as work content and deadlines.

[1517] A "subordinate" is someone who performs tasks under the direction of a superior within an organization.

[1518] A "skill set" refers to the totality of a subordinate's abilities and expertise.

[1519] "Task status" refers to the progress and completion status of the work that a subordinate is currently responsible for.

[1520] "Progress" refers to the degree of progress and completion of tasks assigned to subordinates.

[1521] "Feedback" refers to evaluation and advice provided on progress and work content.

[1522] "Evaluation" is the process of judging the quality or success of a completed task.

[1523] "AI boss type" refers to the leadership style and teaching methods simulated by the AI.

[1524] "Notifications" refer to messages or alerts that communicate information about task assignments and progress to subordinates.

[1525] A "database" is a system that efficiently stores and manages digital information.

[1526] An "algorithm" refers to a computational procedure or process for solving a particular problem.

[1527] "Machine learning" is a technology that allows computers to learn by themselves using empirical data and improve their performance.

[1528] This invention is a system that improves psychological safety in the workplace by constructing an AI system that takes on the role of middle managers and eliminates direct intervention by superiors. As an implementation form of this system, we will explain how the server, terminals, and users participate in this system and play their respective roles.

[1529] Server Roles

[1530] The server receives task instructions from superiors and analyzes them using a natural language processing engine. This system preferably uses natural language processing engines such as Google NLP API or IBM Watson. The analyzed task instructions are broken down into work content and deadlines and stored in a database (MySQL, PostgreSQL, etc.). The server then retrieves the subordinates' skill sets and current task status from the database and identifies the most suitable subordinates using machine learning algorithms (e.g., Random Forest, SVM).

[1531] As subordinates progress with assigned tasks, the server receives progress reports and evaluates the progress using an evaluation algorithm (e.g., linear regression model). Feedback is generated as needed and sent to the subordinate's device. Furthermore, when the subordinate completes the task, the server evaluates the results and generates a report based on the evaluation results to be sent to the superior's device.

[1532] Device Role

[1533] The terminals are devices primarily used by subordinates and superiors. The subordinate's terminal receives notifications of new tasks sent from the server and provides an interface for reporting progress to the server. The superior's terminal receives task analysis results and progress evaluation reports. It also provides an interface for subordinates to send requests from the settings screen if they want to change the type of AI boss.

[1534] User Roles

[1535] The user (subordinate) reports the progress of assigned tasks to the server from their device and proceeds with the work while checking feedback as needed. When the task is completed, the user reports the completion and sends the results to the server. In addition, the user can change the type of AI boss from the settings screen.

[1536] As a concrete example, if a superior inputs a task instruction into an AI system, such as "Conduct market research for a new product and submit a report within two weeks," the server will analyze the instruction using natural language processing and record the task content and deadline in a database. If a subordinate reports progress as "Task ID 5678 is 30% complete," the server will evaluate the progress, generate feedback such as "Progress is behind schedule. Do you need assistance?" and send it to the subordinate's device.

[1537] In this way, the AI ​​system of the present invention aims to improve workplace productivity and mental health by effectively managing tasks while ensuring the psychological safety of subordinates.

[1538] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1539] Step 1: Receiving task instructions

[1540] The server receives task instructions from superiors. The input is a natural language task instruction such as "I want you to create a new market analysis report by next week." The server receives this input and passes it on to the next step.

[1541] Step 2: Parsing task instructions

[1542] The server analyzes the received task instructions using a natural language processing engine (e.g., Google NLP API, IBM Watson). The input is the task instruction received in step 1, which is analyzed and broken down into the work content ("Create a market analysis report") and deadline ("By next week"). The output is the analyzed task content and deadline, which are stored in a database.

[1543] Step 3: Obtain information about your subordinates

[1544] The server retrieves the subordinate's skill set and current task status from the database. The input is the subordinate's information stored in the database, including data on the skill set and progress status. The output is the retrieved subordinate's information, which is passed to the next task assignment step.

[1545] Step 4: Assign tasks

[1546] The server uses a machine learning algorithm (e.g., Random Forest, SVM) to identify the most suitable subordinate based on the acquired information about the subordinates. The input is the analyzed task content and deadline, and the acquired information about the subordinates. The algorithm identifies which subordinate to assign the task to. The output is the task assignment result, and the information about the task assigned to the specific subordinate is saved in a database.

[1547] Step 5: Sending task notifications

[1548] The server sends a notification of a new task to the subordinate's terminal. The input is the result of task assignment, which is information about which task has been assigned to which subordinate. The output is the task notification message sent to the subordinate's terminal. The notification includes the task content and deadline.

[1549] Step 6: Enter progress reports

[1550] The user (subordinate) inputs the progress status of a task from a terminal. For example, "Progress of task ID 1234 is 50%." The input is a progress report, indicating the progress of each task and how much progress has been made. The output is progress information, which is sent to the server.

[1551] Step 7: Record your progress

[1552] The server stores the received progress reports in a database. The input is the progress information sent in step 6. The output is the progress information recorded in the database, which is passed to the next progress evaluation step.

