system

Abstract

Provide a system. 【Solution means】 Means for automatically generating a project plan, Means for analyzing dependencies between tasks and optimizing schedules, Means for performing real-time risk assessment and displaying alerts to the user, Means for visualizing and updating the progress of the project in real time, Means for automatically generating a progress report and distributing it to the user, Means for collecting machine operation data and automatically generating maintenance tasks, Means for optimally allocating and managing the resources required for each task A system including.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In project management, formulating plans, optimizing schedules, risk management, visualizing progress, and efficiently allocating resources are important. However, when these tasks are carried out manually, they not only take time and effort but may also lead to human errors and delays in decision - making. Also, especially in small and medium - sized enterprises, project teams, and freelancers, there is a problem that it is difficult to effectively execute all of these within limited resources.

Means for Solving the Problems

[0005] This invention solves these problems by providing an automated method for project management. The system of this invention includes means for automatically generating a project plan, analyzing dependencies between tasks, and optimizing the schedule. It also has a function to perform real-time risk assessment and display alerts to the user. Furthermore, it makes it possible to always keep track of progress by visualizing and updating the project progress in real time. By adding a function to automatically generate progress reports and distribute them to the user periodically, the overall management of the project can be made more efficient. This makes it possible to improve the transparency and efficiency of the entire project.

[0006] Project management is a systematic approach to planning, organizing, directing, coordinating, and managing a project to achieve its objectives.

[0007] "Automatically generated" refers to a system creating plans and data without human intervention, based on pre-set conditions and rules.

[0008] "Task dependencies" indicate the order and relationships between tasks.

[0009] "Schedule optimization" is the process of efficiently arranging project tasks and adjusting them to minimize the time it takes to complete them.

[0010] "Risk assessment" is the process of identifying potential risk factors in a project and measuring their impact and probability of occurrence.

[0011] "Displaying alerts" means that the system visually or audibly notifies the user of important information or warnings.

[0012] "Real-time visualization" is a function that instantly displays data and situations that are currently in progress, allowing users to check the situation on the spot.

[0013] A "progress report" is a document or data set that summarizes the progress and results of a project, and is intended to provide information to stakeholders. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This 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] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] 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.

[0018] 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.

[0019] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] 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).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] A project management system, as a specific embodiment of the present invention, is comprised of cooperation between a server, a terminal, and a user. The operation of each component is described below.

[0036] server:

[0037] The server automatically generates project plans. First, based on the basic project information entered by the user, it automatically divides tasks and generates a schedule by analyzing dependencies. It also analyzes available resource information and optimally allocates resources to each task. Furthermore, the server aggregates project progress data, analyzes the risks derived from it, and sends risk alerts to the user. This allows the user to understand the potential risks facing the project in real time.

[0038] Terminal:

[0039] The terminal provides a user-facing interface. When a user inputs task information and progress, the terminal immediately sends it to the server, displaying a real-time updated visualization of the project status. For example, if task progress changes, the terminal provides a Gantt chart or dashboard showing the updated progress. The terminal also receives and displays risk alerts and periodic progress reports from the server, making it easy for users to understand the current situation.

[0040] User:

[0041] Users input project information and manage progress through the system. At the start of a project, they input basic information such as the project name, purpose, start date, and end date into the terminal. Progress updates and resource adjustments are also done via the terminal, and the latest status is processed by the server. Users make decisions as needed based on the progress status and risk information displayed on the terminal.

[0042] For example, when a user attempts to schedule a new project, they can view the details of each task on their terminal based on the automatically generated plan suggested by the server, and make adjustments as needed. The server monitors progress in real time and continuously performs risk assessments, allowing the user to respond quickly to unintended delays or risks. By implementing this invention, project management efficiency is improved, and limited resources can be utilized to their fullest potential.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The user enters basic project information into the terminal. This includes the project name, purpose, start date, end date, and key milestones.

[0046] Step 2:

[0047] The terminal sends the entered information to the server. The server analyzes the received information and starts automatically generating tasks based on the project's goals.

[0048] Step 3:

[0049] As a result of task generation, the server divides the project into multiple phases and tasks. During this process, it considers the dependencies between tasks and creates an initial schedule proposal.

[0050] Step 4:

[0051] The server calculates the optimal resource allocation for each task based on available resource information. The results are then sent to the terminal for user review.

[0052] Step 5:

[0053] The terminal displays the schedule and resource allocation sent from the server in a visual format. The user reviews this and makes adjustments as needed.

[0054] Step 6:

[0055] When a user makes adjustments, the terminal resends the change information to the server. The server then receives this information and updates the project plan based on the latest information.

[0056] Step 7:

[0057] The server continuously monitors project progress and aggregates and analyzes progress data in real time. If a risk is detected, it immediately generates a risk alert.

[0058] Step 8:

[0059] The terminal displays risk alerts sent from the server to the user, enabling the user to respond quickly to problems.

[0060] Step 9:

[0061] The server generates periodic reports based on progress data and delivers them to users via their terminals. This allows users to understand the overall status of the project.

[0062] Step 10:

[0063] Upon project completion or milestone, users input feedback into their terminals. The server collects and analyzes this feedback to help improve future project planning.

[0064] (Example 1)

[0065] 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."

[0066] Modern project management requires efficiently managing numerous tasks and optimizing limited resources. However, as projects grow larger, manual planning, progress tracking, and risk assessment tend to become time-consuming, labor-intensive, and inefficient. Therefore, automating project planning and managing progress and risks in real time are essential.

[0067] 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.

[0068] In this invention, the server includes means for automatically generating a project plan based on an AI model, means for dividing tasks based on basic project information input by the user, analyzing dependencies, and creating a schedule, and means for analyzing the user's resource information and optimizing resource allocation for the entire project. This improves the efficiency of project management, enables real-time progress monitoring, and allows for appropriate risk management.

[0069] A "project plan" is an overall blueprint that outlines the tasks, resources, and schedule necessary to achieve the project's objectives.

[0070] A "generative AI model" is an algorithm or computer program that uses artificial intelligence technology to analyze data and automatically generate plans and tasks based on project-related information.

[0071] "Task splitting" is the process of breaking down an entire project into smaller, manageable work units and defining each task in detail.

[0072] "Dependency" refers to the relationship that indicates the execution order and interrelationships of each task, and describes the conditions under which a particular task depends on other tasks.

[0073] "Creating a schedule" is the act of planning the work to be done over a period of time from the start date to the end date of a project, based on tasks and resource allocation.

[0074] "Optimal resource allocation" is the process of allocating available resources, such as personnel, budget, and equipment, to each task in the most effective way in order to efficiently advance project work.

[0075] "Receiving progress updates in real time" means instantly obtaining project progress data and continuously staying informed of the latest status.

[0076] "Risk assessment" is the process of predicting potential problems and obstacles that may arise during the course of a project and analyzing their impact.

[0077] "Information for decision-making" refers to the information necessary for project managers and stakeholders to make optimal decisions, and includes data such as progress, risks, and resource status.

[0078] In an embodiment of this invention, the project management system is primarily composed of cooperation between a server, a terminal, and a user. This system enables the automatic generation of project plans using a generative AI model, real-time management of progress, risk assessment, and optimal allocation of resources.

[0079] The server automatically generates a project plan based on basic project information entered by the user, using a generative AI model. The hardware and software used in this process are expected to include cloud computing services and AI platforms, which will be used for task division and dependency analysis. Furthermore, the server aggregates project progress data, assesses risks using data analysis tools, and generates real-time risk alerts.

[0080] The terminal provides an intuitive interface for users to manage projects. When users input task information and progress via the terminal, it is sent to the server, and the latest project information is updated instantly. For example, the terminal uses visualization tools to provide users with Gantt charts and dashboards that show progress.

[0081] Users input basic project information and resource details into a terminal and manage progress through the system. Users make project decisions based on risk alerts and progress reports displayed on the terminal. For example, when a user tries to schedule a new project, they review the details of each task on the terminal based on the automatically generated plan suggested by the server and make adjustments as needed.

[0082] As a concrete example, when a user inputs a prompt into the AI ​​model saying, "I want you to automatically generate a plan for a new marketing campaign and set up risk alerts," the server automatically divides the task, proposes a plan, and sets up a risk management system. In this way, project management efficiency is improved, and limited resources can be used to their fullest potential.

[0083] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0084] Step 1:

[0085] The user enters basic project information into the terminal. The user enters basic data such as project name, purpose, start date, and end date, and uses this information to perform initial setup. This input data is then transferred to the server as foundational information for use in subsequent processing steps.

[0086] Step 2:

[0087] The server generates a project plan based on the basic information received from the user. Specifically, it utilizes a generation AI model to automatically divide tasks using prompt messages. The server creates a project schedule, analyzes dependencies, and determines the order of each task. In this process, it creates output data (a list of planned project tasks and schedule) from input data (basic project information).

[0088] Step 3:

[0089] The server analyzes the resource information required for the project. The server analyzes the resource information entered by the user on the terminal and performs data calculations to determine the optimal resource allocation. This generates output data for efficiently allocating the necessary personnel, equipment, budget, etc., for each task.

[0090] Step 4:

[0091] Users input progress information via their terminals. Users update task completion status and progress in real time and send this information to the server. Based on this input data, the server checks the current progress of the project.

[0092] Step 5:

[0093] The terminal visually displays the latest project information received from the server. Using visualization tools, it provides Gantt charts and dashboards showing the progress of ongoing projects. This output data serves as visual information that allows users to easily understand the status of projects.

[0094] Step 6:

[0095] The server analyzes progress data in real time and assesses risks. It performs data calculations to predict potential problems that may arise during project progress and generates risk alerts. These alerts are provided to the user as output data to aid in user decision-making.

[0096] Step 7:

[0097] Based on risk alerts presented from the terminal, the user makes necessary decisions. The user optimizes project progress by rescheduling tasks and reallocating resources as needed. In this step, the user generates new input data (decisions and adjustments) based on risk information output from the server.

[0098] (Application Example 1)

[0099] 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."

[0100] The present invention aims to improve overall production efficiency by preventing project delays and unexpected equipment failures through efficient resource allocation and progress management in project management, as well as optimizing machine maintenance schedules.

[0101] 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.

[0102] In this invention, the server includes means for automatically generating a project plan, means for analyzing dependencies between tasks and optimizing the schedule, means for performing real-time risk assessments and displaying alerts to the user, means for collecting machine operation data and automatically generating maintenance tasks, and means for optimally allocating and managing the resources required for each task. This makes it possible to integrate project management and machine maintenance.

[0103] "Means for automatically generating project plans" refer to devices or software that, based on basic information entered by the user, divide project tasks, analyze dependencies, and create a schedule.

[0104] "Means for analyzing dependencies between tasks and optimizing schedules" refer to devices or software that analyze how each task within a project relates to other tasks and enable efficient scheduling.

[0105] "A means of performing real-time risk assessment and displaying alerts to users" refers to devices or software that constantly monitor the status of ongoing projects or machinery and issue warnings when potential problems or risks are detected.

[0106] "Means for visualizing and updating project progress in real time" refers to devices or software that visually display the current progress and status of a project and keep the information constantly updated to the latest version.

[0107] "Means for automatically generating and distributing progress reports to users" refers to devices or software that collect project progress data and provide it to users as a report.

[0108] "Means for collecting machine operation data and automatically generating maintenance tasks" refers to devices or software that collect operational data from machines in a factory or facility and automatically plan maintenance tasks based on that data.

[0109] "Means for optimally allocating and managing the resources required for each task" refers to devices and software that efficiently allocate and monitor resources such as personnel and materials required for projects and maintenance tasks.

[0110] The server provides the central functionality of this invention. Based on the basic project information entered by the user using a terminal, the server automatically divides each task and generates a schedule by analyzing their dependencies. Furthermore, the server constantly evaluates the progress of the project and machinery using real-time streaming data and warns the user of potential risks.

[0111] The server also collects and analyzes machine operation data for preventative maintenance. The collected data is then used with AI to automatically generate maintenance tasks and optimally allocate the necessary resources. This improves the operational efficiency and safety of the machines. In particular, the system utilizes AWS Lambda on the AWS® cloud platform, providing a scalable and reliable environment through a web application for data processing and notifications. This program, written in Python and Flask, uses Plotly Dash for visualization.

[0112] The terminal functions as an interface for direct user interaction. This interface visualizes the progress, allowing users to make decisions based on that visualization. For example, if a machine failure is predicted, an alert is displayed on the terminal, and a notification is immediately sent to the maintenance personnel. The data received by the terminal is immediately sent to the server, and the status is updated in real time.

[0113] A concrete example is a system that monitors the operation of multiple robots installed in a factory. This system automatically checks the inventory status of parts and schedules replacements when a robot's parts are nearing the end of their lifespan. This prevents robot downtime and enables efficient operations.

[0114] An example of a prompt that utilizes a generative AI model is: "Design a system to proactively manage the maintenance of factory robots. Explain how to automatically generate maintenance tasks based on operational data and how to optimally allocate resources."

