system

Abstract

We provide the system. [Solution] A means of collecting multiple data and analyzing the content of each data, A means for detecting and adjusting for duplicate data based on the analysis results, A means for generating an optimized schedule using adjusted data, A means of distributing the generated schedule to stakeholders, A means to continuously monitor progress and readjust the schedule as needed, A means of automatically generating and distributing progress reports, A system that includes this.

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, when multiple departments are involved, there are problems such as easy occurrence of information duplication or shortage, lack of schedule adjustment, and delay in grasping the progress status. As a result, the efficiency of the project decreases and there is a risk of delay. Solving such problems and effectively and efficiently managing the project are required.

Means for Solving the Problems

[0005] This system provides means for collecting multiple data sets and analyzing the content of each data set, for detecting and adjusting duplicate data based on the analysis results, and for generating an optimized schedule using the adjusted data. Furthermore, it provides means for distributing the generated schedule to stakeholders, continuously monitoring progress, and readjusting the schedule as needed. In this way, it provides a system that solves these problems by automatically generating and distributing progress reports.

[0006] "Data" refers to the form of numbers, strings, and other information collected to represent information.

[0007] "Analysis" is the process of examining the content and structure of data and extracting useful information and patterns.

[0008] "Duplicate" refers to a state in which identical or similar information exists multiple times.

[0009] "Adjustment" refers to making changes to make different elements or conditions compatible with each other.

[0010] A "schedule" is a plan that outlines the order and timeline for the execution of activities and tasks.

[0011] An "interested party" is an individual or organization that influences, or is influenced by, the progress of a particular project or operation.

[0012] "Progress status" refers to the current level of completion or outcome of a project or task.

[0013] "Monitoring" means continuously observing a specific object or process and recording any changes.

[0014] A "report" is a document that summarizes specific events or results and presents them in a specified format. [Brief explanation of the drawing]

[0015] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a tagged 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.

[0019] In the following embodiments, a tagged RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, a tagged storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0021] 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).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] This invention relates to a system using an AI agent for optimizing project management. This system can prevent information duplication and omissions in projects involving multiple departments, and efficiently manage schedules.

[0037] The system begins with the server collecting task information from terminals in each department. This task information includes the task name, content, due date, and assigned person. The terminals transmit the information entered by the user to the server using a stable communication method. The server then passes the collected information to an AI model for analysis. This AI model uses natural language processing to examine the task content and identify duplications and dependencies.

[0038] Based on the analysis results from the AI ​​model, the server consolidates tasks that have been identified as duplicates and generates an efficient schedule. The generated schedule is automatically displayed on the screen. The server also continuously monitors the progress and understands the status of each task in real time.

[0039] Users can check the status of ongoing tasks through their terminals and send feedback to the server as needed. Based on this feedback, the server immediately readjusts the schedule and optimizes resource allocation. Furthermore, the system automatically generates progress reports and distributes them regularly to stakeholders. These reports visually represent project progress and risks, supporting stakeholder decision-making.

[0040] As a concrete example, consider a new product development project. If the sales department enters market research tasks, and the marketing department also enters similar tasks, the server will detect and consolidate these duplicates, optimizing resources. Progress is updated regularly, allowing each person in charge to always have access to the latest information.

[0041] Thus, the present invention enables the efficient and smooth progress of projects.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The terminal collects task information entered by the user. This includes the task name, content, deadline, and assigned person. This information is then transmitted to the server using a stable communication method.

[0045] Step 2:

[0046] The server stores task information received from terminals in a database. Simultaneously, it collects necessary data from project management tools and external systems.

[0047] Step 3:

[0048] The server sends the collected task data to an AI model for analysis using natural language processing. At this stage, the content of the tasks is reviewed, and duplicates and dependencies are identified.

[0049] Step 4:

[0050] The server detects task duplication based on the AI ​​model's analysis results and merges tasks if necessary. It also generates an optimal task schedule, taking dependencies into consideration.

[0051] Step 5:

[0052] The server notifies stakeholders of the generated schedule. Users can check the updated schedule via their terminals and understand their respective work responsibilities.

[0053] Step 6:

[0054] The server monitors the project's progress in real time and continuously collects progress data. If necessary, it suggests reallocating available resources and adjusting the schedule.

[0055] Step 7:

[0056] Users can monitor the status of ongoing tasks through their devices and send any problems or comments as feedback to the server. Based on this feedback, the server readjusts the schedule.

[0057] Step 8:

[0058] The server automatically generates project progress reports and distributes them regularly to stakeholders. These reports include progress status, risk analysis, and next steps.

[0059] (Example 1)

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

[0061] In project management, when multiple departments are involved, challenges arise such as overlapping tasks and lack of information, leading to wasted resources and decreased efficiency. Furthermore, it becomes difficult to track the progress of each task in real time and smoothly adjust the plan.

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

[0063] In this invention, the server includes means for collecting multiple pieces of information and analyzing the attributes of each piece of information; means for detecting and integrating the duplication of each piece of information based on the analysis results; and means for generating an optimized plan using the integrated information. This makes it possible to avoid information duplication and adjust an efficient plan in real time.

[0064] "Information" refers to all data related to a project, including tasks and their attributes.

[0065] "Attributes" refer to the characteristics of each element of information, such as name, content, date, and person in charge.

[0066] "Analysis" refers to the process of evaluating the attributes of information to identify duplication and dependencies.

[0067] "Duplicate" refers to a state where multiple pieces of information contain the same content.

[0068] "Integration" refers to the process of combining redundant information into a single, more efficient format.

[0069] "Planning" refers to a set of scheduled steps designed to efficiently carry out the tasks of a project.

[0070] "Providing" refers to the process of communicating the generated plans and progress reports to stakeholders.

[0071] "Progress status" refers to the current state of the project's tasks.

[0072] "Tracking" refers to the process of monitoring the progress of a task in real time.

[0073] "Adjustment" refers to the process of updating and optimizing the plan according to the progress made.

[0074] A "progress report" refers to a document that summarizes the current status of a project.

[0075] A "visual interface" refers to a screen display format that allows users to visually confirm information.

[0076] "Opinions" refer to feedback and suggestions provided by stakeholders.

[0077] An "artificial intelligence model" refers to an algorithm used for analyzing information and identifying dependencies.

[0078] This invention provides a system for streamlining project management, in which a server collects information from multiple terminals and performs analysis using an artificial intelligence model. Terminals transmit information about tasks entered by users to the server via a stable communication method (e.g., HTTPS). The server receives this information and passes it to a generative AI model implemented in a programming language such as Python. The AI ​​model uses natural language processing techniques to identify information duplication and dependencies.

[0079] Specifically, the server analyzes information, integrates similar information, and reorganizes it into a single plan. This plan is provided to the user using visualization tools such as Gantt charts, enabling efficient project management. Users can review this plan and progress reports through a visual interface and send feedback to the server as needed. Based on this feedback, the server readjusts the plan and optimizes resources.

[0080] For example, in a new product development project, if the sales and marketing departments each enter market research tasks separately, the server will detect any overlap and consolidate them. This allows users to always have the latest progress information, reduce wasted resources, and manage projects efficiently.

[0081] An example of a prompt to input into a generating AI model is, "Please tell me how to detect and consolidate overlapping market research tasks in a new product development project." This prompt allows the AI ​​model to perform appropriate analysis and help in formulating an optimal plan.

[0082] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0083] Step 1:

[0084] The terminal retrieves task information entered by the user. The user enters the task name, content, due date, assignee, etc. into the terminal. The terminal formats this data and prepares it to be sent to the server using a stable communication method (e.g., HTTPS protocol).

[0085] Step 2:

[0086] The server receives task information sent from the terminal. The received information is stored in a temporary database, and its reliability is verified. For example, the server checks for missing data or format inconsistencies. This ensures that the data is valid for the next analysis stage.

[0087] Step 3:

[0088] The server provides task information stored in the database to the generating AI model. Specifically, the server applies natural language processing algorithms via programming languages ​​such as Python to analyze information duplication and dependencies. Here, the input is task information, and the output is a list of duplicate tasks and a dependency map.

[0089] Step 4:

[0090] The server consolidates overlapping tasks and creates an efficient plan based on the analysis results from the AI ​​model. During this process, the server generates a schedule and visualizes it in a Gantt chart, etc. The input is information on overlapping tasks, and the output is an optimized schedule.

[0091] Step 5:

[0092] The server provides the generated plan to stakeholders and monitors the overall project progress. Here, it's crucial to collect data in real time and track progress to ensure the plan is implemented accurately.

[0093] Step 6:

[0094] Users check plans and progress through their terminals and send feedback to the server as needed. This feedback includes task progress and new requests. The input is user feedback, and the output is an updated plan.

[0095] Step 7:

[0096] The server receives user feedback, readjusts the plan, and reconsiders the appropriate allocation of resources. Finally, it automatically generates a progress report and provides it to stakeholders. The report details the current progress and risks of the project.

[0097] (Application Example 1)

[0098] 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."

[0099] In modern manufacturing environments, managing production processes involving multiple work lines and departments often leads to problems such as information redundancy, omissions, and insufficient communication among stakeholders. Furthermore, the difficulty in flexibly adjusting plans in line with progress contributes to decreased production efficiency. Therefore, efficient methods for solving these problems are needed.

[0100] 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.

[0101] In this invention, the server includes a device for collecting multiple pieces of information and analyzing the content of each piece of information, a device for detecting and adjusting the redundancy of each piece of information based on the analysis results, and a device for generating an optimized plan using the adjusted information. This enables efficient plan management and flexible plan modification in the production environment.

[0102] "Information" is a collection of data, something that is collected and analyzed within a system.

[0103] A "device" is a combination of hardware and software used to perform a specific function.

[0104] "Analysis" is the process of examining information in detail and clarifying its characteristics and relationships.

[0105] "Redundancy" refers to a state where there is an excess of information or functions, and it is a factor that hinders efficiency.

[0106] "Adjustment" is the act of organizing and integrating information to resolve duplication and contradictions and achieve an optimal state.

[0107] A "plan" is a set of guidelines, including procedures and schedules, established to achieve a specific objective.

[0108] "Stakeholders" refers to individuals or groups involved in the use and management of the plan or system.

[0109] A "report" is a document or data used to record and present progress and results, and to communicate them to relevant parties.

[0110] In the system that realizes this invention, a server plays a central role. The server receives multiple pieces of information from terminals and analyzes their contents. Specifically, it uses an AI model to detect redundancy and dependencies in the information and generates an optimized plan. The generated plan is distributed to stakeholders through a visual interface. Furthermore, the server monitors the progress in real time and modifies the plan as needed.

[0111] The terminal's role is to transmit information entered by the user to the server. This information concerns ongoing tasks and their details, and is analyzed on the server for efficient management. Users can check the latest schedule through the terminal and provide feedback back to the server.

[0112] This system uses Python and specific task analysis models to optimize planning for a particular work line, enabling stakeholders to work efficiently. The reports generated by the server also use a graphical interface to visually represent progress. A concrete example is streamlining processes in a product assembly line. An example of a prompt is: "Propose an optimal schedule for a project with the following task information: task name, details, due date, and assignee."

[0113] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0114] Step 1:

[0115] The terminal receives work information from the user. The user enters data such as the task name, details, deadline, and assigned person, and sends it to the server using a stable communication method. JSON format is the most common input data.

[0116] Step 2:

[0117] The server receives information sent from the terminal. The server analyzes the information using a generative AI model to identify redundancy and dependencies. For data processing, natural language processing is used to structure the text data and detect specific patterns. The analysis results output information on whether the task is novel or duplicates an existing task.

