system

Abstract

We provide the system. [Solution] Means for receiving multiple commands, A means of analyzing received instructions and breaking them down into detailed work items, A means of assigning subdivided work items to human resources or artificial intelligence, A means of monitoring the progress of work items and adjusting their priorities, A method for automating completed work items and maintaining them as templates for future use, A system that includes means for interpreting instructions in the logistics process and for executing the optimal allocation of logistics equipment and personnel.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Improving business efficiency in enterprises is an important issue, especially in modern times when appropriate cooperation between human resources and artificial intelligence is required. However, due to ambiguous high - level business instructions from management positions or inappropriate task allocation, task distribution is often unbalanced. As a result, while human resources bear an excessive burden, the problem that artificial intelligence is not fully utilized has occurred. Therefore, there is a need for a system that can improve business productivity by accurately analyzing instructions and effectively allocating tasks.

Means for Solving the Problems

[0005] This invention provides a means for receiving multiple instructions, analyzing them, and breaking them down into detailed work items. The analysis is performed with high accuracy using natural language processing technology. Next, the invention includes means for appropriately allocating the decomposed work items, taking into account the capabilities of human resources and artificial intelligence. Furthermore, the system monitors the progress of the work items in real time and dynamically adjusts priorities and makes new assignments as needed to achieve efficient work allocation. In addition, completed work items are automated and saved as templates that can be used in the future, thereby promoting standardization and increasing reusability. In this way, the invention provides a system that maximizes business productivity.

[0006] An "instruction" is a command from a manager that outlines the policies and content for carrying out a task or work.

[0007] "Analysis" is the process of breaking down complex information or data into easily understandable elements and understanding their content and structure.

[0008] A "work item" is a specific, actionable task or action extracted from a work instruction.

[0009] "Human resources" refer to the labor, knowledge, and skills of people working in a company or organization.

[0010] Artificial intelligence is a system or technology that imitates human intellectual activity and automatically solves various problems.

[0011] "Assignment" is the process of distributing tasks and resources to the appropriate personnel or functions.

[0012] "Progress status" refers to the current state of how far along a task or project is.

[0013] "Priority" refers to the criteria or order used to determine which of several tasks or items should be handled first, or in what order.

[0014] "Dynamic" refers to having the characteristic of freely changing according to situations and conditions.

[0015] "Automation" means enabling a system or process to execute operations and processing on its own without human intervention.

[0016] "Template" is a fixed format or prototype that can be used multiple times and can be easily reused in new situations.

Brief Description of Drawings

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

Mode for Carrying Out the Invention

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

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

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

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

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

[0023] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0025] [First Embodiment]

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

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

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

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

[0030] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

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

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

[0038] This invention relates to an autonomously operating information processing system that receives multiple instructions, analyzes them, breaks them down into detailed work items, and assigns them to appropriate human resources or artificial intelligence. This system consists of a server, terminals, and users, and each component works together to improve the efficiency of operations.

[0039] The server first receives instructions from management. These instructions are analyzed using natural language processing technology, breaking them down into detailed work items. The server then appropriately assigns each work item, taking into account human resources and the capabilities of artificial intelligence. This process involves referencing employee schedules and AI agent functions from a database to ensure optimal allocation.

[0040] The terminal notifies the user that a work item has been assigned and provides detailed information and materials for the necessary task. Users can report their progress through the terminal. This report is reflected in the server dashboard in real time, and the overall progress is monitored by managers.

[0041] For example, if instructed to check project progress, the server breaks this down into individual tasks such as "data collection," "analysis," and "report creation." It then assigns "data collection" to an AI agent and "analysis" to an analyst. The user can monitor the task progress on their terminal and provide additional instructions if any information is missing.

[0042] Once the task is complete, the server automates the task using the AI ​​agent and saves it as a template, creating a system that allows for immediate response when similar tasks arise in the future. This template helps standardize operations and contributes to improved efficiency.

[0043] In this way, the present invention enables companies to perform their operations with high efficiency and supports the optimal collaboration between human resources and artificial intelligence.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The server receives work instructions from management. These instructions are obtained via email or messaging apps, and the data is registered in a central database.

[0047] Step 2:

[0048] The server analyzes the received instructions using natural language processing. During the analysis, it extracts relevant keywords and context from the instructions and breaks them down into specific work items.

[0049] Step 3:

[0050] The server optimally allocates the broken-down work items based on the capabilities of human resources and artificial intelligence agents retrieved from the database, taking into account current resource availability and priorities.

[0051] Step 4:

[0052] The terminal sends notifications to the user regarding assigned work items. These notifications include detailed task information and necessary documents, which the user can access.

[0053] Step 5:

[0054] Users perform tasks through their terminals and report their progress to the server. Progress reports are a means of recording task completion rates and any problems in real time.

[0055] Step 6:

[0056] The server monitors overall progress and allocation status, readjusting task priorities and reallocating resources as needed. This enables efficient and flexible responses.

[0057] Step 7:

[0058] Once a task is completed, the server aggregates the results and provides them to management as a report. Tasks involving the AI ​​agent are templated and prepared for reuse in future tasks.

[0059] (Example 1)

[0060] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0061] In today's work environment, where efficient work management and resource allocation are essential, many organizations face the challenge of properly analyzing information and making optimal allocations using human resources and artificial intelligence. This results in problems such as delays in progress management, uneven workload distribution, and decreased operational efficiency.

[0062] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0063] In this invention, the server includes means for receiving information, means for analyzing the received information and breaking it down into detailed tasks, and means for assigning the broken-down tasks to personnel or intelligent systems. This enables efficient task allocation and real-time progress management.

[0064] "Information" refers to instructions and data received by a system, which are the subject of analysis and processing.

[0065] "Analysis" is the process of understanding received information, extracting its meaning, and breaking it down into detailed tasks.

[0066] "Detailed work" refers to specific tasks and actions extracted from the analyzed information, and represents actionable units.

[0067] "Personnel" refers to the human resources that perform the tasks assigned by the system.

[0068] An "intelligent system" is a computer-based system that uses artificial intelligence to perform tasks.

[0069] "Assignment" is the process of distributing broken-down tasks to the appropriate personnel or intelligent systems.

[0070] "Progress management" is the activity of tracking the progress of a task and understanding its status until completion.

[0071] A "template" is a template that automates completed tasks or processes and saves them in a reusable format.

[0072] This invention is an information processing system that improves business efficiency through the automation of information processing. This system consists of a server, a terminal, and a user working together.

[0073] The server receives instructions from the management side and analyzes them. Natural language processing technology is used for the analysis, and a natural language processing engine can be used as a specific service. The analyzed instructions are broken down into detailed tasks, which are then appropriately assigned to personnel or intelligent systems. A general-purpose artificial intelligence platform can be used as the intelligent system. For example, cloud-based AI services can be used to perform tasks such as data collection and analysis.

[0074] The terminal notifies the user of tasks assigned by the server. This notification is done, for example, through a team communication platform. The user uses the terminal to report progress and provide feedback on their work, and this information is shared in real time with management via the server's dashboard.

[0075] Users can manage their progress based on the tasks assigned to them and provide additional information or instructions as needed.

[0076] As a concrete example of operation, consider a scenario where an administrator issues a command to "conduct market research for a new product." This command is broken down into specific tasks such as "data collection," "market analysis," and "report creation," and each task is assigned to an appropriate resource. An example of a prompt message might be, "You have received a command from management to 'conduct market research for a new product.' How should you break down the task and assign it to whom?"

[0077] This invention will standardize and streamline operations, while simultaneously enabling the effective use of human resources and the integrated operation of artificial intelligence.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] The server receives information from the management side. This information is often input as instructions in natural language. The server analyzes this received information using natural language processing techniques to determine how it can be broken down into tasks. Specifically, it inputs the information into an analysis engine and outputs a detailed task list as a result of the analysis.

[0081] Step 2:

[0082] Based on the analysis results, the server individually identifies detailed tasks and assigns each task to the appropriate personnel or intelligent system. Specifically, it retrieves employee schedule information and intelligent system availability from a database, and uses this information, along with data processing, to determine the optimal allocation. The output is a list of task assignments.

[0083] Step 3:

[0084] The terminal receives information about assigned tasks from the server and notifies the user. This includes details of the tasks assigned to the user, their deadlines, and necessary documents. Specifically, this is done by generating a notification message and distributing it to the user using the communication platform.

[0085] Step 4:

[0086] The user receives the notification for a task and begins to perform it. As the user progresses through the task, they report their progress via their terminal. This reporting involves inputting data such as the task's progress and completion status, and sending this information as digital data to the server. As output, a progress report is generated and stored on the server.

[0087] Step 5:

[0088] The server updates the overall progress dashboard based on the collected progress reports. Specifically, it analyzes the progress data and generates graphs and charts to visualize the overall progress. This allows administrators to understand the current status at a glance.

[0089] Step 6:

[0090] Once a task is complete, the server generates and saves a template for automating that task. This involves templating the completed task's steps and processes so that intelligent systems can automatically handle it. Specifically, it standardizes and records the process. The output is a reusable template file.

[0091] (Application Example 1)

[0092] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0093] In the logistics industry, when multiple orders are given, it is required to quickly and appropriately break them down into detailed work items and allocate them optimally to personnel resources and logistics equipment. However, in reality, many orders are not differentiated, and reliance on human judgment often compromises the efficiency and accuracy of work. Furthermore, insufficient monitoring of progress and adjustment of priorities can lead to delays in operations and waste of resources.

[0094] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0095] In this invention, the server includes means for receiving multiple instructions, means for analyzing the received instructions and subdividing them into detailed work items, and means for interpreting instructions in the logistics process and for optimizing the allocation of logistics equipment and personnel. This enables efficient breakdown of instructions and optimal resource allocation in logistics operations.

[0096] "Means for receiving multiple commands" refers to a function for acquiring multiple instructions and commands related to logistics operations electronically or via a network, and converting them into a format that can be processed within the system.

[0097] "Means for analyzing received instructions and subdividing them into detailed work items" refers to a function that analyzes multiple acquired instructions using natural language processing technology and algorithms, and breaks them down into concrete and actionable units of work.

[0098] "Means for interpreting instructions in the logistics process and executing the optimal allocation of logistics equipment and personnel" refers to the function of determining and executing the most efficient allocation and roles based on the current status of machinery and personnel involved in logistics, using the decomposed work items as a basis.

[0099] "Means for monitoring the progress of work items and adjusting priorities" refers to a function that constantly checks whether each task is progressing according to plan, and re-evaluates and optimizes the order and importance of actions as needed.

[0100] "Means of maintaining as templates for future use" refers to a function that saves a completed series of work procedures and instructions in a standardized format so that they can be reused in similar tasks later on.

[0101] The system for realizing this invention consists of a server, a terminal, and a user. The server first receives multiple commands related to logistics operations. These commands are analyzed using natural language processing technology and broken down into detailed work items. The SpaCy library is used in this process to efficiently analyze the content of the instructions.

[0102] The detailed work items received are appropriately assigned by the server to robots and personnel within the logistics center. A web-based dashboard using Django is used to monitor the progress of work items in real time. This centralizes progress management and enables optimal prioritization.

[0103] The terminal notifies the user of assigned work items and provides detailed information and materials related to the work. The user reports the progress of tasks to the server via the terminal, and the overall work is adjusted based on this information. Completed work items are saved as templates by the server and managed in a form that can be reused in the future.

[0104] For example, when a large quantity of new products arrives at a logistics center, the server breaks down the tasks into "receiving goods," "inspecting," and "stocking," and allocates them to the most appropriate equipment and personnel. This improves operational efficiency.

[0105] For example, by inputting a prompt such as, "Given a large-scale receiving order, how should the work be broken down and managed efficiently by combining human and robotic personnel?" into the generating AI model, it is possible to obtain the optimal response.

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] The server receives multiple instructions related to logistics operations. The input is provided as text-based data containing the instructions. The server then converts the data format for storage in its database. To efficiently process the received instructions, the server performs operations such as data formatting and adding necessary metadata.