[0115] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0116] Step 1:

[0117] The server receives basic project information entered by the user via their terminal. This input data includes the project name, duration, budget, and required deliverables. Based on this data, the server uses a generative AI model to automatically divide tasks and establish dependencies between them. As a result, an initial project schedule is generated.

[0118] Step 2:

[0119] The server collects real-time operational data from the machine and stores it in a database. This data includes uptime, performance metrics, and error codes. Based on the collected data, analysis is performed to detect machine anomalies and generate maintenance tasks. As output, a task list prioritized by a maintenance prediction model is generated.

[0120] Step 3:

[0121] The terminal visualizes task lists and project progress information sent from the server and provides it to the user. Based on this information, the user can instantly check the progress and risk assessment. The visualization is provided in the form of Gantt charts and dashboards, allowing for an intuitive understanding of the current situation.

[0122] Step 4:

[0123] Users update progress via their devices, adding new information and resources. Information from the devices is immediately sent to the server, where it is integrated with the existing database and the data is updated. The updated data triggers risk alerts and resource reallocations, enabling dynamic project management.

[0124] Step 5:

[0125] The server optimizes scheduling and resource allocation, and generates progress reports as needed. These reports include information on the progress of tasks, which resources are in excess, and which are insufficient. These reports are automatically sent to the user to support project management decision-making.

[0126] 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.

[0127] To implement the present invention, we will describe a configuration that provides project management that takes user emotions into consideration by incorporating an emotion engine into the project management system. The operation of each component will be specifically described below.

[0128] server:

[0129] The server is equipped with an emotion engine that analyzes user input and communication data to detect the user's emotional state. Based on this information, it proposes appropriate communication tailored to the project's progress. For example, if a user is experiencing stress, the server suggests reviewing task priorities or redistributing tasks among team members. The server also uses emotional data to adjust risk assessments in the current project environment in real time.

[0130] Terminal:

[0131] The device provides an interface for receiving emotional input from the user. It features pull-down menus and emotional icons to allow users to easily record their emotional state. The device sends this emotional data to a server, where an emotional engine analyzes it and displays appropriate improvement suggestions to the user. For example, if it determines that project members' morale is low, it might suggest team-building activities to promote unity.

[0132] User:

[0133] Users record their emotions that may influence project progress and receive feedback from the system. They then review the task management improvements recommended by the emotion engine and share them with team members as needed to streamline the project.

[0134] For example, if a user is feeling pressured by a particular task, the emotion engine analyzes that emotional data and suggests adding resources or adjusting the schedule to alleviate the pressure. This allows the project management system to perform comprehensive management that considers not only technical elements but also human emotions, thereby improving the success rate of projects.

[0135] The following describes the processing flow.

[0136] Step 1:

[0137] The user inputs their emotional state into the device. The device displays an interface for selecting an emotion, and the user records their current emotion using an emotion icon or text.

[0138] Step 2:

[0139] The device sends emotion data entered by the user to the server. The transmitted data includes details of the emotion and information about related projects and tasks.

[0140] Step 3:

[0141] The server analyzes the received emotional data using an emotion engine. Based on the user's emotional state, it evaluates the risks and impacts on project progress and performance.

[0142] Step 4:

[0143] Based on the sentiment analysis results, the server generates risk alerts and proposes task prioritization adjustments. If necessary, it also considers reallocating resources and relaxing schedules.

[0144] Step 5:

[0145] The terminal notifies the user of suggestions from the server. The user reviews the presented information and considers and implements specific improvement measures.

[0146] Step 6:

[0147] If a user decides to implement a recommended improvement, they can use a feature to share that information with project members from their device. This information may include a revised task schedule and recommended team activities.

[0148] Step 7:

[0149] The server continues to monitor project progress and user sentiment data. It tracks progress and changes in sentiment at each project phase and suggests further interventions as needed.

[0150] Step 8:

[0151] This process is repeated at key milestones in the project. Based on user feedback and new sentiment data, project management strategies can be flexibly adjusted.

[0152] (Example 2)

[0153] 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".

[0154] In project management, not only technical factors but also the emotional state of individual users involved in the project can significantly impact its success. However, conventional project management systems do not adequately address user emotions. Therefore, there is a risk that emotional stress and anxiety experienced by users could negatively affect project progress. A mechanism to address this is needed.

[0155] 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.

[0156] In this invention, the server includes means for analyzing the user's emotional state and proposing improvements to project management based on the results, means for proposing changes in task priorities and the addition of resources, and means for analyzing feedback and presenting improvements that take into account emotional factors in project management. This makes it possible to manage projects while taking the user's emotions into consideration.

[0157] "Project planning" is the process of systematically organizing the tasks, resources, and schedule necessary to achieve the project's objectives.

[0158] "Task dependencies" refer to relationships where the completion of one task influences the start or progress of other tasks.

[0159] "Optimizing a schedule" refers to the process of adjusting the start and end times of each task to the most optimal form in order to ensure the efficient progress of a project.

[0160] "Analyzing a user's emotional state" refers to the process of analyzing and understanding a user's psychological state based on emotional data entered by the user.

[0161] "Proposing improvements to project management" means analyzing the current situation and presenting concrete action plans in order to improve the progress and results of a project.

[0162] "Real-time risk assessment" refers to the process of continuously reviewing and evaluating potential problems and obstacles in accordance with the progress of the project.

[0163] "Displaying alerts to users" refers to the act of providing users with real-time notifications to inform them of important points or dangers.

[0164] "Visualizing and updating project progress" refers to the process of clearly displaying the current progress of a project and ensuring that the latest information is always reflected.

[0165] "Generating and distributing progress reports to users" refers to the act of creating a report summarizing information about the progress of a project and delivering it to users.

[0166] "Analyzing feedback" refers to the process of analyzing opinions and information received from users and stakeholders, understanding their content, and using that information to inform future strategies.

[0167] This invention provides a project management system that integrates an emotion engine. The system consists of a server, terminals, and users, and each element works in cooperation with the others.

[0168] The server runs an analysis system equipped with an emotion engine, analyzing user input and communication history using data analysis algorithms such as natural language processing tools and machine learning algorithms. This identifies the user's emotional state and generates improvement strategies for project management as needed. These improvement strategies range from re-evaluating task priorities and reallocating resources to proposing team-building activities. The server manages emotion data and project data using an SQL database.

[0169] The device accepts emotional input through its user interface and enables intuitive operation using pull-down menus and emotional icon functions. The collected emotional data is transmitted to the server in real time, and the analysis results and improvement suggestions from the server are notified on the display device. The device can be deployed as a browser application or mobile application and uses web technologies such as HTML5 and JavaScript (registered trademark).

[0170] Users input and record their emotional data through their devices and consider improvement measures suggested by the server. For example, if a user inputs emotional data indicating they are feeling "pressure towards Task A," the server analyzes this data and suggests rescheduling the task or adding resources. The suggested changes are also notified to the user via push notification and displayed on the project management interface.

[0171] A concrete example of a prompt would be, "When a user feels anxious about a meeting, how does the server identify the cause and what solutions does it offer?" Inputting such prompts into an AI model can sometimes yield further suggestions for improvement.

[0172] This system achieves more effective project management by actively incorporating not only technical elements but also user emotions.

[0173] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0174] Step 1:

[0175] The device receives emotional data from the user as input. The user selects their emotional state using pull-down menus or emotional icon functions. This input data is comprised of an interface that enables accurate recording of the user's emotions. Specifically, the type and intensity of the selected emotion, as well as any additional comments, are recorded as data.

[0176] Step 2:

[0177] The device encrypts the collected emotional data and sends it to the server, ensuring the security of the emotional data. The transmitted data is received by the server and processed as input data for analysis. During this process, pre-processing is performed, such as standardizing the data format and removing redundant data.

[0178] Step 3:

[0179] The server analyzes input data using an emotion engine. It employs natural language processing algorithms and machine learning models to identify emotional states as numerical data, and uses this to understand the user's psychological tendencies. The generative AI model is controlled by prompts, ensuring that appropriate analysis results are obtained. The analysis results include classifications of emotional states and related topics.

[0180] Step 4:

[0181] The server generates improvement proposals for project management based on the analysis results. These proposals include suggestions for prioritizing tasks, reviewing responsibilities, and reallocating resources. These proposals are generated using integrated information on sentiment data and project progress and are output as improvement proposals.

[0182] Step 5:

[0183] The terminal displays improvement suggestions received from the server in its user interface. The user reviews these suggestions and, if necessary, accepts them and incorporates them into their task plan. The terminal is designed to display the improvement suggestions in charts and lists for intuitive understanding. Buttons and links are also provided for selecting specific actions.

[0184] (Application Example 2)

[0185] 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".

[0186] In modern manufacturing, human emotions can influence work efficiency and safety. However, conventional automation systems are unable to adequately address worker emotions, and therefore do not provide effective support for human motivation and stress management. For this reason, there is a need to provide an efficient work environment that takes worker emotions into consideration.

[0187] 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.

[0188] In this invention, the server includes means for automatically generating a project plan, means for detecting the user's emotional state and suggesting work optimizations, and means for adjusting the robot's actions based on emotional data. This makes it possible to provide an efficient and flexible work environment based on the worker's emotions.

[0189] 1. "Means for automatically generating project plans" refers to a function that has an algorithm that efficiently and automatically allocates the work timeline and necessary resources by inputting project requirements.

[0190] 2. "Methods for analyzing dependencies between tasks and optimizing the schedule" refers to the process of analyzing the order, priority, and interrelationships of individual tasks to ensure optimal work progress.

[0191] 3. "A means of performing real-time risk assessment and displaying alerts to users" refers to a system that assesses the current work status and potential problems in real time and notifies users of appropriate warnings and improvement suggestions.

[0192] 4. "Means for visualizing and updating project progress in real time" refers to technologies that allow users to check the progress of work in real time and display the latest information on the user interface.

[0193] 5. "Means for automatically generating and distributing progress reports to users" refers to a method of organizing progress information based on collected data and efficiently providing reports to users.

[0194] 6. "Means for detecting the user's emotional state and proposing work optimization" refers to technology that senses the user's psychological state and presents work improvement suggestions aimed at reducing stress and motivating them.

[0195] 7. "Means for adjusting robot operation based on emotional data" refers to a mechanism that adjusts and optimizes the operation of automated work equipment according to collected emotional information.

[0196] 8. "Means for efficient work distribution according to the work environment" refers to a system that optimally allocates work resources according to the current operating status and individual workloads, thereby increasing productivity.

[0197] This invention is a system that detects the emotional state of workers in a factory environment in real time and optimizes work processes based on that data.

[0198] First, the smart glasses, acting as the device, are equipped with a facial recognition camera and a pulse rate measurement function. This hardware allows factory workers to efficiently detect changes in their emotions while they are working. The emotional data is transmitted from the device to a server via Wi-Fi.

[0199] On the server, analysis software equipped with an emotion engine is running. Emotional data is input into a machine learning model using TENSORFLOW® and analyzed in real time. The analysis results are fed back to supervisors and automated work machines, enabling adjustments to work speed and task reallocation.

[0200] The user, i.e., the factory supervisor, receives emotion-based improvement suggestions from this system. The user can then adjust the work environment according to these suggestions, aiming to improve work efficiency and reduce the mental burden on workers.

[0201] For example, if a worker's stress level exceeds a certain level, the server can transmit this information to the robot, which can then take measures to reduce its work speed by 20%. In this way, flexible work processing can be performed in response to the environment.

[0202] Examples of prompts for a generative AI model are as follows:

[0203] "Design a system that utilizes worker emotional data in a factory environment to optimize the operation of production robots. How can this system improve work efficiency and reduce worker stress?"

[0204] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0205] Step 1:

[0206] The smart glasses, acting as the terminal, detect the worker's facial expressions and pulse rate in real time and acquire them as emotion data. Inputs are video data from a facial recognition camera and pulse rate measurement data, while output is emotion data for analysis. Specifically, the facial expression data is analyzed and classified using a video processing algorithm.

[0207] Step 2:

[0208] The device transmits the acquired emotion data to the server via Wi-Fi. The input is the emotion data obtained in step 1, and the output is the data transmitted to the server. Specifically, data encryption is performed to securely establish data communication to the server.

[0209] Step 3:

[0210] The server drives an emotion engine and analyzes the received emotion data. The input is emotion data sent from the terminal, and the output is evaluation data of the emotional state as a result of the analysis. Specifically, it uses TensorFlow to input data into a machine learning model and estimate the emotional state.

[0211] Step 4:

[0212] The server proposes optimized movements for the production robot based on the analyzed emotional state. The input is the emotional state evaluation data obtained in step 3, and the output is the instruction for movement optimization. Specifically, it activates an algorithm that adjusts the robot's work speed and work distribution according to the emotional evaluation.

[0213] Step 5:

[0214] The factory supervisor, acting as the user, adjusts the work environment based on suggestions received from the server. The input is operation optimization instructions from the server, and the output is the adjusted work environment. Specifically, the supervisor uses the robot's control panel to set speed and task allocation.