[0118] Step 3:

[0119] The server generates an optimized plan based on the analysis results. The server uses a scheduling algorithm to calculate the optimal task sequence between processes. Using the analysis results as input, it generates a proposed schedule as output. The generated schedule includes a detailed timeline with tasks assigned to each person.

[0120] Step 4:

[0121] The server visualizes the generated plan and sends it to the user's terminal for review. Using a graphical user interface, the schedule is displayed visually. Each task's progress, deadline, and assigned person are color-coded for easy identification. The output is provided on a user-operable dashboard screen.

[0122] Step 5:

[0123] The user reviews the displayed plan using their device and provides feedback if necessary. The feedback information is sent to the server and used as input for revising the plan.

[0124] Step 6:

[0125] The server readjusts the plan based on the feedback it receives. It analyzes the feedback, modifies the baseline parameters, and generates a new schedule. This enables efficient plan revisions. The server then sends the final plan back to the terminal.

[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] This invention is a project management system incorporating an emotion engine, which aims to optimize project management based on user emotion information. This system utilizes emotion data to more accurately coordinate information and manage schedules among stakeholders in a project.

[0128] First, the terminal receives task information input from the user. The emotion engine then identifies the user's emotional state based on the user's input and data obtained from the terminal. This emotional data is analyzed using a specific algorithm and sent to the server.

[0129] In addition to collecting regular task information, the server also receives user sentiment information. This sentiment information is input into an AI model to understand the overall sentiment trend of the project. Task progress management and scheduling are optimized to take user sentiment into account.

[0130] For example, if a user is experiencing high levels of stress, the server will detect this and readjust the schedule to reduce the workload. This may involve changing task assignments or adjusting resource allocation. Furthermore, if a user is highly motivated towards a particular task, the server will take this into consideration and improve project efficiency by changing its priority.

[0131] Furthermore, the server monitors progress and user feedback, and by taking emotional states into account in the feedback, it provides more appropriate adjustments. Emotional data is also reflected in project reports, making it easier for stakeholders to visually understand the project's status.

[0132] For example, in a new product development project, adjustments are made based on the emotional state of team members. Highly motivated members are assigned more challenging tasks, while members experiencing stress receive more support.

[0133] In this configuration, the system takes into account the human factors of the project and supports efficient and smooth project execution.

[0134] The following describes the processing flow.

[0135] Step 1:

[0136] The device uses its camera and microphone to collect the user's facial expressions and voice as they input task information. This data is then sent to an emotion engine for analysis.

[0137] Step 2:

[0138] The emotion engine analyzes collected facial and voice data to identify the user's emotional state. This emotion data is then sent to the server for subsequent processing.

[0139] Step 3:

[0140] The server receives emotional information and regular task data and inputs it into the AI ​​model. The AI ​​model uses the emotional information, along with the task content and priorities, to optimize the entire project.

[0141] Step 4:

[0142] The server adjusts schedules and resource allocation based on user emotional information. For example, it reduces the workload for highly stressed users and assigns challenging tasks to highly motivated users.

[0143] Step 5:

[0144] The server distributes the adjusted schedule to stakeholders. Through their terminals, users can check the latest schedule and confirm that sentiment-based optimizations have been reflected.

[0145] Step 6:

[0146] The server re-evaluates user feedback along with the sentiment engine throughout the project, and makes further adjustments to the schedule and resource allocation as needed.

[0147] Step 7:

[0148] The server automatically generates reports summarizing project progress and emotional data, and distributes them regularly to stakeholders. These reports visually communicate the emotional health of the project along with its progress.

[0149] (Example 2)

[0150] 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".

[0151] In project management, optimizing progress and processes without considering the emotional states of stakeholders can lead to decreased efficiency and cause anxiety and stress among stakeholders. Furthermore, traditional systems often lack clarity on how to reflect stakeholders' emotional information in process management, potentially hindering project progress. To address this challenge, a system capable of analyzing and reflecting stakeholders' emotional states in real time is needed.

[0152] 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.

[0153] In this invention, the server includes means for collecting multiple pieces of information and analyzing the content of each piece of information; means for identifying and adjusting the emotional state of each piece of information based on the analysis results; and means for generating an optimized schedule using the identified emotional data. This enables efficient project management and smooth progress that takes into account the emotional states of stakeholders.

[0154] "Information" refers to data related to project management and data that expresses the associated emotional states.

[0155] "Analysis" is the act of extracting meaning from information and then performing data processing or making judgments based on that meaning.

[0156] "Emotional state" refers to data that indicates the psychological and emotional condition of those involved.

[0157] "Adjustment" refers to the process of reviewing and optimizing project steps and tasks based on analysis and identified information.

[0158] A "project schedule" is a plan used to manage the progress and schedule of a project, and is distributed to stakeholders for the purpose of optimization.

[0159] "Stakeholders" refers to all individuals and organizations participating in or related to the project.

[0160] This project management system utilizes an emotion engine and generative AI models to achieve optimized project management that takes into account the emotional states of stakeholders.

[0161] Terminal role and operation:

[0162] The terminal accepts task information directly entered by the user. For example, when a user enters a new task, they write the task name, due date, importance level, and related documents in the input fields. The terminal receives this information and activates the emotion engine. This emotion engine analyzes subtle changes during input, such as the speed and strength of keystrokes, and the tone of voice in voice input, to identify the user's emotional state.

[0163] Server roles and operations:

[0164] The server receives emotional data and task information transmitted from terminals. This received data is analyzed through a generative AI model to identify the overall emotional trend in the project. Based on this emotional trend, the server optimizes the project schedule and adjusts task assignments and schedules as needed. For example, if some users show high stress levels, the server readjusts the schedule to reduce their burden. It also assigns higher-priority tasks to users who are deemed highly motivated.

[0165] Examples of specific cases and prompt statements:

[0166] In project management for a new product development team, the system tracks each member's emotional state in real time and adjusts the schedule based on that emotional data. Support resources are added for members experiencing stress, and challenging tasks are prioritized for highly motivated members. This improves overall team efficiency and morale.

[0167] An example of a prompt message would be, "Analyze the emotional state of each member in the current project and propose the optimal task assignments and schedule adjustments."

[0168] In this way, this system enables project management that incorporates emotions, supporting efficient and harmonious progress.

[0169] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0170] Step 1:

[0171] The terminal receives task information from the user. The user enters detailed information about the project task (e.g., task name, deadline, priority, etc.) into the terminal. The terminal passes this information to the emotion engine, which analyzes the keystrokes and voice tone during input. The emotion engine analyzes this input data to identify the user's emotional state (e.g., joy, stress). The identified emotion data and task information are sent from the terminal to the server.

[0172] Step 2:

[0173] The server receives emotional data and task information sent from the terminal. The server analyzes the emotional data through a generative AI model. This extracts the emotional trends for the entire project and aggregates the individual emotional data of each member. This data processing makes it possible to understand the emotional trends and prepares the data for use in project management.

[0174] Step 3:

[0175] The server uses a generative AI model to optimize the schedule based on sentiment data. Specifically, it adjusts task schedules and changes task assignments as needed based on sentiment trends predicted by the model. The input is sentiment and task data, and the output is the optimized schedule. The server updates this schedule in real time, allowing stakeholders to stay informed of the situation.

[0176] Step 4:

[0177] The server distributes the optimized schedule to stakeholders. The schedule is distributed via email or a dedicated project management tool. The server periodically collects feedback from stakeholders and prepares to revise the schedule again, incorporating sentiment information. Further improvements are then made based on this information.

[0178] Step 5:

[0179] The server continuously monitors progress and compares AI model predictions with actual results. If emotional fluctuations affecting project progress are detected, the server automatically readjusts the schedule. Output regarding the readjustment is generated and notified to stakeholders. This ensures that optimization is always maintained.

[0180] (Application Example 2)

[0181] 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".

[0182] In modern project management and production line operations, simply managing schedules presents a challenge: it fails to take into account human emotional states and motivations. This makes it difficult to adjust tasks or adapt machine operations to the emotions of stakeholders, ultimately hindering efficient and flexible operations.

[0183] 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.

[0184] In this invention, the server includes means for collecting multiple pieces of information and analyzing the content of each piece of information, means for identifying emotional states and adjusting workload and priorities, and means for adjusting the operation of machines / devices using emotional data. This enables flexible and efficient project management that reflects the emotional states of stakeholders.

[0185] "Information" refers to data related to a project or production, showing the progress of work and the status of stakeholders.

[0186] "Analysis" is the act of thoroughly evaluating the content of collected information and identifying specific patterns or trends.

[0187] "Schedule" refers to a time plan for carrying out each task in the execution of a project or task.

[0188] "Stakeholders" refers to all individuals and groups affected by or involved in a project or production activity.

[0189] "Progress status" refers to information indicating the degree of progress in work within a project or production line.

[0190] "Emotional state" refers to data that indicates the psychological and emotional condition of those involved.

[0191] "Workload" refers to the quantity and quality of specific tasks and work assigned to stakeholders.

[0192] "Priority" refers to the order in which tasks and projects are carried out, determined based on their importance and urgency.

[0193] "Machine / device operation adjustment" refers to the act of making changes to optimize the movement of machines or devices based on collected data.

[0194] The system realizing this invention first receives task information input from the user via a terminal. At this time, to identify the user's emotional state, it utilizes cameras and sensors to acquire facial expression data and biometric information. The hardware can be general-purpose camera devices and biosensors, and the software combines OpenCV and TENSORFLOW® for emotion analysis. This data is sent to a server, where further data analysis is performed.

[0195] The server uses this data to analyze multiple pieces of information and understand the progress of projects and tasks. Emotional data is input into a generative AI model, which generates an optimized schedule that reflects the emotional state of stakeholders. During this process, the workload and priorities are adjusted considering the stress levels and motivation of workers, and the operation of machines and equipment is adjusted as needed.

[0196] For example, in an assembly line for new equipment, if an emotion analysis system detects a worker experiencing high stress levels, the work sequence and machine operating speed are adjusted to reduce that worker's workload. This adjustment improves work efficiency and increases satisfaction among stakeholders.

[0197] An example of a prompt message from a generated AI model is: "Consider the emotional state of the workers on the current production line and propose specific improvement plans to increase efficiency."

[0198] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0199] Step 1:

[0200] The terminal receives task information from the user. During this process, the terminal uses its camera and sensors to acquire the user's facial expression data and biometric information, which is then added to the input information. The input data includes task information along with images and biometric information necessary for emotion estimation.

[0201] Step 2:

[0202] The device uses OpenCV and TensorFlow to analyze the user's emotional state from acquired facial expression data and biometric information. In this emotion analysis step, image processing is performed to extract features, and an AI model is used to estimate emotion labels. The analyzed emotional state is output, and data with that information attached is generated.

[0203] Step 3:

[0204] Based on the analysis results, the terminal sends relevant data to the server. The server organizes the received task information and sentiment data and inputs it into the schedule generation algorithm. The server then outputs a schedule that optimizes task priorities and worker assignments.

[0205] Step 4:

[0206] The server generates an optimized schedule and distributes it to stakeholders. This distribution step sends schedule details to stakeholders' devices, allowing them to visually review the schedule. Information is organized in an easy-to-understand format to ensure project efficiency.

[0207] Step 5:

[0208] The server continuously monitors the progress and the sentiment of stakeholders, and readjusts the schedule as needed. In this step, data analysis is performed again, and an optimized schedule is generated based on the newly obtained information.

[0209] Step 6:

[0210] The server automatically generates and distributes a final progress report to stakeholders. This report includes information on project progress and changes in each worker's sentiment. The report is presented in a clear and intuitive format, allowing stakeholders to easily grasp the overall situation.

[0211] 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.

[0212] Data generation model 58 is a 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.

