system

Abstract

We provide the system. [Solution] A means of receiving project information and storing project information in a database, A means of adding tasks related to a project and managing the task list, A means to update the progress of a task and analyze that progress, A method for automatically generating reports based on project progress and task information, 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 method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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, there are problems such as insufficient planning, complexity of resource management, opacity of task progress, inadequacy of risk management, and insufficient communication between teams. These problems are the main causes of project delays and failures, thus hindering efficient project operation in small and medium-sized enterprises and project teams. Therefore, a system for solving such problems is required.

Means for Solving the Problems

[0005] This invention provides a means for receiving project information and storing it in a database. It also includes means for adding project-related tasks and managing task lists, thereby clarifying the plan. Furthermore, it incorporates means for updating task progress and analyzing that progress, improving the transparency of the project's progress. In addition, it includes means for automatically generating reports based on project progress and task information, facilitating a smooth understanding of the entire project. This effectively solves various challenges in project management and supports project success.

[0006] "Project information" refers to basic data about a specific project, including details such as the project name, deadline, objectives, and participating members.

[0007] A "database" is a system or container for systematically organizing, storing, and retrieving related information.

[0008] A "task" is a specific activity or unit of work necessary to achieve a project, and it has a set of specific goals and deadlines.

[0009] A "task list" is a compilation of all tasks related to a project in a list format, and it is a management tool used to track the progress of each task.

[0010] "Progress" is an indicator that shows the current degree of completion for a particular task or project.

[0011] "Analysis" is the process of revealing the structure and relationships of data and information by examining and breaking them down in detail.

[0012] A "report" is a document created to compile and record information about a specific event or task, with the purpose of communication and preservation.

[0013] "Automatic generation" refers to the process where a system autonomously creates content or data according to pre-defined rules and programs, without human intervention. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when combined with an emotion engine.

Embodiments for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0017] In the following embodiments, a processor with a reference number (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of a plurality of types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, a RAM (Random Access Memory) with a reference number is a memory in which information is temporarily stored and is used as a work memory by the processor.

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

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

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

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention is a system designed to solve the project management challenges faced by small and medium-sized enterprises and project teams. This system centrally manages project information, enabling efficient task addition and management, progress analysis, and automated report generation.

[0036] The system's main components consist of a database, a user interface, and a server program. Users input project information via a terminal, and the server stores this information in the database. This allows for centralized information management and facilitates information sharing among stakeholders.

[0037] When a user creates a new project or task, they input the information through an interface on their terminal. The server receives this input and updates the data for the corresponding project. The server also monitors the progress and deadlines of specific tasks and sends notifications to the user as needed.

[0038] Furthermore, the server periodically analyzes the project's progress and calculates the progress rate. Based on this information, the server automatically generates reports that include project progress and task status. These reports are provided to the user via their terminal, helping them to understand the overall picture of the project.

[0039] As a concrete example, let's say a user creates a "website development project." In this case, the user uses a terminal to input the project name, deadline, participating members, etc. The server stores this as project information in the database. Later, the user adds "design complete" as a task related to the project. The server updates the task list and monitors the task's deadline.

[0040] In this way, this system simplifies task management and visualizes progress in projects, thereby supporting efficient project operation.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The user uses their device to begin creating a new project. They enter details such as the project name, deadline, and participating members.

[0044] Step 2:

[0045] The terminal sends the entered project information to the server. The server stores the received information in a database and generates a project ID. This ID is used for subsequent management.

[0046] Step 3:

[0047] When a user wants to add a task to a specific project via their device, they enter the task name, due date, and assignee.

[0048] Step 4:

[0049] When the server receives input, it uses the corresponding project ID to add a new task to the project's task list. The database is also updated.

[0050] Step 5:

[0051] The server monitors changes to the task list and periodically checks the progress of tasks. It sends notifications to users when tasks are completed or when deadlines are approaching.

[0052] Step 6:

[0053] The user requests a report on their device to check the progress.

[0054] Step 7:

[0055] The server analyzes data from the entire project and calculates the percentage of completed tasks, among other things. Based on this, it automatically generates a report summarizing the progress.

[0056] Step 8:

[0057] The server sends the generated report to the terminal, allowing the user to view detailed project progress information.

[0058] Step 9:

[0059] Users can continue project management as needed, such as adding new tasks or updating the status of existing tasks.

[0060] In this way, the system supports everything from project information registration to progress monitoring and report generation, enabling efficient project management.

[0061] (Example 1)

[0062] 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."

[0063] In project management, small organizations and teams face a wide range of problems. Information is often scattered, making management cumbersome and frequently resulting in an inability to properly track task progress and deadlines. Furthermore, inadequate resource management can lead to project delays and cost overruns. Additionally, spending excessive time on progress reporting is inefficient and hinders rapid decision-making.

[0064] 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.

[0065] In this invention, the server includes means for receiving project information and storing it in a data set; means for adding tasks related to the project and managing a task list; means for updating the progress of tasks and analyzing that progress; means for providing reports to user terminals; and means for periodically monitoring the project progress and issuing warnings to users. This enables more efficient project management, task visibility, resource optimization, and rapid decision-making.

[0066] "Project information" refers to basic information related to the project, such as its name, start date, end date, participating members, and objectives.

[0067] A "data set" is a storage area used to centrally manage information, and includes databases.

[0068] "Work" refers to individual tasks or operations performed within a project.

[0069] A "task list" is a list of all tasks and activities related to a project.

[0070] A "report" is a document that summarizes the project's progress, task status, and other relevant information.

[0071] A "user terminal" is a device that a user uses to access and operate the project system.