[0215] Step 6:

[0216] The server waits for new emotion input and repeats the process from step 1. The input is the latest emotion data, and the output is the updated robot behavior parameters. Specifically, it maintains a continuous real-time data processing cycle.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] [Second Embodiment]

[0221] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0222] 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.

[0223] 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).

[0224] 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.

[0225] 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.

[0226] 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).

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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".

[0233] A project management system, as a specific embodiment of the present invention, is comprised of cooperation between a server, a terminal, and a user. The operation of each component is described below.

[0234] server:

[0235] The server automatically generates project plans. First, based on the basic project information entered by the user, it automatically divides tasks and generates a schedule by analyzing dependencies. It also analyzes available resource information and optimally allocates resources to each task. Furthermore, the server aggregates project progress data, analyzes the risks derived from it, and sends risk alerts to the user. This allows the user to understand the potential risks facing the project in real time.

[0236] Terminal:

[0237] The terminal provides a user-facing interface. When a user inputs task information and progress, the terminal immediately sends it to the server, displaying a real-time updated visualization of the project status. For example, if task progress changes, the terminal provides a Gantt chart or dashboard showing the updated progress. The terminal also receives and displays risk alerts and periodic progress reports from the server, making it easy for users to understand the current situation.

[0238] User:

[0239] Users input project information and manage progress through the system. At the start of a project, they input basic information such as the project name, purpose, start date, and end date into the terminal. Progress updates and resource adjustments are also done via the terminal, and the latest status is processed by the server. Users make decisions as needed based on the progress status and risk information displayed on the terminal.

[0240] For example, when a user attempts to schedule a new project, they can view the details of each task on their terminal based on the automatically generated plan suggested by the server, and make adjustments as needed. The server monitors progress in real time and continuously performs risk assessments, allowing the user to respond quickly to unintended delays or risks. By implementing this invention, project management efficiency is improved, and limited resources can be utilized to their fullest potential.

[0241] The following describes the processing flow.

[0242] Step 1:

[0243] The user enters basic project information into the terminal. This includes the project name, purpose, start date, end date, and key milestones.

[0244] Step 2:

[0245] The terminal sends the entered information to the server. The server analyzes the received information and starts automatically generating tasks based on the project's goals.

[0246] Step 3:

[0247] As a result of task generation, the server divides the project into multiple phases and tasks. During this process, it considers the dependencies between tasks and creates an initial schedule proposal.

[0248] Step 4:

[0249] The server calculates the optimal resource allocation for each task based on available resource information. The results are then sent to the terminal for user review.

[0250] Step 5:

[0251] The terminal displays the schedule and resource allocation sent from the server in a visual format. The user reviews this and makes adjustments as needed.

[0252] Step 6:

[0253] When a user makes adjustments, the terminal resends the change information to the server. The server then receives this information and updates the project plan based on the latest information.

[0254] Step 7:

[0255] The server continuously monitors project progress and aggregates and analyzes progress data in real time. If a risk is detected, it immediately generates a risk alert.

[0256] Step 8:

[0257] The terminal displays risk alerts sent from the server to the user, enabling the user to respond quickly to problems.

[0258] Step 9:

[0259] The server generates periodic reports based on progress data and delivers them to users via their terminals. This allows users to understand the overall status of the project.

[0260] Step 10:

[0261] Upon project completion or milestone, users input feedback into their terminals. The server collects and analyzes this feedback to help improve future project planning.

[0262] (Example 1)

[0263] 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."

[0264] Modern project management requires efficiently managing numerous tasks and optimizing limited resources. However, as projects grow larger, manual planning, progress tracking, and risk assessment tend to become time-consuming, labor-intensive, and inefficient. Therefore, automating project planning and managing progress and risks in real time are essential.

[0265] 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.

[0266] In this invention, the server includes means for automatically generating a project plan based on an AI model, means for dividing tasks based on basic project information input by the user, analyzing dependencies, and creating a schedule, and means for analyzing the user's resource information and optimizing resource allocation for the entire project. This improves the efficiency of project management, enables real-time progress monitoring, and allows for appropriate risk management.

[0267] A "project plan" is an overall blueprint that outlines the tasks, resources, and schedule necessary to achieve the project's objectives.

[0268] A "generative AI model" is an algorithm or computer program that uses artificial intelligence technology to analyze data and automatically generate plans and tasks based on project-related information.

[0269] "Task splitting" is the process of breaking down an entire project into smaller, manageable work units and defining each task in detail.

[0270] "Dependency" refers to the relationship that indicates the execution order and interrelationships of each task, and describes the conditions under which a particular task depends on other tasks.

[0271] "Creating a schedule" is the act of planning the work to be done over a period of time from the start date to the end date of a project, based on tasks and resource allocation.

[0272] "Optimal resource allocation" is the process of allocating available resources, such as personnel, budget, and equipment, to each task in the most effective way in order to efficiently advance project work.

[0273] "Receiving progress updates in real time" means instantly obtaining project progress data and continuously staying informed of the latest status.

[0274] "Risk assessment" is the process of predicting potential problems and obstacles that may arise during the course of a project and analyzing their impact.

[0275] "Information for decision-making" refers to the information necessary for project managers and stakeholders to make optimal decisions, and includes data such as progress, risks, and resource status.

[0276] In an embodiment of this invention, the project management system is primarily composed of cooperation between a server, a terminal, and a user. This system enables the automatic generation of project plans using a generative AI model, real-time management of progress, risk assessment, and optimal allocation of resources.

[0277] The server automatically generates a project plan based on basic project information entered by the user, using a generative AI model. The hardware and software used in this process are expected to include cloud computing services and AI platforms, which will be used for task division and dependency analysis. Furthermore, the server aggregates project progress data, assesses risks using data analysis tools, and generates real-time risk alerts.

[0278] The terminal provides an intuitive interface for users to manage projects. When a user inputs task information and progress status via the terminal, it is sent to the server, and the latest project information is immediately updated. For example, the terminal uses visualization tools to provide users with Gantt charts and dashboards showing the progress status.

[0279] Users input basic project information and resource information into the terminal and manage the progress through the system. Users make decisions in the project based on the risk alerts and progress reports presented on the terminal. For example, when a user tries to schedule a new project, based on the automatically generated plan proposed by the server, the user checks the details of each task on the terminal and makes adjustments if necessary.

[0280] As a specific example, when a user inputs a prompt sentence such as "Automatically generate a plan for a new marketing campaign and set risk alerts." into the generative AI model, the server automatically divides the tasks, proposes a plan, and arranges the risk management system. In this way, the efficiency of project management is improved, and it is possible to make the most of limited resources.

[0281] The flow of the specific process in Example 1 will be described using FIG. 11.

[0282] Step 1:

[0283] The user inputs the basic information of the project into the terminal. The user inputs basic data such as the project name, purpose, start date, and end date, and makes initial settings based on this. This input data is transferred to the server as basic information for use in subsequent processing steps.

[0284] Step 2:

[0285] The server generates a project plan based on the basic information received from the user. Specifically, it utilizes a generation AI model and automatically divides tasks using prompt sentences. The server creates a project schedule, analyzes dependencies, and determines the order of each task. At this time, it creates output data (a list of tasks and schedule for the planned project) from the input data (basic project information).

[0286] Step 3:

[0287] The server analyzes the resource information required for the project. The server analyzes the resource information input by the user on the terminal and performs data operations to calculate the optimal resource allocation. Thereby, it generates output data for efficiently allocating the personnel, equipment, budget, etc. required for each task.

[0288] Step 4:

[0289] The user inputs progress information via the terminal. The user updates the completion status and progress details of the tasks in real-time and sends that information to the server. Based on this input data, the server checks the current progress of the project.

[0290] Step 5:

[0291] The terminal visually displays the latest project information received from the server. Using a visualization tool, it provides a Gantt chart or dashboard showing the progress of the ongoing project. This output data becomes visual information to enable the user to easily grasp the status of the project.

[0292] Step 6:

[0293] The server analyzes progress data in real time and assesses risks. It performs data calculations to predict potential problems that may arise during project progress and generates risk alerts. These alerts are provided to the user as output data to aid in user decision-making.

[0294] Step 7:

[0295] Based on risk alerts presented from the terminal, the user makes necessary decisions. The user optimizes project progress by rescheduling tasks and reallocating resources as needed. In this step, the user generates new input data (decisions and adjustments) based on risk information output from the server.

[0296] (Application Example 1)

[0297] 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."

[0298] The present invention aims to improve overall production efficiency by preventing project delays and unexpected equipment failures through efficient resource allocation and progress management in project management, as well as optimizing machine maintenance schedules.

[0299] 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.

[0300] In this invention, the server includes means for automatically generating a project plan, means for analyzing dependencies between tasks and optimizing the schedule, means for performing real-time risk assessments and displaying alerts to the user, means for collecting machine operation data and automatically generating maintenance tasks, and means for optimally allocating and managing the resources required for each task. This makes it possible to integrate project management and machine maintenance.

[0301] "Means for automatically generating project plans" refer to devices or software that, based on basic information entered by the user, divide project tasks, analyze dependencies, and create a schedule.

[0302] "Means for analyzing dependencies between tasks and optimizing schedules" refer to devices or software that analyze how each task within a project relates to other tasks and enable efficient scheduling.

[0303] "A means of performing real-time risk assessment and displaying alerts to users" refers to devices or software that constantly monitor the status of ongoing projects or machinery and issue warnings when potential problems or risks are detected.

[0304] "Means for visualizing and updating project progress in real time" refers to devices or software that visually display the current progress and status of a project and keep the information constantly updated to the latest version.

[0305] "Means for automatically generating and distributing progress reports to users" refers to devices or software that collect project progress data and provide it to users as a report.

[0306] The means for "collecting operation data of machines and automatically generating maintenance tasks" refers to devices or software that collect operation data from machines in factories or facilities and automatically plan tasks that require maintenance based on that data.

[0307] The means for "optimally allocating and managing resources required for each task" refers to devices or software that efficiently distribute and monitor resources such as human resources and materials required for projects or maintenance tasks.

[0308] The server provides functions that play a central role in the present invention. The server has a function of automatically dividing each task and generating a schedule by analyzing their dependency relationships based on the basic information of the project input by the user using a terminal. Furthermore, the server constantly evaluates the progress of the project and machines using real-time streaming data and issues warnings to the user about potential risks.

[0309] In addition, the server collects and analyzes the operation data of machines for preventive maintenance. The collected data utilizes AI to automatically generate maintenance tasks and optimally allocate the resources required for them. In this way, the operation efficiency and safety of the machines are improved. In particular, by using AWS Lambda on the AWS cloud platform and performing data processing and notifications through a web application, a scalable and highly reliable environment is provided. This program composed of Python and Flask performs visualization using Plotly Dash.

[0310] The terminal functions as an interface for the user to directly operate. In this interface, the progress is visualized, and the user makes judgments based on it. For example, when a machine failure is predicted, an alert is displayed on the terminal and immediately sent as a notification to the maintenance staff. The data received by the terminal is immediately sent to the server, and the situation is updated in real time.

[0311] A concrete example is a system that monitors the operation of multiple robots installed in a factory. This system automatically checks the inventory status of parts and schedules replacements when a robot's parts are nearing the end of their lifespan. This prevents robot downtime and enables efficient operations.

[0312] An example of a prompt that utilizes a generative AI model is: "Design a system to proactively manage the maintenance of factory robots. Explain how to automatically generate maintenance tasks based on operational data and how to optimally allocate resources."

[0313] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0314] Step 1:

[0315] The server receives basic project information entered by the user via their terminal. This input data includes the project name, duration, budget, and required deliverables. Based on this data, the server uses a generative AI model to automatically divide tasks and establish dependencies between them. As a result, an initial project schedule is generated.

[0316] Step 2:

[0317] The server collects real-time operational data from the machine and stores it in a database. This data includes uptime, performance metrics, and error codes. Based on the collected data, analysis is performed to detect machine anomalies and generate maintenance tasks. As output, a task list prioritized by a maintenance prediction model is generated.

[0318] Step 3:

[0319] The terminal visualizes task lists and project progress information sent from the server and provides it to the user. Based on this information, the user can instantly check the progress and risk assessment. The visualization is provided in the form of Gantt charts and dashboards, allowing for an intuitive understanding of the current situation.

[0320] Step 4:

[0321] Users update progress via their devices, adding new information and resources. Information from the devices is immediately sent to the server, where it is integrated with the existing database and the data is updated. The updated data triggers risk alerts and resource reallocations, enabling dynamic project management.

[0322] Step 5:

[0323] The server optimizes scheduling and resource allocation, and generates progress reports as needed. These reports include information on the progress of tasks, which resources are in excess, and which are insufficient. These reports are automatically sent to the user to support project management decision-making.

[0324] 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.

[0325] To implement the present invention, we will describe a configuration that provides project management that takes user emotions into consideration by incorporating an emotion engine into the project management system. The operation of each component will be specifically described below.