[1553] Step 8: Evaluate progress and generate feedback

[1554] The server evaluates the progress information using a progress evaluation algorithm (e.g., a linear regression model). The input is the progress information and the task deadlines recorded in a dictionary. The algorithm compares the progress with the deadlines and generates feedback, if necessary, such as "At this rate, you may not meet the deadline." The output is the generated feedback message, which is passed to the next feedback sending step.

[1555] Step 9: Submit your feedback

[1556] The server sends the generated feedback to the terminal (subordinate's device). The input is the evaluated progress information and the generated feedback message. The output is the feedback message sent to the subordinate's terminal. The subordinate checks this and adjusts the work pace as necessary.

[1557] Step 10: Reporting Task Completion

[1558] When a user (subordinate) completes a task, he / she reports the result to the server. The input is a task completion report such as "I have completed the market analysis report." The output is the report data of the completed task, which is sent to the server.

[1559] Step 11: Evaluation and Report Generation

[1560] The server receives the task completion reports and evaluates the quality and performance of the tasks using an evaluation algorithm (e.g., a rule-based evaluation model). The input is the completion report data. The algorithm generates an evaluation score, which is then used to generate a report to be reported to superiors. The output is the generated evaluation report.

[1561] Step 12: Submit the report

[1562] The server sends the evaluation report to the superior's terminal. The input is the generated evaluation report. The output is the evaluation report sent to the superior's terminal. The superior checks it and provides necessary feedback or additional instructions.

[1563] Step 13: Request a change of manager type

[1564] The user (subordinate) selects the "Change supervisor type" option from the settings screen, selects a new supervisor type, and submits it. The input is a request to change supervisor type. The output is the request data, which is sent to the server.

[1565] Step 14: Update Manager Type

[1566] The server receives the boss type change request and updates the database settings. The input is the boss type change request. The output is the new boss type setting stored in the database, and the task management algorithm is updated.

[1567] (Application example 1)

[1568] 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."

[1569] In today's factory work environment, efficient task management and appropriate feedback to workers are difficult. Line workers, in particular, need to receive real-time instructions and manage progress, which can lead to stress and mistakes. There is a need for a system that minimizes direct intervention from superiors, ensures psychological safety for workers, and improves factory productivity.

[1570] 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.

[1571] In this invention, the server includes means for receiving task instructions entered by a superior, means for analyzing the received task instructions using natural language processing, means for assigning tasks to appropriate subordinates based on the analyzed task instructions, means for managing task progress reported by subordinates and providing feedback on the progress, means for evaluating tasks completed by subordinates and reporting the evaluation results to a superior, means for changing the AI ​​boss type according to the subordinate's selection, means for installing the AI ​​system in terminals used for work in the factory, means for visually presenting work content in real time via devices worn by line workers in the factory, and means for workers to report progress by voice input. This makes it easier for workers to understand task content in real time, enabling efficient progress management and appropriate feedback.

[1572] A "server" is a central processing unit that receives task instructions from superiors, analyzes them, and assigns tasks and manages their progress.

[1573] "Task instructions" are documents or orders that show specific work instructions given by superiors to subordinates.

[1574] "Natural language processing" is the technology for understanding, analyzing, and generating human language.

[1575] A "subordinate" is an employee who performs specific tasks under the direction of a superior.

[1576] "Progress" is a report that shows how much of a task a subordinate has completed.

[1577] "Feedback" is advice or notification from superiors to subordinates based on progress.

[1578] "Evaluation" means judging the quality and success of tasks completed by subordinates.

[1579] "AI Boss" is a system that uses artificial intelligence to take on the role of middle management.

[1580] "Terminal" refers to a device used by a worker, including, for example, smart glasses and a head-mounted display.

[1581] "Devices" refers to hardware such as smart glasses and head-mounted displays worn by line workers.

[1582] "Voice input" is an input method in which workers communicate information to the system by speaking.

[1583] "Real-time" refers to a state in which data transmission, reception, and processing are carried out immediately without delay.

[1584] The present invention is a system for receiving task instructions entered by superiors, assigning appropriate tasks to subordinates, managing progress, and providing feedback. The system is intended to be used by line workers in factories wearing smart glasses.

[1585] The server receives task instructions from superiors and analyzes them using natural language processing. The analyzed task instructions are stored in a database within the server. The server then retrieves the subordinates' skill sets and current task status from the database and assigns the tasks to the appropriate subordinates. The assigned tasks are then notified to the subordinates' smart glasses.

[1586] The smart glasses, which serve as terminals, visually display the work content to subordinates in real time. Workers use the smart glasses to check specific instructions and progress as they go about their work. Progress reports are made via voice input. For example, a report such as "Progress on task ID 5678 is 30%." The server receives the progress reports and uses a progress evaluation algorithm to compare the task progress with the deadline. If necessary, it generates feedback such as "Progress is behind schedule. Do you need help?" and sends it to the subordinate's terminal.

[1587] When the task is completed, the subordinate reports through the smart glasses, "I have completed the market analysis report." The server receives this report and evaluates the task. The evaluation results are then reported to the superior in the form of a report generated by the AI ​​boss. This allows the superior to check the subordinate's performance and provide appropriate feedback.

[1588] Subordinates can also change the type of their AI boss from the settings screen. This change request is sent to the server, and the new AI boss type is set in the database. The subordinate's task management algorithm is updated based on the new boss type.