[0072] "Progress status" refers to information indicating the degree to which tasks have been completed or the progress toward achieving project goals.

[0073] A "warning" is a notification sent to a user when work is not progressing as planned or when a task deadline is approaching.

[0074] "Resources" refer to the means, such as personnel, materials, or time, necessary to carry out a project.

[0075] This invention is a system for effectively managing projects. This system primarily consists of servers, terminals, and data sets, and aims to improve the efficiency of information aggregation and processing.

[0076] Hardware and software:

[0077] The server can be a high-performance cloud server or an on-premises server machine, and the data set uses a database management system such as MySQL® or PostgreSQL. Terminals include computers and mobile devices that users directly interact with, and these have a web browser or dedicated application installed.

[0078] Data processing and computation:

[0079] Users input project information and tasks using a terminal and send the information to the server through the interface. Upon receiving this input information, the server performs a data storage process on the data set and executes an algorithm to analyze the progress of tasks in real time. Scripting languages ​​such as Python and JavaScript (registered trademark) can be used for this process.

[0080] The server utilizes a generative AI model to aggregate data and automatically generate reports for users. This AI model enables efficient and highly accurate analysis of progress data. It also optimizes resource data to support efficient resource management.

[0081] Specific example:

[0082] For example, if a user registers a new project called "Website Development Project" from their terminal, they enter information such as the project name, start date, and participating members. The server then saves this information to a data set, and the basic project information is organized. Subsequently, if the user adds tasks such as "Design Completion" and sets deadlines, the server can monitor these tasks, evaluate their progress, and issue warnings to the user as needed.

[0083] Example of a prompt:

[0084] When using a generative AI model, a prompt message such as "Please prepare an updated project progress report for the next meeting" can be used.

[0085] This invention aims to support efficient project management by enabling users to quickly grasp the overall picture of a project through centralized information management.

[0086] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0087] Step 1:

[0088] Users input project and task information through the terminal's user interface. Specifically, they enter the project name, start date, end date, participating members, task details, and deadlines. The entered information is sent from the terminal to the server, thereby collecting basic project information.

[0089] Step 2:

[0090] The server analyzes the received project information and stores it in a data set. For storage in the database, a data management system such as MySQL is used, ensuring centralized information management. Based on the input, project and task data structures are generated and recorded in the database. This process establishes a well-organized and easily accessible information state.

[0091] Step 3:

[0092] When a user wants to add a new task to a project, they use a terminal to enter the task name and related information. The server adds the task information received from the terminal to the existing project data and updates the task list. During this process, alerts are also set based on the task's priority and schedule.

[0093] Step 4:

[0094] The server periodically analyzes the project's progress from the data set, generating progress percentages and lists of incomplete tasks. This analysis uses scripts such as Python to process the data using statistical methods. The output progress information is essential for understanding the overall project status.

[0095] Step 5:

[0096] The server utilizes a generative AI model to automatically generate reports based on analyzed progress information. The reports show the project status, including graphs and charts. It's also possible to create customized reports that highlight specific information requested by the user based on prompts. Accuracy and relevance of the reports are ensured at this stage.

[0097] Step 6:

[0098] Finally, the server sends the generated report to the terminal, where the user can view it and grasp the overall picture of the project. The report is provided as a PDF file or through a web-based interface. The user can check the project's health and adjust its direction as needed.

[0099] (Application Example 1)

[0100] 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."

[0101] In project management at home or in small groups, there are challenges in visualizing progress, effectively dividing tasks, and checking progress in real time. As a result, delays in work and inappropriate resource allocation occur, leading to decreased project efficiency.

[0102] 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.

[0103] In this invention, the server includes means for receiving project information and storing the project information in a data set, means for adding tasks related to the project and managing a list of tasks, and means for updating the progress of tasks and analyzing that progress. This enables real-time monitoring of project progress and efficient task management within a home or small group.

[0104] "Information" refers to data and metadata related to projects and tasks, which are the objects that the system manages and analyzes.

[0105] A "data collection" is a digital storage system for systematically storing and managing information.

[0106] A "task" is a specific unit of work or activity that needs to be performed within a project.

[0107] A "task list" is a list used to systematically manage all tasks related to a project.

[0108] "Progress" is an indicator that shows how far along a task or project is in relation to the plan.

[0109] "Analysis" is the process of thoroughly analyzing progress and resource data to identify patterns and trends.

[0110] A "report" is a document that organizes the progress and work information of a project and provides it to the relevant parties.

[0111] "Prediction" is the process of estimating future progress or conditions based on existing data.

[0112] An "alert" is a notification that draws attention to a user when certain conditions are met.

[0113] The system for implementing this invention consists of a server for managing projects, terminals for users to input information, and a data set for storing data. This system is built using Python programming and the Django framework. PostgreSQL is used as the database to achieve highly efficient data management.

[0114] The server stores project and work information entered by users through their terminals into a data set. In this process, the information is stored in a structured format, making it easily accessible and analyzable. The server also uses machine learning libraries such as Scikit-learn to predict progress based on past project data. This allows for the generation of alerts to prevent delays and decreased efficiency. For notifications, the Twilio API is utilized to send real-time alerts to users as push notifications.

[0115] For example, if a user is managing a "home renovation project," they would use their smartphone to enter the project name and work deadlines. The server stores this information in a database and continuously monitors whether the project is progressing according to plan. If delays are anticipated, alerts can be sent to all family members' smartphones to prompt immediate action.

[0116] Examples of prompts for a generative AI model include the following:

[0117] "Please give me ideas for designing an app that optimizes project management within the home. This app would allow users to easily manage projects and check task progress in real time."