[0326] server:

[0327] The server is equipped with an emotion engine that analyzes user input and communication data to detect the user's emotional state. Based on this information, it proposes appropriate communication tailored to the project's progress. For example, if a user is experiencing stress, the server suggests reviewing task priorities or redistributing tasks among team members. The server also uses emotional data to adjust risk assessments in the current project environment in real time.

[0328] Terminal:

[0329] The device provides an interface for receiving emotional input from the user. It features pull-down menus and emotional icons to allow users to easily record their emotional state. The device sends this emotional data to a server, where an emotional engine analyzes it and displays appropriate improvement suggestions to the user. For example, if it determines that project members' morale is low, it might suggest team-building activities to promote unity.

[0330] User:

[0331] Users record their emotions that may influence project progress and receive feedback from the system. They then review the task management improvements recommended by the emotion engine and share them with team members as needed to streamline the project.

[0332] For example, if a user is feeling pressured by a particular task, the emotion engine analyzes that emotional data and suggests adding resources or adjusting the schedule to alleviate the pressure. This allows the project management system to perform comprehensive management that considers not only technical elements but also human emotions, thereby improving the success rate of projects.

[0333] The following describes the processing flow.

[0334] Step 1:

[0335] The user inputs their emotional state into the device. The device displays an interface for selecting an emotion, and the user records their current emotion using an emotion icon or text.

[0336] Step 2:

[0337] The device sends emotion data entered by the user to the server. The transmitted data includes details of the emotion and information about related projects and tasks.

[0338] Step 3:

[0339] The server analyzes the received emotional data using an emotion engine. Based on the user's emotional state, it evaluates the risks and impacts on project progress and performance.

[0340] Step 4:

[0341] Based on the sentiment analysis results, the server generates risk alerts and proposes task prioritization adjustments. If necessary, it also considers reallocating resources and relaxing schedules.

[0342] Step 5:

[0343] The terminal notifies the user of suggestions from the server. The user reviews the presented information and considers and implements specific improvement measures.

[0344] Step 6:

[0345] If a user decides to implement a recommended improvement, they can use a feature to share that information with project members from their device. This information may include a revised task schedule and recommended team activities.

[0346] Step 7:

[0347] The server continues to monitor project progress and user sentiment data. It tracks progress and changes in sentiment at each project phase and suggests further interventions as needed.

[0348] Step 8:

[0349] This process is repeated at key milestones in the project. Based on user feedback and new sentiment data, project management strategies can be flexibly adjusted.

[0350] (Example 2)

[0351] 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".

[0352] In project management, not only technical factors but also the emotional state of individual users involved in the project can significantly impact its success. However, conventional project management systems do not adequately address user emotions. Therefore, there is a risk that emotional stress and anxiety experienced by users could negatively affect project progress. A mechanism to address this is needed.

[0353] 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.

[0354] In this invention, the server includes means for analyzing the user's emotional state and proposing improvements to project management based on the results, means for proposing changes in task priorities and the addition of resources, and means for analyzing feedback and presenting improvements that take into account emotional factors in project management. This makes it possible to manage projects while taking the user's emotions into consideration.

[0355] "Project planning" is the process of systematically organizing the tasks, resources, and schedule necessary to achieve the project's objectives.

[0356] "Task dependencies" refer to relationships where the completion of one task influences the start or progress of other tasks.

[0357] "Optimizing a schedule" refers to the process of adjusting the start and end times of each task to the most optimal form in order to ensure the efficient progress of a project.

[0358] "Analyzing a user's emotional state" refers to the process of analyzing and understanding a user's psychological state based on emotional data entered by the user.

[0359] "Proposing improvements to project management" means analyzing the current situation and presenting concrete action plans in order to improve the progress and results of a project.

[0360] "Real-time risk assessment" refers to the process of continuously reviewing and evaluating potential problems and obstacles in accordance with the progress of the project.

[0361] "Displaying alerts to users" refers to the act of providing users with real-time notifications to inform them of important points or dangers.

[0362] "Visualizing and updating project progress" refers to the process of clearly displaying the current progress of a project and ensuring that the latest information is always reflected.

[0363] "Generating and distributing progress reports to users" refers to the act of creating a report summarizing information about the progress of a project and delivering it to users.

[0364] "Analyzing feedback" refers to the process of analyzing opinions and information received from users and stakeholders, understanding their content, and using that information to inform future strategies.

[0365] This invention provides a project management system that integrates an emotion engine. The system consists of a server, terminals, and users, and each element works in cooperation with the others.

[0366] The server runs an analysis system equipped with an emotion engine, analyzing user input and communication history using data analysis algorithms such as natural language processing tools and machine learning algorithms. This identifies the user's emotional state and generates improvement strategies for project management as needed. These improvement strategies range from re-evaluating task priorities and reallocating resources to proposing team-building activities. The server manages emotion data and project data using an SQL database.

[0367] The device accepts emotional input through its user interface and enables intuitive operation using pull-down menus and emotional icon functions. The collected emotional data is sent to the server in real time, and the analysis results and improvement suggestions from the server are notified on the display device. The device can be deployed as a browser application or mobile application, using web technologies such as HTML5 and JavaScript.

[0368] Users input and record their emotional data through their devices and consider improvement measures suggested by the server. For example, if a user inputs emotional data indicating they are feeling "pressure towards Task A," the server analyzes this data and suggests rescheduling the task or adding resources. The suggested changes are also notified to the user via push notification and displayed on the project management interface.

[0369] A concrete example of a prompt would be, "When a user feels anxious about a meeting, how does the server identify the cause and what solutions does it offer?" Inputting such prompts into an AI model can sometimes yield further suggestions for improvement.

[0370] This system achieves more effective project management by actively incorporating not only technical elements but also user emotions.

[0371] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0372] Step 1:

[0373] The device receives emotional data from the user as input. The user selects their emotional state using pull-down menus or emotional icon functions. This input data is comprised of an interface that enables accurate recording of the user's emotions. Specifically, the type and intensity of the selected emotion, as well as any additional comments, are recorded as data.

[0374] Step 2:

[0375] The device encrypts the collected emotional data and sends it to the server, ensuring the security of the emotional data. The transmitted data is received by the server and processed as input data for analysis. During this process, pre-processing is performed, such as standardizing the data format and removing redundant data.

[0376] Step 3:

[0377] The server analyzes input data using an emotion engine. It employs natural language processing algorithms and machine learning models to identify emotional states as numerical data, and uses this to understand the user's psychological tendencies. The generative AI model is controlled by prompts, ensuring that appropriate analysis results are obtained. The analysis results include classifications of emotional states and related topics.

[0378] Step 4:

[0379] The server generates improvement proposals for project management based on the analysis results. These proposals include suggestions for prioritizing tasks, reviewing responsibilities, and reallocating resources. These proposals are generated using integrated information on sentiment data and project progress and are output as improvement proposals.

[0380] Step 5:

[0381] The terminal displays improvement suggestions received from the server in its user interface. The user reviews these suggestions and, if necessary, accepts them and incorporates them into their task plan. The terminal is designed to display the improvement suggestions in charts and lists for intuitive understanding. Buttons and links are also provided for selecting specific actions.

[0382] (Application Example 2)

[0383] 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."

[0384] In modern manufacturing, human emotions can influence work efficiency and safety. However, conventional automation systems are unable to adequately address worker emotions, and therefore do not provide effective support for human motivation and stress management. For this reason, there is a need to provide an efficient work environment that takes worker emotions into consideration.

[0385] 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.

[0386] In this invention, the server includes means for automatically generating a project plan, means for detecting the user's emotional state and suggesting work optimizations, and means for adjusting the robot's actions based on emotional data. This makes it possible to provide an efficient and flexible work environment based on the worker's emotions.

[0387] 1. "Means for automatically generating project plans" refers to a function that has an algorithm that efficiently and automatically allocates the work timeline and necessary resources by inputting project requirements.

[0388] 2. "Methods for analyzing dependencies between tasks and optimizing the schedule" refers to the process of analyzing the order, priority, and interrelationships of individual tasks to ensure optimal work progress.

[0389] 3. "A means of performing real-time risk assessment and displaying alerts to users" refers to a system that assesses the current work status and potential problems in real time and notifies users of appropriate warnings and improvement suggestions.

[0390] 4. "Means for visualizing and updating project progress in real time" refers to technologies that allow users to check the progress of work in real time and display the latest information on the user interface.

[0391] 5. "Means for automatically generating and distributing progress reports to users" refers to a method of organizing progress information based on collected data and efficiently providing reports to users.

[0392] 6. "Means for detecting the user's emotional state and proposing work optimization" refers to technology that senses the user's psychological state and presents work improvement suggestions aimed at reducing stress and motivating them.

[0393] 7. "Means for adjusting robot operation based on emotional data" refers to a mechanism that adjusts and optimizes the operation of automated work equipment according to collected emotional information.

[0394] 8. "Means for efficient work distribution according to the work environment" refers to a system that optimally allocates work resources according to the current operating status and individual workloads, thereby increasing productivity.

[0395] This invention is a system that detects the emotional state of workers in a factory environment in real time and optimizes work processes based on that data.

[0396] First, the smart glasses, acting as the device, are equipped with a facial recognition camera and a pulse rate measurement function. This hardware allows factory workers to efficiently detect changes in their emotions while they are working. The emotional data is transmitted from the device to a server via Wi-Fi.

[0397] On the server, analysis software equipped with an emotion engine is running. Emotional data is input into a machine learning model using TensorFlow and analyzed in real time. The analysis results are fed back to supervisors and automated work machines, enabling adjustments to work speed and task reallocation.

[0398] The user, i.e., the factory supervisor, receives emotion-based improvement suggestions from this system. The user can then adjust the work environment according to these suggestions, aiming to improve work efficiency and reduce the mental burden on workers.

[0399] For example, if a worker's stress level exceeds a certain level, the server can transmit this information to the robot, which can then take measures to reduce its work speed by 20%. In this way, flexible work processing can be performed in response to the environment.

[0400] Examples of prompts for a generative AI model are as follows:

[0401] "Design a system that utilizes worker emotional data in a factory environment to optimize the operation of production robots. How can this system improve work efficiency and reduce worker stress?"

[0402] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0403] Step 1:

[0404] The smart glasses, acting as the terminal, detect the worker's facial expressions and pulse rate in real time and acquire them as emotion data. Inputs are video data from a facial recognition camera and pulse rate measurement data, while output is emotion data for analysis. Specifically, the facial expression data is analyzed and classified using a video processing algorithm.

[0405] Step 2:

[0406] The device transmits the acquired emotion data to the server via Wi-Fi. The input is the emotion data obtained in step 1, and the output is the data transmitted to the server. Specifically, data encryption is performed to securely establish data communication to the server.

[0407] Step 3:

[0408] The server drives an emotion engine and analyzes the received emotion data. The input is emotion data sent from the terminal, and the output is evaluation data of the emotional state as a result of the analysis. Specifically, it uses TensorFlow to input data into a machine learning model and estimate the emotional state.

[0409] Step 4:

[0410] The server proposes optimized movements for the production robot based on the analyzed emotional state. The input is the emotional state evaluation data obtained in step 3, and the output is the instruction for movement optimization. Specifically, it activates an algorithm that adjusts the robot's work speed and work distribution according to the emotional evaluation.

[0411] Step 5:

[0412] The factory supervisor, acting as the user, adjusts the work environment based on suggestions received from the server. The input is operation optimization instructions from the server, and the output is the adjusted work environment. Specifically, the supervisor uses the robot's control panel to set speed and task allocation.

[0413] Step 6:

[0414] The server waits for new emotion input and repeats the process from step 1. The input is the latest emotion data, and the output is the updated robot behavior parameters. Specifically, it maintains a continuous real-time data processing cycle.

[0415] 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.

[0416] 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.

[0417] 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.

[0418] [Third Embodiment]

[0419] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0420] 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.

[0421] 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).

[0422] 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.

[0423] 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.

[0424] 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).

[0425] 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.

[0426] 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.

[0427] 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.

[0428] 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.

[0429] 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.

[0430] 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".

[0431] A project management system, as a specific embodiment of the present invention, is comprised of cooperation between a server, a terminal, and a user. The operation of each component is described below.

[0432] server:

[0433] The server automatically generates project plans. First, based on the basic project information entered by the user, it automatically divides tasks and generates a schedule by analyzing dependencies. It also analyzes available resource information and optimally allocates resources to each task. Furthermore, the server aggregates project progress data, analyzes the risks derived from it, and sends risk alerts to the user. This allows the user to understand the potential risks facing the project in real time.

[0434] Terminal:

[0435] The terminal provides a user-facing interface. When a user inputs task information and progress, the terminal immediately sends it to the server, displaying a real-time updated visualization of the project status. For example, if task progress changes, the terminal provides a Gantt chart or dashboard showing the updated progress. The terminal also receives and displays risk alerts and periodic progress reports from the server, making it easy for users to understand the current situation.

[0436] User:

[0437] Users input project information and manage progress through the system. At the start of a project, they input basic information such as the project name, purpose, start date, and end date into the terminal. Progress updates and resource adjustments are also done via the terminal, and the latest status is processed by the server. Users make decisions as needed based on the progress status and risk information displayed on the terminal.