[1589] This system configuration improves work efficiency in the factory and also ensures psychological safety for workers. As a concrete example, the server can receive, analyze, and assign the following task instructions to subordinates:

[1590] "Try out the new line work procedure and let us know the results within a week."

[1591] "Please inspect the product and report the results within two days."

[1592] This allows for timely feedback on progress, enabling efficient task management.

[1593] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1594] Step 1:

[1595] The server receives task instructions from superiors. The input task instructions are written in natural language and contain specific content such as "Please create a new market analysis report by next week." The server receives this task instruction and stores it in a database.

[1596] Step 2:

[1597] The server analyzes the received task instructions using a natural language processing engine (e.g., Spacy). It receives the task instructions as input and generates the analyzed task content and deadline information as output. It breaks them down into specific elements such as "Create a market analysis report" and "By next week." The analysis results are stored in a database.

[1598] Step 3:

[1599] The server assigns tasks to the most suitable subordinates based on the analyzed task instructions. The subordinates' skill sets and current task status are retrieved from the database and input into the algorithm. The algorithm evaluates the skill sets and task status, selects the most suitable subordinates, and outputs the assigned tasks. The subordinates' devices are notified of the assigned tasks.

[1600] Step 4:

[1601] The subordinate's device (smart glasses) receives the task notification sent from the server and visually presents it to the worker. The device displays task details and deadline information in an easy-to-read format for the worker. Once the worker confirms the information, the task is ready to begin.

[1602] Step 5:

[1603] The user (subordinate) reports the progress of the work to the server through the terminal. The progress is reported using the voice input function, and the user inputs "The progress of task ID 5678 is 30%." The server receives the report and stores it in the database.

[1604] Step 6:

[1605] The server manages progress based on the received progress status. Using a progress evaluation algorithm, it compares the reported progress with the deadline and generates feedback. For example, feedback such as "Progress is behind schedule. Do you need help?" is generated and sent to the subordinate's device.

[1606] Step 7:

[1607] When a user (subordinate) completes a task, he / she reports it to the server via a terminal. For example, he / she may type, "I have completed the market analysis report." The server receives this report and evaluates the quality and success of the task using an evaluation algorithm.

[1608] Step 8:

[1609] The server generates a report for the superior based on the evaluation results. The server retrieves the evaluation results from the database, compiles them into a report, and sends it to the superior's terminal. The superior can check this report to understand the performance of his subordinates.

[1610] Step 9:

[1611] Users (subordinates) can change the type of their AI boss from the settings screen. They input a change request on their device and send it to the server. The server receives this request and sets the new boss type in the database. When the setting is changed, the task management algorithm is updated based on the new boss type.

[1612] 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.

[1613] The present invention builds an AI system that takes on the role of middle managers, eliminating direct intervention by superiors to improve psychological safety in the workplace, and also incorporates an emotion engine to recognize the user's emotions and provide appropriate feedback and task management based on those emotions. Specific embodiments for implementing the present invention are described below.

[1614] Receiving and analyzing task instructions

[1615] The server receives task instructions from a superior. These task instructions have specific content, such as "Please create a new market analysis report by next week." The server receives these instructions and analyzes them using a natural language processing engine. The analyzed task instructions are broken down into the work content, "Create a market analysis report," and the deadline, "By next week." The results of this analysis are saved in the server's database.

[1616] Task assignment

[1617] The server then assigns the analyzed tasks to the appropriate subordinates. It retrieves the subordinates' skill sets and current task status from the database and runs an algorithm to identify the most suitable subordinate. Once the most suitable subordinate is identified, the task is assigned and the results are saved in the database. At the same time, a notification of the new task is sent to the subordinate's device.

[1618] Task progress management and feedback

[1619] The terminal (subordinate device) reports the task progress to the server. For example, input "Progress on task ID 1234 is 50%." The server receives the progress report and manages progress based on it. The server uses a progress evaluation algorithm to compare the reported progress with the deadline, and generates feedback as needed, such as "At this rate, you may not meet the deadline." This feedback is sent to the subordinate device.

[1620] Feedback using an emotion engine

[1621] The server is equipped with an emotion engine that obtains emotional data from subordinates' input, facial expressions, tone of voice, etc. The server uses this emotional data to adjust the feedback method. For example, if the server recognizes that a subordinate is feeling stressed, it can provide gentle feedback such as, "You seem to be behind on progress. Do you need any support?" This allows subordinates to work with greater peace of mind.

[1622] Evaluation and Reporting

[1623] After completing a task, the user (subordinate) reports the results to the server. For example, the user might type, "I have completed a market analysis report." The server receives this report and uses an evaluation algorithm to evaluate the quality and success of the task. Based on this evaluation, the server generates a report to be sent to the superior's terminal and sends it from the server to the superior's terminal.

[1624] Change boss type

[1625] The user (subordinate) can change the type of AI boss from the settings screen. For example, if a subordinate thinks, "I want to change my leadership style," he or she clicks the "Change boss type" option from the settings menu. The device sends this change request to the server. The server receives this request, sets the new boss type in the database, and changes the task management algorithm.