[0118] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0119] Step 1:

[0120] Users enter project information and related work details via a terminal. This input includes the project name, deadline, and work description. The data entered by the user is sent to a data set in a structured format.

[0121] Step 2:

[0122] The server stores the received project information in a data set. During this process, data validation is performed as needed to check for any duplication or inconsistencies with existing project data. After validation is complete, the data is securely stored.

[0123] Step 3:

[0124] The server analyzes progress using existing data within the data set. Here, Scikit-learn is used to run a machine learning model that predicts progress based on previous project data. The analysis results are output as a percentage indicating the expected progress.

[0125] Step 4:

[0126] Based on the progress forecast, the server uses the Twilio API to send alerts to the user if there are delays or risks. These notifications appear as push notifications on the device, enabling quick action.

[0127] Step 5:

[0128] The user can review the received alerts and modify the project plan as needed. If additional work or reconfigurations are required, the user will re-enter the updated information from the terminal. The updated information will then be returned to step 2, where it will be stored in the data set and progress analysis will be performed again.

[0129] 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.

[0130] This invention integrates an emotion engine into a project management system to understand user emotions and optimize feedback and project management based on those emotions. In addition to the basic functions of project management, this system provides new insights using user emotion data.

[0131] The system consists of a user interface, server, database, and emotion engine. Users input project and task information via a terminal, which the server receives and stores in the database. In addition to conventional task management functions, the emotion engine analyzes the user's emotional data, which is used to improve task management and communication.

[0132] When a user enters information about a project, the device collects not only the user's input but also data related to their emotions. This includes, for example, facial recognition for expression analysis and voice tone analysis. The emotion engine analyzes this data in real time to estimate the user's stress levels, satisfaction levels, and other factors.

[0133] The server evaluates progress and task assignments based on the collected emotional data, and makes adjustments as needed. It also generates feedback for users regarding project progress and provides suggestions for maintaining motivation and reducing stress.

[0134] For example, if the emotion engine detects high stress levels while a user is conducting an important project review, the server will either notify the user of an appropriate break or re-evaluate task priorities to reduce the stress load.

[0135] Thus, this system, which incorporates an emotion engine, enables project management that takes the user's psychological state into account, thereby improving the overall efficiency and success rate of projects.

[0136] The following describes the processing flow.

[0137] Step 1:

[0138] Users input project and task information using their devices. Simultaneously, emotion-related data is collected. This data collection utilizes facial recognition for expression analysis and voice tone analysis.

[0139] Step 2:

[0140] The terminal sends collected project information and emotional data to the server. The server stores this information in a database. This process accumulates basic project information and the user's emotional state.

[0141] Step 3:

[0142] The server uses an emotion engine to analyze emotional data in real time. This allows it to estimate the user's emotional state, such as stress levels and satisfaction levels.

[0143] Step 4:

[0144] If the emotion engine determines that the user's stress level is high, the server will generate appropriate feedback. For example, it might suggest improvements to time management or suggest taking short breaks.

[0145] Step 5:

[0146] The server receives feedback generated by the user through their device and selects the necessary actions. It then records these selections to help improve the system for future use.

[0147] Step 6:

[0148] As the project progresses, the server continuously analyzes sentiment data and prioritizes tasks and reallocates resources as needed. This process optimizes the project's efficiency.

[0149] Step 7:

[0150] Users view reports on their devices that reflect project progress and analysis results based on sentiment data. These reports provide suggestions for improving project management and maintaining the user's emotional well-being.

[0151] In this way, the system closely links project information and sentiment data, enabling continuous optimization.

[0152] (Example 2)

[0153] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0154] In project management, it's not enough to simply manage the progress of work items; efficient and flexible management that takes into account the emotions of users is required. However, conventional methods have lacked sufficient feedback and adjustments based on the psychological state and emotions of users, which has sometimes led to decreased project efficiency and success rates. This problem needs to be solved.

[0155] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0156] In this invention, the server includes means for receiving project information and storing it in a memory device, means for collecting and analyzing user emotional data using an emotional analysis device, and means for adjusting work items and evaluating progress based on the analyzed emotional data. This makes it possible to manage projects while taking user emotions into consideration, thereby improving the overall efficiency and success rate of the project.

[0157] "Project information" refers to all data related to the plans, progress, resources, and schedules associated with a specific activity or task.

[0158] A "storage device" is a physical or virtual device used to store and manage data.

[0159] A "work item" refers to an individual task or activity that is performed within a project.

[0160] A "work item list" is a list that encompasses all work items managed within a project.

[0161] An "emotion analysis device" is a device or software that uses facial recognition, voice analysis, or other methods to determine a user's emotional state.

[0162] "User sentiment data" refers to data that shows information related to the user's psychological state and emotions.

[0163] "Analyzed emotional data" refers to data processed by an emotional analysis device, which specifically evaluates the user's emotional state.

[0164] "Resource management" refers to management methods for effectively allocating and utilizing the resources needed within a project.

[0165] "Resource data" refers to data that includes information related to the resources required for a project, such as personnel, equipment, time, and budget.

[0166] This invention provides a system for considering user emotions in project management. This system consists of a terminal, server, storage device, and emotion analysis device, and effectively manages project information and user emotion data to optimize project progress.

[0167] Users input project details and individual work items using a terminal, and emotional data is collected during this process. For this purpose, the terminal is equipped with a camera and microphone, and facial recognition and voice analysis software are used to analyze the user's expressions and voice. This data is sent in real time to an emotion analysis device, where the user's emotional state is interpreted.