[0109] Step 2:

[0110] The server analyzes received instructions using natural language processing techniques and breaks them down into detailed work items. The input is text data, and the output is a list of the decomposed work items. Specifically, the SpaCy library is used to perform text analysis, identify the content of the instructions, and classify them into items.

[0111] Step 3:

[0112] The server optimally allocates robots and personnel within the logistics center based on the broken-down work items. Inputs are a list of work items and resource information within the center, and output is an allocation plan. The allocation process is executed through a dashboard built with Django, taking into account the schedules and capabilities of the relevant resources.

[0113] Step 4:

[0114] The terminal notifies the user of assigned work items. The input is the assignment plan, and the output is notification information displayed to the user. Based on this, the terminal performs actions to present the user with any necessary detailed information or documents.

[0115] Step 5:

[0116] Users report task progress to the server via their terminal. The input is progress information from the user, which is sent to the server, and the output is real-time progress data for the entire system. Users operate the interface to record progress and perform reporting actions.

[0117] Step 6:

[0118] The server adjusts the overall priority of work items based on the obtained progress information. The input is progress data, and the output is an updated priority list. The system executes an AI algorithm based on the progress and schedule to optimize the schedule.

[0119] Step 7:

[0120] The server maintains data from completed work items as templates for future use. The input is the completed data, and the output is the data stored in template format. The system then generates a standardized process and saves the templates to a database.

[0121] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0122] This invention is an information processing system that efficiently manages work instructions and optimally controls task progress, using an emotion engine to recognize user emotions and reflect them in the system's operation. The system consists of a server, terminals, and users.

[0123] The server receives instructions from management and analyzes them using natural language processing technology. The instructions are broken down into detailed work items, and each item is assigned to human resources or artificial intelligence. The broken-down tasks are optimally distributed based on the availability of resources referenced from a database. The assigned work items are then notified to the terminal, allowing the user to immediately confirm them.

[0124] Furthermore, this system includes an emotion engine, which allows the terminal to monitor the user's emotional state in real time. The recognized emotional state is sent to the server and used for task assignment and schedule adjustments. For example, if the user is feeling stressed, the server will take measures to reduce the load, such as lowering the priority of tasks or reducing the workload.

[0125] For example, if a user is simultaneously tasked with "creating sales reports" and "customer analysis," the emotion engine detects the user is under high stress. The server then temporarily transfers the "sales report creation" task to an AI agent, reducing the user's workload. As a result, the user can focus on the "customer analysis task" without feeling burdened.

[0126] Once a work item is completed, the server creates a template of it and saves it for use in related tasks in the future. Templated work items improve work efficiency and promote the standardization of operations.

[0127] In this way, the present invention makes it possible to efficiently perform tasks while adapting to the user's emotional state, thereby simultaneously improving the comfort and productivity of the work environment.

[0128] The following describes the processing flow.

[0129] Step 1:

[0130] The server receives work instructions from management via email or messaging apps. The instructions are registered in the database and prepared for processing.

[0131] Step 2:

[0132] The server analyzes the received instructions using natural language processing techniques and breaks them down into detailed work items. During this process, relevant keywords and task priorities are identified.

[0133] Step 3:

[0134] The server optimally allocates the broken-down work items based on human and artificial intelligence resources. User-specific schedules and the capabilities of AI agents are also taken into consideration.

[0135] Step 4:

[0136] The terminal notifies the user of assigned tasks. This notification includes task details, priority, and relevant documentation.

[0137] Step 5:

[0138] The device monitors the user's emotional state in real time through an emotion engine. Emotional data is analyzed and sent to the server as needed.

[0139] Step 6:

[0140] The server receives emotional data and dynamically adjusts task assignments and priorities based on the user's emotional state. It implements measures to reduce the user's burden, such as adjusting the workload when stress levels are high.

[0141] Step 7:

[0142] Users perform tasks through their devices and report their progress to the server. This allows for real-time management of task completion status.

[0143] Step 8:

[0144] After all tasks are completed, the server aggregates the results and saves the work items as templates for reuse in the future. These templates help improve work efficiency.

[0145] (Example 2)

[0146] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0147] In today's work environment, multiple different instructions and tasks arise simultaneously, making it difficult to manage them efficiently. Furthermore, assigning tasks uniformly without considering the emotional state of individual users leads to excessive stress and inefficiency. This invention aims to improve work efficiency and achieve task management that takes into account the emotional state of users.

[0148] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0149] In this invention, the server includes means for receiving multiple instructions, means for analyzing the received instructions using a language analysis method and breaking them down into detailed work elements, and means for assigning the broken-down work elements to personnel or intelligent functions. This enables efficient management of tasks and dynamic task reallocation in accordance with the emotional state of the user.

[0150] "Instructions" refer to information that specifies the concrete actions and conditions necessary to carry out a task.

[0151] "Linguistic analysis techniques" are technologies that use computers to analyze and interpret text written in natural language based on its meaning and structure.

[0152] A "work element" refers to a specific task or action derived from the analyzed instructions.

[0153] "Human resources" refer to the human resources required to perform specific tasks within a company or organization.

[0154] "Intelligent function" refers to a system that has the ability to perform or assist in specific tasks using artificial intelligence.

[0155] "Emotional state" refers to the psychological and mental state or emotions that the user is experiencing at a particular point in time.

[0156] "Dynamic reassignment" is a process that flexibly changes assigned tasks in response to changes in circumstances and conditions.

[0157] This invention is an information processing system that enables efficient business management and task management tailored to the emotional state of the user. This system consists of a server, terminals, and users.

[0158] The server receives instructions and analyzes them using language analysis techniques. Specifically, it uses programming languages ​​such as Python and common language analysis APIs to analyze the received instructions and break them down into detailed work elements. These decomposed work elements are then optimally assigned by the server to human resources or intelligent functions. During this process, a database service is used to efficiently distribute tasks while monitoring resource availability.

[0159] The device is equipped with an emotion engine, which is used to monitor the user's emotional state in real time. Specifically, it uses a software development kit with facial recognition technology and voice analysis tools to detect the user's emotions and send the data to a server.

[0160] If the emotion engine determines that a user is experiencing high stress, the server will adjust task priorities or transfer tasks to AI agents. This allows the user to focus on other tasks without feeling overwhelmed.

[0161] As a concrete example, let's consider a situation where a user has to simultaneously handle "creating sales reports" and "customer analysis tasks." In this case, the emotion engine detects stress, and the server reduces the user's task load by transferring the "creating sales reports" task to an AI agent. As a result, the user can concentrate on the "customer analysis task."

[0162] Examples of prompts include, "Please suggest ways to reduce stress related to customer analysis tasks," and "Please describe how to reallocate tasks based on the user's emotional state."

[0163] In this way, the present invention realizes improved work efficiency and the provision of a comfortable work environment.

[0164] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0165] Step 1:

[0166] The server receives work instructions sent from the administrator. These instructions are obtained via email or a specific work management system. The input is natural language text data, and receiving this data yields the instructions to be analyzed. The output is the text data of the received instructions.

[0167] Step 2:

[0168] The server analyzes the received work instructions using language analysis techniques. Specifically, it uses a natural language processing model to tokenize the instructions and convert them into structured information. The input is the text data of the instructions obtained in step 1. The output is a list of the analyzed work elements.

[0169] Step 3:

[0170] The server reviews the analyzed work elements and assigns them to personnel or intelligent functions. This includes database access to check resource utilization. The input is a list of work elements obtained in step 2. The output is the assignment information for the personnel or system resources to which each work element is assigned.

[0171] Step 4:

[0172] The terminal notifies the user of the work elements assigned to them based on instructions from the server. The terminal utilizes the user interface to quickly display tasks. The input is the assignment information generated in step 3. The output is the task information displayed in the user interface.

[0173] Step 5:

[0174] The device monitors the user's emotional state using an emotion engine. The emotion engine collects data using cameras and microphones, analyzes it, and determines the emotional state. Inputs are sensor data such as facial recognition and voice input. Outputs are the detected user's emotional state information.

[0175] Step 6:

[0176] The server receives emotional state data sent from the terminal and adjusts task assignments and priorities. If a stressed state is detected, the server takes measures such as transferring part of the work to the AI ​​agent. The input is the emotional state information obtained in step 5. The output is the adjusted task assignment information.

[0177] Step 7:

[0178] The server templates completed work elements and saves them to a database. This template is used for similar tasks in the future. This process summarizes the details and workflow of completed tasks. The input is the data of the completed tasks. The output is the templated work element data.

[0179] (Application Example 2)

[0180] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0181] In today's work environment, imbalances in workload and employee emotional stress are contributing factors to decreased productivity. Furthermore, traditional systems have a static approach to work progress management and task assignment, failing to reflect individual circumstances, making efficient operations difficult. To address these issues, a system is needed that can grasp workers' emotional states in real time and dynamically adjust work assignments accordingly.

[0182] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0183] In this invention, the server includes means for receiving multiple commands, means for analyzing the received commands and breaking them down into detailed work elements, and means for monitoring the user's emotional state using emotion recognition technology and dynamically adjusting the assignment of work elements based on that state. This enables work adjustments in response to the user's real-time emotional state.

[0184] A "means for receiving multiple commands" refers to a mechanism for aggregating various commands transmitted from external sources.

[0185] "Means for analyzing received instructions and breaking them down into detailed work elements" refers to a mechanism for analyzing received instructions and breaking them down into smaller, manageable work parts.

[0186] "Means of allocating to human resources or machine learning systems" refers to a mechanism for distributing the broken-down work elements to appropriate personnel or AI systems.

[0187] "Means for monitoring the progress of work elements and adjusting priorities" refers to a function that monitors how work is progressing and changes the order of tasks as needed.

[0188] "Emotion recognition technology" refers to a method of detecting a user's emotional state using various sensors and algorithms.

[0189] "Means for monitoring the user's emotional state and dynamically adjusting the assignment of work elements based on that state" refers to a mechanism for observing the user's current emotions and flexibly changing the workload accordingly.

[0190] "Means of automating completed work elements and saving them as templates for future use" refers to methods for organizing already completed work into a format that can be used repeatedly.

[0191] The system for implementing this invention consists of a server, a terminal, and a user. The server first receives commands from an external source. After receiving the commands, the server analyzes and breaks them down into detailed work elements and appropriately assigns these elements to human resources or a machine learning system. During this process, the server monitors the progress of the work in real time and dynamically adjusts priorities as needed. A device worn by the user, such as smart glasses or a personal digital assistant, monitors the user's emotional state using emotion recognition technology.

[0192] The device analyzes the user's facial expressions and voice, and sends their emotional state to the server. The server dynamically adjusts the assignment of work elements based on the emotional data, ensuring that the workload is appropriately distributed. For example, if the server detects from the emotional data that the user is fatigued, it can automatically assign part of the task to a robot. This allows the user to perform tasks efficiently while reducing their burden. Completed work elements are saved on the server as automated templates and reused for similar tasks in the future. This system is implemented using programming languages ​​such as Python and utilizes hardware sensors such as smart glasses and cameras.

[0193] As a concrete example, in an automobile parts factory, workers perform assembly tasks while simultaneously conducting quality inspections. If smart glasses detect a decrease in the worker's concentration, the server automatically hands over the assembly task to a robot, allowing the worker to focus on quality inspections.

[0194] An example of a prompt for a generative AI model is: "Explain how an emotion recognition system can detect worker fatigue in a factory and assign tasks to robots. Please provide a specific example in a peaceful scenario."

[0195] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0196] Step 1:

[0197] The server receives external commands and analyzes their content. The received commands may be something like "assemble parts," and the server uses natural language processing to break down the commands into specific work elements. In this process, the input is the text data of the command, and the output is a list of individual work elements such as "tighten screws" and "place parts."

[0198] Step 2:

[0199] The device monitors the user's emotional state in real time using an emotion recognition sensor. The user wears smart glasses, and the device measures stress levels and fatigue based on facial expressions and voice tone. Input is the user's biometric data, and output is an emotional state such as "relaxed," "stressed," or "fatigued." This data is transmitted to a server.