[0438] For example, when a user attempts to schedule a new project, they can view the details of each task on their terminal based on the automatically generated plan suggested by the server, and make adjustments as needed. The server monitors progress in real time and continuously performs risk assessments, allowing the user to respond quickly to unintended delays or risks. By implementing this invention, project management efficiency is improved, and limited resources can be utilized to their fullest potential.

[0439] The following describes the processing flow.

[0440] Step 1:

[0441] The user enters basic project information into the terminal. This includes the project name, purpose, start date, end date, and key milestones.

[0442] Step 2:

[0443] The terminal sends the entered information to the server. The server analyzes the received information and starts automatically generating tasks based on the project's goals.

[0444] Step 3:

[0445] As a result of task generation, the server divides the project into multiple phases and tasks. During this process, it considers the dependencies between tasks and creates an initial schedule proposal.

[0446] Step 4:

[0447] The server calculates the optimal resource allocation for each task based on available resource information. The results are then sent to the terminal for user review.

[0448] Step 5:

[0449] The terminal displays the schedule and resource allocation sent from the server in a visual format. The user reviews this and makes adjustments as needed.

[0450] Step 6:

[0451] When a user makes adjustments, the terminal resends the change information to the server. The server then receives this information and updates the project plan based on the latest information.

[0452] Step 7:

[0453] The server continuously monitors project progress and aggregates and analyzes progress data in real time. If a risk is detected, it immediately generates a risk alert.

[0454] Step 8:

[0455] The terminal displays risk alerts sent from the server to the user, enabling the user to respond quickly to problems.

[0456] Step 9:

[0457] The server generates periodic reports based on progress data and delivers them to users via their terminals. This allows users to understand the overall status of the project.

[0458] Step 10:

[0459] Upon project completion or milestone, users input feedback into their terminals. The server collects and analyzes this feedback to help improve future project planning.

[0460] (Example 1)

[0461] 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."

[0462] Modern project management requires efficiently managing numerous tasks and optimizing limited resources. However, as projects grow larger, manual planning, progress tracking, and risk assessment tend to become time-consuming, labor-intensive, and inefficient. Therefore, automating project planning and managing progress and risks in real time are essential.

[0463] 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.

[0464] In this invention, the server includes means for automatically generating a project plan based on an AI model, means for dividing tasks based on basic project information input by the user, analyzing dependencies, and creating a schedule, and means for analyzing the user's resource information and optimizing resource allocation for the entire project. This improves the efficiency of project management, enables real-time progress monitoring, and allows for appropriate risk management.

[0465] A "project plan" is an overall blueprint that outlines the tasks, resources, and schedule necessary to achieve the project's objectives.

[0466] A "generative AI model" is an algorithm or computer program that uses artificial intelligence technology to analyze data and automatically generate plans and tasks based on project-related information.

[0467] "Task splitting" is the process of breaking down an entire project into smaller, manageable work units and defining each task in detail.

[0468] "Dependency" refers to the relationship that indicates the execution order and interrelationships of each task, and describes the conditions under which a particular task depends on other tasks.

[0469] "Creating a schedule" is the act of planning the work to be done over a period of time from the start date to the end date of a project, based on tasks and resource allocation.

[0470] "Optimal resource allocation" is the process of allocating available resources, such as personnel, budget, and equipment, to each task in the most effective way in order to efficiently advance project work.

[0471] "Receiving progress updates in real time" means instantly obtaining project progress data and continuously staying informed of the latest status.

[0472] "Risk assessment" is the process of predicting potential problems and obstacles that may arise during the course of a project and analyzing their impact.

[0473] "Information for decision-making" refers to the information necessary for project managers and stakeholders to make optimal decisions, and includes data such as progress, risks, and resource status.

[0474] In an embodiment of this invention, the project management system is primarily composed of cooperation between a server, a terminal, and a user. This system enables the automatic generation of project plans using a generative AI model, real-time management of progress, risk assessment, and optimal allocation of resources.

[0475] The server automatically generates a project plan based on basic project information entered by the user, using a generative AI model. The hardware and software used in this process are expected to include cloud computing services and AI platforms, which will be used for task division and dependency analysis. Furthermore, the server aggregates project progress data, assesses risks using data analysis tools, and generates real-time risk alerts.

[0476] The terminal provides an intuitive interface for users to manage projects. When users input task information and progress via the terminal, it is sent to the server, and the latest project information is updated instantly. For example, the terminal uses visualization tools to provide users with Gantt charts and dashboards that show progress.

[0477] Users input basic project information and resource details into a terminal and manage progress through the system. Users make project decisions based on risk alerts and progress reports displayed on the terminal. For example, when a user tries to schedule a new project, they review the details of each task on the terminal based on the automatically generated plan suggested by the server and make adjustments as needed.

[0478] As a concrete example, when a user inputs a prompt into the AI ​​model saying, "I want you to automatically generate a plan for a new marketing campaign and set up risk alerts," the server automatically divides the task, proposes a plan, and sets up a risk management system. In this way, project management efficiency is improved, and limited resources can be used to their fullest potential.

[0479] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0480] Step 1:

[0481] The user enters basic project information into the terminal. The user enters basic data such as project name, purpose, start date, and end date, and uses this information to perform initial setup. This input data is then transferred to the server as foundational information for use in subsequent processing steps.

[0482] Step 2:

[0483] The server generates a project plan based on the basic information received from the user. Specifically, it utilizes a generation AI model to automatically divide tasks using prompt messages. The server creates a project schedule, analyzes dependencies, and determines the order of each task. In this process, it creates output data (a list of planned project tasks and schedule) from input data (basic project information).

[0484] Step 3:

[0485] The server analyzes the resource information required for the project. The server analyzes the resource information entered by the user on the terminal and performs data calculations to determine the optimal resource allocation. This generates output data for efficiently allocating the necessary personnel, equipment, budget, etc., for each task.

[0486] Step 4:

[0487] Users input progress information via their terminals. Users update task completion status and progress in real time and send this information to the server. Based on this input data, the server checks the current progress of the project.

[0488] Step 5:

[0489] The terminal visually displays the latest project information received from the server. Using visualization tools, it provides Gantt charts and dashboards showing the progress of ongoing projects. This output data serves as visual information that allows users to easily understand the status of projects.

[0490] Step 6:

[0491] The server analyzes progress data in real time and assesses risks. It performs data calculations to predict potential problems that may arise during project progress and generates risk alerts. These alerts are provided to the user as output data to aid in user decision-making.

[0492] Step 7:

[0493] Based on risk alerts presented from the terminal, the user makes necessary decisions. The user optimizes project progress by rescheduling tasks and reallocating resources as needed. In this step, the user generates new input data (decisions and adjustments) based on risk information output from the server.

[0494] (Application Example 1)

[0495] 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."

[0496] The present invention aims to improve overall production efficiency by preventing project delays and unexpected equipment failures through efficient resource allocation and progress management in project management, as well as optimizing machine maintenance schedules.

[0497] 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.

[0498] In this invention, the server includes means for automatically generating a project plan, means for analyzing dependencies between tasks and optimizing the schedule, means for performing real-time risk assessments and displaying alerts to the user, means for collecting machine operation data and automatically generating maintenance tasks, and means for optimally allocating and managing the resources required for each task. This makes it possible to integrate project management and machine maintenance.

[0499] "Means for automatically generating project plans" refer to devices or software that, based on basic information entered by the user, divide project tasks, analyze dependencies, and create a schedule.

[0500] "Means for analyzing dependencies between tasks and optimizing schedules" refer to devices or software that analyze how each task within a project relates to other tasks and enable efficient scheduling.

[0501] "A means of performing real-time risk assessment and displaying alerts to users" refers to devices or software that constantly monitor the status of ongoing projects or machinery and issue warnings when potential problems or risks are detected.

[0502] "Means for visualizing and updating project progress in real time" refers to devices or software that visually display the current progress and status of a project and keep the information constantly updated to the latest version.

[0503] "Means for automatically generating and distributing progress reports to users" refers to devices or software that collect project progress data and provide it to users as a report.

[0504] "Means for collecting machine operation data and automatically generating maintenance tasks" refers to devices or software that collect operational data from machines in a factory or facility and automatically plan maintenance tasks based on that data.

[0505] "Means for optimally allocating and managing the resources required for each task" refers to devices and software that efficiently allocate and monitor resources such as personnel and materials required for projects and maintenance tasks.

[0506] The server provides the central functionality of this invention. Based on the basic project information entered by the user using a terminal, the server automatically divides each task and generates a schedule by analyzing their dependencies. Furthermore, the server constantly evaluates the progress of the project and machinery using real-time streaming data and warns the user of potential risks.

[0507] The server also collects and analyzes machine operation data for preventative maintenance. The collected data is then used with AI to automatically generate maintenance tasks and optimally allocate the necessary resources. This improves the operational efficiency and safety of the machines. In particular, using AWS Lambda on the AWS cloud platform and performing data processing and notifications via a web application provides a scalable and reliable environment. This program, written in Python and Flask, uses Plotly Dash for visualization.

[0508] The terminal functions as an interface for direct user interaction. This interface visualizes the progress, allowing users to make decisions based on that visualization. For example, if a machine failure is predicted, an alert is displayed on the terminal, and a notification is immediately sent to the maintenance personnel. The data received by the terminal is immediately sent to the server, and the status is updated in real time.

[0509] A concrete example is a system that monitors the operation of multiple robots installed in a factory. This system automatically checks the inventory status of parts and schedules replacements when a robot's parts are nearing the end of their lifespan. This prevents robot downtime and enables efficient operations.

[0510] An example of a prompt that utilizes a generative AI model is: "Design a system to proactively manage the maintenance of factory robots. Explain how to automatically generate maintenance tasks based on operational data and how to optimally allocate resources."

[0511] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0512] Step 1:

[0513] The server receives basic project information entered by the user via their terminal. This input data includes the project name, duration, budget, and required deliverables. Based on this data, the server uses a generative AI model to automatically divide tasks and establish dependencies between them. As a result, an initial project schedule is generated.

[0514] Step 2:

[0515] The server collects real-time operational data from the machine and stores it in a database. This data includes uptime, performance metrics, and error codes. Based on the collected data, analysis is performed to detect machine anomalies and generate maintenance tasks. As output, a task list prioritized by a maintenance prediction model is generated.

[0516] Step 3:

[0517] The terminal visualizes task lists and project progress information sent from the server and provides it to the user. Based on this information, the user can instantly check the progress and risk assessment. The visualization is provided in the form of Gantt charts and dashboards, allowing for an intuitive understanding of the current situation.

[0518] Step 4:

[0519] Users update progress via their devices, adding new information and resources. Information from the devices is immediately sent to the server, where it is integrated with the existing database and the data is updated. The updated data triggers risk alerts and resource reallocations, enabling dynamic project management.

[0520] Step 5:

[0521] The server optimizes scheduling and resource allocation, and generates progress reports as needed. These reports include information on the progress of tasks, which resources are in excess, and which are insufficient. These reports are automatically sent to the user to support project management decision-making.

[0522] 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.

[0523] To implement the present invention, we will describe a configuration that provides project management that takes user emotions into consideration by incorporating an emotion engine into the project management system. The operation of each component will be specifically described below.

[0524] server:

[0525] The server is equipped with an emotion engine that analyzes user input and communication data to detect the user's emotional state. Based on this information, it proposes appropriate communication tailored to the project's progress. For example, if a user is experiencing stress, the server suggests reviewing task priorities or redistributing tasks among team members. The server also uses emotional data to adjust risk assessments in the current project environment in real time.

[0526] Terminal:

[0527] The device provides an interface for receiving emotional input from the user. It features pull-down menus and emotional icons to allow users to easily record their emotional state. The device sends this emotional data to a server, where an emotional engine analyzes it and displays appropriate improvement suggestions to the user. For example, if it determines that project members' morale is low, it might suggest team-building activities to promote unity.

[0528] User:

[0529] Users record their emotions that may influence project progress and receive feedback from the system. They then review the task management improvements recommended by the emotion engine and share them with team members as needed to streamline the project.

[0530] For example, if a user is feeling pressured by a particular task, the emotion engine analyzes that emotional data and suggests adding resources or adjusting the schedule to alleviate the pressure. This allows the project management system to perform comprehensive management that considers not only technical elements but also human emotions, thereby improving the success rate of projects.

[0531] The following describes the processing flow.

[0532] Step 1:

[0533] The user inputs their emotional state into the device. The device displays an interface for selecting an emotion, and the user records their current emotion using an emotion icon or text.

[0534] Step 2:

[0535] The device sends emotion data entered by the user to the server. The transmitted data includes details of the emotion and information about related projects and tasks.

[0536] Step 3:

[0537] The server analyzes the received emotional data using an emotion engine. Based on the user's emotional state, it evaluates the risks and impacts on project progress and performance.