[0213] 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.

[0214] [Second Embodiment]

[0215] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0216] 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.

[0217] 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).

[0218] 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.

[0219] 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.

[0220] 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).

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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".

[0227] This invention relates to a system using an AI agent for optimizing project management. This system can prevent information duplication and omissions in projects involving multiple departments, and efficiently manage schedules.

[0228] The system begins with the server collecting task information from terminals in each department. This task information includes the task name, content, due date, and assigned person. The terminals transmit the information entered by the user to the server using a stable communication method. The server then passes the collected information to an AI model for analysis. This AI model uses natural language processing to examine the task content and identify duplications and dependencies.

[0229] Based on the analysis results from the AI ​​model, the server consolidates tasks that have been identified as duplicates and generates an efficient schedule. The generated schedule is automatically displayed on the screen. The server also continuously monitors the progress and understands the status of each task in real time.

[0230] Users can check the status of ongoing tasks through their terminals and send feedback to the server as needed. Based on this feedback, the server immediately readjusts the schedule and optimizes resource allocation. Furthermore, the system automatically generates progress reports and distributes them regularly to stakeholders. These reports visually represent project progress and risks, supporting stakeholder decision-making.

[0231] As a concrete example, consider a new product development project. If the sales department enters market research tasks, and the marketing department also enters similar tasks, the server will detect and consolidate these duplicates, optimizing resources. Progress is updated regularly, allowing each person in charge to always have access to the latest information.

[0232] Thus, the present invention enables the efficient and smooth progress of projects.

[0233] The following describes the processing flow.

[0234] Step 1:

[0235] The terminal collects task information entered by the user. This includes the task name, content, deadline, and assigned person. This information is then transmitted to the server using a stable communication method.

[0236] Step 2:

[0237] The server stores task information received from terminals in a database. Simultaneously, it collects necessary data from project management tools and external systems.

[0238] Step 3:

[0239] The server sends the collected task data to an AI model for analysis using natural language processing. At this stage, the content of the tasks is reviewed, and duplicates and dependencies are identified.

[0240] Step 4:

[0241] The server detects task duplication based on the AI ​​model's analysis results and merges tasks if necessary. It also generates an optimal task schedule, taking dependencies into consideration.

[0242] Step 5:

[0243] The server notifies stakeholders of the generated schedule. Users can check the updated schedule via their terminals and understand their respective work responsibilities.

[0244] Step 6:

[0245] The server monitors the project's progress in real time and continuously collects progress data. If necessary, it suggests reallocating available resources and adjusting the schedule.

[0246] Step 7:

[0247] Users can monitor the status of ongoing tasks through their devices and send any problems or comments as feedback to the server. Based on this feedback, the server readjusts the schedule.

[0248] Step 8:

[0249] The server automatically generates project progress reports and distributes them regularly to stakeholders. These reports include progress status, risk analysis, and next steps.

[0250] (Example 1)

[0251] 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."

[0252] In project management, when multiple departments are involved, challenges arise such as overlapping tasks and lack of information, leading to wasted resources and decreased efficiency. Furthermore, it becomes difficult to track the progress of each task in real time and smoothly adjust the plan.

[0253] 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.

[0254] In this invention, the server includes means for collecting multiple pieces of information and analyzing the attributes of each piece of information; means for detecting and integrating the duplication of each piece of information based on the analysis results; and means for generating an optimized plan using the integrated information. This makes it possible to avoid information duplication and adjust an efficient plan in real time.

[0255] "Information" refers to all data related to a project, including tasks and their attributes.

[0256] "Attributes" refer to the characteristics of each element of information, such as name, content, date, and person in charge.

[0257] "Analysis" refers to the process of evaluating the attributes of information to identify duplication and dependencies.

[0258] "Duplicate" refers to a state where multiple pieces of information contain the same content.

[0259] "Integration" refers to the process of combining redundant information into a single, more efficient format.

[0260] "Planning" refers to a set of scheduled steps designed to efficiently carry out the tasks of a project.

[0261] "Providing" refers to the process of communicating the generated plans and progress reports to stakeholders.

[0262] "Progress status" refers to the current state of the project's tasks.

[0263] "Tracking" refers to the process of monitoring the progress of a task in real time.

[0264] "Adjustment" refers to the process of updating and optimizing the plan according to the progress made.

[0265] A "progress report" refers to a document that summarizes the current status of a project.

[0266] A "visual interface" refers to a screen display format that allows users to visually confirm information.

[0267] "Opinions" refer to feedback and suggestions provided by stakeholders.

[0268] An "artificial intelligence model" refers to an algorithm used for analyzing information and identifying dependencies.

[0269] This invention provides a system for streamlining project management, in which a server collects information from multiple terminals and performs analysis using an artificial intelligence model. Terminals transmit information about tasks entered by users to the server via a stable communication method (e.g., HTTPS). The server receives this information and passes it to a generative AI model implemented in a programming language such as Python. The AI ​​model uses natural language processing techniques to identify information duplication and dependencies.

[0270] Specifically, the server analyzes information, integrates similar information, and reorganizes it into a single plan. This plan is provided to the user using visualization tools such as Gantt charts, enabling efficient project management. Users can review this plan and progress reports through a visual interface and send feedback to the server as needed. Based on this feedback, the server readjusts the plan and optimizes resources.

[0271] For example, in a new product development project, if the sales and marketing departments each enter market research tasks separately, the server will detect any overlap and consolidate them. This allows users to always have the latest progress information, reduce wasted resources, and manage projects efficiently.

[0272] An example of a prompt to input into a generating AI model is, "Please tell me how to detect and consolidate overlapping market research tasks in a new product development project." This prompt allows the AI ​​model to perform appropriate analysis and help in formulating an optimal plan.

[0273] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0274] Step 1:

[0275] The terminal retrieves task information entered by the user. The user enters the task name, content, due date, assignee, etc. into the terminal. The terminal formats this data and prepares it to be sent to the server using a stable communication method (e.g., HTTPS protocol).

[0276] Step 2:

[0277] The server receives task information sent from the terminal. The received information is stored in a temporary database, and its reliability is verified. For example, the server checks for missing data or format inconsistencies. This ensures that the data is valid for the next analysis stage.

[0278] Step 3:

[0279] The server provides the task information stored in the database to the generative AI model. Specifically, the server applies natural language processing algorithms via a programming language such as Python to analyze information duplication and dependency relationships. Here, the input is the task information, and as output, a list of duplicate tasks and a map of dependency relationships can be obtained.

[0280] Step 4:

[0281] Based on the analysis results from the AI model, the server integrates duplicate tasks and creates an efficient plan. During this process, the server generates a schedule and visualizes it in a Gantt chart or the like. The input is the task information with duplication, and the output is the optimized schedule.

[0282] Step 5:

[0283] The server provides the generated plan to the relevant parties and monitors the progress of the entire project. Here, it is important to collect data in real time and track the progress to ensure that the plan is accurately implemented.

[0284] Step 6:

[0285] The user checks the plan and progress through the terminal and sends feedback to the server as needed. The feedback includes the progress of tasks and new requests. The input is the feedback from the user, and as output, an updated plan can be obtained.

[0286] Step 7:

[0287] The server receives the feedback from the user, re-adjusts the plan, and reconsiders the appropriate allocation of resources. Finally, it automatically generates a progress report and provides this to the relevant parties. The report details the current progress status and risks of the project.

[0288] (Application Example 1)

[0289] 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."

[0290] In modern manufacturing environments, managing production processes involving multiple work lines and departments often leads to problems such as information redundancy, omissions, and insufficient communication among stakeholders. Furthermore, the difficulty in flexibly adjusting plans in line with progress contributes to decreased production efficiency. Therefore, efficient methods for solving these problems are needed.

[0291] 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.

[0292] In this invention, the server includes a device for collecting multiple pieces of information and analyzing the content of each piece of information, a device for detecting and adjusting the redundancy of each piece of information based on the analysis results, and a device for generating an optimized plan using the adjusted information. This enables efficient plan management and flexible plan modification in the production environment.

[0293] "Information" is a collection of data, something that is collected and analyzed within a system.

[0294] A "device" is a combination of hardware and software used to perform a specific function.

[0295] "Analysis" is the process of examining information in detail and clarifying its characteristics and relationships.

[0296] "Redundancy" refers to a state where there is an excess of information or functions, and it is a factor that hinders efficiency.

[0297] "Adjustment" is the act of organizing and integrating information to resolve duplication and contradictions and achieve an optimal state.

[0298] A "plan" is a set of guidelines, including procedures and schedules, established to achieve a specific objective.

[0299] "Stakeholders" refers to individuals or groups involved in the use and management of the plan or system.

[0300] A "report" is a document or data used to record and present progress and results, and to communicate them to relevant parties.

[0301] In the system that realizes this invention, a server plays a central role. The server receives multiple pieces of information from terminals and analyzes their contents. Specifically, it uses an AI model to detect redundancy and dependencies in the information and generates an optimized plan. The generated plan is distributed to stakeholders through a visual interface. Furthermore, the server monitors the progress in real time and modifies the plan as needed.

[0302] The terminal's role is to transmit information entered by the user to the server. This information concerns ongoing tasks and their details, and is analyzed on the server for efficient management. Users can check the latest schedule through the terminal and provide feedback back to the server.

[0303] This system uses Python and specific task analysis models to optimize planning for a particular work line, enabling stakeholders to work efficiently. The reports generated by the server also use a graphical interface to visually represent progress. A concrete example is streamlining processes in a product assembly line. An example of a prompt is: "Propose an optimal schedule for a project with the following task information: task name, details, due date, and assignee."

[0304] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0305] Step 1:

[0306] The terminal inputs work information from the user. The user inputs data such as task name, details, deadline, and responsible person, and transmits it to the server using a stable communication method. As input data, information in JSON format is common.

[0307] Step 2:

[0308] The server receives the information transmitted from the terminal. The server uses a generation AI model to analyze the information and identify redundancy and dependencies. For data processing, natural language processing is used to structure text data and detect specific patterns. As a result of the analysis, information on whether the task is new or duplicates an existing task is output.

[0309] Step 3:

[0310] Based on the analysis results, the server generates an optimized plan. The server uses a scheduling algorithm to calculate the optimal task order between processes. Using the analysis results as input, a schedule proposal is generated as output. The generated schedule includes a detailed timeline with tasks assigned to each responsible person.

[0311] Step 4:

[0312] The server visualizes the generated plan and transmits it to the terminal so that the user can view it. Using a graphical user interface, the schedule is visually displayed. Each task's progress, deadline, and responsible person are color-coded for easy understanding. The output is provided on a dashboard screen that the user can operate.

[0313] Step 5:

[0314] The user reviews the displayed plan using their device and provides feedback if necessary. The feedback information is sent to the server and used as input for revising the plan.

[0315] Step 6:

[0316] The server readjusts the plan based on the feedback it receives. It analyzes the feedback, modifies the baseline parameters, and generates a new schedule. This enables efficient plan revisions. The server then sends the final plan back to the terminal.

[0317] 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.

[0318] This invention is a project management system incorporating an emotion engine, which aims to optimize project management based on user emotion information. This system utilizes emotion data to more accurately coordinate information and manage schedules among stakeholders in a project.

[0319] First, the terminal receives task information input from the user. The emotion engine then identifies the user's emotional state based on the user's input and data obtained from the terminal. This emotional data is analyzed using a specific algorithm and sent to the server.

[0320] In addition to collecting regular task information, the server also receives user sentiment information. This sentiment information is input into an AI model to understand the overall sentiment trend of the project. Task progress management and scheduling are optimized to take user sentiment into account.