[1626] Specific examples

[1627] Example of receiving and analyzing task instructions

[1628] A superior gives instructions to the AI ​​system: "Conduct market research for a new product and submit a report within two weeks."

[1629] The server analyzes: The task is "conduct market research" and the deadline is "submit the report within two weeks" and records this in the database.

[1630] Examples of task progress management and feedback

[1631] Subordinate reports progress: Enters "Task ID 5678 is 30% complete."

[1632] The server evaluates and provides feedback: "Progress is behind schedule. Do you need help?" feedback is generated and sent to the subordinate's device.

[1633] Examples of emotion engines

[1634] Subordinate reports progress: Enters "Task ID 5678 is 30% complete."

[1635] The server analyzes using an emotion engine: it recognizes that "your subordinate is feeling stressed" and provides gentle feedback such as "progress seems to be lagging behind. Do you need any support?"

[1636] Through these processes, the AI ​​boss can effectively manage tasks and provide appropriate feedback when necessary while ensuring psychological safety for subordinates, thereby improving organizational productivity and workplace mental health.

[1637] The processing flow will be explained below.

[1638] The present invention is a system that builds an AI system that plays the role of middle management, eliminates direct intervention by superiors, and recognizes the user's emotions by combining an emotion engine, and provides appropriate feedback and task management based on the emotions. Specific embodiments for implementing the present invention will be described below, divided into processing steps.

[1639] Receiving and analyzing task instructions

[1640] Step 1:

[1641] A task instruction is input from the superior's terminal and sent to the AI ​​system. For example, "Please create a new market analysis report by next week."

[1642] Step 2:

[1643] The server receives task instructions sent by superiors and stores them in a task instruction database.

[1644] Step 3:

[1645] The server passes the received task instructions to a natural language processing engine, which analyzes the task content and deadline. For example, it extracts "Create a market analysis report" and "By next week."

[1646] Step 4:

[1647] The server stores the analysis results in a database.

[1648] Task assignment

[1649] Step 5:

[1650] The server obtains the subordinate's skill set and current task status from the database in order to appropriately assign the analyzed task to the subordinate.

[1651] Step 6:

[1652] The server runs an algorithm to identify the most suitable subordinate based on the acquired skill set and task situation.

[1653] Step 7:

[1654] The server assigns tasks to the identified subordinates and stores the results in a database.

[1655] Step 8:

[1656] Send a notification of a new task to the terminal (subordinate device).

[1657] Task progress management and feedback

[1658] Step 9:

[1659] The device reports the task progress to the server. Example: "Task ID 1234 progress is 50%."

[1660] Step 10:

[1661] The server receives the progress reports and records them in a database.

[1662] Step 11:

[1663] The server uses a progress evaluation algorithm to compare the reported progress with the deadline.

[1664] Step 12:

[1665] If the progress is not as planned, the server uses a feedback generation engine to generate appropriate feedback, e.g., "At this rate, you may not meet the deadline."

[1666] Step 13:

[1667] The terminal (subordinate device) receives a feedback message from the server as a notification.

[1668] Feedback using an emotion engine

[1669] Step 14:

[1670] The terminal (subordinate's device) collects the subordinate's emotion data through an emotion tracking device during task progress reports and other interactions.

[1671] Step 15:

[1672] The server uses an emotion engine to analyze the subordinates' emotional data, e.g., to detect stress or anxiety from their facial expressions, tone of voice, and text input.

[1673] Step 16:

[1674] The server adjusts the feedback method based on the emotional data. For example, if a subordinate feels stressed, the server provides feedback such as, "You seem to be behind in your progress. Do you need any support?"

[1675] Evaluation and Reporting

[1676] Step 17:

[1677] After completing a task, the user (subordinate) reports the results of the work to the server. For example, the user might write, "I have completed the market analysis report."

[1678] Step 18:

[1679] The server receives the reports and uses a rating algorithm to evaluate the quality and success of the tasks.

[1680] Step 19:

[1681] The server generates a report based on the evaluation results and sends it to the superior's terminal, which includes the evaluation results and a link to the completed market analysis report.

[1682] Change boss type

[1683] Step 20:

[1684] The user (subordinate) can change the type of AI boss from the settings screen. For example, if you think, "I want to change my leadership style," click the "Change boss type" option from the settings menu.

[1685] Step 21:

[1686] The terminal transmits the selection of the new boss type to the server.

[1687] Step 22:

[1688] The server receives the new manager type setting and updates the database with the changes.

[1689] Step 23:

[1690] The server changes the task management algorithm of the subordinates based on the new boss type.

[1691] Through these processes, the AI ​​boss can effectively manage tasks and provide feedback using an emotion engine while ensuring psychological safety for subordinates, thereby improving organizational productivity and workplace mental health.

[1692] Example 2

[1693] 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."

[1694] In traditional management systems, superiors often intervene directly, which can undermine the psychological safety of subordinates. Furthermore, traditional systems do not provide feedback that takes into account the emotions of subordinates, making effective task management difficult.

[1695] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1696] In this invention, the server includes means for receiving task instructions entered by a superior, means for analyzing the received task instructions using natural language processing, means for assigning tasks to appropriate subordinates based on the analyzed task instructions, means for managing task progress reported by subordinates and providing feedback on the progress, means for collecting emotional data on subordinates and adjusting the feedback method based on the data, means for evaluating tasks completed by subordinates and reporting the evaluation results to the superior, and means for changing the type of AI boss according to the subordinate's selection. This enables effective task management and appropriate feedback while ensuring psychological safety for subordinates.