[0168] The server receives this input data and analyzed sentiment data and stores it in its memory. The server also uses a generative AI model to automatically generate feedback for the user from the accumulated data. This feedback includes advice to maintain motivation and suggestions for stress reduction, and is communicated to the user along with project progress information.

[0169] For example, if a high level of stress is detected while a user is conducting a project review, the server will notify them to "take a few minutes' relaxation break." This notification is based on a prompt generated by a generative AI model. An example of a prompt would be, "Please describe the emotions the user is experiencing during the project. In particular, please advise on what measures should be taken if high levels of stress are detected."

[0170] In this way, the system enables advanced project management based on the user's psychological state, aiming to improve operational efficiency and increase the success rate of projects.

[0171] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0172] Step 1:

[0173] The user uses a terminal to input project information and work items. The terminal receives the user's input data and saves its contents to its storage device. This input includes the project name, deadline, and specific task list. The output is digital data of the project information. In addition, the terminal prepares to collect data for facial recognition and voice analysis.

[0174] Step 2:

[0175] The device uses facial recognition to capture the user's facial expressions and voice analysis to record their tone of voice. This emotion-related data is transmitted in real time to an emotion analysis device. The input consists of facial image and voice data, and the output is initial data indicating the user's emotions. The device also filters out ambient noise during this process to improve analysis accuracy.

[0176] Step 3:

[0177] The emotion analysis device analyzes collected facial recognition data and voice data to determine the user's emotional state. Specifically, it uses various algorithms to quantify and evaluate stress levels and satisfaction levels. The input is emotion-related data obtained in the previous step, and the output is structured emotional information. The emotion analysis device learns similar patterns during the analysis process to improve the accuracy of its judgments.

[0178] Step 4:

[0179] The server integrates analyzed sentiment data and project data to generate optimal feedback for the user. This step utilizes a generative AI model to create prompts tailored to the user's situation. Inputs are sentiment information and project progress data sent from the sentiment analyzer, while output is an appropriate feedback message for the user. Based on this feedback, the server automatically performs tasks such as resetting work item priorities.

[0180] Step 5:

[0181] The server sends a generated feedback message to the terminal, notifying the user. The user receives the notification on the terminal and can take action based on the feedback. The notification may include specific instructions, such as "Take a break." The input is the generated prompt, and the output is the notification displayed on the user's screen. The terminal manages the notification history for later reference.

[0182] (Application Example 2)

[0183] 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".

[0184] In modern project management, progress is often managed without considering the psychological state of workers, leading to challenges such as decreased worker motivation and stress, which can impact work efficiency and safety. Furthermore, traditional monitoring systems lack the ability to adjust for workers' emotions, making it difficult to achieve project optimization.

[0185] 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.

[0186] In this invention, the server includes means for receiving project information and storing the project information in information storage, means for adding tasks related to the project and managing a list of tasks, and means for analyzing user emotional data and generating work adjustment proposals based on the user's psychological state. This makes it possible to adjust tasks while taking into account the emotional state of the workers, thereby improving the safety and efficiency of the project.

[0187] A "project" is a set of work processes that are planned to achieve a specific goal and include multiple tasks.

[0188] "Information storage" refers to a medium or system for storing project information and related data.

[0189] "Work" refers to specific tasks or tasks related to a project.

[0190] "Methods for managing a list of tasks" refer to methods and tools for organizing all tasks related to a project and tracking their progress.

[0191] "Emotional data" refers to information that indicates the user's psychological state, and is obtained from facial expressions, voice tone, and other similar data.

[0192] "Psychological state" refers to the emotional and mental health condition that a user experiences in a particular situation or under specific circumstances.

[0193] "Means for generating work adjustment proposals" refer to methods and tools for analyzing user emotional data and revising work content and schedules accordingly.

[0194] This invention is a system that understands the emotional state of workers in a factory environment and improves work efficiency and safety based on that understanding. The server first receives project information and stores it in information storage. This information includes the project name, deadline, and related tasks.

[0195] Next, the terminal monitors input from the worker and collects emotional data using facial recognition and speech analysis technologies. This uses sensors equipped with high-precision cameras and microphones. A facial recognition system using OpenCV and a speech recognition system equipped with Google® Speech-to-Text API process this data in real time to infer the worker's emotional state.

[0196] The server uses an emotion engine to evaluate the analysis results and generates suggested adjustments to the work based on the worker's stress level and satisfaction level. These adjustments may include pausing work, recommending breaks, or changing work priorities. This allows for efficient production work while maintaining the worker's psychological well-being.

[0197] For example, if a worker on a production line exhibits persistent stress, the system recommends temporarily suspending work and taking a break. If the emotional state does not improve, a manager will be notified, and further action will be considered.

[0198] An example of a prompt for a generating AI model is, "Analyze worker stress levels from camera feeds on the manufacturing line and suggest work adjustments if stress is detected." This prompt guides how the AI ​​interprets the data and generates countermeasures.

[0199] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0200] Step 1:

[0201] The terminal receives project information.

[0202] The user enters the project name, deadline, related tasks, etc., and the terminal sends this information to the information storage. The input data is formatted as a string and converted into a format that can be stored in the information storage as output.

[0203] Step 2:

[0204] The server stores project information in its information storage.

[0205] The server stores project information received from the terminal in a database. It converts the entered project information into an appropriate data structure and stores it in the database for efficient access.

[0206] Step 3:

[0207] The device collects emotional data from the workers.

[0208] While the user is working, the device uses a high-precision camera and microphone to collect facial expressions and audio data. This data is input as image and audio files and processed in real time.

[0209] Step 4:

[0210] The device analyzes emotional data.