[0321] For example, if a user is experiencing high levels of stress, the server will detect this and readjust the schedule to reduce the workload. This may involve changing task assignments or adjusting resource allocation. Furthermore, if a user is highly motivated towards a particular task, the server will take this into consideration and improve project efficiency by changing its priority.

[0322] Furthermore, the server monitors progress and user feedback, and by taking emotional states into account in the feedback, it provides more appropriate adjustments. Emotional data is also reflected in project reports, making it easier for stakeholders to visually understand the project's status.

[0323] For example, in a new product development project, adjustments are made based on the emotional state of team members. Highly motivated members are assigned more challenging tasks, while members experiencing stress receive more support.

[0324] In this configuration, the system takes into account the human factors of the project and supports efficient and smooth project execution.

[0325] The following describes the processing flow.

[0326] Step 1:

[0327] The device uses its camera and microphone to collect the user's facial expressions and voice as they input task information. This data is then sent to an emotion engine for analysis.

[0328] Step 2:

[0329] The emotion engine analyzes collected facial and voice data to identify the user's emotional state. This emotion data is then sent to the server for subsequent processing.

[0330] Step 3:

[0331] The server receives emotional information and regular task data and inputs it into the AI ​​model. The AI ​​model uses the emotional information, along with the task content and priorities, to optimize the entire project.

[0332] Step 4:

[0333] The server adjusts schedules and resource allocation based on user emotional information. For example, it reduces the workload for highly stressed users and assigns challenging tasks to highly motivated users.

[0334] Step 5:

[0335] The server distributes the adjusted schedule to stakeholders. Through their terminals, users can check the latest schedule and confirm that sentiment-based optimizations have been reflected.

[0336] Step 6:

[0337] The server re-evaluates user feedback along with the sentiment engine throughout the project, and makes further adjustments to the schedule and resource allocation as needed.

[0338] Step 7:

[0339] The server automatically generates reports summarizing project progress and emotional data, and distributes them regularly to stakeholders. These reports visually communicate the emotional health of the project along with its progress.

[0340] (Example 2)

[0341] 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".

[0342] In project management, optimizing progress and processes without considering the emotional states of stakeholders can lead to decreased efficiency and cause anxiety and stress among stakeholders. Furthermore, traditional systems often lack clarity on how to reflect stakeholders' emotional information in process management, potentially hindering project progress. To address this challenge, a system capable of analyzing and reflecting stakeholders' emotional states in real time is needed.

[0343] 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.

[0344] In this invention, the server includes means for collecting multiple pieces of information and analyzing the content of each piece of information; means for identifying and adjusting the emotional state of each piece of information based on the analysis results; and means for generating an optimized schedule using the identified emotional data. This enables efficient project management and smooth progress that takes into account the emotional states of stakeholders.

[0345] "Information" refers to data related to project management and data that expresses the associated emotional states.

[0346] "Analysis" is the act of extracting meaning from information and then performing data processing or making judgments based on that meaning.

[0347] "Emotional state" refers to data that indicates the psychological and emotional condition of those involved.

[0348] "Adjustment" refers to the process of reviewing and optimizing project steps and tasks based on analysis and identified information.

[0349] A "project schedule" is a plan used to manage the progress and schedule of a project, and is distributed to stakeholders for the purpose of optimization.

[0350] "Stakeholders" refers to all individuals and organizations participating in or related to the project.

[0351] This project management system utilizes an emotion engine and generative AI models to achieve optimized project management that takes into account the emotional states of stakeholders.

[0352] Terminal role and operation:

[0353] The terminal accepts task information directly entered by the user. For example, when a user enters a new task, they write the task name, due date, importance level, and related documents in the input fields. The terminal receives this information and activates the emotion engine. This emotion engine analyzes subtle changes during input, such as the speed and strength of keystrokes, and the tone of voice in voice input, to identify the user's emotional state.

[0354] Server roles and operations:

[0355] The server receives emotional data and task information transmitted from terminals. This received data is analyzed through a generative AI model to identify the overall emotional trend in the project. Based on this emotional trend, the server optimizes the project schedule and adjusts task assignments and schedules as needed. For example, if some users show high stress levels, the server readjusts the schedule to reduce their burden. It also assigns higher-priority tasks to users who are deemed highly motivated.

[0356] Examples of specific cases and prompt statements:

[0357] In project management for a new product development team, the system tracks each member's emotional state in real time and adjusts the schedule based on that emotional data. Support resources are added for members experiencing stress, and challenging tasks are prioritized for highly motivated members. This improves overall team efficiency and morale.

[0358] An example of a prompt message would be, "Analyze the emotional state of each member in the current project and propose the optimal task assignments and schedule adjustments."

[0359] In this way, this system enables project management that incorporates emotions, supporting efficient and harmonious progress.

[0360] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0361] Step 1:

[0362] The terminal receives task information from the user. The user enters detailed information about the project task (e.g., task name, deadline, priority, etc.) into the terminal. The terminal passes this information to the emotion engine, which analyzes the keystrokes and voice tone during input. The emotion engine analyzes this input data to identify the user's emotional state (e.g., joy, stress). The identified emotion data and task information are sent from the terminal to the server.

[0363] Step 2:

[0364] The server receives emotional data and task information sent from the terminal. The server analyzes the emotional data through a generative AI model. This extracts the emotional trends for the entire project and aggregates the individual emotional data of each member. This data processing makes it possible to understand the emotional trends and prepares the data for use in project management.

[0365] Step 3:

[0366] The server uses a generative AI model to optimize the schedule based on sentiment data. Specifically, it adjusts task schedules and changes task assignments as needed based on sentiment trends predicted by the model. The input is sentiment and task data, and the output is the optimized schedule. The server updates this schedule in real time, allowing stakeholders to stay informed of the situation.

[0367] Step 4:

[0368] The server distributes the optimized schedule to stakeholders. The schedule is distributed via email or a dedicated project management tool. The server periodically collects feedback from stakeholders and prepares to revise the schedule again, incorporating sentiment information. Further improvements are then made based on this information.

[0369] Step 5:

[0370] The server continuously monitors progress and compares AI model predictions with actual results. If emotional fluctuations affecting project progress are detected, the server automatically readjusts the schedule. Output regarding the readjustment is generated and notified to stakeholders. This ensures that optimization is always maintained.

[0371] (Application Example 2)

[0372] 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."

[0373] In modern project management and production line operations, simply managing schedules presents a challenge: it fails to take into account human emotional states and motivations. This makes it difficult to adjust tasks or adapt machine operations to the emotions of stakeholders, ultimately hindering efficient and flexible operations.

[0374] 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.

[0375] In this invention, the server includes means for collecting multiple pieces of information and analyzing the content of each piece of information, means for identifying emotional states and adjusting workload and priorities, and means for adjusting the operation of machines / devices using emotional data. This enables flexible and efficient project management that reflects the emotional states of stakeholders.

[0376] "Information" refers to data related to a project or production, showing the progress of work and the status of stakeholders.

[0377] "Analysis" is the act of thoroughly evaluating the content of collected information and identifying specific patterns or trends.

[0378] "Schedule" refers to a time plan for carrying out each task in the execution of a project or task.

[0379] "Stakeholders" refers to all individuals and groups affected by or involved in a project or production activity.

[0380] "Progress status" refers to information indicating the degree of progress in work within a project or production line.

[0381] "Emotional state" refers to data that indicates the psychological and emotional condition of those involved.

[0382] "Workload" refers to the quantity and quality of specific tasks and work assigned to stakeholders.

[0383] "Priority" refers to the order in which tasks and projects are carried out, determined based on their importance and urgency.

[0384] "Machine / device operation adjustment" refers to the act of making changes to optimize the movement of machines or devices based on collected data.

[0385] The system realizing this invention first receives task information input from the user via a terminal. At this time, to identify the user's emotional state, it utilizes cameras and sensors to acquire facial expression data and biometric information. The hardware can be general-purpose camera devices and biosensors, and the software combines OpenCV and TensorFlow for emotion analysis. This data is sent to a server, where further data analysis is performed.

[0386] The server uses this data to analyze multiple pieces of information and understand the progress of projects and tasks. Emotional data is input into a generative AI model, which generates an optimized schedule that reflects the emotional state of stakeholders. During this process, the workload and priorities are adjusted considering the stress levels and motivation of workers, and the operation of machines and equipment is adjusted as needed.

[0387] For example, in an assembly line for new equipment, if an emotion analysis system detects a worker experiencing high stress levels, the work sequence and machine operating speed are adjusted to reduce that worker's workload. This adjustment improves work efficiency and increases satisfaction among stakeholders.

[0388] An example of a prompt message from a generated AI model is: "Consider the emotional state of the workers on the current production line and propose specific improvement plans to increase efficiency."

[0389] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0390] Step 1:

[0391] The terminal receives task information from the user. During this process, the terminal uses its camera and sensors to acquire the user's facial expression data and biometric information, which is then added to the input information. The input data includes task information along with images and biometric information necessary for emotion estimation.

[0392] Step 2:

[0393] The device uses OpenCV and TensorFlow to analyze the user's emotional state from acquired facial expression data and biometric information. In this emotion analysis step, image processing is performed to extract features, and an AI model is used to estimate emotion labels. The analyzed emotional state is output, and data with that information attached is generated.

[0394] Step 3:

[0395] Based on the analysis results, the terminal sends relevant data to the server. The server organizes the received task information and sentiment data and inputs it into the schedule generation algorithm. The server then outputs a schedule that optimizes task priorities and worker assignments.

[0396] Step 4:

[0397] The server generates an optimized schedule and distributes it to stakeholders. This distribution step sends schedule details to stakeholders' devices, allowing them to visually review the schedule. Information is organized in an easy-to-understand format to ensure project efficiency.

[0398] Step 5:

[0399] The server continuously monitors the progress and the sentiment of stakeholders, and readjusts the schedule as needed. In this step, data analysis is performed again, and an optimized schedule is generated based on the newly obtained information.

[0400] Step 6:

[0401] The server automatically generates and distributes a final progress report to stakeholders. This report includes information on project progress and changes in each worker's sentiment. The report is presented in a clear and intuitive format, allowing stakeholders to easily grasp the overall situation.

[0402] 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.

[0403] 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.

[0404] 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.

[0405] [Third Embodiment]

[0406] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0407] 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.

[0408] 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).

[0409] 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.

[0410] 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.

[0411] 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).

[0412] 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.

[0413] 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.

[0414] 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.

[0415] 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.

[0416] 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.

[0417] 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".

[0418] This invention relates to a system using an AI agent for optimizing project management. This system can prevent information duplication and omissions in projects involving multiple departments, and efficiently manage schedules.

[0419] The system begins with the server collecting task information from terminals in each department. This task information includes the task name, content, due date, and assigned person. The terminals transmit the information entered by the user to the server using a stable communication method. The server then passes the collected information to an AI model for analysis. This AI model uses natural language processing to examine the task content and identify duplications and dependencies.

[0420] Based on the analysis results from the AI ​​model, the server consolidates tasks that have been identified as duplicates and generates an efficient schedule. The generated schedule is automatically displayed on the screen. The server also continuously monitors the progress and understands the status of each task in real time.

[0421] Users can check the status of ongoing tasks through their terminals and send feedback to the server as needed. Based on this feedback, the server immediately readjusts the schedule and optimizes resource allocation. Furthermore, the system automatically generates progress reports and distributes them regularly to stakeholders. These reports visually represent project progress and risks, supporting stakeholder decision-making.

[0422] As a concrete example, consider a new product development project. If the sales department enters market research tasks, and the marketing department also enters similar tasks, the server will detect and consolidate these duplicates, optimizing resources. Progress is updated regularly, allowing each person in charge to always have access to the latest information.