[1697] A "superior" is a person in an organization who commands and orders subordinates.

[1698] "Task instructions" are instructions given by a superior to a subordinate that specify specific work content and deadlines.

[1699] "Natural language processing" is a technology that analyzes and understands human language on a computer.

[1700] A "subordinate" is someone who performs work under the command and order of a superior.

[1701] "Task assignment" refers to assigning a specific task to an appropriate person from among multiple subordinates.

[1702] "Progress" is information indicating the degree of completion of the tasks assigned to a subordinate.

[1703] "Feedback" refers to evaluation and advice on progress and results, with the aim of improving and supporting work.

[1704] "Emotion data" is data that indicates the emotional state of a subordinate, as determined from facial expressions, tone of voice, etc.

[1705] "Evaluation" is the process of making judgments based on the quality and results of completed tasks.

[1706] "Boss type" refers to the type of leadership style and management method that an AI boss possesses.

[1707] This invention aims to improve psychological safety in the workplace by reducing direct intervention by superiors using an artificial intelligence (AI) system that plays the role of middle managers. The system recognizes users' emotions through an emotion engine and provides appropriate feedback and task management based on that information.

[1708] Receiving and analyzing task instructions

[1709] The server receives specific task instructions from its superior, which are sent as HTTP requests to API endpoints.

[1710] For example, a user might receive an instruction such as "Create a new market analysis report by next week." The instruction is parsed using the Google Cloud Natural Language API and broken down into tasks and deadlines. The analysis results are then stored in a MySQL database.

[1711] Task assignment

[1712] The server retrieves the subordinates' skill sets and task status from a MySQL database, executes a custom algorithm written in Python, and assigns tasks to the most suitable subordinates. The assignment results are stored in the database, and new task notifications are sent to the subordinates' devices via a push notification service.

[1713] Task progress management and feedback

[1714] The terminal (subordinate device) reports its progress to the server. For example, it inputs and sends "Task ID 1234 is 50% complete." The server receives the progress report and evaluates it using a progress evaluation algorithm. If necessary, it generates feedback such as "At this rate, you may not meet the deadline," and sends it to the subordinate device.

[1715] Feedback using an emotion engine

[1716] The server uses the Affectiva SDK to collect emotional data from subordinates' input, facial expressions, and tone of voice. Based on this emotional data, the server adjusts the feedback provided to the subordinate's device, providing gentle feedback such as, "You seem to be behind schedule. Do you need any help?"

[1717] Evaluation and Reporting

[1718] After completing the task, the user (subordinate) reports to the server, "I have completed the market analysis report." The server receives the report and performs task evaluation using a Python script. Based on the evaluation results, a report to be reported to the superior is generated and sent to the superior's terminal in HTML or PDF format.

[1719] Change boss type

[1720] The user (subordinate) requests a change of supervisor type from the settings screen. The subordinate's device sends this change request to the server. The server receives the request and updates the database using an SQL UPDATE statement to change the supervisor type.

[1721] Specific examples

[1722] Example of receiving and analyzing task instructions

[1723] A superior gives instructions to the AI ​​system: "Conduct market research for a new product and submit a report within two weeks."

[1724] The server analyzes: The task "conduct market research" and the deadline "submit report within two weeks" are analyzed and recorded in a database.

[1725] Examples of task progress management and feedback

[1726] Subordinate reports progress: Enters "Task ID 5678 is 30% complete."

[1727] The server evaluates and provides feedback: "Progress is behind schedule. Do you need help?" feedback is generated and sent to the subordinate's device.

[1728] Examples of emotion engines

[1729] Subordinate reports progress: Enters "Task ID 5678 is 30% complete."

[1730] The server analyzes using an emotion engine: it recognizes that "your subordinate is feeling stressed" and provides gentle feedback such as "progress seems to be lagging behind. Do you need any support?"

[1731] Example of input prompt for generative AI model

[1732] "We've received a request to create a new market analysis report by next week. Please parse this request using a natural language processing engine to extract the task and deadline."

[1733] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1734] Step 1:

[1735] The server receives a task instruction from a superior saying, "I want you to create a new market analysis report by next week."

[1736] Specific operation: A task instruction is sent as an HTTP request to an API endpoint.

[1737] Input: Task instructions from superiors.

[1738] Output: An HTTP request containing the task instructions.

[1739] Step 2:

[1740] The server parses the received task instructions using the Google Cloud Natural Language API.

[1741] Specific operation: Send an API request and receive the analysis results as a response.

[1742] Input: An HTTP request containing task instructions.

[1743] Output: Job description: "Create a market analysis report" with deadline "by next week."

[1744] Step 3:

[1745] The server stores the analysis results in a MySQL database.

[1746] Specific operation: Inserts data into the database using the SQL INSERT statement.

[1747] Input: "Create market analysis report" and analysis results "by next week".

[1748] Output: Task instructions and deadlines recorded in a database.

[1749] Step 4:

[1750] The server retrieves the subordinate's skill set and current task status from the database.