[0211] The device uses OpenCV for facial recognition and the Google Speech-to-Text API for speech analysis. By extracting facial features from input image data and analyzing the tone of the speech data, it infers the user's emotional state and outputs an estimated emotion value.

[0212] Step 5:

[0213] The server generates work adjustment proposals based on emotional data.

[0214] The server uses an emotion engine to evaluate the analysis results and generates work adjustment suggestions based on the worker's stress level and satisfaction level. It takes estimated emotions as input and determines and outputs the necessary adjustment actions (such as pausing work or recommending a break).

[0215] Step 6:

[0216] The server notifies the user of the proposed adjustments it has generated.

[0217] The server notifies the user of the generated adjustment proposal via the terminal. The terminal visualizes it for the user, provides feedback to adjust the work content and schedule, and presents the adjustment proposal.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] [Second Embodiment]

[0222] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0223] 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.

[0224] 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).

[0225] 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.

[0226] 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.

[0227] 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).

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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".

[0234] This invention is a system designed to solve the project management challenges faced by small and medium-sized enterprises and project teams. This system centrally manages project information, enabling efficient task addition and management, progress analysis, and automated report generation.

[0235] The system's main components consist of a database, a user interface, and a server program. Users input project information via a terminal, and the server stores this information in the database. This allows for centralized information management and facilitates information sharing among stakeholders.

[0236] When a user creates a new project or task, they input the information through an interface on their terminal. The server receives this input and updates the data for the corresponding project. The server also monitors the progress and deadlines of specific tasks and sends notifications to the user as needed.

[0237] Furthermore, the server periodically analyzes the project's progress and calculates the progress rate. Based on this information, the server automatically generates reports that include project progress and task status. These reports are provided to the user via their terminal, helping them to understand the overall picture of the project.

[0238] As a concrete example, let's say a user creates a "website development project." In this case, the user uses a terminal to input the project name, deadline, participating members, etc. The server stores this as project information in the database. Later, the user adds "design complete" as a task related to the project. The server updates the task list and monitors the task's deadline.

[0239] In this way, this system simplifies task management and visualizes progress in projects, thereby supporting efficient project operation.

[0240] The following describes the processing flow.

[0241] Step 1:

[0242] The user uses their device to begin creating a new project. They enter details such as the project name, deadline, and participating members.

[0243] Step 2:

[0244] The terminal sends the entered project information to the server. The server stores the received information in a database and generates a project ID. This ID is used for subsequent management.

[0245] Step 3:

[0246] When a user wants to add a task to a specific project via their device, they enter the task name, due date, and assignee.

[0247] Step 4:

[0248] When the server receives input, it uses the corresponding project ID to add a new task to the project's task list. The database is also updated.

[0249] Step 5:

[0250] The server monitors changes to the task list and periodically checks the progress of tasks. It sends notifications to users when tasks are completed or when deadlines are approaching.

[0251] Step 6:

[0252] The user requests a report on their device to check the progress.

[0253] Step 7:

[0254] The server analyzes data from the entire project and calculates the percentage of completed tasks, among other things. Based on this, it automatically generates a report summarizing the progress.

[0255] Step 8:

[0256] The server sends the generated report to the terminal, allowing the user to view detailed project progress information.

[0257] Step 9:

[0258] Users can continue project management as needed, such as adding new tasks or updating the status of existing tasks.

[0259] In this way, the system supports everything from project information registration to progress monitoring and report generation, enabling efficient project management.

[0260] (Example 1)

[0261] 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."

[0262] In project management, small organizations and teams face a wide range of problems. Information is often scattered, making management cumbersome and frequently resulting in an inability to properly track task progress and deadlines. Furthermore, inadequate resource management can lead to project delays and cost overruns. Additionally, spending excessive time on progress reporting is inefficient and hinders rapid decision-making.

[0263] 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.

[0264] In this invention, the server includes means for receiving project information and storing it in a data set; means for adding tasks related to the project and managing a task list; means for updating the progress of tasks and analyzing that progress; means for providing reports to user terminals; and means for periodically monitoring the project progress and issuing warnings to users. This enables more efficient project management, task visibility, resource optimization, and rapid decision-making.

[0265] "Project information" refers to basic information related to the project, such as its name, start date, end date, participating members, and objectives.

[0266] A "data set" is a storage area used to centrally manage information, and includes databases.

[0267] "Work" refers to individual tasks or operations performed within a project.

[0268] A "task list" is a list of all tasks and activities related to a project.

[0269] A "report" is a document that summarizes the project's progress, task status, and other relevant information.

[0270] A "user terminal" is a device that a user uses to access and operate the project system.

[0271] "Progress status" refers to information indicating the degree to which tasks have been completed or the progress toward achieving project goals.

[0272] A "warning" is a notification sent to a user when work is not progressing as planned or when a task deadline is approaching.

[0273] "Resources" refer to the means, such as personnel, materials, or time, necessary to carry out a project.

[0274] This invention is a system for effectively managing projects. This system primarily consists of servers, terminals, and data sets, and aims to improve the efficiency of information aggregation and processing.

[0275] Hardware and software:

[0276] The server can be a high-performance cloud server or an on-premises server machine, and the data set uses a database management system such as MySQL or PostgreSQL. Terminals include computers and mobile devices that users directly interact with, and these have a web browser or dedicated application installed.

[0277] Data processing and computation:

[0278] The user uses the terminal to input project information and operations, and sends the information to the server through the interface. When the server receives this input information, it performs a storage process on the data set and executes an algorithm to analyze the progress of the operations in real time. For this process, script languages such as Python and JavaScript can be used.