[0423] Thus, the present invention enables the efficient and smooth progress of projects.

[0424] The following describes the processing flow.

[0425] Step 1:

[0426] The terminal collects task information entered by the user. This includes the task name, content, deadline, and assigned person. This information is then transmitted to the server using a stable communication method.

[0427] Step 2:

[0428] The server stores task information received from terminals in a database. Simultaneously, it collects necessary data from project management tools and external systems.

[0429] Step 3:

[0430] The server sends the collected task data to an AI model for analysis using natural language processing. At this stage, the content of the tasks is reviewed, and duplicates and dependencies are identified.

[0431] Step 4:

[0432] The server detects task duplication based on the AI ​​model's analysis results and merges tasks if necessary. It also generates an optimal task schedule, taking dependencies into consideration.

[0433] Step 5:

[0434] The server notifies stakeholders of the generated schedule. Users can check the updated schedule via their terminals and understand their respective work responsibilities.

[0435] Step 6:

[0436] The server monitors the project's progress in real time and continuously collects progress data. If necessary, it suggests reallocating available resources and adjusting the schedule.

[0437] Step 7:

[0438] Users can monitor the status of ongoing tasks through their devices and send any problems or comments as feedback to the server. Based on this feedback, the server readjusts the schedule.

[0439] Step 8:

[0440] The server automatically generates project progress reports and distributes them regularly to stakeholders. These reports include progress status, risk analysis, and next steps.

[0441] (Example 1)

[0442] 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."

[0443] In project management, when multiple departments are involved, challenges arise such as overlapping tasks and lack of information, leading to wasted resources and decreased efficiency. Furthermore, it becomes difficult to track the progress of each task in real time and smoothly adjust the plan.

[0444] 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.

[0445] In this invention, the server includes means for collecting multiple pieces of information and analyzing the attributes of each piece of information; means for detecting and integrating the duplication of each piece of information based on the analysis results; and means for generating an optimized plan using the integrated information. This makes it possible to avoid information duplication and adjust an efficient plan in real time.

[0446] "Information" refers to all data related to a project, including tasks and their attributes.

[0447] "Attributes" refer to the characteristics of each element of information, such as name, content, date, and person in charge.

[0448] "Analysis" refers to the process of evaluating the attributes of information to identify duplication and dependencies.

[0449] "Duplicate" refers to a state where multiple pieces of information contain the same content.

[0450] "Integration" refers to the process of combining redundant information into a single, more efficient format.

[0451] "Planning" refers to a set of scheduled steps designed to efficiently carry out the tasks of a project.

[0452] "Providing" refers to the process of communicating the generated plans and progress reports to stakeholders.

[0453] "Progress status" refers to the current state of the project's tasks.

[0454] "Tracking" refers to the process of monitoring the progress of a task in real time.

[0455] "Adjustment" refers to the process of updating and optimizing the plan according to the progress made.

[0456] A "progress report" refers to a document that summarizes the current status of a project.

[0457] A "visual interface" refers to a screen display format that allows users to visually confirm information.

[0458] "Opinions" refer to feedback and suggestions provided by stakeholders.

[0459] An "artificial intelligence model" refers to an algorithm used for analyzing information and identifying dependencies.

[0460] This invention provides a system for streamlining project management, in which a server collects information from multiple terminals and performs analysis using an artificial intelligence model. Terminals transmit information about tasks entered by users to the server via a stable communication method (e.g., HTTPS). The server receives this information and passes it to a generative AI model implemented in a programming language such as Python. The AI ​​model uses natural language processing techniques to identify information duplication and dependencies.

[0461] Specifically, the server analyzes information, integrates similar information, and reorganizes it into a single plan. This plan is provided to the user using visualization tools such as Gantt charts, enabling efficient project management. Users can review this plan and progress reports through a visual interface and send feedback to the server as needed. Based on this feedback, the server readjusts the plan and optimizes resources.

[0462] For example, in a new product development project, if the sales and marketing departments each enter market research tasks separately, the server will detect any overlap and consolidate them. This allows users to always have the latest progress information, reduce wasted resources, and manage projects efficiently.

[0463] An example of a prompt to input into a generating AI model is, "Please tell me how to detect and consolidate overlapping market research tasks in a new product development project." This prompt allows the AI ​​model to perform appropriate analysis and help in formulating an optimal plan.

[0464] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0465] Step 1:

[0466] The terminal retrieves task information entered by the user. The user enters the task name, content, due date, assignee, etc. into the terminal. The terminal formats this data and prepares it to be sent to the server using a stable communication method (e.g., HTTPS protocol).

[0467] Step 2:

[0468] The server receives task information sent from the terminal. The received information is stored in a temporary database, and its reliability is verified. For example, the server checks for missing data or format inconsistencies. This ensures that the data is valid for the next analysis stage.

[0469] Step 3:

[0470] The server provides task information stored in the database to the generating AI model. Specifically, the server applies natural language processing algorithms via programming languages ​​such as Python to analyze information duplication and dependencies. Here, the input is task information, and the output is a list of duplicate tasks and a dependency map.

[0471] Step 4:

[0472] The server consolidates overlapping tasks and creates an efficient plan based on the analysis results from the AI ​​model. During this process, the server generates a schedule and visualizes it in a Gantt chart, etc. The input is information on overlapping tasks, and the output is an optimized schedule.

[0473] Step 5:

[0474] The server provides the generated plan to stakeholders and monitors the overall project progress. Here, it's crucial to collect data in real time and track progress to ensure the plan is implemented accurately.

[0475] Step 6:

[0476] Users check plans and progress through their terminals and send feedback to the server as needed. This feedback includes task progress and new requests. The input is user feedback, and the output is an updated plan.

[0477] Step 7:

[0478] The server receives user feedback, readjusts the plan, and reconsiders the appropriate allocation of resources. Finally, it automatically generates a progress report and provides it to stakeholders. The report details the current progress and risks of the project.

[0479] (Application Example 1)

[0480] 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."

[0481] In modern manufacturing environments, managing production processes involving multiple work lines and departments often leads to problems such as information redundancy, omissions, and insufficient communication among stakeholders. Furthermore, the difficulty in flexibly adjusting plans in line with progress contributes to decreased production efficiency. Therefore, efficient methods for solving these problems are needed.

[0482] 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.

[0483] In this invention, the server includes a device for collecting multiple pieces of information and analyzing the content of each piece of information, a device for detecting and adjusting the redundancy of each piece of information based on the analysis results, and a device for generating an optimized plan using the adjusted information. This enables efficient plan management and flexible plan modification in the production environment.

[0484] "Information" is a collection of data, something that is collected and analyzed within a system.

[0485] A "device" is a combination of hardware and software used to perform a specific function.

[0486] "Analysis" is the process of examining information in detail and clarifying its characteristics and relationships.

[0487] "Redundancy" refers to a state where there is an excess of information or functions, and it is a factor that hinders efficiency.

[0488] "Adjustment" is the act of organizing and integrating information to resolve duplication and contradictions and achieve an optimal state.

[0489] A "plan" is a set of guidelines, including procedures and schedules, established to achieve a specific objective.

[0490] "Stakeholders" refers to individuals or groups involved in the use and management of the plan or system.

[0491] A "report" is a document or data used to record and present progress and results, and to communicate them to relevant parties.

[0492] In the system that realizes this invention, a server plays a central role. The server receives multiple pieces of information from terminals and analyzes their contents. Specifically, it uses an AI model to detect redundancy and dependencies in the information and generates an optimized plan. The generated plan is distributed to stakeholders through a visual interface. Furthermore, the server monitors the progress in real time and modifies the plan as needed.

[0493] The terminal's role is to transmit information entered by the user to the server. This information concerns ongoing tasks and their details, and is analyzed on the server for efficient management. Users can check the latest schedule through the terminal and provide feedback back to the server.

[0494] This system uses Python and specific task analysis models to optimize planning for a particular work line, enabling stakeholders to work efficiently. The reports generated by the server also use a graphical interface to visually represent progress. A concrete example is streamlining processes in a product assembly line. An example of a prompt is: "Propose an optimal schedule for a project with the following task information: task name, details, due date, and assignee."

[0495] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0496] Step 1:

[0497] The terminal receives work information from the user. The user enters data such as the task name, details, deadline, and assigned person, and sends it to the server using a stable communication method. JSON format is the most common input data.

[0498] Step 2:

[0499] The server receives information sent from the terminal. The server analyzes the information using a generative AI model to identify redundancy and dependencies. For data processing, natural language processing is used to structure the text data and detect specific patterns. The analysis results output information on whether the task is novel or duplicates an existing task.

[0500] Step 3:

[0501] The server generates an optimized plan based on the analysis results. The server uses a scheduling algorithm to calculate the optimal task sequence between processes. Using the analysis results as input, it generates a proposed schedule as output. The generated schedule includes a detailed timeline with tasks assigned to each person.

[0502] Step 4:

[0503] The server visualizes the generated plan and sends it to the user's terminal for review. Using a graphical user interface, the schedule is displayed visually. Each task's progress, deadline, and assigned person are color-coded for easy identification. The output is provided on a user-operable dashboard screen.

[0504] Step 5:

[0505] The user reviews the displayed plan using their device and provides feedback if necessary. The feedback information is sent to the server and used as input for revising the plan.

[0506] Step 6:

[0507] The server readjusts the plan based on the feedback it receives. It analyzes the feedback, modifies the baseline parameters, and generates a new schedule. This enables efficient plan revisions. The server then sends the final plan back to the terminal.

[0508] 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.

[0509] This invention is a project management system incorporating an emotion engine, which aims to optimize project management based on user emotion information. This system utilizes emotion data to more accurately coordinate information and manage schedules among stakeholders in a project.

[0510] First, the terminal receives task information input from the user. The emotion engine then identifies the user's emotional state based on the user's input and data obtained from the terminal. This emotional data is analyzed using a specific algorithm and sent to the server.

[0511] In addition to collecting regular task information, the server also receives user sentiment information. This sentiment information is input into an AI model to understand the overall sentiment trend of the project. Task progress management and scheduling are optimized to take user sentiment into account.

[0512] For example, if a user is experiencing high levels of stress, the server will detect this and readjust the schedule to reduce the workload. This may involve changing task assignments or adjusting resource allocation. Furthermore, if a user is highly motivated towards a particular task, the server will take this into consideration and improve project efficiency by changing its priority.

[0513] Furthermore, the server monitors progress and user feedback, and by taking emotional states into account in the feedback, it provides more appropriate adjustments. Emotional data is also reflected in project reports, making it easier for stakeholders to visually understand the project's status.

[0514] For example, in a new product development project, adjustments are made based on the emotional state of team members. Highly motivated members are assigned more challenging tasks, while members experiencing stress receive more support.

[0515] In this configuration, the system takes into account the human factors of the project and supports efficient and smooth project execution.

[0516] The following describes the processing flow.

[0517] Step 1:

[0518] The device uses its camera and microphone to collect the user's facial expressions and voice as they input task information. This data is then sent to an emotion engine for analysis.

[0519] Step 2:

[0520] The emotion engine analyzes collected facial and voice data to identify the user's emotional state. This emotion data is then sent to the server for subsequent processing.

[0521] Step 3:

[0522] The server receives emotional information and regular task data and inputs it into the AI ​​model. The AI ​​model uses the emotional information, along with the task content and priorities, to optimize the entire project.

[0523] Step 4:

[0524] The server adjusts schedules and resource allocation based on user emotional information. For example, it reduces the workload for highly stressed users and assigns challenging tasks to highly motivated users.

[0525] Step 5:

[0526] The server distributes the adjusted schedule to stakeholders. Through their terminals, users can check the latest schedule and confirm that sentiment-based optimizations have been reflected.