[1751] What it does: Uses a SQL SELECT statement to query for the information you need.

[1752] Input: Subordinate ID information.

[1753] Output: Skill set and task status data for each subordinate.

[1754] Step 5:

[1755] The server runs a custom algorithm written in Python to assign tasks to the best subordinates.

[1756] Specific behavior: The algorithm evaluates subordinates' skill sets and task situations and selects the most suitable subordinate.

[1757] Inputs: Subordinate skill set, current task status, task instructions.

[1758] Output: Information about the subordinates who have been assigned the task.

[1759] Step 6:

[1760] The server stores the task assignment results in a database and sends new task notifications to the subordinate terminals.

[1761] Specific operation: Inserts data into the database using an SQL INSERT statement and sends a notification to the subordinate's device via the push notification service.

[1762] Input: Task assignment results.

[1763] Output: Task assignment results stored in the database and notifications sent to subordinates' devices.

[1764] Step 7:

[1765] The terminals (subordinate devices) report their progress to the server.

[1766] Specific operation: The subordinate enters the progress status and sends it to the server as an HTTP POST request.

[1767] Input: A progress report such as "Task ID 1234 is 50% complete."

[1768] Output: Progress report sent to the server.

[1769] Step 8:

[1770] The server receives the progress reports and evaluates them using a progress evaluation algorithm.

[1771] Specific actions: Compare progress with deadlines and generate evaluation results.

[1772] Input: Progress report from subordinate.

[1773] Output: Feedback such as "At this rate, you may not meet the deadline."

[1774] Step 9:

[1775] The server generates feedback as needed and sends it to the subordinate terminals.

[1776] Specific actions: Feedback is automatically generated based on progress evaluation results and sent to subordinates via push notification or email.

[1777] Input: Progress assessment results.

[1778] Output: Feedback sent to subordinate devices.

[1779] Step 10:

[1780] The server uses the Affectiva SDK to collect emotional data from subordinates' inputs, facial expressions, and tone of voice.

[1781] Specific operation: Data is acquired from subordinate devices via the camera and microphone, and analyzed using the SDK.

[1782] Input: Subordinates' facial expressions and tone of voice.

[1783] Output: Parsed emotion data.

[1784] Step 11:

[1785] The server adjusts the feedback content based on the emotional data and sends it to the subordinate's device.

[1786] Specific action: Analyze emotional data and generate appropriate feedback content.

[1787] Input: Emotion data.

[1788] Output: Gentle feedback such as, "You seem to be making slow progress, do you need any help?"

[1789] Step 12:

[1790] After completing the task, the user (subordinate) reports to the server, "I have completed the market analysis report."

[1791] Specific operation: Enter a task completion report and send it to the server as an HTTP POST request.

[1792] Input: Task completion report.

[1793] Output: Task completion report sent to the server.

[1794] Step 13:

[1795] The server evaluates the report and evaluates the task quality and performance.

[1796] Specific operation: A Python script is used to analyze the report content and generate a rating score.

[1797] Input: Task completion report.

[1798] Output: Evaluation score.

[1799] Step 14:

[1800] The server generates a report to report to the superior based on the evaluation results and transmits it to the superior's terminal.

[1801] Specific Actions: Based on the assessment results, a report is generated in HTML or PDF format and sent via email or internal messaging system.

[1802] Input: Rating score.

[1803] Output: Report sent to superior's terminal.

[1804] Step 15:

[1805] The user (subordinate) requests a change of superior type from the settings screen.

[1806] Specific actions: Select the Change Manager Type option and submit a change request.

[1807] Input: Supervisor type change request.

[1808] Output: The change request sent to the server.

[1809] Step 16:

[1810] The terminal sends a change request to the server.

[1811] Specific operation: A change request is sent to the server as an HTTP POST request.

[1812] Input: Supervisor type change request.

[1813] Output: The change request sent to the server.

[1814] Step 17:

[1815] The server receives the request and updates the database with the new boss type.

[1816] Specific Actions: Update the database using a SQL UPDATE statement to apply the task management algorithm based on the new supervisor type.

[1817] Input: Supervisor type change request.

[1818] Output: The manager type information updated in the database.

[1819] (Application example 2)

[1820] 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."

[1821] The problem to be solved by this invention is to eliminate direct intervention by superiors, improve psychological safety in the workplace, and provide appropriate task management and feedback. In particular, the object is to reduce stress for subordinates and support efficient work performance by recognizing subordinates' emotions in real time and adjusting feedback methods based on those emotions.

[1822] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1823] In this invention, the server includes means for receiving task instructions entered by a superior, means for analyzing the received task instructions using natural language processing, means for assigning tasks to appropriate subordinates based on the analyzed task instructions, means for managing task progress reported by subordinates and providing feedback on the progress, means for evaluating tasks completed by subordinates and reporting the evaluation results to the superior, means for using an emotion recognition engine that recognizes the emotions of subordinates and adjusts the feedback content based on the emotions, and means for changing the type of AI boss according to the selection of the subordinate. This enables flexible feedback and progress management according to the emotions of subordinates.

[1824] "Task instructions" refer to specific work content and deadlines instructed by a superior to a subordinate.