[0200] Step 3:

[0201] The server integrates emotional data with previously broken-down work elements to determine task assignments. The input is the user's emotional state and a list of work elements, while the output is the optimal assignment result for each work element. For example, if the user is determined to be "stressed," a portion of the task will be automatically assigned to the robot.

[0202] Step 4:

[0203] The user receives task execution instructions from the terminal. The instructions are based on pre-determined assignments, and the user checks the task progress through a dedicated application. In this process, the input is instruction data from the server, and the output is detailed task information notified to the user.

[0204] Step 5:

[0205] The server saves the completed work element as an automation template, allowing it to be reused for similar tasks in the future. The input is the completed work element, and the output is a new automation template saved in the database.

[0206] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0207] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0208] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0209] [Second Embodiment]

[0210] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0211] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0212] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0213] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0214] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0215] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0216] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0217] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0218] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0219] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0220] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0221] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0222] This invention relates to an autonomously operating information processing system that receives multiple instructions, analyzes them, breaks them down into detailed work items, and assigns them to appropriate human resources or artificial intelligence. This system consists of a server, terminals, and users, and each component works together to improve the efficiency of operations.

[0223] The server first receives instructions from management. These instructions are analyzed using natural language processing technology, breaking them down into detailed work items. The server then appropriately assigns each work item, taking into account human resources and the capabilities of artificial intelligence. This process involves referencing employee schedules and AI agent functions from a database to ensure optimal allocation.

[0224] The terminal notifies the user that a work item has been assigned and provides detailed information and materials for the necessary task. Users can report their progress through the terminal. This report is reflected in the server dashboard in real time, and the overall progress is monitored by managers.

[0225] For example, if instructed to check project progress, the server breaks this down into individual tasks such as "data collection," "analysis," and "report creation." It then assigns "data collection" to an AI agent and "analysis" to an analyst. The user can monitor the task progress on their terminal and provide additional instructions if any information is missing.

[0226] Once the task is complete, the server automates the task using the AI ​​agent and saves it as a template, creating a system that allows for immediate response when similar tasks arise in the future. This template helps standardize operations and contributes to improved efficiency.

[0227] In this way, the present invention enables companies to perform their operations with high efficiency and supports the optimal collaboration between human resources and artificial intelligence.

[0228] The following describes the processing flow.

[0229] Step 1:

[0230] The server receives work instructions from management. These instructions are obtained via email or messaging apps, and the data is registered in a central database.

[0231] Step 2:

[0232] The server analyzes the received instructions using natural language processing. During the analysis, it extracts relevant keywords and context from the instructions and breaks them down into specific work items.

[0233] Step 3:

[0234] The server optimally allocates the broken-down work items based on the capabilities of human resources and artificial intelligence agents retrieved from the database, taking into account current resource availability and priorities.

[0235] Step 4:

[0236] The terminal sends notifications to the user regarding assigned work items. These notifications include detailed task information and necessary documents, which the user can access.

[0237] Step 5:

[0238] Users perform tasks through their terminals and report their progress to the server. Progress reports are a means of recording task completion rates and any problems in real time.

[0239] Step 6:

[0240] The server monitors overall progress and allocation status, readjusting task priorities and reallocating resources as needed. This enables efficient and flexible responses.

[0241] Step 7:

[0242] Once a task is completed, the server aggregates the results and provides them to management as a report. Tasks involving the AI ​​agent are templated and prepared for reuse in future tasks.

[0243] (Example 1)

[0244] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0245] In today's work environment, where efficient work management and resource allocation are essential, many organizations face the challenge of properly analyzing information and making optimal allocations using human resources and artificial intelligence. This results in problems such as delays in progress management, uneven workload distribution, and decreased operational efficiency.

[0246] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0247] In this invention, the server includes means for receiving information, means for analyzing the received information and breaking it down into detailed tasks, and means for assigning the broken-down tasks to personnel or intelligent systems. This enables efficient task allocation and real-time progress management.

[0248] "Information" refers to instructions and data received by a system, which are the subject of analysis and processing.

[0249] "Analysis" is the process of understanding received information, extracting its meaning, and breaking it down into detailed tasks.

[0250] "Detailed work" refers to specific tasks and actions extracted from the analyzed information, and represents actionable units.

[0251] "Personnel" refers to the human resources that perform the tasks assigned by the system.

[0252] An "intelligent system" is a computer-based system that uses artificial intelligence to perform tasks.

[0253] "Assignment" is the process of distributing broken-down tasks to the appropriate personnel or intelligent systems.

[0254] "Progress management" is the activity of tracking the progress of a task and understanding its status until completion.

[0255] A "template" is a template that automates completed tasks or processes and saves them in a reusable format.

[0256] This invention is an information processing system that improves business efficiency through the automation of information processing. This system consists of a server, a terminal, and a user working together.

[0257] The server receives instructions from the management side and analyzes them. Natural language processing technology is used for the analysis, and a natural language processing engine can be used as a specific service. The analyzed instructions are broken down into detailed tasks, which are then appropriately assigned to personnel or intelligent systems. A general-purpose artificial intelligence platform can be used as the intelligent system. For example, cloud-based AI services can be used to perform tasks such as data collection and analysis.

[0258] The terminal notifies the user of tasks assigned by the server. This notification is done, for example, through a team communication platform. The user uses the terminal to report progress and provide feedback on their work, and this information is shared in real time with management via the server's dashboard.

[0259] Users can manage their progress based on the tasks assigned to them and provide additional information or instructions as needed.

[0260] As a concrete example of operation, consider a scenario where an administrator issues a command to "conduct market research for a new product." This command is broken down into specific tasks such as "data collection," "market analysis," and "report creation," and each task is assigned to an appropriate resource. An example of a prompt message might be, "You have received a command from management to 'conduct market research for a new product.' How should you break down the task and assign it to whom?"

[0261] This invention will standardize and streamline operations, while simultaneously enabling the effective use of human resources and the integrated operation of artificial intelligence.

[0262] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0263] Step 1:

[0264] The server receives information from the management side. This information is often input as instructions in natural language. The server analyzes this received information using natural language processing techniques to determine how it can be broken down into tasks. Specifically, it inputs the information into an analysis engine and outputs a detailed task list as a result of the analysis.

[0265] Step 2:

[0266] Based on the analysis results, the server individually identifies detailed tasks and assigns each task to the appropriate personnel or intelligent system. Specifically, it retrieves employee schedule information and intelligent system availability from a database, and uses this information, along with data processing, to determine the optimal allocation. The output is a list of task assignments.

[0267] Step 3:

[0268] The terminal receives information about assigned tasks from the server and notifies the user. This includes details of the tasks assigned to the user, their deadlines, and necessary documents. Specifically, this is done by generating a notification message and distributing it to the user using the communication platform.

[0269] Step 4:

[0270] The user receives the notification for a task and begins to perform it. As the user progresses through the task, they report their progress via their terminal. This reporting involves inputting data such as the task's progress and completion status, and sending this information as digital data to the server. As output, a progress report is generated and stored on the server.

[0271] Step 5:

[0272] The server updates the overall progress dashboard based on the collected progress reports. Specifically, it analyzes the progress data and generates graphs and charts to visualize the overall progress. This allows administrators to understand the current status at a glance.

[0273] Step 6:

[0274] Once a task is complete, the server generates and saves a template for automating that task. This involves templating the completed task's steps and processes so that intelligent systems can automatically handle it. Specifically, it standardizes and records the process. The output is a reusable template file.

[0275] (Application Example 1)

[0276] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0277] In the logistics industry, when multiple orders are given, it is required to quickly and appropriately break them down into detailed work items and allocate them optimally to personnel resources and logistics equipment. However, in reality, many orders are not differentiated, and reliance on human judgment often compromises the efficiency and accuracy of work. Furthermore, insufficient monitoring of progress and adjustment of priorities can lead to delays in operations and waste of resources.

[0278] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0279] In this invention, the server includes means for receiving multiple instructions, means for analyzing the received instructions and subdividing them into detailed work items, and means for interpreting instructions in the logistics process and for optimizing the allocation of logistics equipment and personnel. This enables efficient breakdown of instructions and optimal resource allocation in logistics operations.

[0280] "Means for receiving multiple commands" refers to a function for acquiring multiple instructions and commands related to logistics operations electronically or via a network, and converting them into a format that can be processed within the system.

[0281] "Means for analyzing received instructions and subdividing them into detailed work items" refers to a function that analyzes multiple acquired instructions using natural language processing technology and algorithms, and breaks them down into concrete and actionable units of work.

[0282] "Means for interpreting instructions in the logistics process and executing the optimal allocation of logistics equipment and personnel" refers to the function of determining and executing the most efficient allocation and roles based on the current status of machinery and personnel involved in logistics, using the decomposed work items as a basis.

[0283] "Means for monitoring the progress of work items and adjusting priorities" refers to a function that constantly checks whether each task is progressing according to plan, and re-evaluates and optimizes the order and importance of actions as needed.

[0284] The means of "conserving as a template for future use" is a function for storing a completed series of work procedures and instructions in a standardized format so that they can be reused in similar operations later.

[0285] The system for implementing this invention consists of a server, a terminal, and a user. First, the server receives a plurality of instructions related to logistics operations. These instructions are analyzed using natural language processing technology and decomposed into detailed work items. In this process, the SpaCy library is used to efficiently analyze the content of the instructions.

[0286] The received detailed work items are appropriately assigned by the server to robots and personnel within the logistics center. A web-based dashboard using Django is used to monitor the progress of work items in real time. This enables unified progress management and optimal prioritization.

[0287] The terminal notifies the user of the assignment information for work items and provides detailed work information and materials. The user reports the progress of tasks to the server through the terminal, and based on this, the overall operation is adjusted. Completed work items are stored as templates by the server and managed in a form that can be reused in the future.

[0288] For example, when a large quantity of new products is received at the logistics center, the server decomposes the work items into "receiving goods", "inspection", and "storage", and assigns them to the relevant equipment and personnel in an optimal way. This improves the efficiency of operations.

[0289] As an example of a prompt sentence, by inputting a question such as "When a large-scale inbound instruction is given, how do you decompose the work and efficiently manage the combination of people and robots?" into the generative AI model, it is possible to obtain an optimal countermeasure.

[0290] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0291] Step 1:

[0292] The server receives multiple instructions related to logistics operations. The input is provided as text-based data containing the instructions. The server then converts the data format for storage in its database. To efficiently process the received instructions, the server performs operations such as data formatting and adding necessary metadata.

[0293] Step 2:

[0294] The server analyzes received instructions using natural language processing techniques and breaks them down into detailed work items. The input is text data, and the output is a list of the decomposed work items. Specifically, the SpaCy library is used to perform text analysis, identify the content of the instructions, and classify them into items.

[0295] Step 3:

[0296] The server optimally allocates robots and personnel within the logistics center based on the broken-down work items. Inputs are a list of work items and resource information within the center, and output is an allocation plan. The allocation process is executed through a dashboard built with Django, taking into account the schedules and capabilities of the relevant resources.

[0297] Step 4:

[0298] The terminal notifies the user of assigned work items. The input is the assignment plan, and the output is notification information displayed to the user. Based on this, the terminal performs actions to present the user with any necessary detailed information or documents.

[0299] Step 5:

[0300] Users report task progress to the server via their terminal. The input is progress information from the user, which is sent to the server, and the output is real-time progress data for the entire system. Users operate the interface to record progress and perform reporting actions.

[0301] Step 6:

[0302] The server adjusts the overall priority of work items based on the obtained progress information. The input is progress data, and the output is an updated priority list. The system executes an AI algorithm based on the progress and schedule to optimize the schedule.

[0303] Step 7:

[0304] The server maintains data from completed work items as templates for future use. The input is the completed data, and the output is the data stored in template format. The system then generates a standardized process and saves the templates to a database.

[0305] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0306] This invention is an information processing system that efficiently manages work instructions and optimally controls task progress, using an emotion engine to recognize user emotions and reflect them in the system's operation. The system consists of a server, terminals, and users.

[0307] The server receives instructions from management and analyzes them using natural language processing technology. The instructions are decomposed into detailed work items, and each work item is assigned to human resources or artificial intelligence. At that time, the decomposed tasks are optimally allocated based on the resource status referenced from the database. Then, the assigned work items are notified to the terminal, and the user can check them immediately.