[0527] Step 6:

[0528] The server re-evaluates user feedback along with the sentiment engine throughout the project, and makes further adjustments to the schedule and resource allocation as needed.

[0529] Step 7:

[0530] The server automatically generates reports summarizing project progress and emotional data, and distributes them regularly to stakeholders. These reports visually communicate the emotional health of the project along with its progress.

[0531] (Example 2)

[0532] 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."

[0533] In project management, optimizing progress and processes without considering the emotional states of stakeholders can lead to decreased efficiency and cause anxiety and stress among stakeholders. Furthermore, traditional systems often lack clarity on how to reflect stakeholders' emotional information in process management, potentially hindering project progress. To address this challenge, a system capable of analyzing and reflecting stakeholders' emotional states in real time is needed.

[0534] 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.

[0535] In this invention, the server includes means for collecting multiple pieces of information and analyzing the content of each piece of information; means for identifying and adjusting the emotional state of each piece of information based on the analysis results; and means for generating an optimized schedule using the identified emotional data. This enables efficient project management and smooth progress that takes into account the emotional states of stakeholders.

[0536] "Information" refers to data related to project management and data that expresses the associated emotional states.

[0537] "Analysis" is the act of extracting meaning from information and then performing data processing or making judgments based on that meaning.

[0538] "Emotional state" refers to data that indicates the psychological and emotional condition of those involved.

[0539] "Adjustment" refers to the process of reviewing and optimizing project steps and tasks based on analysis and identified information.

[0540] A "project schedule" is a plan used to manage the progress and schedule of a project, and is distributed to stakeholders for the purpose of optimization.

[0541] "Stakeholders" refers to all individuals and organizations participating in or related to the project.

[0542] This project management system utilizes an emotion engine and generative AI models to achieve optimized project management that takes into account the emotional states of stakeholders.

[0543] Terminal role and operation:

[0544] The terminal accepts task information directly entered by the user. For example, when a user enters a new task, they write the task name, due date, importance level, and related documents in the input fields. The terminal receives this information and activates the emotion engine. This emotion engine analyzes subtle changes during input, such as the speed and strength of keystrokes, and the tone of voice in voice input, to identify the user's emotional state.

[0545] Server roles and operations:

[0546] The server receives emotional data and task information transmitted from terminals. This received data is analyzed through a generative AI model to identify the overall emotional trend in the project. Based on this emotional trend, the server optimizes the project schedule and adjusts task assignments and schedules as needed. For example, if some users show high stress levels, the server readjusts the schedule to reduce their burden. It also assigns higher-priority tasks to users who are deemed highly motivated.

[0547] Examples of specific cases and prompt statements:

[0548] In project management for a new product development team, the system tracks each member's emotional state in real time and adjusts the schedule based on that emotional data. Support resources are added for members experiencing stress, and challenging tasks are prioritized for highly motivated members. This improves overall team efficiency and morale.

[0549] An example of a prompt message would be, "Analyze the emotional state of each member in the current project and propose the optimal task assignments and schedule adjustments."

[0550] In this way, this system enables project management that incorporates emotions, supporting efficient and harmonious progress.

[0551] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0552] Step 1:

[0553] The terminal receives task information from the user. The user enters detailed information about the project task (e.g., task name, deadline, priority, etc.) into the terminal. The terminal passes this information to the emotion engine, which analyzes the keystrokes and voice tone during input. The emotion engine analyzes this input data to identify the user's emotional state (e.g., joy, stress). The identified emotion data and task information are sent from the terminal to the server.

[0554] Step 2:

[0555] The server receives emotional data and task information sent from the terminal. The server analyzes the emotional data through a generative AI model. This extracts the emotional trends for the entire project and aggregates the individual emotional data of each member. This data processing makes it possible to understand the emotional trends and prepares the data for use in project management.

[0556] Step 3:

[0557] The server uses a generative AI model to optimize the schedule based on sentiment data. Specifically, it adjusts task schedules and changes task assignments as needed based on sentiment trends predicted by the model. The input is sentiment and task data, and the output is the optimized schedule. The server updates this schedule in real time, allowing stakeholders to stay informed of the situation.

[0558] Step 4:

[0559] The server distributes the optimized schedule to stakeholders. The schedule is distributed via email or a dedicated project management tool. The server periodically collects feedback from stakeholders and prepares to revise the schedule again, incorporating sentiment information. Further improvements are then made based on this information.

[0560] Step 5:

[0561] The server continuously monitors progress and compares AI model predictions with actual results. If emotional fluctuations affecting project progress are detected, the server automatically readjusts the schedule. Output regarding the readjustment is generated and notified to stakeholders. This ensures that optimization is always maintained.

[0562] (Application Example 2)

[0563] 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."

[0564] In modern project management and production line operations, simply managing schedules presents a challenge: it fails to take into account human emotional states and motivations. This makes it difficult to adjust tasks or adapt machine operations to the emotions of stakeholders, ultimately hindering efficient and flexible operations.

[0565] 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.

[0566] In this invention, the server includes means for collecting multiple pieces of information and analyzing the content of each piece of information, means for identifying emotional states and adjusting workload and priorities, and means for adjusting the operation of machines / devices using emotional data. This enables flexible and efficient project management that reflects the emotional states of stakeholders.

[0567] "Information" refers to data related to a project or production, showing the progress of work and the status of stakeholders.

[0568] "Analysis" is the act of thoroughly evaluating the content of collected information and identifying specific patterns or trends.

[0569] "Schedule" refers to a time plan for carrying out each task in the execution of a project or task.

[0570] "Stakeholders" refers to all individuals and groups affected by or involved in a project or production activity.

[0571] "Progress status" refers to information indicating the degree of progress in work within a project or production line.

[0572] "Emotional state" refers to data that indicates the psychological and emotional condition of those involved.

[0573] "Workload" refers to the quantity and quality of specific tasks and work assigned to stakeholders.

[0574] "Priority" refers to the order in which tasks and projects are carried out, determined based on their importance and urgency.

[0575] "Machine / device operation adjustment" refers to the act of making changes to optimize the movement of machines or devices based on collected data.

[0576] The system realizing this invention first receives task information input from the user via a terminal. At this time, to identify the user's emotional state, it utilizes cameras and sensors to acquire facial expression data and biometric information. The hardware can be general-purpose camera devices and biosensors, and the software combines OpenCV and TensorFlow for emotion analysis. This data is sent to a server, where further data analysis is performed.

[0577] The server uses this data to analyze multiple pieces of information and understand the progress of projects and tasks. Emotional data is input into a generative AI model, which generates an optimized schedule that reflects the emotional state of stakeholders. During this process, the workload and priorities are adjusted considering the stress levels and motivation of workers, and the operation of machines and equipment is adjusted as needed.

[0578] For example, in an assembly line for new equipment, if an emotion analysis system detects a worker experiencing high stress levels, the work sequence and machine operating speed are adjusted to reduce that worker's workload. This adjustment improves work efficiency and increases satisfaction among stakeholders.

[0579] An example of a prompt message from a generated AI model is: "Consider the emotional state of the workers on the current production line and propose specific improvement plans to increase efficiency."

[0580] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0581] Step 1:

[0582] The terminal receives task information from the user. During this process, the terminal uses its camera and sensors to acquire the user's facial expression data and biometric information, which is then added to the input information. The input data includes task information along with images and biometric information necessary for emotion estimation.

[0583] Step 2:

[0584] The device uses OpenCV and TensorFlow to analyze the user's emotional state from acquired facial expression data and biometric information. In this emotion analysis step, image processing is performed to extract features, and an AI model is used to estimate emotion labels. The analyzed emotional state is output, and data with that information attached is generated.

[0585] Step 3:

[0586] Based on the analysis results, the terminal sends relevant data to the server. The server organizes the received task information and sentiment data and inputs it into the schedule generation algorithm. The server then outputs a schedule that optimizes task priorities and worker assignments.

[0587] Step 4:

[0588] The server generates an optimized schedule and distributes it to stakeholders. This distribution step sends schedule details to stakeholders' devices, allowing them to visually review the schedule. Information is organized in an easy-to-understand format to ensure project efficiency.

[0589] Step 5:

[0590] The server continuously monitors the progress and the sentiment of stakeholders, and readjusts the schedule as needed. In this step, data analysis is performed again, and an optimized schedule is generated based on the newly obtained information.

[0591] Step 6:

[0592] The server automatically generates and distributes a final progress report to stakeholders. This report includes information on project progress and changes in each worker's sentiment. The report is presented in a clear and intuitive format, allowing stakeholders to easily grasp the overall situation.

[0593] 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.

[0594] 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.

[0595] 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.

[0596] [Fourth Embodiment]

[0597] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0598] 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.

[0599] 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).

[0600] 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.

[0601] 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.

[0602] 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).

[0603] 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.

[0604] 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.

[0605] 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.

[0606] 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.

[0607] 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.

[0608] 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.

[0609] 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".

[0610] This invention relates to a system using an AI agent for optimizing project management. This system can prevent information duplication and omissions in projects involving multiple departments, and efficiently manage schedules.

[0611] The system begins with the server collecting task information from terminals in each department. This task information includes the task name, content, due date, and assigned person. The terminals transmit the information entered by the user to the server using a stable communication method. The server then passes the collected information to an AI model for analysis. This AI model uses natural language processing to examine the task content and identify duplications and dependencies.

[0612] Based on the analysis results from the AI ​​model, the server consolidates tasks that have been identified as duplicates and generates an efficient schedule. The generated schedule is automatically displayed on the screen. The server also continuously monitors the progress and understands the status of each task in real time.

[0613] Users can check the status of ongoing tasks through their terminals and send feedback to the server as needed. Based on this feedback, the server immediately readjusts the schedule and optimizes resource allocation. Furthermore, the system automatically generates progress reports and distributes them regularly to stakeholders. These reports visually represent project progress and risks, supporting stakeholder decision-making.

[0614] As a concrete example, consider a new product development project. If the sales department enters market research tasks, and the marketing department also enters similar tasks, the server will detect and consolidate these duplicates, optimizing resources. Progress is updated regularly, allowing each person in charge to always have access to the latest information.

[0615] Thus, the present invention enables the efficient and smooth progress of projects.

[0616] The following describes the processing flow.

[0617] Step 1:

[0618] The terminal collects task information entered by the user. This includes the task name, content, deadline, and assigned person. This information is then transmitted to the server using a stable communication method.

[0619] Step 2:

[0620] The server stores task information received from terminals in a database. Simultaneously, it collects necessary data from project management tools and external systems.

[0621] Step 3:

[0622] The server sends the collected task data to an AI model for analysis using natural language processing. At this stage, the content of the tasks is reviewed, and duplicates and dependencies are identified.

[0623] Step 4:

[0624] The server detects task duplication based on the AI ​​model's analysis results and merges tasks if necessary. It also generates an optimal task schedule, taking dependencies into consideration.

[0625] Step 5:

[0626] The server notifies stakeholders of the generated schedule. Users can check the updated schedule via their terminals and understand their respective work responsibilities.

[0627] Step 6:

[0628] The server monitors the project's progress in real time and continuously collects progress data. If necessary, it suggests reallocating available resources and adjusting the schedule.

[0629] Step 7:

[0630] Users can monitor the status of ongoing tasks through their devices and send any problems or comments as feedback to the server. Based on this feedback, the server readjusts the schedule.

[0631] Step 8:

[0632] The server automatically generates project progress reports and distributes them regularly to stakeholders. These reports include progress status, risk analysis, and next steps.

[0633] (Example 1)

[0634] 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".

[0635] In project management, when multiple departments are involved, challenges arise such as overlapping tasks and lack of information, leading to wasted resources and decreased efficiency. Furthermore, it becomes difficult to track the progress of each task in real time and smoothly adjust the plan.