[0538] Step 4:

[0539] Based on the sentiment analysis results, the server generates risk alerts and proposes task prioritization adjustments. If necessary, it also considers reallocating resources and relaxing schedules.

[0540] Step 5:

[0541] The terminal notifies the user of suggestions from the server. The user reviews the presented information and considers and implements specific improvement measures.

[0542] Step 6:

[0543] If a user decides to implement a recommended improvement, they can use a feature to share that information with project members from their device. This information may include a revised task schedule and recommended team activities.

[0544] Step 7:

[0545] The server continues to monitor project progress and user sentiment data. It tracks progress and changes in sentiment at each project phase and suggests further interventions as needed.

[0546] Step 8:

[0547] This process is repeated at key milestones in the project. Based on user feedback and new sentiment data, project management strategies can be flexibly adjusted.

[0548] (Example 2)

[0549] 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."

[0550] In project management, not only technical factors but also the emotional state of individual users involved in the project can significantly impact its success. However, conventional project management systems do not adequately address user emotions. Therefore, there is a risk that emotional stress and anxiety experienced by users could negatively affect project progress. A mechanism to address this is needed.

[0551] 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.

[0552] In this invention, the server includes means for analyzing the user's emotional state and proposing improvements to project management based on the results, means for proposing changes in task priorities and the addition of resources, and means for analyzing feedback and presenting improvements that take into account emotional factors in project management. This makes it possible to manage projects while taking the user's emotions into consideration.

[0553] "Project planning" is the process of systematically organizing the tasks, resources, and schedule necessary to achieve the project's objectives.

[0554] "Task dependencies" refer to relationships where the completion of one task influences the start or progress of other tasks.

[0555] "Optimizing a schedule" refers to the process of adjusting the start and end times of each task to the most optimal form in order to ensure the efficient progress of a project.

[0556] "Analyzing a user's emotional state" refers to the process of analyzing and understanding a user's psychological state based on emotional data entered by the user.

[0557] "Proposing improvements to project management" means analyzing the current situation and presenting concrete action plans in order to improve the progress and results of a project.

[0558] "Real-time risk assessment" refers to the process of continuously reviewing and evaluating potential problems and obstacles in accordance with the progress of the project.

[0559] "Displaying alerts to users" refers to the act of providing users with real-time notifications to inform them of important points or dangers.

[0560] "Visualizing and updating project progress" refers to the process of clearly displaying the current progress of a project and ensuring that the latest information is always reflected.

[0561] "Generating and distributing progress reports to users" refers to the act of creating a report summarizing information about the progress of a project and delivering it to users.

[0562] "Analyzing feedback" refers to the process of analyzing opinions and information received from users and stakeholders, understanding their content, and using that information to inform future strategies.

[0563] This invention provides a project management system that integrates an emotion engine. The system consists of a server, terminals, and users, and each element works in cooperation with the others.

[0564] The server runs an analysis system equipped with an emotion engine, analyzing user input and communication history using data analysis algorithms such as natural language processing tools and machine learning algorithms. This identifies the user's emotional state and generates improvement strategies for project management as needed. These improvement strategies range from re-evaluating task priorities and reallocating resources to proposing team-building activities. The server manages emotion data and project data using an SQL database.

[0565] The device accepts emotional input through its user interface and enables intuitive operation using pull-down menus and emotional icon functions. The collected emotional data is sent to the server in real time, and the analysis results and improvement suggestions from the server are notified on the display device. The device can be deployed as a browser application or mobile application, using web technologies such as HTML5 and JavaScript.

[0566] Users input and record their emotional data through their devices and consider improvement measures suggested by the server. For example, if a user inputs emotional data indicating they are feeling "pressure towards Task A," the server analyzes this data and suggests rescheduling the task or adding resources. The suggested changes are also notified to the user via push notification and displayed on the project management interface.

[0567] A concrete example of a prompt would be, "When a user feels anxious about a meeting, how does the server identify the cause and what solutions does it offer?" Inputting such prompts into an AI model can sometimes yield further suggestions for improvement.

[0568] This system achieves more effective project management by actively incorporating not only technical elements but also user emotions.

[0569] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0570] Step 1:

[0571] The device receives emotional data from the user as input. The user selects their emotional state using pull-down menus or emotional icon functions. This input data is comprised of an interface that enables accurate recording of the user's emotions. Specifically, the type and intensity of the selected emotion, as well as any additional comments, are recorded as data.

[0572] Step 2:

[0573] The device encrypts the collected emotional data and sends it to the server, ensuring the security of the emotional data. The transmitted data is received by the server and processed as input data for analysis. During this process, pre-processing is performed, such as standardizing the data format and removing redundant data.

[0574] Step 3:

[0575] The server analyzes input data using an emotion engine. It employs natural language processing algorithms and machine learning models to identify emotional states as numerical data, and uses this to understand the user's psychological tendencies. The generative AI model is controlled by prompts, ensuring that appropriate analysis results are obtained. The analysis results include classifications of emotional states and related topics.

[0576] Step 4:

[0577] The server generates improvement proposals for project management based on the analysis results. These proposals include suggestions for prioritizing tasks, reviewing responsibilities, and reallocating resources. These proposals are generated using integrated information on sentiment data and project progress and are output as improvement proposals.

[0578] Step 5:

[0579] The terminal displays improvement suggestions received from the server in its user interface. The user reviews these suggestions and, if necessary, accepts them and incorporates them into their task plan. The terminal is designed to display the improvement suggestions in charts and lists for intuitive understanding. Buttons and links are also provided for selecting specific actions.

[0580] (Application Example 2)

[0581] 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."

[0582] In modern manufacturing, human emotions can influence work efficiency and safety. However, conventional automation systems are unable to adequately address worker emotions, and therefore do not provide effective support for human motivation and stress management. For this reason, there is a need to provide an efficient work environment that takes worker emotions into consideration.

[0583] 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.

[0584] In this invention, the server includes means for automatically generating a project plan, means for detecting the user's emotional state and suggesting work optimizations, and means for adjusting the robot's actions based on emotional data. This makes it possible to provide an efficient and flexible work environment based on the worker's emotions.

[0585] 1. "Means for automatically generating project plans" refers to a function that has an algorithm that efficiently and automatically allocates the work timeline and necessary resources by inputting project requirements.

[0586] 2. "Methods for analyzing dependencies between tasks and optimizing the schedule" refers to the process of analyzing the order, priority, and interrelationships of individual tasks to ensure optimal work progress.

[0587] 3. "A means of performing real-time risk assessment and displaying alerts to users" refers to a system that assesses the current work status and potential problems in real time and notifies users of appropriate warnings and improvement suggestions.

[0588] 4. "Means for visualizing and updating project progress in real time" refers to technologies that allow users to check the progress of work in real time and display the latest information on the user interface.

[0589] 5. "Means for automatically generating and distributing progress reports to users" refers to a method of organizing progress information based on collected data and efficiently providing reports to users.

[0590] 6. "Means for detecting the user's emotional state and proposing work optimization" refers to technology that senses the user's psychological state and presents work improvement suggestions aimed at reducing stress and motivating them.

[0591] 7. "Means for adjusting robot operation based on emotional data" refers to a mechanism that adjusts and optimizes the operation of automated work equipment according to collected emotional information.

[0592] 8. "Means for efficient work distribution according to the work environment" refers to a system that optimally allocates work resources according to the current operating status and individual workloads, thereby increasing productivity.

[0593] This invention is a system that detects the emotional state of workers in a factory environment in real time and optimizes work processes based on that data.

[0594] First, the smart glasses, acting as the device, are equipped with a facial recognition camera and a pulse rate measurement function. This hardware allows factory workers to efficiently detect changes in their emotions while they are working. The emotional data is transmitted from the device to a server via Wi-Fi.

[0595] On the server, analysis software equipped with an emotion engine is running. Emotional data is input into a machine learning model using TensorFlow and analyzed in real time. The analysis results are fed back to supervisors and automated work machines, enabling adjustments to work speed and task reallocation.

[0596] The user, i.e., the factory supervisor, receives emotion-based improvement suggestions from this system. The user can then adjust the work environment according to these suggestions, aiming to improve work efficiency and reduce the mental burden on workers.

[0597] For example, if a worker's stress level exceeds a certain level, the server can transmit this information to the robot, which can then take measures to reduce its work speed by 20%. In this way, flexible work processing can be performed in response to the environment.

[0598] Examples of prompts for a generative AI model are as follows:

[0599] "Design a system that utilizes worker emotional data in a factory environment to optimize the operation of production robots. How can this system improve work efficiency and reduce worker stress?"

[0600] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0601] Step 1:

[0602] The smart glasses, acting as the terminal, detect the worker's facial expressions and pulse rate in real time and acquire them as emotion data. Inputs are video data from a facial recognition camera and pulse rate measurement data, while output is emotion data for analysis. Specifically, the facial expression data is analyzed and classified using a video processing algorithm.

[0603] Step 2:

[0604] The device transmits the acquired emotion data to the server via Wi-Fi. The input is the emotion data obtained in step 1, and the output is the data transmitted to the server. Specifically, data encryption is performed to securely establish data communication to the server.

[0605] Step 3:

[0606] The server drives an emotion engine and analyzes the received emotion data. The input is emotion data sent from the terminal, and the output is evaluation data of the emotional state as a result of the analysis. Specifically, it uses TensorFlow to input data into a machine learning model and estimate the emotional state.

[0607] Step 4:

[0608] The server proposes optimized movements for the production robot based on the analyzed emotional state. The input is the emotional state evaluation data obtained in step 3, and the output is the instruction for movement optimization. Specifically, it activates an algorithm that adjusts the robot's work speed and work distribution according to the emotional evaluation.

[0609] Step 5:

[0610] The factory supervisor, acting as the user, adjusts the work environment based on suggestions received from the server. The input is operation optimization instructions from the server, and the output is the adjusted work environment. Specifically, the supervisor uses the robot's control panel to set speed and task allocation.

[0611] Step 6:

[0612] The server waits for new emotion input and repeats the process from step 1. The input is the latest emotion data, and the output is the updated robot behavior parameters. Specifically, it maintains a continuous real-time data processing cycle.

[0613] 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.

[0614] 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.

[0615] 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.

[0616] [Fourth Embodiment]

[0617] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0618] 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.

[0619] 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).

[0620] 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.

[0621] 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.

[0622] 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).

[0623] 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.

[0624] 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.

[0625] 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.

[0626] 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.

[0627] 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.

[0628] 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.

[0629] 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".

[0630] A project management system, as a specific embodiment of the present invention, is comprised of cooperation between a server, a terminal, and a user. The operation of each component is described below.

[0631] server:

[0632] The server automatically generates project plans. First, based on the basic project information entered by the user, it automatically divides tasks and generates a schedule by analyzing dependencies. It also analyzes available resource information and optimally allocates resources to each task. Furthermore, the server aggregates project progress data, analyzes the risks derived from it, and sends risk alerts to the user. This allows the user to understand the potential risks facing the project in real time.

[0633] Terminal:

[0634] The terminal provides a user-facing interface. When a user inputs task information and progress, the terminal immediately sends it to the server, displaying a real-time updated visualization of the project status. For example, if task progress changes, the terminal provides a Gantt chart or dashboard showing the updated progress. The terminal also receives and displays risk alerts and periodic progress reports from the server, making it easy for users to understand the current situation.

[0635] User:

[0636] Users input project information and manage progress through the system. At the start of a project, they input basic information such as the project name, purpose, start date, and end date into the terminal. Progress updates and resource adjustments are also done via the terminal, and the latest status is processed by the server. Users make decisions as needed based on the progress status and risk information displayed on the terminal.

[0637] For example, when a user attempts to schedule a new project, they can view the details of each task on their terminal based on the automatically generated plan suggested by the server, and make adjustments as needed. The server monitors progress in real time and continuously performs risk assessments, allowing the user to respond quickly to unintended delays or risks. By implementing this invention, project management efficiency is improved, and limited resources can be utilized to their fullest potential.

[0638] The following describes the processing flow.

[0639] Step 1:

[0640] The user enters basic project information into the terminal. This includes the project name, purpose, start date, end date, and key milestones.

[0641] Step 2:

[0642] The terminal sends the entered information to the server. The server analyzes the received information and starts automatically generating tasks based on the project's goals.

[0643] Step 3:

[0644] As a result of task generation, the server divides the project into multiple phases and tasks. During this process, it considers the dependencies between tasks and creates an initial schedule proposal.

[0645] Step 4:

[0646] The server calculates the optimal resource allocation for each task based on available resource information. The results are then sent to the terminal for user review.

[0647] Step 5:

[0648] The terminal displays the schedule and resource allocation sent from the server in a visual format. The user reviews this and makes adjustments as needed.

[0649] Step 6:

[0650] When a user makes adjustments, the terminal resends the change information to the server. The server then receives this information and updates the project plan based on the latest information.

[0651] Step 7:

[0652] The server continuously monitors project progress and aggregates and analyzes progress data in real time. If a risk is detected, it immediately generates a risk alert.

[0653] Step 8:

[0654] The terminal displays risk alerts sent from the server to the user, enabling the user to respond quickly to problems.