[0308] Furthermore, this system includes an emotion engine, which enables the terminal to monitor the user's emotional state in real time. The recognized emotional state is transmitted to the server and utilized for task assignment and schedule adjustment. For example, when the user is feeling stressed, the server takes measures such as lowering the priority of tasks or reducing the workload to relieve the load.

[0309] As a specific example, when the user has both "creating a sales report" and "customer analysis task" at the same time, if the emotion engine detects the user's high-stress state, the server temporarily transfers "creating a sales report" to the AI agent to reduce the user's task load. As a result, the user can concentrate on the "customer analysis task" without feeling burdened.

[0310] When a work item is completed, the server templates it and saves it for use in related tasks in the future. The templated work items improve work efficiency and promote business standardization.

[0311] In this way, the present invention can efficiently perform operations while adapting to the user's emotional state, and can simultaneously improve the comfort and productivity of the business environment.

[0312] The following describes the processing flow.

[0313] Step 1:

[0314] The server receives business instructions from management via email or a messaging app. The instructions are registered in the database and are ready for processing.

[0315] Step 2:

[0316] The server analyzes the received instructions using natural language processing techniques and breaks them down into detailed work items. During this process, relevant keywords and task priorities are identified.

[0317] Step 3:

[0318] The server optimally allocates the broken-down work items based on human and artificial intelligence resources. User-specific schedules and the capabilities of AI agents are also taken into consideration.

[0319] Step 4:

[0320] The terminal notifies the user of assigned tasks. This notification includes task details, priority, and relevant documentation.

[0321] Step 5:

[0322] The device monitors the user's emotional state in real time through an emotion engine. Emotional data is analyzed and sent to the server as needed.

[0323] Step 6:

[0324] The server receives emotional data and dynamically adjusts task assignments and priorities based on the user's emotional state. It implements measures to reduce the user's burden, such as adjusting the workload when stress levels are high.

[0325] Step 7:

[0326] Users perform tasks through their devices and report their progress to the server. This allows for real-time management of task completion status.

[0327] Step 8:

[0328] After all tasks are completed, the server aggregates the results and saves the work items as templates for reuse in the future. These templates help improve work efficiency.

[0329] (Example 2)

[0330] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0331] In today's work environment, multiple different instructions and tasks arise simultaneously, making it difficult to manage them efficiently. Furthermore, assigning tasks uniformly without considering the emotional state of individual users leads to excessive stress and inefficiency. This invention aims to improve work efficiency and achieve task management that takes into account the emotional state of users.

[0332] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0333] In this invention, the server includes means for receiving multiple instructions, means for analyzing the received instructions using a language analysis method and breaking them down into detailed work elements, and means for assigning the broken-down work elements to personnel or intelligent functions. This enables efficient management of tasks and dynamic task reallocation in accordance with the emotional state of the user.

[0334] "Instructions" refer to information that specifies the concrete actions and conditions necessary to carry out a task.

[0335] "Linguistic analysis techniques" are technologies that use computers to analyze and interpret text written in natural language based on its meaning and structure.

[0336] A "work element" refers to a specific task or action derived from the analyzed instructions.

[0337] "Human resources" refer to the human resources required to perform specific tasks within a company or organization.

[0338] "Intelligent function" refers to a system that has the ability to perform or assist in specific tasks using artificial intelligence.

[0339] "Emotional state" refers to the psychological and mental state or emotions that the user is experiencing at a particular point in time.

[0340] "Dynamic reassignment" is a process that flexibly changes assigned tasks in response to changes in circumstances and conditions.

[0341] This invention is an information processing system that enables efficient business management and task management tailored to the emotional state of the user. This system consists of a server, terminals, and users.

[0342] The server receives instructions and analyzes them using language analysis techniques. Specifically, it uses programming languages ​​such as Python and common language analysis APIs to analyze the received instructions and break them down into detailed work elements. These decomposed work elements are then optimally assigned by the server to human resources or intelligent functions. During this process, a database service is used to efficiently distribute tasks while monitoring resource availability.

[0343] The device is equipped with an emotion engine, which is used to monitor the user's emotional state in real time. Specifically, it uses a software development kit with facial recognition technology and voice analysis tools to detect the user's emotions and send the data to a server.

[0344] If the emotion engine determines that a user is experiencing high stress, the server will adjust task priorities or transfer tasks to AI agents. This allows the user to focus on other tasks without feeling overwhelmed.

[0345] As a concrete example, let's consider a situation where a user has to simultaneously handle "creating sales reports" and "customer analysis tasks." In this case, the emotion engine detects stress, and the server reduces the user's task load by transferring the "creating sales reports" task to an AI agent. As a result, the user can concentrate on the "customer analysis task."

[0346] Examples of prompts include, "Please suggest ways to reduce stress related to customer analysis tasks," and "Please describe how to reallocate tasks based on the user's emotional state."

[0347] In this way, the present invention realizes improved work efficiency and the provision of a comfortable work environment.

[0348] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0349] Step 1:

[0350] The server receives work instructions sent from the administrator. These instructions are obtained via email or a specific work management system. The input is natural language text data, and receiving this data yields the instructions to be analyzed. The output is the text data of the received instructions.

[0351] Step 2:

[0352] The server analyzes the received work instructions using language analysis techniques. Specifically, it uses a natural language processing model to tokenize the instructions and convert them into structured information. The input is the text data of the instructions obtained in step 1. The output is a list of the analyzed work elements.

[0353] Step 3:

[0354] The server reviews the analyzed work elements and assigns them to personnel or intelligent functions. This includes database access to check resource utilization. The input is a list of work elements obtained in step 2. The output is the assignment information for the personnel or system resources to which each work element is assigned.

[0355] Step 4:

[0356] The terminal notifies the user of the work elements assigned to them based on instructions from the server. The terminal utilizes the user interface to quickly display tasks. The input is the assignment information generated in step 3. The output is the task information displayed in the user interface.

[0357] Step 5:

[0358] The device monitors the user's emotional state using an emotion engine. The emotion engine collects data using cameras and microphones, analyzes it, and determines the emotional state. Inputs are sensor data such as facial recognition and voice input. Outputs are the detected user's emotional state information.

[0359] Step 6:

[0360] The server receives emotional state data sent from the terminal and adjusts task assignments and priorities. If a stressed state is detected, the server takes measures such as transferring part of the work to the AI ​​agent. The input is the emotional state information obtained in step 5. The output is the adjusted task assignment information.

[0361] Step 7:

[0362] The server templates completed work elements and saves them to a database. This template is used for similar tasks in the future. This process summarizes the details and workflow of completed tasks. The input is the data of the completed tasks. The output is the templated work element data.

[0363] (Application Example 2)

[0364] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0365] In today's work environment, imbalances in workload and employee emotional stress are contributing factors to decreased productivity. Furthermore, traditional systems have a static approach to work progress management and task assignment, failing to reflect individual circumstances, making efficient operations difficult. To address these issues, a system is needed that can grasp workers' emotional states in real time and dynamically adjust work assignments accordingly.

[0366] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0367] In this invention, the server includes means for receiving multiple commands, means for analyzing the received commands and breaking them down into detailed work elements, and means for monitoring the user's emotional state using emotion recognition technology and dynamically adjusting the assignment of work elements based on that state. This enables work adjustments in response to the user's real-time emotional state.

[0368] A "means for receiving multiple commands" refers to a mechanism for aggregating various commands transmitted from external sources.

[0369] "Means for analyzing received instructions and breaking them down into detailed work elements" refers to a mechanism for analyzing received instructions and breaking them down into smaller, manageable work parts.

[0370] "Means of allocating to human resources or machine learning systems" refers to a mechanism for distributing the broken-down work elements to appropriate personnel or AI systems.

[0371] "Means for monitoring the progress of work elements and adjusting priorities" refers to a function that monitors how work is progressing and changes the order of tasks as needed.

[0372] "Emotion recognition technology" refers to a method of detecting a user's emotional state using various sensors and algorithms.

[0373] "Means for monitoring the user's emotional state and dynamically adjusting the assignment of work elements based on that state" refers to a mechanism for observing the user's current emotions and flexibly changing the workload accordingly.

[0374] "Means of automating completed work elements and saving them as templates for future use" refers to methods for organizing already completed work into a format that can be used repeatedly.

[0375] The system for implementing this invention consists of a server, a terminal, and a user. The server first receives commands from an external source. After receiving the commands, the server analyzes and breaks them down into detailed work elements and appropriately assigns these elements to human resources or a machine learning system. During this process, the server monitors the progress of the work in real time and dynamically adjusts priorities as needed. A device worn by the user, such as smart glasses or a personal digital assistant, monitors the user's emotional state using emotion recognition technology.

[0376] The device analyzes the user's facial expressions and voice, and sends their emotional state to the server. The server dynamically adjusts the assignment of work elements based on the emotional data, ensuring that the workload is appropriately distributed. For example, if the server detects from the emotional data that the user is fatigued, it can automatically assign part of the task to a robot. This allows the user to perform tasks efficiently while reducing their burden. Completed work elements are saved on the server as automated templates and reused for similar tasks in the future. This system is implemented using programming languages ​​such as Python and utilizes hardware sensors such as smart glasses and cameras.

[0377] As a concrete example, in an automobile parts factory, workers perform assembly tasks while simultaneously conducting quality inspections. If smart glasses detect a decrease in the worker's concentration, the server automatically hands over the assembly task to a robot, allowing the worker to focus on quality inspections.

[0378] An example of a prompt for a generative AI model is: "Explain how an emotion recognition system can detect worker fatigue in a factory and assign tasks to robots. Please provide a specific example in a peaceful scenario."

[0379] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0380] Step 1:

[0381] The server receives external commands and analyzes their content. The received commands may be something like "assemble parts," and the server uses natural language processing to break down the commands into specific work elements. In this process, the input is the text data of the command, and the output is a list of individual work elements such as "tighten screws" and "place parts."

[0382] Step 2:

[0383] The device monitors the user's emotional state in real time using an emotion recognition sensor. The user wears smart glasses, and the device measures stress levels and fatigue based on facial expressions and voice tone. Input is the user's biometric data, and output is an emotional state such as "relaxed," "stressed," or "fatigued." This data is transmitted to a server.

[0384] Step 3:

[0385] The server integrates emotional data with previously broken-down work elements to determine task assignments. The input is the user's emotional state and a list of work elements, while the output is the optimal assignment result for each work element. For example, if the user is determined to be "stressed," a portion of the task will be automatically assigned to the robot.

[0386] Step 4:

[0387] The user receives task execution instructions from the terminal. The instructions are based on pre-determined assignments, and the user checks the task progress through a dedicated application. In this process, the input is instruction data from the server, and the output is detailed task information notified to the user.

[0388] Step 5:

[0389] The server saves the completed work element as an automation template, allowing it to be reused for similar tasks in the future. The input is the completed work element, and the output is a new automation template saved in the database.

[0390] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0391] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0392] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0393] [Third Embodiment]

[0394] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0395] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0396] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0397] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0398] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0399] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0400] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0401] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0402] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0403] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0404] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0405] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0406] This invention relates to an autonomously operating information processing system that receives multiple instructions, analyzes them, breaks them down into detailed work items, and assigns them to appropriate human resources or artificial intelligence. This system consists of a server, terminals, and users, and each component works together to improve the efficiency of operations.

[0407] The server first receives instructions from management. These instructions are analyzed using natural language processing technology, breaking them down into detailed work items. The server then appropriately assigns each work item, taking into account human resources and the capabilities of artificial intelligence. This process involves referencing employee schedules and AI agent functions from a database to ensure optimal allocation.

[0408] The terminal notifies the user that a work item has been assigned and provides detailed information and materials for the necessary task. Users can report their progress through the terminal. This report is reflected in the server dashboard in real time, and the overall progress is monitored by managers.

[0409] For example, if instructed to check project progress, the server breaks this down into individual tasks such as "data collection," "analysis," and "report creation." It then assigns "data collection" to an AI agent and "analysis" to an analyst. The user can monitor the task progress on their terminal and provide additional instructions if any information is missing.