[0636] 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.

[0637] In this invention, the server includes means for collecting multiple pieces of information and analyzing the attributes of each piece of information; means for detecting and integrating the duplication of each piece of information based on the analysis results; and means for generating an optimized plan using the integrated information. This makes it possible to avoid information duplication and adjust an efficient plan in real time.

[0638] "Information" refers to all data related to a project, including tasks and their attributes.

[0639] "Attributes" refer to the characteristics of each element of information, such as name, content, date, and person in charge.

[0640] "Analysis" refers to the process of evaluating the attributes of information to identify duplication and dependencies.

[0641] "Duplicate" refers to a state where multiple pieces of information contain the same content.

[0642] "Integration" refers to the process of combining redundant information into a single, more efficient format.

[0643] "Planning" refers to a set of scheduled steps designed to efficiently carry out the tasks of a project.

[0644] "Providing" refers to the process of communicating the generated plans and progress reports to stakeholders.

[0645] "Progress status" refers to the current state of the project's tasks.

[0646] "Tracking" refers to the process of monitoring the progress of a task in real time.

[0647] "Adjustment" refers to the process of updating and optimizing the plan according to the progress made.

[0648] A "progress report" refers to a document that summarizes the current status of a project.

[0649] A "visual interface" refers to a screen display format that allows users to visually confirm information.

[0650] "Opinions" refer to feedback and suggestions provided by stakeholders.

[0651] An "artificial intelligence model" refers to an algorithm used for analyzing information and identifying dependencies.

[0652] This invention provides a system for streamlining project management, in which a server collects information from multiple terminals and performs analysis using an artificial intelligence model. Terminals transmit information about tasks entered by users to the server via a stable communication method (e.g., HTTPS). The server receives this information and passes it to a generative AI model implemented in a programming language such as Python. The AI ​​model uses natural language processing techniques to identify information duplication and dependencies.

[0653] Specifically, the server analyzes information, integrates similar information, and reorganizes it into a single plan. This plan is provided to the user using visualization tools such as Gantt charts, enabling efficient project management. Users can review this plan and progress reports through a visual interface and send feedback to the server as needed. Based on this feedback, the server readjusts the plan and optimizes resources.

[0654] For example, in a new product development project, if the sales and marketing departments each enter market research tasks separately, the server will detect any overlap and consolidate them. This allows users to always have the latest progress information, reduce wasted resources, and manage projects efficiently.

[0655] An example of a prompt to input into a generating AI model is, "Please tell me how to detect and consolidate overlapping market research tasks in a new product development project." This prompt allows the AI ​​model to perform appropriate analysis and help in formulating an optimal plan.

[0656] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0657] Step 1:

[0658] The terminal retrieves task information entered by the user. The user enters the task name, content, due date, assignee, etc. into the terminal. The terminal formats this data and prepares it to be sent to the server using a stable communication method (e.g., HTTPS protocol).

[0659] Step 2:

[0660] The server receives task information sent from the terminal. The received information is stored in a temporary database, and its reliability is verified. For example, the server checks for missing data or format inconsistencies. This ensures that the data is valid for the next analysis stage.

[0661] Step 3:

[0662] The server provides task information stored in the database to the generating AI model. Specifically, the server applies natural language processing algorithms via programming languages ​​such as Python to analyze information duplication and dependencies. Here, the input is task information, and the output is a list of duplicate tasks and a dependency map.

[0663] Step 4:

[0664] The server consolidates overlapping tasks and creates an efficient plan based on the analysis results from the AI ​​model. During this process, the server generates a schedule and visualizes it in a Gantt chart, etc. The input is information on overlapping tasks, and the output is an optimized schedule.

[0665] Step 5:

[0666] The server provides the generated plan to stakeholders and monitors the overall project progress. Here, it's crucial to collect data in real time and track progress to ensure the plan is implemented accurately.

[0667] Step 6:

[0668] Users check plans and progress through their terminals and send feedback to the server as needed. This feedback includes task progress and new requests. The input is user feedback, and the output is an updated plan.

[0669] Step 7:

[0670] The server receives user feedback, readjusts the plan, and reconsiders the appropriate allocation of resources. Finally, it automatically generates a progress report and provides it to stakeholders. The report details the current progress and risks of the project.

[0671] (Application Example 1)

[0672] 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".

[0673] In modern manufacturing environments, managing production processes involving multiple work lines and departments often leads to problems such as information redundancy, omissions, and insufficient communication among stakeholders. Furthermore, the difficulty in flexibly adjusting plans in line with progress contributes to decreased production efficiency. Therefore, efficient methods for solving these problems are needed.

[0674] 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.

[0675] In this invention, the server includes a device for collecting multiple pieces of information and analyzing the content of each piece of information, a device for detecting and adjusting the redundancy of each piece of information based on the analysis results, and a device for generating an optimized plan using the adjusted information. This enables efficient plan management and flexible plan modification in the production environment.

[0676] "Information" is a collection of data, something that is collected and analyzed within a system.

[0677] A "device" is a combination of hardware and software used to perform a specific function.

[0678] "Analysis" is the process of examining information in detail and clarifying its characteristics and relationships.

[0679] "Redundancy" refers to a state where there is an excess of information or functions, and it is a factor that hinders efficiency.

[0680] "Adjustment" is the act of organizing and integrating information to resolve duplication and contradictions and achieve an optimal state.

[0681] A "plan" is a set of guidelines, including procedures and schedules, established to achieve a specific objective.

[0682] "Stakeholders" refers to individuals or groups involved in the use and management of the plan or system.

[0683] A "report" is a document or data used to record and present progress and results, and to communicate them to relevant parties.

[0684] In the system that realizes this invention, a server plays a central role. The server receives multiple pieces of information from terminals and analyzes their contents. Specifically, it uses an AI model to detect redundancy and dependencies in the information and generates an optimized plan. The generated plan is distributed to stakeholders through a visual interface. Furthermore, the server monitors the progress in real time and modifies the plan as needed.

[0685] The terminal's role is to transmit information entered by the user to the server. This information concerns ongoing tasks and their details, and is analyzed on the server for efficient management. Users can check the latest schedule through the terminal and provide feedback back to the server.

[0686] This system uses Python and specific task analysis models to optimize planning for a particular work line, enabling stakeholders to work efficiently. The reports generated by the server also use a graphical interface to visually represent progress. A concrete example is streamlining processes in a product assembly line. An example of a prompt is: "Propose an optimal schedule for a project with the following task information: task name, details, due date, and assignee."

[0687] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0688] Step 1:

[0689] The terminal receives work information from the user. The user enters data such as the task name, details, deadline, and assigned person, and sends it to the server using a stable communication method. JSON format is the most common input data.

[0690] Step 2:

[0691] The server receives information sent from the terminal. The server analyzes the information using a generative AI model to identify redundancy and dependencies. For data processing, natural language processing is used to structure the text data and detect specific patterns. The analysis results output information on whether the task is novel or duplicates an existing task.

[0692] Step 3:

[0693] The server generates an optimized plan based on the analysis results. The server uses a scheduling algorithm to calculate the optimal task sequence between processes. Using the analysis results as input, it generates a proposed schedule as output. The generated schedule includes a detailed timeline with tasks assigned to each person.

[0694] Step 4:

[0695] The server visualizes the generated plan and sends it to the user's terminal for review. Using a graphical user interface, the schedule is displayed visually. Each task's progress, deadline, and assigned person are color-coded for easy identification. The output is provided on a user-operable dashboard screen.

[0696] Step 5:

[0697] The user reviews the displayed plan using their device and provides feedback if necessary. The feedback information is sent to the server and used as input for revising the plan.

[0698] Step 6:

[0699] The server readjusts the plan based on the feedback it receives. It analyzes the feedback, modifies the baseline parameters, and generates a new schedule. This enables efficient plan revisions. The server then sends the final plan back to the terminal.

[0700] 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.

[0701] This invention is a project management system incorporating an emotion engine, which aims to optimize project management based on user emotion information. This system utilizes emotion data to more accurately coordinate information and manage schedules among stakeholders in a project.

[0702] First, the terminal receives task information input from the user. The emotion engine then identifies the user's emotional state based on the user's input and data obtained from the terminal. This emotional data is analyzed using a specific algorithm and sent to the server.

[0703] In addition to collecting regular task information, the server also receives user sentiment information. This sentiment information is input into an AI model to understand the overall sentiment trend of the project. Task progress management and scheduling are optimized to take user sentiment into account.

[0704] For example, if a user is experiencing high levels of stress, the server will detect this and readjust the schedule to reduce the workload. This may involve changing task assignments or adjusting resource allocation. Furthermore, if a user is highly motivated towards a particular task, the server will take this into consideration and improve project efficiency by changing its priority.

[0705] Furthermore, the server monitors progress and user feedback, and by taking emotional states into account in the feedback, it provides more appropriate adjustments. Emotional data is also reflected in project reports, making it easier for stakeholders to visually understand the project's status.

[0706] For example, in a new product development project, adjustments are made based on the emotional state of team members. Highly motivated members are assigned more challenging tasks, while members experiencing stress receive more support.

[0707] In this configuration, the system takes into account the human factors of the project and supports efficient and smooth project execution.

[0708] The following describes the processing flow.

[0709] Step 1:

[0710] The device uses its camera and microphone to collect the user's facial expressions and voice as they input task information. This data is then sent to an emotion engine for analysis.

[0711] Step 2:

[0712] The emotion engine analyzes collected facial and voice data to identify the user's emotional state. This emotion data is then sent to the server for subsequent processing.

[0713] Step 3:

[0714] The server receives emotional information and regular task data and inputs it into the AI ​​model. The AI ​​model uses the emotional information, along with the task content and priorities, to optimize the entire project.

[0715] Step 4:

[0716] The server adjusts schedules and resource allocation based on user emotional information. For example, it reduces the workload for highly stressed users and assigns challenging tasks to highly motivated users.

[0717] Step 5:

[0718] The server distributes the adjusted schedule to stakeholders. Through their terminals, users can check the latest schedule and confirm that sentiment-based optimizations have been reflected.

[0719] Step 6:

[0720] The server re-evaluates user feedback along with the sentiment engine throughout the project, and makes further adjustments to the schedule and resource allocation as needed.

[0721] Step 7:

[0722] The server automatically generates reports summarizing project progress and emotional data, and distributes them regularly to stakeholders. These reports visually communicate the emotional health of the project along with its progress.

[0723] (Example 2)

[0724] 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".

[0725] In project management, optimizing progress and processes without considering the emotional states of stakeholders can lead to decreased efficiency and cause anxiety and stress among stakeholders. Furthermore, traditional systems often lack clarity on how to reflect stakeholders' emotional information in process management, potentially hindering project progress. To address this challenge, a system capable of analyzing and reflecting stakeholders' emotional states in real time is needed.

[0726] 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.

[0727] In this invention, the server includes means for collecting multiple pieces of information and analyzing the content of each piece of information; means for identifying and adjusting the emotional state of each piece of information based on the analysis results; and means for generating an optimized schedule using the identified emotional data. This enables efficient project management and smooth progress that takes into account the emotional states of stakeholders.

[0728] "Information" refers to data related to project management and data that expresses the associated emotional states.

[0729] "Analysis" is the act of extracting meaning from information and then performing data processing or making judgments based on that meaning.

[0730] "Emotional state" refers to data that indicates the psychological and emotional condition of those involved.

[0731] "Adjustment" refers to the process of reviewing and optimizing project steps and tasks based on analysis and identified information.

[0732] A "project schedule" is a plan used to manage the progress and schedule of a project, and is distributed to stakeholders for the purpose of optimization.