[0655] Step 9:

[0656] The server generates periodic reports based on progress data and delivers them to users via their terminals. This allows users to understand the overall status of the project.

[0657] Step 10:

[0658] Upon project completion or milestone, users input feedback into their terminals. The server collects and analyzes this feedback to help improve future project planning.

[0659] (Example 1)

[0660] 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".

[0661] Modern project management requires efficiently managing numerous tasks and optimizing limited resources. However, as projects grow larger, manual planning, progress tracking, and risk assessment tend to become time-consuming, labor-intensive, and inefficient. Therefore, automating project planning and managing progress and risks in real time are essential.

[0662] 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.

[0663] In this invention, the server includes means for automatically generating a project plan based on an AI model, means for dividing tasks based on basic project information input by the user, analyzing dependencies, and creating a schedule, and means for analyzing the user's resource information and optimizing resource allocation for the entire project. This improves the efficiency of project management, enables real-time progress monitoring, and allows for appropriate risk management.

[0664] A "project plan" is an overall blueprint that outlines the tasks, resources, and schedule necessary to achieve the project's objectives.

[0665] A "generative AI model" is an algorithm or computer program that uses artificial intelligence technology to analyze data and automatically generate plans and tasks based on project-related information.

[0666] "Task splitting" is the process of breaking down an entire project into smaller, manageable work units and defining each task in detail.

[0667] "Dependency" refers to the relationship that indicates the execution order and interrelationships of each task, and describes the conditions under which a particular task depends on other tasks.

[0668] "Creating a schedule" is the act of planning the work to be done over a period of time from the start date to the end date of a project, based on tasks and resource allocation.

[0669] "Optimal resource allocation" is the process of allocating available resources, such as personnel, budget, and equipment, to each task in the most effective way in order to efficiently advance project work.

[0670] "Receiving progress updates in real time" means instantly obtaining project progress data and continuously staying informed of the latest status.

[0671] "Risk assessment" is the process of predicting potential problems and obstacles that may arise during the course of a project and analyzing their impact.

[0672] "Information for decision-making" refers to the information necessary for project managers and stakeholders to make optimal decisions, and includes data such as progress, risks, and resource status.

[0673] In an embodiment of this invention, the project management system is primarily composed of cooperation between a server, a terminal, and a user. This system enables the automatic generation of project plans using a generative AI model, real-time management of progress, risk assessment, and optimal allocation of resources.

[0674] The server automatically generates a project plan based on basic project information entered by the user, using a generative AI model. The hardware and software used in this process are expected to include cloud computing services and AI platforms, which will be used for task division and dependency analysis. Furthermore, the server aggregates project progress data, assesses risks using data analysis tools, and generates real-time risk alerts.

[0675] The terminal provides an intuitive interface for users to manage projects. When users input task information and progress via the terminal, it is sent to the server, and the latest project information is updated instantly. For example, the terminal uses visualization tools to provide users with Gantt charts and dashboards that show progress.

[0676] Users input basic project information and resource details into a terminal and manage progress through the system. Users make project decisions based on risk alerts and progress reports displayed on the terminal. For example, when a user tries to schedule a new project, they review the details of each task on the terminal based on the automatically generated plan suggested by the server and make adjustments as needed.

[0677] As a concrete example, when a user inputs a prompt into the AI ​​model saying, "I want you to automatically generate a plan for a new marketing campaign and set up risk alerts," the server automatically divides the task, proposes a plan, and sets up a risk management system. In this way, project management efficiency is improved, and limited resources can be used to their fullest potential.

[0678] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0679] Step 1:

[0680] The user enters basic project information into the terminal. The user enters basic data such as project name, purpose, start date, and end date, and uses this information to perform initial setup. This input data is then transferred to the server as foundational information for use in subsequent processing steps.

[0681] Step 2:

[0682] The server generates a project plan based on the basic information received from the user. Specifically, it utilizes a generation AI model to automatically divide tasks using prompt messages. The server creates a project schedule, analyzes dependencies, and determines the order of each task. In this process, it creates output data (a list of planned project tasks and schedule) from input data (basic project information).

[0683] Step 3:

[0684] The server analyzes the resource information required for the project. The server analyzes the resource information entered by the user on the terminal and performs data calculations to determine the optimal resource allocation. This generates output data for efficiently allocating the necessary personnel, equipment, budget, etc., for each task.

[0685] Step 4:

[0686] Users input progress information via their terminals. Users update task completion status and progress in real time and send this information to the server. Based on this input data, the server checks the current progress of the project.

[0687] Step 5:

[0688] The terminal visually displays the latest project information received from the server. Using visualization tools, it provides Gantt charts and dashboards showing the progress of ongoing projects. This output data serves as visual information that allows users to easily understand the status of projects.

[0689] Step 6:

[0690] The server analyzes progress data in real time and assesses risks. It performs data calculations to predict potential problems that may arise during project progress and generates risk alerts. These alerts are provided to the user as output data to aid in user decision-making.

[0691] Step 7:

[0692] Based on risk alerts presented from the terminal, the user makes necessary decisions. The user optimizes project progress by rescheduling tasks and reallocating resources as needed. In this step, the user generates new input data (decisions and adjustments) based on risk information output from the server.

[0693] (Application Example 1)

[0694] 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".

[0695] The present invention aims to improve overall production efficiency by preventing project delays and unexpected equipment failures through efficient resource allocation and progress management in project management, as well as optimizing machine maintenance schedules.

[0696] 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.

[0697] In this invention, the server includes means for automatically generating a project plan, means for analyzing dependencies between tasks and optimizing the schedule, means for performing real-time risk assessments and displaying alerts to the user, means for collecting machine operation data and automatically generating maintenance tasks, and means for optimally allocating and managing the resources required for each task. This makes it possible to integrate project management and machine maintenance.

[0698] "Means for automatically generating project plans" refer to devices or software that, based on basic information entered by the user, divide project tasks, analyze dependencies, and create a schedule.

[0699] "Means for analyzing dependencies between tasks and optimizing schedules" refer to devices or software that analyze how each task within a project relates to other tasks and enable efficient scheduling.

[0700] "A means of performing real-time risk assessment and displaying alerts to users" refers to devices or software that constantly monitor the status of ongoing projects or machinery and issue warnings when potential problems or risks are detected.

[0701] "Means for visualizing and updating project progress in real time" refers to devices or software that visually display the current progress and status of a project and keep the information constantly updated to the latest version.

[0702] "Means for automatically generating and distributing progress reports to users" refers to devices or software that collect project progress data and provide it to users as a report.

[0703] "Means for collecting machine operation data and automatically generating maintenance tasks" refers to devices or software that collect operational data from machines in a factory or facility and automatically plan maintenance tasks based on that data.

[0704] "Means for optimally allocating and managing the resources required for each task" refers to devices and software that efficiently allocate and monitor resources such as personnel and materials required for projects and maintenance tasks.

[0705] The server provides the central functionality of this invention. Based on the basic project information entered by the user using a terminal, the server automatically divides each task and generates a schedule by analyzing their dependencies. Furthermore, the server constantly evaluates the progress of the project and machinery using real-time streaming data and warns the user of potential risks.

[0706] The server also collects and analyzes machine operation data for preventative maintenance. The collected data is then used with AI to automatically generate maintenance tasks and optimally allocate the necessary resources. This improves the operational efficiency and safety of the machines. In particular, using AWS Lambda on the AWS cloud platform and performing data processing and notifications via a web application provides a scalable and reliable environment. This program, written in Python and Flask, uses Plotly Dash for visualization.

[0707] The terminal functions as an interface for direct user interaction. This interface visualizes the progress, allowing users to make decisions based on that visualization. For example, if a machine failure is predicted, an alert is displayed on the terminal, and a notification is immediately sent to the maintenance personnel. The data received by the terminal is immediately sent to the server, and the status is updated in real time.

[0708] A concrete example is a system that monitors the operation of multiple robots installed in a factory. This system automatically checks the inventory status of parts and schedules replacements when a robot's parts are nearing the end of their lifespan. This prevents robot downtime and enables efficient operations.

[0709] An example of a prompt that utilizes a generative AI model is: "Design a system to proactively manage the maintenance of factory robots. Explain how to automatically generate maintenance tasks based on operational data and how to optimally allocate resources."

[0710] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0711] Step 1:

[0712] The server receives basic project information entered by the user via their terminal. This input data includes the project name, duration, budget, and required deliverables. Based on this data, the server uses a generative AI model to automatically divide tasks and establish dependencies between them. As a result, an initial project schedule is generated.

[0713] Step 2:

[0714] The server collects real-time operational data from the machine and stores it in a database. This data includes uptime, performance metrics, and error codes. Based on the collected data, analysis is performed to detect machine anomalies and generate maintenance tasks. As output, a task list prioritized by a maintenance prediction model is generated.

[0715] Step 3:

[0716] The terminal visualizes task lists and project progress information sent from the server and provides it to the user. Based on this information, the user can instantly check the progress and risk assessment. The visualization is provided in the form of Gantt charts and dashboards, allowing for an intuitive understanding of the current situation.

[0717] Step 4:

[0718] Users update progress via their devices, adding new information and resources. Information from the devices is immediately sent to the server, where it is integrated with the existing database and the data is updated. The updated data triggers risk alerts and resource reallocations, enabling dynamic project management.

[0719] Step 5:

[0720] The server optimizes scheduling and resource allocation, and generates progress reports as needed. These reports include information on the progress of tasks, which resources are in excess, and which are insufficient. These reports are automatically sent to the user to support project management decision-making.

[0721] 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.

[0722] To implement the present invention, we will describe a configuration that provides project management that takes user emotions into consideration by incorporating an emotion engine into the project management system. The operation of each component will be specifically described below.

[0723] server:

[0724] The server is equipped with an emotion engine that analyzes user input and communication data to detect the user's emotional state. Based on this information, it proposes appropriate communication tailored to the project's progress. For example, if a user is experiencing stress, the server suggests reviewing task priorities or redistributing tasks among team members. The server also uses emotional data to adjust risk assessments in the current project environment in real time.

[0725] Terminal:

[0726] The device provides an interface for receiving emotional input from the user. It features pull-down menus and emotional icons to allow users to easily record their emotional state. The device sends this emotional data to a server, where an emotional engine analyzes it and displays appropriate improvement suggestions to the user. For example, if it determines that project members' morale is low, it might suggest team-building activities to promote unity.

[0727] User:

[0728] Users record their emotions that may influence project progress and receive feedback from the system. They then review the task management improvements recommended by the emotion engine and share them with team members as needed to streamline the project.

[0729] For example, if a user is feeling pressured by a particular task, the emotion engine analyzes that emotional data and suggests adding resources or adjusting the schedule to alleviate the pressure. This allows the project management system to perform comprehensive management that considers not only technical elements but also human emotions, thereby improving the success rate of projects.

[0730] The following describes the processing flow.

[0731] Step 1:

[0732] The user inputs their emotional state into the device. The device displays an interface for selecting an emotion, and the user records their current emotion using an emotion icon or text.

[0733] Step 2:

[0734] The device sends emotion data entered by the user to the server. The transmitted data includes details of the emotion and information about related projects and tasks.

[0735] Step 3:

[0736] The server analyzes the received emotional data using an emotion engine. Based on the user's emotional state, it evaluates the risks and impacts on project progress and performance.

[0737] Step 4:

[0738] Based on the sentiment analysis results, the server generates risk alerts and proposes task prioritization adjustments. If necessary, it also considers reallocating resources and relaxing schedules.

[0739] Step 5:

[0740] The terminal notifies the user of suggestions from the server. The user reviews the presented information and considers and implements specific improvement measures.

[0741] Step 6:

[0742] If a user decides to implement a recommended improvement, they can use a feature to share that information with project members from their device. This information may include a revised task schedule and recommended team activities.

[0743] Step 7:

[0744] The server continues to monitor project progress and user sentiment data. It tracks progress and changes in sentiment at each project phase and suggests further interventions as needed.

[0745] Step 8:

[0746] This process is repeated at key milestones in the project. Based on user feedback and new sentiment data, project management strategies can be flexibly adjusted.

[0747] (Example 2)

[0748] 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".

[0749] In project management, not only technical factors but also the emotional state of individual users involved in the project can significantly impact its success. However, conventional project management systems do not adequately address user emotions. Therefore, there is a risk that emotional stress and anxiety experienced by users could negatively affect project progress. A mechanism to address this is needed.

[0750] 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.

[0751] In this invention, the server includes means for analyzing the user's emotional state and proposing improvements to project management based on the results, means for proposing changes in task priorities and the addition of resources, and means for analyzing feedback and presenting improvements that take into account emotional factors in project management. This makes it possible to manage projects while taking the user's emotions into consideration.

[0752] "Project planning" is the process of systematically organizing the tasks, resources, and schedule necessary to achieve the project's objectives.

[0753] "Task dependencies" refer to relationships where the completion of one task influences the start or progress of other tasks.