[0410] Once the task is complete, the server automates the task using the AI ​​agent and saves it as a template, creating a system that allows for immediate response when similar tasks arise in the future. This template helps standardize operations and contributes to improved efficiency.

[0411] In this way, the present invention enables companies to perform their operations with high efficiency and supports the optimal collaboration between human resources and artificial intelligence.

[0412] The following describes the processing flow.

[0413] Step 1:

[0414] The server receives work instructions from management. These instructions are obtained via email or messaging apps, and the data is registered in a central database.

[0415] Step 2:

[0416] The server analyzes the received instructions using natural language processing. During the analysis, it extracts relevant keywords and context from the instructions and breaks them down into specific work items.

[0417] Step 3:

[0418] The server optimally allocates the broken-down work items based on the capabilities of human resources and artificial intelligence agents retrieved from the database, taking into account current resource availability and priorities.

[0419] Step 4:

[0420] The terminal sends notifications to the user regarding assigned work items. These notifications include detailed task information and necessary documents, which the user can access.

[0421] Step 5:

[0422] Users perform tasks through their terminals and report their progress to the server. Progress reports are a means of recording task completion rates and any problems in real time.

[0423] Step 6:

[0424] The server monitors overall progress and allocation status, readjusting task priorities and reallocating resources as needed. This enables efficient and flexible responses.

[0425] Step 7:

[0426] Once a task is completed, the server aggregates the results and provides them to management as a report. Tasks involving the AI ​​agent are templated and prepared for reuse in future tasks.

[0427] (Example 1)

[0428] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0429] In today's work environment, where efficient work management and resource allocation are essential, many organizations face the challenge of properly analyzing information and making optimal allocations using human resources and artificial intelligence. This results in problems such as delays in progress management, uneven workload distribution, and decreased operational efficiency.

[0430] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0431] In this invention, the server includes means for receiving information, means for analyzing the received information and breaking it down into detailed tasks, and means for assigning the broken-down tasks to personnel or intelligent systems. This enables efficient task allocation and real-time progress management.

[0432] "Information" refers to instructions and data received by a system, which are the subject of analysis and processing.

[0433] "Analysis" is the process of understanding received information, extracting its meaning, and breaking it down into detailed tasks.

[0434] "Detailed work" refers to specific tasks and actions extracted from the analyzed information, and represents actionable units.

[0435] "Personnel" refers to the human resources that perform the tasks assigned by the system.

[0436] An "intelligent system" is a computer-based system that uses artificial intelligence to perform tasks.

[0437] "Assignment" is the process of distributing broken-down tasks to the appropriate personnel or intelligent systems.

[0438] "Progress management" is the activity of tracking the progress of a task and understanding its status until completion.

[0439] A "template" is a template that automates completed tasks or processes and saves them in a reusable format.

[0440] This invention is an information processing system that improves business efficiency through the automation of information processing. This system consists of a server, a terminal, and a user working together.

[0441] The server receives instructions from the management side and analyzes them. Natural language processing technology is used for the analysis, and a natural language processing engine can be used as a specific service. The analyzed instructions are broken down into detailed tasks, which are then appropriately assigned to personnel or intelligent systems. A general-purpose artificial intelligence platform can be used as the intelligent system. For example, cloud-based AI services can be used to perform tasks such as data collection and analysis.

[0442] The terminal notifies the user of tasks assigned by the server. This notification is done, for example, through a team communication platform. The user uses the terminal to report progress and provide feedback on their work, and this information is shared in real time with management via the server's dashboard.

[0443] Users can manage their progress based on the tasks assigned to them and provide additional information or instructions as needed.

[0444] As a concrete example of operation, consider a scenario where an administrator issues a command to "conduct market research for a new product." This command is broken down into specific tasks such as "data collection," "market analysis," and "report creation," and each task is assigned to an appropriate resource. An example of a prompt message might be, "You have received a command from management to 'conduct market research for a new product.' How should you break down the task and assign it to whom?"

[0445] This invention will standardize and streamline operations, while simultaneously enabling the effective use of human resources and the integrated operation of artificial intelligence.

[0446] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0447] Step 1:

[0448] The server receives information from the management side. This information is often input as instructions in natural language. The server analyzes this received information using natural language processing techniques to determine how it can be broken down into tasks. Specifically, it inputs the information into an analysis engine and outputs a detailed task list as a result of the analysis.

[0449] Step 2:

[0450] Based on the analysis results, the server individually identifies detailed tasks and assigns each task to the appropriate personnel or intelligent system. Specifically, it retrieves employee schedule information and intelligent system availability from a database, and uses this information, along with data processing, to determine the optimal allocation. The output is a list of task assignments.

[0451] Step 3:

[0452] The terminal receives information about assigned tasks from the server and notifies the user. This includes details of the tasks assigned to the user, their deadlines, and necessary documents. Specifically, this is done by generating a notification message and distributing it to the user using the communication platform.

[0453] Step 4:

[0454] The user receives the notification for a task and begins to perform it. As the user progresses through the task, they report their progress via their terminal. This reporting involves inputting data such as the task's progress and completion status, and sending this information as digital data to the server. As output, a progress report is generated and stored on the server.

[0455] Step 5:

[0456] The server updates the overall progress dashboard based on the collected progress reports. Specifically, it analyzes the progress data and generates graphs and charts to visualize the overall progress. This allows administrators to understand the current status at a glance.

[0457] Step 6:

[0458] Once a task is complete, the server generates and saves a template for automating that task. This involves templating the completed task's steps and processes so that intelligent systems can automatically handle it. Specifically, it standardizes and records the process. The output is a reusable template file.

[0459] (Application Example 1)

[0460] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0461] In the logistics industry, when multiple orders are given, it is required to quickly and appropriately break them down into detailed work items and allocate them optimally to personnel resources and logistics equipment. However, in reality, many orders are not differentiated, and reliance on human judgment often compromises the efficiency and accuracy of work. Furthermore, insufficient monitoring of progress and adjustment of priorities can lead to delays in operations and waste of resources.

[0462] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0463] In this invention, the server includes means for receiving multiple instructions, means for analyzing the received instructions and subdividing them into detailed work items, and means for interpreting instructions in the logistics process and for optimizing the allocation of logistics equipment and personnel. This enables efficient breakdown of instructions and optimal resource allocation in logistics operations.

[0464] "Means for receiving multiple commands" refers to a function for acquiring multiple instructions and commands related to logistics operations electronically or via a network, and converting them into a format that can be processed within the system.

[0465] "Means for analyzing received instructions and subdividing them into detailed work items" refers to a function that analyzes multiple acquired instructions using natural language processing technology and algorithms, and breaks them down into concrete and actionable units of work.

[0466] "Means for interpreting instructions in the logistics process and executing the optimal allocation of logistics equipment and personnel" refers to the function of determining and executing the most efficient allocation and roles based on the current status of machinery and personnel involved in logistics, using the decomposed work items as a basis.

[0467] "Means for monitoring the progress of work items and adjusting priorities" refers to a function that constantly checks whether each task is progressing according to plan, and re-evaluates and optimizes the order and importance of actions as needed.

[0468] "Means of maintaining as templates for future use" refers to a function that saves a completed series of work procedures and instructions in a standardized format so that they can be reused in similar tasks later on.

[0469] The system for realizing this invention consists of a server, a terminal, and a user. The server first receives multiple commands related to logistics operations. These commands are analyzed using natural language processing technology and broken down into detailed work items. The SpaCy library is used in this process to efficiently analyze the content of the instructions.

[0470] The detailed work items received are appropriately assigned by the server to robots and personnel within the logistics center. A web-based dashboard using Django is used to monitor the progress of work items in real time. This centralizes progress management and enables optimal prioritization.

[0471] The terminal notifies the user of assigned work items and provides detailed information and materials related to the work. The user reports the progress of tasks to the server via the terminal, and the overall work is adjusted based on this information. Completed work items are saved as templates by the server and managed in a form that can be reused in the future.

[0472] For example, when a large quantity of new products arrives at a logistics center, the server breaks down the tasks into "receiving goods," "inspecting," and "stocking," and allocates them to the most appropriate equipment and personnel. This improves operational efficiency.

[0473] For example, by inputting a prompt such as, "Given a large-scale receiving order, how should the work be broken down and managed efficiently by combining human and robotic personnel?" into the generating AI model, it is possible to obtain the optimal response.

[0474] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0475] Step 1:

[0476] The server receives multiple instructions related to logistics operations. The input is provided as text-based data containing the instructions. The server then converts the data format for storage in its database. To efficiently process the received instructions, the server performs operations such as data formatting and adding necessary metadata.

[0477] Step 2:

[0478] The server analyzes received instructions using natural language processing techniques and breaks them down into detailed work items. The input is text data, and the output is a list of the decomposed work items. Specifically, the SpaCy library is used to perform text analysis, identify the content of the instructions, and classify them into items.

[0479] Step 3:

[0480] The server optimally allocates robots and personnel within the logistics center based on the broken-down work items. Inputs are a list of work items and resource information within the center, and output is an allocation plan. The allocation process is executed through a dashboard built with Django, taking into account the schedules and capabilities of the relevant resources.

[0481] Step 4:

[0482] The terminal notifies the user of assigned work items. The input is the assignment plan, and the output is notification information displayed to the user. Based on this, the terminal performs actions to present the user with any necessary detailed information or documents.

[0483] Step 5:

[0484] Users report task progress to the server via their terminal. The input is progress information from the user, which is sent to the server, and the output is real-time progress data for the entire system. Users operate the interface to record progress and perform reporting actions.

[0485] Step 6:

[0486] The server adjusts the overall priority of work items based on the obtained progress information. The input is progress data, and the output is an updated priority list. The system executes an AI algorithm based on the progress and schedule to optimize the schedule.

[0487] Step 7:

[0488] The server maintains data from completed work items as templates for future use. The input is the completed data, and the output is the data stored in template format. The system then generates a standardized process and saves the templates to a database.

[0489] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0490] This invention is an information processing system that efficiently manages work instructions and optimally controls task progress, using an emotion engine to recognize user emotions and reflect them in the system's operation. The system consists of a server, terminals, and users.

[0491] The server receives instructions from management and analyzes them using natural language processing technology. The instructions are broken down into detailed work items, and each item is assigned to human resources or artificial intelligence. The broken-down tasks are optimally distributed based on the availability of resources referenced from a database. The assigned work items are then notified to the terminal, allowing the user to immediately confirm them.

[0492] Furthermore, this system includes an emotion engine, which allows the terminal to monitor the user's emotional state in real time. The recognized emotional state is sent to the server and used for task assignment and schedule adjustments. For example, if the user is feeling stressed, the server will take measures to reduce the load, such as lowering the priority of tasks or reducing the workload.

[0493] For example, if a user is simultaneously tasked with "creating sales reports" and "customer analysis," the emotion engine detects the user is under high stress. The server then temporarily transfers the "sales report creation" task to an AI agent, reducing the user's workload. As a result, the user can focus on the "customer analysis task" without feeling burdened.

[0494] Once a work item is completed, the server creates a template of it and saves it for use in related tasks in the future. Templated work items improve work efficiency and promote the standardization of operations.

[0495] In this way, the present invention makes it possible to efficiently perform tasks while adapting to the user's emotional state, thereby simultaneously improving the comfort and productivity of the work environment.

[0496] The following describes the processing flow.

[0497] Step 1:

[0498] The server receives work instructions from management via email or messaging apps. The instructions are registered in the database and prepared for processing.

[0499] Step 2:

[0500] The server analyzes the received instructions using natural language processing techniques and breaks them down into detailed work items. During this process, relevant keywords and task priorities are identified.

[0501] Step 3:

[0502] The server optimally allocates the broken-down work items based on human and artificial intelligence resources. User-specific schedules and the capabilities of AI agents are also taken into consideration.

[0503] Step 4:

[0504] The terminal notifies the user of assigned tasks. This notification includes task details, priority, and relevant documentation.