[0733] "Stakeholders" refers to all individuals and organizations participating in or related to the project.

[0734] This project management system utilizes an emotion engine and generative AI models to achieve optimized project management that takes into account the emotional states of stakeholders.

[0735] Terminal role and operation:

[0736] The terminal accepts task information directly entered by the user. For example, when a user enters a new task, they write the task name, due date, importance level, and related documents in the input fields. The terminal receives this information and activates the emotion engine. This emotion engine analyzes subtle changes during input, such as the speed and strength of keystrokes, and the tone of voice in voice input, to identify the user's emotional state.

[0737] Server roles and operations:

[0738] The server receives emotional data and task information transmitted from terminals. This received data is analyzed through a generative AI model to identify the overall emotional trend in the project. Based on this emotional trend, the server optimizes the project schedule and adjusts task assignments and schedules as needed. For example, if some users show high stress levels, the server readjusts the schedule to reduce their burden. It also assigns higher-priority tasks to users who are deemed highly motivated.

[0739] Examples of specific cases and prompt statements:

[0740] In project management for a new product development team, the system tracks each member's emotional state in real time and adjusts the schedule based on that emotional data. Support resources are added for members experiencing stress, and challenging tasks are prioritized for highly motivated members. This improves overall team efficiency and morale.

[0741] An example of a prompt message would be, "Analyze the emotional state of each member in the current project and propose the optimal task assignments and schedule adjustments."

[0742] In this way, this system enables project management that incorporates emotions, supporting efficient and harmonious progress.

[0743] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0744] Step 1:

[0745] The terminal receives task information from the user. The user enters detailed information about the project task (e.g., task name, deadline, priority, etc.) into the terminal. The terminal passes this information to the emotion engine, which analyzes the keystrokes and voice tone during input. The emotion engine analyzes this input data to identify the user's emotional state (e.g., joy, stress). The identified emotion data and task information are sent from the terminal to the server.

[0746] Step 2:

[0747] The server receives emotional data and task information sent from the terminal. The server analyzes the emotional data through a generative AI model. This extracts the emotional trends for the entire project and aggregates the individual emotional data of each member. This data processing makes it possible to understand the emotional trends and prepares the data for use in project management.

[0748] Step 3:

[0749] The server uses a generative AI model to optimize the schedule based on sentiment data. Specifically, it adjusts task schedules and changes task assignments as needed based on sentiment trends predicted by the model. The input is sentiment and task data, and the output is the optimized schedule. The server updates this schedule in real time, allowing stakeholders to stay informed of the situation.

[0750] Step 4:

[0751] The server distributes the optimized schedule to stakeholders. The schedule is distributed via email or a dedicated project management tool. The server periodically collects feedback from stakeholders and prepares to revise the schedule again, incorporating sentiment information. Further improvements are then made based on this information.

[0752] Step 5:

[0753] The server continuously monitors progress and compares AI model predictions with actual results. If emotional fluctuations affecting project progress are detected, the server automatically readjusts the schedule. Output regarding the readjustment is generated and notified to stakeholders. This ensures that optimization is always maintained.

[0754] (Application Example 2)

[0755] 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".

[0756] In modern project management and production line operations, simply managing schedules presents a challenge: it fails to take into account human emotional states and motivations. This makes it difficult to adjust tasks or adapt machine operations to the emotions of stakeholders, ultimately hindering efficient and flexible operations.

[0757] 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.

[0758] In this invention, the server includes means for collecting multiple pieces of information and analyzing the content of each piece of information, means for identifying emotional states and adjusting workload and priorities, and means for adjusting the operation of machines / devices using emotional data. This enables flexible and efficient project management that reflects the emotional states of stakeholders.

[0759] "Information" refers to data related to a project or production, showing the progress of work and the status of stakeholders.

[0760] "Analysis" is the act of thoroughly evaluating the content of collected information and identifying specific patterns or trends.

[0761] "Schedule" refers to a time plan for carrying out each task in the execution of a project or task.

[0762] "Stakeholders" refers to all individuals and groups affected by or involved in a project or production activity.

[0763] "Progress status" refers to information indicating the degree of progress in work within a project or production line.

[0764] "Emotional state" refers to data that indicates the psychological and emotional condition of those involved.

[0765] "Workload" refers to the quantity and quality of specific tasks and work assigned to stakeholders.

[0766] "Priority" refers to the order in which tasks and projects are carried out, determined based on their importance and urgency.

[0767] "Machine / device operation adjustment" refers to the act of making changes to optimize the movement of machines or devices based on collected data.

[0768] The system realizing this invention first receives task information input from the user via a terminal. At this time, to identify the user's emotional state, it utilizes cameras and sensors to acquire facial expression data and biometric information. The hardware can be general-purpose camera devices and biosensors, and the software combines OpenCV and TensorFlow for emotion analysis. This data is sent to a server, where further data analysis is performed.

[0769] The server uses this data to analyze multiple pieces of information and understand the progress of projects and tasks. Emotional data is input into a generative AI model, which generates an optimized schedule that reflects the emotional state of stakeholders. During this process, the workload and priorities are adjusted considering the stress levels and motivation of workers, and the operation of machines and equipment is adjusted as needed.

[0770] For example, in an assembly line for new equipment, if an emotion analysis system detects a worker experiencing high stress levels, the work sequence and machine operating speed are adjusted to reduce that worker's workload. This adjustment improves work efficiency and increases satisfaction among stakeholders.

[0771] An example of a prompt message from a generated AI model is: "Consider the emotional state of the workers on the current production line and propose specific improvement plans to increase efficiency."

[0772] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0773] Step 1:

[0774] The terminal receives task information from the user. During this process, the terminal uses its camera and sensors to acquire the user's facial expression data and biometric information, which is then added to the input information. The input data includes task information along with images and biometric information necessary for emotion estimation.

[0775] Step 2:

[0776] The device uses OpenCV and TensorFlow to analyze the user's emotional state from acquired facial expression data and biometric information. In this emotion analysis step, image processing is performed to extract features, and an AI model is used to estimate emotion labels. The analyzed emotional state is output, and data with that information attached is generated.

[0777] Step 3:

[0778] Based on the analysis results, the terminal sends relevant data to the server. The server organizes the received task information and sentiment data and inputs it into the schedule generation algorithm. The server then outputs a schedule that optimizes task priorities and worker assignments.

[0779] Step 4:

[0780] The server generates an optimized schedule and distributes it to stakeholders. This distribution step sends schedule details to stakeholders' devices, allowing them to visually review the schedule. Information is organized in an easy-to-understand format to ensure project efficiency.

[0781] Step 5:

[0782] The server continuously monitors the progress and the sentiment of stakeholders, and readjusts the schedule as needed. In this step, data analysis is performed again, and an optimized schedule is generated based on the newly obtained information.

[0783] Step 6:

[0784] The server automatically generates and distributes a final progress report to stakeholders. This report includes information on project progress and changes in each worker's sentiment. The report is presented in a clear and intuitive format, allowing stakeholders to easily grasp the overall situation.

[0785] 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.

[0786] 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.

[0787] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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."

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] The following is further disclosed regarding the embodiments described above.

[0807] (Claim 1)

[0808] A means of collecting multiple data and analyzing the content of each data,

[0809] A means for detecting and adjusting for duplicate data based on the analysis results,

[0810] A means for generating an optimized schedule using adjusted data,

[0811] A means of distributing the generated schedule to stakeholders,

[0812] A means to continuously monitor progress and readjust the schedule as needed,

[0813] A means of automatically generating and distributing progress reports,

[0814] A system that includes this.

[0815] (Claim 2)

[0816] A means for displaying a progress report using a graphical interface in the system according to claim 1.

[0817] (Claim 3)

[0818] A means for collecting feedback from stakeholders and adjusting the schedule based on that feedback in the system according to claim 1.

[0819] "Example 1"

[0820] (Claim 1)

[0821] A means of collecting multiple pieces of information and analyzing the attributes of each piece of information,

[0822] A means for detecting and integrating duplicate information based on the analysis results,

[0823] A means for generating an optimized plan using integrated information,

[0824] Means for providing the generated plan to stakeholders,

[0825] A means to continuously track progress and readjust the plan as needed,

[0826] A means of automatically generating and providing progress reports,

[0827] A means of identifying information dependencies using an artificial intelligence model,

[0828] A system that includes this.

[0829] (Claim 2)

[0830] The system according to claim 1, wherein progress reports are presented using a visual interface.

[0831] (Claim 3)

[0832] The system according to claim 1, which collects opinions from stakeholders and adjusts the plan based on those opinions.

[0833] "Application Example 1"

[0834] (Claim 1)

[0835] A device that collects multiple pieces of information and analyzes the content of each piece of information,

[0836] A device that detects and adjusts the redundancy of each piece of information based on the analysis results,

[0837] A device that generates an optimized plan using adjusted information,

[0838] A device that distributes the generated plan to the relevant parties,

[0839] A device that continuously monitors the progress and readjusts the plan as needed,

[0840] A device that optimizes work planning in the production process and promotes an efficient production flow,

[0841] A device that instantly modifies the plan based on feedback,

[0842] A device that automatically generates and distributes progress reports,

[0843] A system that includes this.

[0844] (Claim 2)

[0845] The system according to claim 1, wherein the progress report is displayed using a visual interface.

[0846] (Claim 3)

[0847] The system according to claim 1, which collects information from stakeholders and modifies the plan based on that information.

[0848] "Example 2 of combining an emotion engine"

[0849] (Claim 1)

[0850] A means of collecting multiple pieces of information and analyzing the content of each piece of information,

[0851] A means for identifying and adjusting the emotional state of each piece of information based on the analysis results,

[0852] A means for generating an optimized process chart using identified emotion data,

[0853] A means of distributing the generated process schedule to relevant parties,

[0854] A means of continuously monitoring progress and readjusting the schedule as needed, taking into account emotional states,

[0855] A means of automatically generating a status report and distributing it in a format that stakeholders can visually understand,

[0856] A system that includes this.

[0857] (Claim 2)

[0858] The system according to claim 1, wherein progress reports are displayed using visualization means.

[0859] (Claim 3)

[0860] The system according to claim 1, which collects opinions from stakeholders and adjusts the schedule based on them.

[0861] "Application example 2 when combining with an emotional engine"

[0862] (Claim 1)

[0863] A means of collecting multiple pieces of information and analyzing the content of each piece of information,

[0864] A means for detecting and adjusting for duplication of information based on the analysis results,

[0865] A means for generating an optimized schedule using adjusted information,

[0866] A means of distributing the generated schedule to relevant parties,

[0867] A means to continuously monitor progress and readjust the schedule as needed,

[0868] A means of automatically generating and distributing progress reports,

[0869] A means of identifying emotional states and adjusting workload and priorities,

[0870] A means of adjusting the operation of a machine / device using emotional data,

[0871] A system that includes this.

[0872] (Claim 2)

[0873] The system according to claim 1, comprising means for visually displaying the report.

[0874] (Claim 3)

[0875] The system according to claim 1, comprising means for collecting responses from stakeholders and adjusting the schedule based thereon. [Explanation of Symbols]

[0876] 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 collecting multiple data and analyzing the content of each data, A means for detecting and adjusting for duplicate data based on the analysis results, A means for generating an optimized schedule using adjusted data, A means of distributing the generated schedule to stakeholders, A means to continuously monitor progress and readjust the schedule as needed, A means of automatically generating and distributing progress reports, A system that includes this.

2. The system according to claim 1, further comprising means for displaying a progress report using a graphical interface.

3. A system according to claim 1, further comprising means for collecting feedback from stakeholders and adjusting the schedule based thereon.

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