[0279] The server aggregates the data and utilizes a generative AI model to automatically generate a report for the user. By using this AI model, efficient and accurate analysis of the progress data becomes possible. Also, in order to support efficient resource management, optimization of the resource data is also performed.

[0280] Specific example:

[0281] For example, when the user registers a new project named "Website Development Project" from the terminal, by inputting information such as the project name, start date, and participating members, the server saves the information in the data set and the basic information of the project is completed. After that, when the user adds an operation such as "Design completed" and sets the deadline, etc., the server can monitor this operation and issue a warning to the user as necessary while evaluating the progress.

[0282] Example of prompt text:

[0283] As a prompt text when using the generative AI model, a format such as "Please create the latest project progress report for the next meeting." can be used.

[0284] The purpose of this invention is to enable the user to quickly grasp the overall picture of the project and support efficient operation through centralized management of information.

[0285] The flow of the specific process in Example 1 will be described using FIG. 11.

[0286] Step 1:

[0287] The user inputs project and task information through the user interface of the terminal. Specifically, the user inputs the project name, start date, end date, participating members, task details, deadlines, etc. The input information is sent from the terminal to the server. Thereby, the basic information of the project is collected.

[0288] Step 2:

[0289] The server analyzes the received project information and stores it in a data set. For storage in the database, a data management system such as MySQL is used, and the information is centrally managed. Based on the input, the data structures of the project and tasks are generated and recorded in the database. This process establishes the organization and easy access of the information.

[0290] Step 3:

[0291] When the user wants to add a new task to the project, the user uses the terminal to input the task name and related information. The server adds the task information received from the terminal to the existing project data and updates the task list. In this process, alerts are also set based on the task priorities and schedules.

[0292] Step 4:

[0293] The server periodically analyzes the progress of the project from the data set and generates the progress rate and a list of unfinished tasks. In this analysis, scripts such as Python are used to process the data using statistical methods. The output progress information is essential for grasping the overall progress of the project.

[0294] Step 5:

[0295] The server utilizes a generative AI model to automatically generate reports based on analyzed progress information. The reports show the project status, including graphs and charts. It's also possible to create customized reports that highlight specific information requested by the user based on prompts. Accuracy and relevance of the reports are ensured at this stage.

[0296] Step 6:

[0297] Finally, the server sends the generated report to the terminal, where the user can view it and grasp the overall picture of the project. The report is provided as a PDF file or through a web-based interface. The user can check the project's health and adjust its direction as needed.

[0298] (Application Example 1)

[0299] 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."

[0300] In project management at home or in small groups, there are challenges in visualizing progress, effectively dividing tasks, and checking progress in real time. As a result, delays in work and inappropriate resource allocation occur, leading to decreased project efficiency.

[0301] 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.

[0302] In this invention, the server includes means for receiving project information and storing the project information in a data set, means for adding tasks related to the project and managing a list of tasks, and means for updating the progress of tasks and analyzing that progress. This enables real-time monitoring of project progress and efficient task management within a home or small group.

[0303] "Information" refers to data and metadata related to projects and tasks, which are the objects managed and analyzed by the system.

[0304] "Data set" is a digital storage for systematically storing and managing information.

[0305] "Task" is a unit of specific tasks or activities to be executed within a project.

[0306] "Task list" is a list for systematically managing all tasks related to a project.

[0307] "Progress" is an indicator showing how far a task or project has progressed against the plan.

[0308] "Analysis" is a process for analyzing progress and resource data in detail to grasp patterns and trends.

[0309] "Report" is a document for organizing the progress of a project and task information and providing it to relevant parties.

[0310] "Prediction" is a process for estimating future progress and status based on existing data.

[0311] "Alert" is a notification for prompting users when specific conditions are met.

[0312] The system for implementing the present invention is composed of a server for managing projects, a terminal for users to input information, and a data set for storing data. This system is constructed by utilizing programming with Python and the Django framework. PostgreSQL is used for the database to achieve efficient data management.

[0313] The server stores project and work information entered by users through their terminals into a data set. In this process, the information is stored in a structured format, making it easily accessible and analyzable. The server also uses machine learning libraries such as Scikit-learn to predict progress based on past project data. This allows for the generation of alerts to prevent delays and decreased efficiency. For notifications, the Twilio API is utilized to send real-time alerts to users as push notifications.

[0314] For example, if a user is managing a "home renovation project," they would use their smartphone to enter the project name and work deadlines. The server stores this information in a database and continuously monitors whether the project is progressing according to plan. If delays are anticipated, alerts can be sent to all family members' smartphones to prompt immediate action.

[0315] Examples of prompts for a generative AI model include the following:

[0316] "Please give me ideas for designing an app that optimizes project management within the home. This app would allow users to easily manage projects and check task progress in real time."

[0317] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0318] Step 1:

[0319] Users enter project information and related work details via a terminal. This input includes the project name, deadline, and work description. The data entered by the user is sent to a data set in a structured format.

[0320] Step 2:

[0321] The server stores the received project information in a data set. During this process, data validation is performed as needed to check for any duplication or inconsistencies with existing project data. After validation is complete, the data is securely stored.

[0322] Step 3:

[0323] The server analyzes progress using existing data within the data set. Here, Scikit-learn is used to run a machine learning model that predicts progress based on previous project data. The analysis results are output as a percentage indicating the expected progress.

[0324] Step 4:

[0325] Based on the progress forecast, the server uses the Twilio API to send alerts to the user if there are delays or risks. These notifications appear as push notifications on the device, enabling quick action.

[0326] Step 5:

[0327] The user can review the received alerts and modify the project plan as needed. If additional work or reconfigurations are required, the user will re-enter the updated information from the terminal. The updated information will then be returned to step 2, where it will be stored in the data set and progress analysis will be performed again.