[0754] "Optimizing a schedule" refers to the process of adjusting the start and end times of each task to the most optimal form in order to ensure the efficient progress of a project.

[0755] "Analyzing a user's emotional state" refers to the process of analyzing and understanding a user's psychological state based on emotional data entered by the user.

[0756] "Proposing improvements to project management" means analyzing the current situation and presenting concrete action plans in order to improve the progress and results of a project.

[0757] "Real-time risk assessment" refers to the process of continuously reviewing and evaluating potential problems and obstacles in accordance with the progress of the project.

[0758] "Displaying alerts to users" refers to the act of providing users with real-time notifications to inform them of important points or dangers.

[0759] "Visualizing and updating project progress" refers to the process of clearly displaying the current progress of a project and ensuring that the latest information is always reflected.

[0760] "Generating and distributing progress reports to users" refers to the act of creating a report summarizing information about the progress of a project and delivering it to users.

[0761] "Analyzing feedback" refers to the process of analyzing opinions and information received from users and stakeholders, understanding their content, and using that information to inform future strategies.

[0762] This invention provides a project management system that integrates an emotion engine. The system consists of a server, terminals, and users, and each element works in cooperation with the others.

[0763] The server runs an analysis system equipped with an emotion engine, analyzing user input and communication history using data analysis algorithms such as natural language processing tools and machine learning algorithms. This identifies the user's emotional state and generates improvement strategies for project management as needed. These improvement strategies range from re-evaluating task priorities and reallocating resources to proposing team-building activities. The server manages emotion data and project data using an SQL database.

[0764] The device accepts emotional input through its user interface and enables intuitive operation using pull-down menus and emotional icon functions. The collected emotional data is sent to the server in real time, and the analysis results and improvement suggestions from the server are notified on the display device. The device can be deployed as a browser application or mobile application, using web technologies such as HTML5 and JavaScript.

[0765] Users input and record their emotional data through their devices and consider improvement measures suggested by the server. For example, if a user inputs emotional data indicating they are feeling "pressure towards Task A," the server analyzes this data and suggests rescheduling the task or adding resources. The suggested changes are also notified to the user via push notification and displayed on the project management interface.

[0766] A concrete example of a prompt would be, "When a user feels anxious about a meeting, how does the server identify the cause and what solutions does it offer?" Inputting such prompts into an AI model can sometimes yield further suggestions for improvement.

[0767] This system achieves more effective project management by actively incorporating not only technical elements but also user emotions.

[0768] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0769] Step 1:

[0770] The device receives emotional data from the user as input. The user selects their emotional state using pull-down menus or emotional icon functions. This input data is comprised of an interface that enables accurate recording of the user's emotions. Specifically, the type and intensity of the selected emotion, as well as any additional comments, are recorded as data.

[0771] Step 2:

[0772] The device encrypts the collected emotional data and sends it to the server, ensuring the security of the emotional data. The transmitted data is received by the server and processed as input data for analysis. During this process, pre-processing is performed, such as standardizing the data format and removing redundant data.

[0773] Step 3:

[0774] The server analyzes input data using an emotion engine. It employs natural language processing algorithms and machine learning models to identify emotional states as numerical data, and uses this to understand the user's psychological tendencies. The generative AI model is controlled by prompts, ensuring that appropriate analysis results are obtained. The analysis results include classifications of emotional states and related topics.

[0775] Step 4:

[0776] The server generates improvement proposals for project management based on the analysis results. These proposals include suggestions for prioritizing tasks, reviewing responsibilities, and reallocating resources. These proposals are generated using integrated information on sentiment data and project progress and are output as improvement proposals.

[0777] Step 5:

[0778] The terminal displays improvement suggestions received from the server in its user interface. The user reviews these suggestions and, if necessary, accepts them and incorporates them into their task plan. The terminal is designed to display the improvement suggestions in charts and lists for intuitive understanding. Buttons and links are also provided for selecting specific actions.

[0779] (Application Example 2)

[0780] 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".

[0781] In modern manufacturing, human emotions can influence work efficiency and safety. However, conventional automation systems are unable to adequately address worker emotions, and therefore do not provide effective support for human motivation and stress management. For this reason, there is a need to provide an efficient work environment that takes worker emotions into consideration.

[0782] 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.

[0783] In this invention, the server includes means for automatically generating a project plan, means for detecting the user's emotional state and suggesting work optimizations, and means for adjusting the robot's actions based on emotional data. This makes it possible to provide an efficient and flexible work environment based on the worker's emotions.

[0784] 1. "Means for automatically generating project plans" refers to a function that has an algorithm that efficiently and automatically allocates the work timeline and necessary resources by inputting project requirements.

[0785] 2. "Methods for analyzing dependencies between tasks and optimizing the schedule" refers to the process of analyzing the order, priority, and interrelationships of individual tasks to ensure optimal work progress.

[0786] 3. "A means of performing real-time risk assessment and displaying alerts to users" refers to a system that assesses the current work status and potential problems in real time and notifies users of appropriate warnings and improvement suggestions.

[0787] 4. "Means for visualizing and updating project progress in real time" refers to technologies that allow users to check the progress of work in real time and display the latest information on the user interface.

[0788] 5. "Means for automatically generating and distributing progress reports to users" refers to a method of organizing progress information based on collected data and efficiently providing reports to users.

[0789] 6. "Means for detecting the user's emotional state and proposing work optimization" refers to technology that senses the user's psychological state and presents work improvement suggestions aimed at reducing stress and motivating them.

[0790] 7. "Means for adjusting robot operation based on emotional data" refers to a mechanism that adjusts and optimizes the operation of automated work equipment according to collected emotional information.

[0791] 8. "Means for efficient work distribution according to the work environment" refers to a system that optimally allocates work resources according to the current operating status and individual workloads, thereby increasing productivity.

[0792] This invention is a system that detects the emotional state of workers in a factory environment in real time and optimizes work processes based on that data.

[0793] First, the smart glasses, acting as the device, are equipped with a facial recognition camera and a pulse rate measurement function. This hardware allows factory workers to efficiently detect changes in their emotions while they are working. The emotional data is transmitted from the device to a server via Wi-Fi.

[0794] On the server, analysis software equipped with an emotion engine is running. Emotional data is input into a machine learning model using TensorFlow and analyzed in real time. The analysis results are fed back to supervisors and automated work machines, enabling adjustments to work speed and task reallocation.

[0795] The user, i.e., the factory supervisor, receives emotion-based improvement suggestions from this system. The user can then adjust the work environment according to these suggestions, aiming to improve work efficiency and reduce the mental burden on workers.

[0796] For example, if a worker's stress level exceeds a certain level, the server can transmit this information to the robot, which can then take measures to reduce its work speed by 20%. In this way, flexible work processing can be performed in response to the environment.

[0797] Examples of prompts for a generative AI model are as follows:

[0798] "Design a system that utilizes worker emotional data in a factory environment to optimize the operation of production robots. How can this system improve work efficiency and reduce worker stress?"

[0799] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0800] Step 1:

[0801] The smart glasses, acting as the terminal, detect the worker's facial expressions and pulse rate in real time and acquire them as emotion data. Inputs are video data from a facial recognition camera and pulse rate measurement data, while output is emotion data for analysis. Specifically, the facial expression data is analyzed and classified using a video processing algorithm.

[0802] Step 2:

[0803] The device transmits the acquired emotion data to the server via Wi-Fi. The input is the emotion data obtained in step 1, and the output is the data transmitted to the server. Specifically, data encryption is performed to securely establish data communication to the server.

[0804] Step 3:

[0805] The server drives an emotion engine and analyzes the received emotion data. The input is emotion data sent from the terminal, and the output is evaluation data of the emotional state as a result of the analysis. Specifically, it uses TensorFlow to input data into a machine learning model and estimate the emotional state.

[0806] Step 4:

[0807] The server proposes optimized movements for the production robot based on the analyzed emotional state. The input is the emotional state evaluation data obtained in step 3, and the output is the instruction for movement optimization. Specifically, it activates an algorithm that adjusts the robot's work speed and work distribution according to the emotional evaluation.

[0808] Step 5:

[0809] The factory supervisor, acting as the user, adjusts the work environment based on suggestions received from the server. The input is operation optimization instructions from the server, and the output is the adjusted work environment. Specifically, the supervisor uses the robot's control panel to set speed and task allocation.

[0810] Step 6:

[0811] The server waits for new emotion input and repeats the process from step 1. The input is the latest emotion data, and the output is the updated robot behavior parameters. Specifically, it maintains a continuous real-time data processing cycle.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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."

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] The following is further disclosed regarding the embodiments described above.

[0834] (Claim 1)

[0835] A means of automatically generating project plans,

[0836] A means to analyze the dependencies between tasks and optimize the schedule,

[0837] A means of performing real-time risk assessment and displaying alerts to users,

[0838] A means to visualize and update the project's progress in real time,

[0839] A system that includes means for automatically generating and distributing progress reports to users.

[0840] (Claim 2)

[0841] The system according to claim 1, which analyzes resource information entered by the user and proposes the optimal resource allocation.

[0842] (Claim 3)

[0843] The system according to claim 1, which extracts feedback on a project and presents it as a measure to improve project management.

[0844] "Example 1"

[0845] (Claim 1)

[0846] A means of automatically generating project plans based on an AI model,

[0847] A means of dividing tasks based on basic project information entered by the user, analyzing dependencies, and creating a schedule,

[0848] A means of analyzing user resource information and optimizing resource allocation for the entire project,

[0849] A means to receive project progress in real time and visualize the progress,

[0850] A means of analyzing progress data to assess risks in real time and notifying users with alerts,

[0851] A system that includes means of providing users with information for decision-making based on project progress and risk information.

[0852] (Claim 2)

[0853] The system according to claim 1, which transmits task information and progress status entered by the user to the server in real time and displays the updated information.

[0854] (Claim 3)

[0855] The system according to claim 1, which automatically generates project progress reports using a generative AI model and delivers them to users.

[0856] "Application Example 1"

[0857] (Claim 1)

[0858] A means of automatically generating project plans,

[0859] A means to analyze the dependencies between tasks and optimize the schedule,

[0860] A means of performing real-time risk assessment and displaying alerts to users,

[0861] A means to visualize and update the project's progress in real time,

[0862] A means of automatically generating and distributing progress reports to users,

[0863] A means for collecting machine operation data and automatically generating maintenance tasks,

[0864] A means of optimally allocating and managing the resources required for each task.

[0865] A system that includes this.

[0866] (Claim 2)

[0867] The system according to claim 1, which analyzes resource information entered by the user, proposes the optimal resource allocation, and efficiently schedules machine maintenance.

[0868] (Claim 3)

[0869] The system according to claim 1, which extracts feedback on a project, presents it as a measure to improve project management, and proposes preventive maintenance based on the operating status of the machinery.

[0870] "Example 2 of combining an emotion engine"

[0871] (Claim 1)

[0872] A means of automatically generating project plans,

[0873] A means to analyze the dependencies between tasks and optimize the schedule,

[0874] A means of analyzing the emotional state of users and proposing improvement measures for project management based on the results,

[0875] A means of performing real-time risk assessment and displaying alerts to users,

[0876] A means to visualize and update the project's progress in real time,

[0877] A system that includes means for automatically generating and distributing progress reports to users.

[0878] (Claim 2)

[0879] The system according to claim 1, which analyzes emotional data entered by the user and proposes changes in task priorities or the addition of resources based on the results.

[0880] (Claim 3)

[0881] The system according to claim 1, which analyzes feedback collected from users and presents improvement measures that take into account emotional factors in project management.

[0882] "Application example 2 when combining with an emotional engine"

[0883] (Claim 1)

[0884] A means of automatically generating project plans,

[0885] A means to analyze the dependencies between tasks and optimize the schedule,

[0886] A means of performing real-time risk assessment and displaying alerts to users,

[0887] A means to visualize and update the project's progress in real time,

[0888] A means of automatically generating and distributing progress reports to users,

[0889] A means to detect the user's emotional state and suggest optimizations for their work,

[0890] A means of adjusting the robot's actions based on emotional data,

[0891] A system that includes means for efficiently dividing tasks according to the work environment.

[0892] (Claim 2)

[0893] The system according to claim 1, which analyzes resource information entered by the user and proposes the optimal resource allocation.

[0894] (Claim 3)

[0895] The system according to claim 1, which extracts feedback on a project and presents it as a measure to improve project management. [Explanation of Symbols]

[0896] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of automatically generating project plans, A means to analyze the dependencies between tasks and optimize the schedule, A means of performing real-time risk assessment and displaying alerts to users, A means to visualize and update the project's progress in real time, A means of automatically generating and distributing progress reports to users, A means for collecting machine operation data and automatically generating maintenance tasks, A means of optimally allocating and managing the resources required for each task. A system that includes this.

2. The system according to claim 1, which analyzes resource information entered by the user, proposes the optimal resource allocation, and efficiently schedules machine maintenance.

3. The system according to claim 1, which extracts feedback on a project, presents it as a measure to improve project management, and proposes preventive maintenance based on the operating status of the machinery.

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