[1825] "Natural language processing" is a technology that uses computers to analyze, understand, and generate human language (natural language).

[1826] "Task progress" refers to the status that indicates how much a subordinate has performed on a given task and how close they are to completing it.

[1827] "Feedback" refers to the guidance and evaluation provided to subordinates by superiors or systems based on task progress.

[1828] An "emotion recognition engine" is a technology that analyzes a subordinate's emotional state from their input, facial expressions, tone of voice, etc.

[1829] A "generative AI model" is an artificial intelligence model that has been trained to generate results for a specific task.

[1830] A "skill set" is a collection of knowledge, abilities, experience, etc. that a subordinate possesses that are relevant to a specific job.

[1831] "AI Boss" is a management system with artificial intelligence that assigns tasks and provides feedback to subordinates.

[1832] "Task management" is the process of monitoring task progress and providing adjustments and feedback.

[1833] "Psychological safety" refers to a state in the workplace where members feel safe to express their opinions and feelings.

[1834] "Progress management" is the process of making sure that tasks are progressing as planned and making adjustments as necessary.

[1835] An "evaluation algorithm" is a calculation method for quantitatively or qualitatively evaluating the work performance of subordinates upon task completion.

[1836] The present invention is a system that builds an AI system that takes on the role of middle managers, improves psychological safety in the workplace by eliminating direct intervention by superiors, and recognizes the user's emotions by combining it with an emotion recognition engine, and provides appropriate feedback and task management based on the emotions. Specific embodiments for implementing the present invention are described in detail below.

[1837] Receiving and analyzing task instructions

[1838] The server receives task instructions input by a superior. For example, the superior's input may be specific, such as "Please create a new market analysis report by next week." The server receives this instruction and analyzes it using a natural language processing engine. The analyzed task instruction is broken down into the work content, "Create a market analysis report," and the deadline, "By next week," and the analysis results are saved in the server's database.

[1839] Task assignment

[1840] The server assigns the analyzed tasks to subordinates. It retrieves the subordinates' skill sets and current task status from the database and runs an algorithm to identify the most suitable subordinate. Once the most suitable subordinate is identified, the task is assigned to that subordinate and the result is saved in the database. At the same time, a notification of the new task is sent to the subordinate's device.

[1841] Task progress management and feedback

[1842] The subordinate's device reports the task progress to the server. For example, "Progress on task ID 1234 is 50%." The server receives the progress report and manages the progress based on it. The server uses a progress evaluation algorithm to compare the reported progress with the deadline, and generates feedback such as "At this rate, you may not meet the deadline." This feedback is sent to the subordinate's device.

[1843] Feedback using an emotion recognition engine

[1844] The server is equipped with an emotion recognition engine that obtains emotional data from subordinates' input, facial expressions, tone of voice, etc. The server uses this emotional data to adjust the feedback method. For example, if the server recognizes that a subordinate is feeling stressed, it can provide gentle feedback such as, "You seem to be behind on progress. Do you need any support?"

[1845] Evaluation and Reporting

[1846] After completing the task, the subordinate reports the results to the server, entering "I have completed the market analysis report." The server receives this report and uses an evaluation algorithm to evaluate the quality and results of the task. Based on this evaluation result, a report is generated for reporting to the superior, and sent from the server to the superior's terminal.

[1847] Change boss type

[1848] Subordinates can change the type of their AI boss from the settings screen. For example, if a subordinate decides to "change their leadership style," they can click the "Change boss type" option from the settings menu. The device then sends this change request to the server. The server receives this request, sets the new boss type in the database, and changes the task management algorithm.

[1849] Specific examples

[1850] Example of receiving and analyzing task instructions

[1851] A superior gives instructions to the AI ​​system: "Conduct market research for a new product and submit a report within two weeks."

[1852] The server analyzes: The task is "conduct market research" and the deadline is "submit the report within two weeks" and records this in the database.

[1853] Examples of task progress management and feedback

[1854] Subordinate reports progress: Enters "Task ID 5678 is 30% complete."

[1855] The server evaluates and provides feedback: "Progress is behind schedule. Do you need help?" feedback is generated and sent to the subordinate's device.

[1856] Example of an emotion recognition engine

[1857] Subordinate reports progress: Enters "Task ID 5678 is 30% complete."

[1858] The server analyzes using an emotion recognition engine: it recognizes that "your subordinate is feeling stressed" and provides gentle feedback such as "You seem to be making slow progress. Do you need any support?"

[1859] Through these processes, the AI ​​boss can effectively manage tasks and provide appropriate feedback when necessary while ensuring psychological safety for subordinates, thereby improving organizational productivity and workplace mental health.

[1860] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1861] Step 1:

[1862] The server receives task instructions entered by superiors.

[1863] Specifically, when a superior inputs an instruction into the system such as "Please create a new market analysis report by next week," the server receives this task instruction as a string of characters.

[1864] Input: Task instructions from superiors

[1865] Output: Raw task instruction data (string format)

[1866] Step 2:

[1867] The server analyzes the received task instructions using a natural language processing engine.

[1868] Specifically, the server breaks down the task instructions into components such as "create a market analysis report" and "by next week," and stores this information in a database.