[0505] Step 5:

[0506] The device monitors the user's emotional state in real time through an emotion engine. Emotional data is analyzed and sent to the server as needed.

[0507] Step 6:

[0508] The server receives emotional data and dynamically adjusts task assignments and priorities based on the user's emotional state. It implements measures to reduce the user's burden, such as adjusting the workload when stress levels are high.

[0509] Step 7:

[0510] Users perform tasks through their devices and report their progress to the server. This allows for real-time management of task completion status.

[0511] Step 8:

[0512] After all tasks are completed, the server aggregates the results and saves the work items as templates for reuse in the future. These templates help improve work efficiency.

[0513] (Example 2)

[0514] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0515] In today's work environment, multiple different instructions and tasks arise simultaneously, making it difficult to manage them efficiently. Furthermore, assigning tasks uniformly without considering the emotional state of individual users leads to excessive stress and inefficiency. This invention aims to improve work efficiency and achieve task management that takes into account the emotional state of users.

[0516] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0517] In this invention, the server includes means for receiving multiple instructions, means for analyzing the received instructions using a language analysis method and breaking them down into detailed work elements, and means for assigning the broken-down work elements to personnel or intelligent functions. This enables efficient management of tasks and dynamic task reallocation in accordance with the emotional state of the user.

[0518] "Instructions" refer to information that specifies the concrete actions and conditions necessary to carry out a task.

[0519] "Linguistic analysis techniques" are technologies that use computers to analyze and interpret text written in natural language based on its meaning and structure.

[0520] A "work element" refers to a specific task or action derived from the analyzed instructions.

[0521] "Human resources" refer to the human resources required to perform specific tasks within a company or organization.

[0522] "Intelligent function" refers to a system that has the ability to perform or assist in specific tasks using artificial intelligence.

[0523] "Emotional state" refers to the psychological and mental state or emotions that the user is experiencing at a particular point in time.

[0524] "Dynamic reassignment" is a process that flexibly changes assigned tasks in response to changes in circumstances and conditions.

[0525] This invention is an information processing system that enables efficient business management and task management tailored to the emotional state of the user. This system consists of a server, terminals, and users.

[0526] The server receives instructions and analyzes them using language analysis techniques. Specifically, it uses programming languages ​​such as Python and common language analysis APIs to analyze the received instructions and break them down into detailed work elements. These decomposed work elements are then optimally assigned by the server to human resources or intelligent functions. During this process, a database service is used to efficiently distribute tasks while monitoring resource availability.

[0527] The device is equipped with an emotion engine, which is used to monitor the user's emotional state in real time. Specifically, it uses a software development kit with facial recognition technology and voice analysis tools to detect the user's emotions and send the data to a server.

[0528] If the emotion engine determines that a user is experiencing high stress, the server will adjust task priorities or transfer tasks to AI agents. This allows the user to focus on other tasks without feeling overwhelmed.

[0529] As a concrete example, let's consider a situation where a user has to simultaneously handle "creating sales reports" and "customer analysis tasks." In this case, the emotion engine detects stress, and the server reduces the user's task load by transferring the "creating sales reports" task to an AI agent. As a result, the user can concentrate on the "customer analysis task."

[0530] Examples of prompts include, "Please suggest ways to reduce stress related to customer analysis tasks," and "Please describe how to reallocate tasks based on the user's emotional state."

[0531] In this way, the present invention realizes improved work efficiency and the provision of a comfortable work environment.

[0532] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0533] Step 1:

[0534] The server receives work instructions sent from the administrator. These instructions are obtained via email or a specific work management system. The input is natural language text data, and receiving this data yields the instructions to be analyzed. The output is the text data of the received instructions.

[0535] Step 2:

[0536] The server analyzes the received work instructions using language analysis techniques. Specifically, it uses a natural language processing model to tokenize the instructions and convert them into structured information. The input is the text data of the instructions obtained in step 1. The output is a list of the analyzed work elements.

[0537] Step 3:

[0538] The server reviews the analyzed work elements and assigns them to personnel or intelligent functions. This includes database access to check resource utilization. The input is a list of work elements obtained in step 2. The output is the assignment information for the personnel or system resources to which each work element is assigned.

[0539] Step 4:

[0540] The terminal notifies the user of the work elements assigned to them based on instructions from the server. The terminal utilizes the user interface to quickly display tasks. The input is the assignment information generated in step 3. The output is the task information displayed in the user interface.

[0541] Step 5:

[0542] The device monitors the user's emotional state using an emotion engine. The emotion engine collects data using cameras and microphones, analyzes it, and determines the emotional state. Inputs are sensor data such as facial recognition and voice input. Outputs are the detected user's emotional state information.

[0543] Step 6:

[0544] The server receives emotional state data sent from the terminal and adjusts task assignments and priorities. If a stressed state is detected, the server takes measures such as transferring part of the work to the AI ​​agent. The input is the emotional state information obtained in step 5. The output is the adjusted task assignment information.

[0545] Step 7:

[0546] The server templates completed work elements and saves them to a database. This template is used for similar tasks in the future. This process summarizes the details and workflow of completed tasks. The input is the data of the completed tasks. The output is the templated work element data.

[0547] (Application Example 2)

[0548] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0549] In today's work environment, imbalances in workload and employee emotional stress are contributing factors to decreased productivity. Furthermore, traditional systems have a static approach to work progress management and task assignment, failing to reflect individual circumstances, making efficient operations difficult. To address these issues, a system is needed that can grasp workers' emotional states in real time and dynamically adjust work assignments accordingly.

[0550] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0551] In this invention, the server includes means for receiving multiple commands, means for analyzing the received commands and breaking them down into detailed work elements, and means for monitoring the user's emotional state using emotion recognition technology and dynamically adjusting the assignment of work elements based on that state. This enables work adjustments in response to the user's real-time emotional state.

[0552] A "means for receiving multiple commands" refers to a mechanism for aggregating various commands transmitted from external sources.

[0553] "Means for analyzing received instructions and breaking them down into detailed work elements" refers to a mechanism for analyzing received instructions and breaking them down into smaller, manageable work parts.

[0554] "Means of allocating to human resources or machine learning systems" refers to a mechanism for distributing the broken-down work elements to appropriate personnel or AI systems.

[0555] "Means for monitoring the progress of work elements and adjusting priorities" refers to a function that monitors how work is progressing and changes the order of tasks as needed.

[0556] "Emotion recognition technology" refers to a method of detecting a user's emotional state using various sensors and algorithms.

[0557] "Means for monitoring the user's emotional state and dynamically adjusting the assignment of work elements based on that state" refers to a mechanism for observing the user's current emotions and flexibly changing the workload accordingly.

[0558] "Means of automating completed work elements and saving them as templates for future use" refers to methods for organizing already completed work into a format that can be used repeatedly.

[0559] The system for implementing this invention consists of a server, a terminal, and a user. The server first receives commands from an external source. After receiving the commands, the server analyzes and breaks them down into detailed work elements and appropriately assigns these elements to human resources or a machine learning system. During this process, the server monitors the progress of the work in real time and dynamically adjusts priorities as needed. A device worn by the user, such as smart glasses or a personal digital assistant, monitors the user's emotional state using emotion recognition technology.

[0560] The device analyzes the user's facial expressions and voice, and sends their emotional state to the server. The server dynamically adjusts the assignment of work elements based on the emotional data, ensuring that the workload is appropriately distributed. For example, if the server detects from the emotional data that the user is fatigued, it can automatically assign part of the task to a robot. This allows the user to perform tasks efficiently while reducing their burden. Completed work elements are saved on the server as automated templates and reused for similar tasks in the future. This system is implemented using programming languages ​​such as Python and utilizes hardware sensors such as smart glasses and cameras.

[0561] As a concrete example, in an automobile parts factory, workers perform assembly tasks while simultaneously conducting quality inspections. If smart glasses detect a decrease in the worker's concentration, the server automatically hands over the assembly task to a robot, allowing the worker to focus on quality inspections.

[0562] An example of a prompt for a generative AI model is: "Explain how an emotion recognition system can detect worker fatigue in a factory and assign tasks to robots. Please provide a specific example in a peaceful scenario."

[0563] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0564] Step 1:

[0565] The server receives external commands and analyzes their content. The received commands may be something like "assemble parts," and the server uses natural language processing to break down the commands into specific work elements. In this process, the input is the text data of the command, and the output is a list of individual work elements such as "tighten screws" and "place parts."

[0566] Step 2:

[0567] The device monitors the user's emotional state in real time using an emotion recognition sensor. The user wears smart glasses, and the device measures stress levels and fatigue based on facial expressions and voice tone. Input is the user's biometric data, and output is an emotional state such as "relaxed," "stressed," or "fatigued." This data is transmitted to a server.

[0568] Step 3:

[0569] The server integrates emotional data with previously broken-down work elements to determine task assignments. The input is the user's emotional state and a list of work elements, while the output is the optimal assignment result for each work element. For example, if the user is determined to be "stressed," a portion of the task will be automatically assigned to the robot.

[0570] Step 4:

[0571] The user receives task execution instructions from the terminal. The instructions are based on pre-determined assignments, and the user checks the task progress through a dedicated application. In this process, the input is instruction data from the server, and the output is detailed task information notified to the user.

[0572] Step 5:

[0573] The server saves the completed work element as an automation template, allowing it to be reused for similar tasks in the future. The input is the completed work element, and the output is a new automation template saved in the database.

[0574] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0575] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0576] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0577] [Fourth Embodiment]

[0578] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0579] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0580] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0581] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0582] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0583] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0584] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0585] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0586] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0587] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0588] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0589] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0590] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0591] This invention relates to an autonomously operating information processing system that receives multiple instructions, analyzes them, breaks them down into detailed work items, and assigns them to appropriate human resources or artificial intelligence. This system consists of a server, terminals, and users, and each component works together to improve the efficiency of operations.

[0592] The server first receives instructions from management. These instructions are analyzed using natural language processing technology, breaking them down into detailed work items. The server then appropriately assigns each work item, taking into account human resources and the capabilities of artificial intelligence. This process involves referencing employee schedules and AI agent functions from a database to ensure optimal allocation.

[0593] The terminal notifies the user that a work item has been assigned and provides detailed information and materials for the necessary task. Users can report their progress through the terminal. This report is reflected in the server dashboard in real time, and the overall progress is monitored by managers.

[0594] For example, if instructed to check project progress, the server breaks this down into individual tasks such as "data collection," "analysis," and "report creation." It then assigns "data collection" to an AI agent and "analysis" to an analyst. The user can monitor the task progress on their terminal and provide additional instructions if any information is missing.

[0595] Once the task is complete, the server automates the task using the AI ​​agent and saves it as a template, creating a system that allows for immediate response when similar tasks arise in the future. This template helps standardize operations and contributes to improved efficiency.

[0596] In this way, the present invention enables companies to perform their operations with high efficiency and supports the optimal collaboration between human resources and artificial intelligence.

[0597] The following describes the processing flow.

[0598] Step 1:

[0599] The server receives work instructions from management. These instructions are obtained via email or messaging apps, and the data is registered in a central database.

[0600] Step 2:

[0601] The server analyzes the received instructions using natural language processing. During the analysis, it extracts relevant keywords and context from the instructions and breaks them down into specific work items.

[0602] Step 3:

[0603] The server optimally allocates the broken-down work items based on the capabilities of human resources and artificial intelligence agents retrieved from the database, taking into account current resource availability and priorities.

[0604] Step 4:

[0605] The terminal sends notifications to the user regarding assigned work items. These notifications include detailed task information and necessary documents, which the user can access.

[0606] Step 5:

[0607] Users perform tasks through their terminals and report their progress to the server. Progress reports are a means of recording task completion rates and any problems in real time.

[0608] Step 6:

[0609] The server monitors overall progress and allocation status, readjusting task priorities and reallocating resources as needed. This enables efficient and flexible responses.

[0610] Step 7:

[0611] Once a task is completed, the server aggregates the results and provides them to management as a report. Tasks involving the AI ​​agent are templated and prepared for reuse in future tasks.