[0328] 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.

[0329] This invention integrates an emotion engine into a project management system to understand user emotions and optimize feedback and project management based on those emotions. In addition to the basic functions of project management, this system provides new insights using user emotion data.

[0330] The system consists of a user interface, server, database, and emotion engine. Users input project and task information via a terminal, which the server receives and stores in the database. In addition to conventional task management functions, the emotion engine analyzes the user's emotional data, which is used to improve task management and communication.

[0331] When a user enters information about a project, the device collects not only the user's input but also data related to their emotions. This includes, for example, facial recognition for expression analysis and voice tone analysis. The emotion engine analyzes this data in real time to estimate the user's stress levels, satisfaction levels, and other factors.

[0332] The server evaluates progress and task assignments based on the collected emotional data, and makes adjustments as needed. It also generates feedback for users regarding project progress and provides suggestions for maintaining motivation and reducing stress.

[0333] For example, if the emotion engine detects high stress levels while a user is conducting an important project review, the server will either notify the user of an appropriate break or re-evaluate task priorities to reduce the stress load.

[0334] Thus, this system, which incorporates an emotion engine, enables project management that takes the user's psychological state into account, thereby improving the overall efficiency and success rate of projects.

[0335] The following describes the processing flow.

[0336] Step 1:

[0337] Users input project and task information using their devices. Simultaneously, emotion-related data is collected. This data collection utilizes facial recognition for expression analysis and voice tone analysis.

[0338] Step 2:

[0339] The terminal sends collected project information and emotional data to the server. The server stores this information in a database. This process accumulates basic project information and the user's emotional state.

[0340] Step 3:

[0341] The server uses an emotion engine to analyze emotional data in real time. This allows it to estimate the user's emotional state, such as stress levels and satisfaction levels.

[0342] Step 4:

[0343] If the emotion engine determines that the user's stress level is high, the server will generate appropriate feedback. For example, it might suggest improvements to time management or suggest taking short breaks.

[0344] Step 5:

[0345] The server receives feedback generated by the user through their device and selects the necessary actions. It then records these selections to help improve the system for future use.

[0346] Step 6:

[0347] As the project progresses, the server continuously analyzes sentiment data and prioritizes tasks and reallocates resources as needed. This process optimizes the project's efficiency.

[0348] Step 7:

[0349] Users view reports on their devices that reflect project progress and analysis results based on sentiment data. These reports provide suggestions for improving project management and maintaining the user's emotional well-being.

[0350] In this way, the system closely links project information and sentiment data, enabling continuous optimization.

[0351] (Example 2)

[0352] 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".

[0353] In project management, it's not enough to simply manage the progress of work items; efficient and flexible management that takes into account the emotions of users is required. However, conventional methods have lacked sufficient feedback and adjustments based on the psychological state and emotions of users, which has sometimes led to decreased project efficiency and success rates. This problem needs to be solved.

[0354] 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.

[0355] In this invention, the server includes means for receiving project information and storing it in a memory device, means for collecting and analyzing user emotional data using an emotional analysis device, and means for adjusting work items and evaluating progress based on the analyzed emotional data. This makes it possible to manage projects while taking user emotions into consideration, thereby improving the overall efficiency and success rate of the project.

[0356] "Project information" refers to all data related to the plans, progress, resources, and schedules associated with a specific activity or task.

[0357] A "storage device" is a physical or virtual device used to store and manage data.

[0358] A "work item" refers to an individual task or activity that is performed within a project.

[0359] A "work item list" is a list that encompasses all work items managed within a project.

[0360] An "emotion analysis device" is a device or software that uses facial recognition, voice analysis, or other methods to determine a user's emotional state.

[0361] "User sentiment data" refers to data that shows information related to the user's psychological state and emotions.

[0362] "Analyzed emotional data" refers to data processed by an emotional analysis device, which specifically evaluates the user's emotional state.

[0363] "Resource management" refers to management methods for effectively allocating and utilizing the resources needed within a project.

[0364] "Resource data" refers to data that includes information related to the resources required for a project, such as personnel, equipment, time, and budget.

[0365] This invention provides a system for considering user emotions in project management. This system consists of a terminal, server, storage device, and emotion analysis device, and effectively manages project information and user emotion data to optimize project progress.

[0366] Users input project details and individual work items using a terminal, and emotional data is collected during this process. For this purpose, the terminal is equipped with a camera and microphone, and facial recognition and voice analysis software are used to analyze the user's expressions and voice. This data is sent in real time to an emotion analysis device, where the user's emotional state is interpreted.

[0367] The server receives this input data and analyzed sentiment data and stores it in its memory. The server also uses a generative AI model to automatically generate feedback for the user from the accumulated data. This feedback includes advice to maintain motivation and suggestions for stress reduction, and is communicated to the user along with project progress information.

[0368] For example, if a high level of stress is detected while a user is conducting a project review, the server will notify them to "take a few minutes' relaxation break." This notification is based on a prompt generated by a generative AI model. An example of a prompt would be, "Please describe the emotions the user is experiencing during the project. In particular, please advise on what measures should be taken if high levels of stress are detected."

[0369] In this way, the system enables advanced project management based on the user's psychological state, aiming to improve operational efficiency and increase the success rate of projects.

[0370] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0371] Step 1:

[0372] The user uses a terminal to input project information and work items. The terminal receives the user's input data and saves its contents to its storage device. This input includes the project name, deadline, and specific task list. The output is digital data of the project information. In addition, the terminal prepares to collect data for facial recognition and voice analysis.