[1869] Input: Raw task instruction data

[1870] Output: Parsed task details and deadlines

[1871] Step 3:

[1872] The server assigns tasks to subordinates based on the parsed task instructions.

[1873] Specifically, the server retrieves the subordinates' skill sets and current task status from the database, identifies the most suitable subordinate, and assigns the task to that subordinate.

[1874] Input: Analyzed task content, subordinate skill set, current task status

[1875] Output: Task assignment notification sent to subordinate's device

[1876] Step 4:

[1877] The subordinate terminals report the progress of the tasks to the server.

[1878] Specifically, the subordinate enters the task progress as "Progress of task ID 1234 is 50%" and sends this progress data to the server.

[1879] Input: Progress report data from subordinates

[1880] Output: Progress data stored on the server

[1881] Step 5:

[1882] The server manages the progress based on the progress reports and generates feedback as needed.

[1883] Specifically, the server uses a progress evaluation algorithm to compare the reported progress with the deadline, generates feedback such as "At this rate, you may not meet the deadline," and sends it to the subordinate's device.

[1884] Input: Progress report data

[1885] Output: Feedback message

[1886] Step 6:

[1887] The server recognizes the emotions of the subordinates using an emotion recognition engine and adjusts the feedback content.

[1888] Specifically, it extracts emotional data from the text of progress reports and the input of subordinates, and if it recognizes emotions such as "feeling stressed," it generates gentle feedback such as "You seem to be making slow progress. Do you need any support?"

[1889] Input: Progress report text data

[1890] Output: Emotion-based regulatory feedback

[1891] Step 7:

[1892] After completing a task, the subordinate reports the result to the server.

[1893] Specifically, the subordinate enters "Market analysis report completed" and sends the task completion report to the server.

[1894] Input: Task completion report data

[1895] Output: Task completion report saved on the server

[1896] Step 8:

[1897] The server receives the task completion reports and evaluates the quality and success of the tasks using an evaluation algorithm.

[1898] Specifically, the task results are evaluated quantitatively or qualitatively using an evaluation algorithm, and a report is generated to report the evaluation results to superiors.

[1899] Input: Task completion report data

[1900] Output: Evaluation report for superiors

[1901] Step 9:

[1902] Subordinates can change the type of AI boss they have from the settings screen.

[1903] Specifically, the subordinate selects the "Change Supervisor Type" option from the settings menu and sends a change request to the server. The server receives this request, sets the new supervisor type in the database, and changes the task management algorithm.

[1904] Input: Request from subordinate to change supervisor type

[1905] Output: New manager type set in the database

[1906] Example prompt sentence:

[1907] "Progress of task ID xxxx is yy%"

[1908] "The user's progress report text is sent to the emotion engine for emotion analysis."

[1909] "Market analysis report completed."

[1910] 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.

[1911] 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.

[1912] 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 robot 414.

[1913] 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.

[1914] FIG. 9 illustrates 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 behaviors 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.

[1915] 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.

[1916] 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).

[1917] 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.

[1918] 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."

[1919] 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.

[1920] 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).

[1921] 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.

[1922] 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.

[1923] 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.

[1924] 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.

[1925] 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.

[1926] 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.

[1927] 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.

[1928] 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.

[1929] 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.

[1930] 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.

[1931] The following is further disclosed regarding the above embodiment.

[1932] (Claim 1)

[1933] means for receiving task instructions entered by a superior;

[1934] means for analyzing the received task instructions using natural language processing;

[1935] means for assigning tasks to appropriate subordinates based on the analyzed task instructions;

[1936] A means of managing the progress of tasks reported by subordinates and providing feedback on progress;

[1937] A means for subordinates to evaluate the tasks they have completed and report the evaluation results to their superiors;

[1938] A way to change the type of AI boss depending on the subordinate's choice,

[1939] A system including:

[1940] (Claim 2)

[1941] 10. The system of claim 1, wherein natural language processing is utilized to analyze task instructions.

[1942] (Claim 3)

[1943] 10. The system of claim 1, wherein tasks are assigned based on the subordinate's skill set and task status.

[1944] "Example 1"

[1945] (Claim 1)

[1946] means for receiving task instructions entered by a superior;

[1947] means for analyzing the received task instructions using natural language processing;

[1948] means for assigning tasks to appropriate subordinates based on the analyzed task instructions;

[1949] A means for acquiring the skill sets and current task status of subordinates and identifying the most suitable subordinates;

[1950] a means for sending notifications of new tasks to subordinates;

[1951] A means of managing the progress of tasks reported by subordinates and providing feedback on progress;

[1952] A means for subordinates to evaluate the tasks they have completed and report the evaluation results to their superiors; ...

Claims

1. means for receiving task instructions entered by a superior; means for analyzing the received task instructions using natural language processing; means for assigning tasks to appropriate subordinates based on the analyzed task instructions; A means of managing the progress of tasks reported by subordinates and providing feedback on progress; A means for subordinates to evaluate the tasks they have completed and report the evaluation results to their superiors; A way to change the type of AI boss depending on the subordinate's choice, A system including:

2. 10. The system of claim 1, wherein natural language processing is utilized to analyze the task instructions.

3. 10. The system of claim 1, wherein tasks are assigned based on the subordinate's skill set and task status.

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