[0612] (Example 1)

[0613] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0614] In today's work environment, where efficient work management and resource allocation are essential, many organizations face the challenge of properly analyzing information and making optimal allocations using human resources and artificial intelligence. This results in problems such as delays in progress management, uneven workload distribution, and decreased operational efficiency.

[0615] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0616] In this invention, the server includes means for receiving information, means for analyzing the received information and breaking it down into detailed tasks, and means for assigning the broken-down tasks to personnel or intelligent systems. This enables efficient task allocation and real-time progress management.

[0617] "Information" refers to instructions and data received by a system, which are the subject of analysis and processing.

[0618] "Analysis" is the process of understanding received information, extracting its meaning, and breaking it down into detailed tasks.

[0619] "Detailed work" refers to specific tasks and actions extracted from the analyzed information, and represents actionable units.

[0620] "Personnel" refers to the human resources that perform the tasks assigned by the system.

[0621] An "intelligent system" is a computer-based system that uses artificial intelligence to perform tasks.

[0622] "Assignment" is the process of distributing broken-down tasks to the appropriate personnel or intelligent systems.

[0623] "Progress management" is the activity of tracking the progress of a task and understanding its status until completion.

[0624] A "template" is a template that automates completed tasks or processes and saves them in a reusable format.

[0625] This invention is an information processing system that improves business efficiency through the automation of information processing. This system consists of a server, a terminal, and a user working together.

[0626] The server receives instructions from the management side and analyzes them. Natural language processing technology is used for the analysis, and a natural language processing engine can be used as a specific service. The analyzed instructions are broken down into detailed tasks, which are then appropriately assigned to personnel or intelligent systems. A general-purpose artificial intelligence platform can be used as the intelligent system. For example, cloud-based AI services can be used to perform tasks such as data collection and analysis.

[0627] The terminal notifies the user of tasks assigned by the server. This notification is done, for example, through a team communication platform. The user uses the terminal to report progress and provide feedback on their work, and this information is shared in real time with management via the server's dashboard.

[0628] Users can manage their progress based on the tasks assigned to them and provide additional information or instructions as needed.

[0629] As a concrete example of operation, consider a scenario where an administrator issues a command to "conduct market research for a new product." This command is broken down into specific tasks such as "data collection," "market analysis," and "report creation," and each task is assigned to an appropriate resource. An example of a prompt message might be, "You have received a command from management to 'conduct market research for a new product.' How should you break down the task and assign it to whom?"

[0630] This invention will standardize and streamline operations, while simultaneously enabling the effective use of human resources and the integrated operation of artificial intelligence.

[0631] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0632] Step 1:

[0633] The server receives information from the management side. This information is often input as instructions in natural language. The server analyzes this received information using natural language processing techniques to determine how it can be broken down into tasks. Specifically, it inputs the information into an analysis engine and outputs a detailed task list as a result of the analysis.

[0634] Step 2:

[0635] Based on the analysis results, the server individually identifies detailed tasks and assigns each task to the appropriate personnel or intelligent system. Specifically, it retrieves employee schedule information and intelligent system availability from a database, and uses this information, along with data processing, to determine the optimal allocation. The output is a list of task assignments.

[0636] Step 3:

[0637] The terminal receives information about assigned tasks from the server and notifies the user. This includes details of the tasks assigned to the user, their deadlines, and necessary documents. Specifically, this is done by generating a notification message and distributing it to the user using the communication platform.

[0638] Step 4:

[0639] The user receives the notification for a task and begins to perform it. As the user progresses through the task, they report their progress via their terminal. This reporting involves inputting data such as the task's progress and completion status, and sending this information as digital data to the server. As output, a progress report is generated and stored on the server.

[0640] Step 5:

[0641] The server updates the overall progress dashboard based on the collected progress reports. Specifically, it analyzes the progress data and generates graphs and charts to visualize the overall progress. This allows administrators to understand the current status at a glance.

[0642] Step 6:

[0643] Once a task is complete, the server generates and saves a template for automating that task. This involves templating the completed task's steps and processes so that intelligent systems can automatically handle it. Specifically, it standardizes and records the process. The output is a reusable template file.

[0644] (Application Example 1)

[0645] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0646] In the logistics industry, when multiple orders are given, it is required to quickly and appropriately break them down into detailed work items and allocate them optimally to personnel resources and logistics equipment. However, in reality, many orders are not differentiated, and reliance on human judgment often compromises the efficiency and accuracy of work. Furthermore, insufficient monitoring of progress and adjustment of priorities can lead to delays in operations and waste of resources.

[0647] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0648] In this invention, the server includes means for receiving multiple instructions, means for analyzing the received instructions and subdividing them into detailed work items, and means for interpreting instructions in the logistics process and for optimizing the allocation of logistics equipment and personnel. This enables efficient breakdown of instructions and optimal resource allocation in logistics operations.

[0649] "Means for receiving multiple commands" refers to a function for acquiring multiple instructions and commands related to logistics operations electronically or via a network, and converting them into a format that can be processed within the system.

[0650] "Means for analyzing received instructions and subdividing them into detailed work items" refers to a function that analyzes multiple acquired instructions using natural language processing technology and algorithms, and breaks them down into concrete and actionable units of work.

[0651] "Means for interpreting instructions in the logistics process and executing the optimal allocation of logistics equipment and personnel" refers to the function of determining and executing the most efficient allocation and roles based on the current status of machinery and personnel involved in logistics, using the decomposed work items as a basis.

[0652] "Means for monitoring the progress of work items and adjusting priorities" refers to a function that constantly checks whether each task is progressing according to plan, and re-evaluates and optimizes the order and importance of actions as needed.

[0653] "Means of maintaining as templates for future use" refers to a function that saves a completed series of work procedures and instructions in a standardized format so that they can be reused in similar tasks later on.

[0654] The system for realizing this invention consists of a server, a terminal, and a user. The server first receives multiple commands related to logistics operations. These commands are analyzed using natural language processing technology and broken down into detailed work items. The SpaCy library is used in this process to efficiently analyze the content of the instructions.

[0655] The detailed work items received are appropriately assigned by the server to robots and personnel within the logistics center. A web-based dashboard using Django is used to monitor the progress of work items in real time. This centralizes progress management and enables optimal prioritization.

[0656] The terminal notifies the user of assigned work items and provides detailed information and materials related to the work. The user reports the progress of tasks to the server via the terminal, and the overall work is adjusted based on this information. Completed work items are saved as templates by the server and managed in a form that can be reused in the future.

[0657] For example, when a large quantity of new products arrives at a logistics center, the server breaks down the tasks into "receiving goods," "inspecting," and "stocking," and allocates them to the most appropriate equipment and personnel. This improves operational efficiency.

[0658] For example, by inputting a prompt such as, "Given a large-scale receiving order, how should the work be broken down and managed efficiently by combining human and robotic personnel?" into the generating AI model, it is possible to obtain the optimal response.

[0659] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0660] Step 1:

[0661] The server receives multiple instructions related to logistics operations. The input is provided as text-based data containing the instructions. The server then converts the data format for storage in its database. To efficiently process the received instructions, the server performs operations such as data formatting and adding necessary metadata.

[0662] Step 2:

[0663] The server analyzes received instructions using natural language processing techniques and breaks them down into detailed work items. The input is text data, and the output is a list of the decomposed work items. Specifically, the SpaCy library is used to perform text analysis, identify the content of the instructions, and classify them into items.

[0664] Step 3:

[0665] The server optimally allocates robots and personnel within the logistics center based on the broken-down work items. Inputs are a list of work items and resource information within the center, and output is an allocation plan. The allocation process is executed through a dashboard built with Django, taking into account the schedules and capabilities of the relevant resources.

[0666] Step 4:

[0667] The terminal notifies the user of assigned work items. The input is the assignment plan, and the output is notification information displayed to the user. Based on this, the terminal performs actions to present the user with any necessary detailed information or documents.

[0668] Step 5:

[0669] Users report task progress to the server via their terminal. The input is progress information from the user, which is sent to the server, and the output is real-time progress data for the entire system. Users operate the interface to record progress and perform reporting actions.

[0670] Step 6:

[0671] The server adjusts the overall priority of work items based on the obtained progress information. The input is progress data, and the output is an updated priority list. The system executes an AI algorithm based on the progress and schedule to optimize the schedule.

[0672] Step 7:

[0673] The server maintains data from completed work items as templates for future use. The input is the completed data, and the output is the data stored in template format. The system then generates a standardized process and saves the templates to a database.

[0674] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0675] This invention is an information processing system that efficiently manages work instructions and optimally controls task progress, using an emotion engine to recognize user emotions and reflect them in the system's operation. The system consists of a server, terminals, and users.

[0676] The server receives instructions from management and analyzes them using natural language processing technology. The instructions are broken down into detailed work items, and each item is assigned to human resources or artificial intelligence. The broken-down tasks are optimally distributed based on the availability of resources referenced from a database. The assigned work items are then notified to the terminal, allowing the user to immediately confirm them.

[0677] Furthermore, this system includes an emotion engine, which allows the terminal to monitor the user's emotional state in real time. The recognized emotional state is sent to the server and used for task assignment and schedule adjustments. For example, if the user is feeling stressed, the server will take measures to reduce the load, such as lowering the priority of tasks or reducing the workload.

[0678] For example, if a user is simultaneously tasked with "creating sales reports" and "customer analysis," the emotion engine detects the user is under high stress. The server then temporarily transfers the "sales report creation" task to an AI agent, reducing the user's workload. As a result, the user can focus on the "customer analysis task" without feeling burdened.

[0679] Once a work item is completed, the server creates a template of it and saves it for use in related tasks in the future. Templated work items improve work efficiency and promote the standardization of operations.

[0680] In this way, the present invention makes it possible to efficiently perform tasks while adapting to the user's emotional state, thereby simultaneously improving the comfort and productivity of the work environment.

[0681] The following describes the processing flow.

[0682] Step 1:

[0683] The server receives work instructions from management via email or messaging apps. The instructions are registered in the database and prepared for processing.

[0684] Step 2:

[0685] The server analyzes the received instructions using natural language processing techniques and breaks them down into detailed work items. During this process, relevant keywords and task priorities are identified.

[0686] Step 3:

[0687] The server optimally allocates the broken-down work items based on human and artificial intelligence resources. User-specific schedules and the capabilities of AI agents are also taken into consideration.

[0688] Step 4:

[0689] The terminal notifies the user of assigned tasks. This notification includes task details, priority, and relevant documentation.

[0690] Step 5:

[0691] The device monitors the user's emotional state in real time through an emotion engine. Emotional data is analyzed and sent to the server as needed.

[0692] Step 6:

[0693] The server receives emotional data and dynamically adjusts task assignments and priorities based on the user's emotional state. It implements measures to reduce the user's burden, such as adjusting the workload when stress levels are high.

[0694] Step 7:

[0695] Users perform tasks through their devices and report their progress to the server. This allows for real-time management of task completion status.

[0696] Step 8:

[0697] After all tasks are completed, the server aggregates the results and saves the work items as templates for reuse in the future. These templates help improve work efficiency.

[0698] (Example 2)

[0699] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0700] In today's work environment, multiple different instructions and tasks arise simultaneously, making it difficult to manage them efficiently. Furthermore, assigning tasks uniformly without considering the emotional state of individual users leads to excessive stress and inefficiency. This invention aims to improve work efficiency and achieve task management that takes into account the emotional state of users.

[0701] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0702] In this invention, the server includes means for receiving multiple instructions, means for analyzing the received instructions using a language analysis method and breaking them down into detailed work elements, and means for assigning the broken-down work elements to personnel or intelligent functions. This enables efficient management of tasks and dynamic task reallocation in accordance with the emotional state of the user.

[0703] "Instructions" refer to information that specifies the concrete actions and conditions necessary to carry out a task.

[0704] "Linguistic analysis techniques" are technologies that use computers to analyze and interpret text written in natural language based on its meaning and structure.

[0705] A "work element" refers to a specific task or action derived from the analyzed instructions.

[0706] "Human resources" refer to the human resources required to perform specific tasks within a company or organization.

[0707] "Intelligent function" refers to a system that has the ability to perform or assist in specific tasks using artificial intelligence.

[0708] "Emotional state" refers to the psychological and mental state or emotions that the user is experiencing at a particular point in time.

[0709] "Dynamic reassignment" is a process that flexibly changes assigned tasks in response to changes in circumstances and conditions.

[0710] This invention is an information processing system that enables efficient business management and task management tailored to the emotional state of the user. This system consists of a server, terminals, and users.

[0711] The server receives instructions and analyzes them using language analysis techniques. Specifically, it uses programming languages ​​such as Python and common language analysis APIs to analyze the received instructions and break them down into detailed work elements. These decomposed work elements are then optimally assigned by the server to human resources or intelligent functions. During this process, a database service is used to efficiently distribute tasks while monitoring resource availability.

[0712] The device is equipped with an emotion engine, which is used to monitor the user's emotional state in real time. Specifically, it uses a software development kit with facial recognition technology and voice analysis tools to detect the user's emotions and send the data to a server.

[0713] If the emotion engine determines that a user is experiencing high stress, the server will adjust task priorities or transfer tasks to AI agents. This allows the user to focus on other tasks without feeling overwhelmed.

[0714] As a concrete example, let's consider a situation where a user has to simultaneously handle "creating sales reports" and "customer analysis tasks." In this case, the emotion engine detects stress, and the server reduces the user's task load by transferring the "creating sales reports" task to an AI agent. As a result, the user can concentrate on the "customer analysis task."

[0715] Examples of prompts include, "Please suggest ways to reduce stress related to customer analysis tasks," and "Please describe how to reallocate tasks based on the user's emotional state."

[0716] In this way, the present invention realizes improved work efficiency and the provision of a comfortable work environment.

[0717] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0718] Step 1:

[0719] The server receives work instructions sent from the administrator. These instructions are obtained via email or a specific work management system. The input is natural language text data, and receiving this data yields the instructions to be analyzed. The output is the text data of the received instructions.

[0720] Step 2:

[0721] The server analyzes the received work instructions using language analysis techniques. Specifically, it uses a natural language processing model to tokenize the instructions and convert them into structured information. The input is the text data of the instructions obtained in step 1. The output is a list of the analyzed work elements.

[0722] Step 3:

[0723] The server reviews the analyzed work elements and assigns them to personnel or intelligent functions. This includes database access to check resource utilization. The input is a list of work elements obtained in step 2. The output is the assignment information for the personnel or system resources to which each work element is assigned.

[0724] Step 4:

[0725] The terminal notifies the user of the work elements assigned to them based on instructions from the server. The terminal utilizes the user interface to quickly display tasks. The input is the assignment information generated in step 3. The output is the task information displayed in the user interface.

[0726] Step 5:

[0727] The device monitors the user's emotional state using an emotion engine. The emotion engine collects data using cameras and microphones, analyzes it, and determines the emotional state. Inputs are sensor data such as facial recognition and voice input. Outputs are the detected user's emotional state information.

[0728] Step 6:

[0729] The server receives emotional state data sent from the terminal and adjusts task assignments and priorities. If a stressed state is detected, the server takes measures such as transferring part of the work to the AI ​​agent. The input is the emotional state information obtained in step 5. The output is the adjusted task assignment information.

[0730] Step 7:

[0731] The server templates completed work elements and saves them to a database. This template is used for similar tasks in the future. This process summarizes the details and workflow of completed tasks. The input is the data of the completed tasks. The output is the templated work element data.

[0732] (Application Example 2)

[0733] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0734] In today's work environment, imbalances in workload and employee emotional stress are contributing factors to decreased productivity. Furthermore, traditional systems have a static approach to work progress management and task assignment, failing to reflect individual circumstances, making efficient operations difficult. To address these issues, a system is needed that can grasp workers' emotional states in real time and dynamically adjust work assignments accordingly.

[0735] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0736] In this invention, the server includes means for receiving multiple commands, means for analyzing the received commands and breaking them down into detailed work elements, and means for monitoring the user's emotional state using emotion recognition technology and dynamically adjusting the assignment of work elements based on that state. This enables work adjustments in response to the user's real-time emotional state.

[0737] A "means for receiving multiple commands" refers to a mechanism for aggregating various commands transmitted from external sources.

[0738] "Means for analyzing received instructions and breaking them down into detailed work elements" refers to a mechanism for analyzing received instructions and breaking them down into smaller, manageable work parts.

[0739] "Means of allocating to human resources or machine learning systems" refers to a mechanism for distributing the broken-down work elements to appropriate personnel or AI systems.

[0740] "Means for monitoring the progress of work elements and adjusting priorities" refers to a function that monitors how work is progressing and changes the order of tasks as needed.

[0741] "Emotion recognition technology" refers to a method of detecting a user's emotional state using various sensors and algorithms.

[0742] "Means for monitoring the user's emotional state and dynamically adjusting the assignment of work elements based on that state" refers to a mechanism for observing the user's current emotions and flexibly changing the workload accordingly.

[0743] "Means of automating completed work elements and saving them as templates for future use" refers to methods for organizing already completed work into a format that can be used repeatedly.

[0744] The system for implementing this invention consists of a server, a terminal, and a user. The server first receives commands from an external source. After receiving the commands, the server analyzes and breaks them down into detailed work elements and appropriately assigns these elements to human resources or a machine learning system. During this process, the server monitors the progress of the work in real time and dynamically adjusts priorities as needed. A device worn by the user, such as smart glasses or a personal digital assistant, monitors the user's emotional state using emotion recognition technology.

[0745] The device analyzes the user's facial expressions and voice, and sends their emotional state to the server. The server dynamically adjusts the assignment of work elements based on the emotional data, ensuring that the workload is appropriately distributed. For example, if the server detects from the emotional data that the user is fatigued, it can automatically assign part of the task to a robot. This allows the user to perform tasks efficiently while reducing their burden. Completed work elements are saved on the server as automated templates and reused for similar tasks in the future. This system is implemented using programming languages ​​such as Python and utilizes hardware sensors such as smart glasses and cameras.

[0746] As a concrete example, in an automobile parts factory, workers perform assembly tasks while simultaneously conducting quality inspections. If smart glasses detect a decrease in the worker's concentration, the server automatically hands over the assembly task to a robot, allowing the worker to focus on quality inspections.

[0747] An example of a prompt for a generative AI model is: "Explain how an emotion recognition system can detect worker fatigue in a factory and assign tasks to robots. Please provide a specific example in a peaceful scenario."

[0748] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0749] Step 1:

[0750] The server receives external commands and analyzes their content. The received commands may be something like "assemble parts," and the server uses natural language processing to break down the commands into specific work elements. In this process, the input is the text data of the command, and the output is a list of individual work elements such as "tighten screws" and "place parts."

[0751] Step 2:

[0752] The device monitors the user's emotional state in real time using an emotion recognition sensor. The user wears smart glasses, and the device measures stress levels and fatigue based on facial expressions and voice tone. Input is the user's biometric data, and output is an emotional state such as "relaxed," "stressed," or "fatigued." This data is transmitted to a server.

[0753] Step 3:

[0754] The server integrates emotional data with previously broken-down work elements to determine task assignments. The input is the user's emotional state and a list of work elements, while the output is the optimal assignment result for each work element. For example, if the user is determined to be "stressed," a portion of the task will be automatically assigned to the robot.

[0755] Step 4:

[0756] The user receives task execution instructions from the terminal. The instructions are based on pre-determined assignments, and the user checks the task progress through a dedicated application. In this process, the input is instruction data from the server, and the output is detailed task information notified to the user.

[0757] Step 5:

[0758] The server saves the completed work element as an automation template, allowing it to be reused for similar tasks in the future. The input is the completed work element, and the output is a new automation template saved in the database.

[0759] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0760] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0761] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0762] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0763] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0764] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0765] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0766] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0767] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0768] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0769] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0770] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0771] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0772] 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.

[0773] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0774] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0775] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0776] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0777] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0778] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0779] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0780] The following is further disclosed regarding the embodiments described above.

[0781] (Claim 1)

[0782] A means for receiving multiple instructions,

[0783] A means of analyzing the received instructions and breaking them down into detailed work items,

[0784] Means for assigning the broken-down work items to human resources or artificial intelligence,

[0785] A means of monitoring the progress of work items and adjusting their priorities,

[0786] A system that includes a means to automate completed work items and save them as templates for future use.

[0787] (Claim 2)

[0788] The system according to claim 1, which analyzes received instructions using natural language processing technology.

[0789] (Claim 3)

[0790] The system according to claim 1, which dynamically adjusts work items and makes new assignments in real time.

[0791] "Example 1"

[0792] (Claim 1)

[0793] Means of receiving information,

[0794] A means of analyzing the received information and breaking it down into detailed tasks,

[0795] Means for assigning the disassembled tasks to personnel or intelligent systems,

[0796] A means of managing the progress of work and adjusting priorities,

[0797] A means to automate completed tasks and save them as templates for future use,

[0798] A system that includes a means for reporting work progress and allowing managers to review it.

[0799] (Claim 2)

[0800] The system according to claim 1, which analyzes received information using natural language processing technology.

[0801] (Claim 3)

[0802] The system according to claim 1, which dynamically adjusts tasks and immediately assigns new tasks.

[0803] "Application Example 1"

[0804] (Claim 1)

[0805] Means for receiving multiple commands,

[0806] A means of analyzing received instructions and breaking them down into detailed work items,

[0807] A means of assigning subdivided work items to human resources or artificial intelligence,

[0808] A means of monitoring the progress of work items and adjusting their priorities,

[0809] A method for automating completed work items and maintaining them as templates for future use,

[0810] A system that includes means for interpreting instructions in the logistics process and for executing the optimal allocation of logistics equipment and personnel.

[0811] (Claim 2)

[0812] The system according to claim 1, which analyzes received commands using natural language processing technology.

[0813] (Claim 3)

[0814] The system according to claim 1, which dynamically adapts work items and implements new assignments in real time.

[0815] "Example 2 of combining an emotion engine"

[0816] (Claim 1)

[0817] A means for receiving multiple instructions,

[0818] A means of analyzing the received instructions using language analysis techniques and breaking them down into detailed work elements,

[0819] Means for assigning the decomposed work elements to personnel or intelligent functions,

[0820] A means of monitoring the progress of work elements and adjusting their priorities,

[0821] A means to automate completed work elements and save them as templates that can be used next time,

[0822] A means for detecting the user's emotional state and dynamically adjusting the assignment of work elements based on that state,

[0823] A means of reducing workload based on emotional state data,

[0824] A system that includes this.

[0825] (Claim 2)

[0826] The system according to claim 1, which analyzes received instructions using a language analysis method and periodically analyzes the user's emotional state.

[0827] (Claim 3)

[0828] The system according to claim 1, which dynamically reassigns work elements and adjusts their priority according to emotional state.

[0829] "Application example 2 when combining with an emotional engine"

[0830] (Claim 1)

[0831] Means for receiving multiple commands,

[0832] A means for analyzing the received command and breaking it down into detailed work elements,

[0833] Means for assigning the decomposed work elements to human resources or machine learning systems,

[0834] A means of monitoring the progress of work elements and adjusting their priorities,

[0835] A means for monitoring the user's emotional state using emotion recognition technology and dynamically adjusting the assignment of work elements based on that state,

[0836] A system that includes means for automating completed work elements and saving them as templates for future use.

[0837] (Claim 2)

[0838] The system according to claim 1, which analyzes received commands using natural language processing technology.

[0839] (Claim 3)

[0840] The system according to claim 1, which dynamically adjusts work elements and makes new assignments in real time. [Explanation of Symbols]

[0841] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means for receiving multiple commands, A means of analyzing received instructions and breaking them down into detailed work items, A means of assigning subdivided work items to human resources or artificial intelligence, A means of monitoring the progress of work items and adjusting their priorities, A method for automating completed work items and maintaining them as templates for future use, A system that includes means for interpreting instructions in the logistics process and for executing the optimal allocation of logistics equipment and personnel.

2. The system according to claim 1, which analyzes received commands using natural language processing technology.

3. The system according to claim 1, which dynamically adapts work items and implements new assignments in real time.

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