[0373] Step 2:

[0374] The device uses facial recognition to capture the user's facial expressions and voice analysis to record their tone of voice. This emotion-related data is transmitted in real time to an emotion analysis device. The input consists of facial image and voice data, and the output is initial data indicating the user's emotions. The device also filters out ambient noise during this process to improve analysis accuracy.

[0375] Step 3:

[0376] The emotion analysis device analyzes collected facial recognition data and voice data to determine the user's emotional state. Specifically, it uses various algorithms to quantify and evaluate stress levels and satisfaction levels. The input is emotion-related data obtained in the previous step, and the output is structured emotional information. The emotion analysis device learns similar patterns during the analysis process to improve the accuracy of its judgments.

[0377] Step 4:

[0378] The server integrates analyzed sentiment data and project data to generate optimal feedback for the user. This step utilizes a generative AI model to create prompts tailored to the user's situation. Inputs are sentiment information and project progress data sent from the sentiment analyzer, while output is an appropriate feedback message for the user. Based on this feedback, the server automatically performs tasks such as resetting work item priorities.

[0379] Step 5:

[0380] The server sends a generated feedback message to the terminal, notifying the user. The user receives the notification on the terminal and can take action based on the feedback. The notification may include specific instructions, such as "Take a break." The input is the generated prompt, and the output is the notification displayed on the user's screen. The terminal manages the notification history for later reference.

[0381] (Application Example 2)

[0382] 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."

[0383] In modern project management, progress is often managed without considering the psychological state of workers, leading to challenges such as decreased worker motivation and stress, which can impact work efficiency and safety. Furthermore, traditional monitoring systems lack the ability to adjust for workers' emotions, making it difficult to achieve project optimization.

[0384] 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.

[0385] In this invention, the server includes means for receiving project information and storing the project information in information storage, means for adding tasks related to the project and managing a list of tasks, and means for analyzing user emotional data and generating work adjustment proposals based on the user's psychological state. This makes it possible to adjust tasks while taking into account the emotional state of the workers, thereby improving the safety and efficiency of the project.

[0386] A "project" is a set of work processes that are planned to achieve a specific goal and include multiple tasks.

[0387] "Information storage" refers to a medium or system for storing project information and related data.

[0388] "Work" refers to specific tasks or tasks related to a project.

[0389] "Methods for managing a list of tasks" refer to methods and tools for organizing all tasks related to a project and tracking their progress.

[0390] "Emotional data" refers to information that indicates the user's psychological state, and is obtained from facial expressions, voice tone, and other similar data.

[0391] "Psychological state" refers to the emotional and mental health condition that a user experiences in a particular situation or under specific circumstances.

[0392] "Means for generating work adjustment proposals" refer to methods and tools for analyzing user emotional data and revising work content and schedules accordingly.

[0393] This invention is a system that understands the emotional state of workers in a factory environment and improves work efficiency and safety based on that understanding. The server first receives project information and stores it in information storage. This information includes the project name, deadline, and related tasks.

[0394] Next, the terminal monitors input from the worker and collects emotional data using facial recognition and speech analysis technologies. This uses sensors equipped with high-precision cameras and microphones. A facial recognition system using OpenCV and a speech recognition system equipped with the Google Speech-to-Text API process this data in real time to infer the worker's emotional state.

[0395] The server uses an emotion engine to evaluate the analysis results and generates suggested adjustments to the work based on the worker's stress level and satisfaction level. These adjustments may include pausing work, recommending breaks, or changing work priorities. This allows for efficient production work while maintaining the worker's psychological well-being.

[0396] For example, if a worker on a production line exhibits persistent stress, the system recommends temporarily suspending work and taking a break. If the emotional state does not improve, a manager will be notified, and further action will be considered.

[0397] An example of a prompt for a generating AI model is, "Analyze worker stress levels from camera feeds on the manufacturing line and suggest work adjustments if stress is detected." This prompt guides how the AI ​​interprets the data and generates countermeasures.

[0398] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0399] Step 1:

[0400] The terminal receives project information.

[0401] The user enters the project name, deadline, related tasks, etc., and the terminal sends this information to the information storage. The input data is formatted as a string and converted into a format that can be stored in the information storage as output.

[0402] Step 2:

[0403] The server stores project information in its information storage.

[0404] The server stores project information received from the terminal in a database. It converts the entered project information into an appropriate data structure and stores it in the database for efficient access.

[0405] Step 3:

[0406] The device collects emotional data from the workers.

[0407] While the user is working, the device uses a high-precision camera and microphone to collect facial expressions and audio data. This data is input as image and audio files and processed in real time.

[0408] Step 4:

[0409] The device analyzes emotional data.

[0410] The device uses OpenCV for facial recognition and the Google Speech-to-Text API for speech analysis. By extracting facial features from input image data and analyzing the tone of the speech data, it infers the user's emotional state and outputs an estimated emotion value.

[0411] Step 5:

[0412] The server generates work adjustment proposals based on emotional data.

[0413] The server uses an emotion engine to evaluate the analysis results and generates work adjustment suggestions based on the worker's stress level and satisfaction level. It takes estimated emotions as input and determines and outputs the necessary adjustment actions (such as pausing work or recommending a break).

[0414] Step 6:

[0415] The server notifies the user of the proposed adjustments it has generated.

[0416] The server notifies the user of the generated adjustment proposal via the terminal. The terminal visualizes it for the user, provides feedback to adjust the work content and schedule, and presents the adjustment proposal.

[0417] 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.

[0418] 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.

[0419] 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.

[0420] [Third Embodiment]

[0421] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0422] 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.

[0423] 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).

[0424] 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.

[0425] 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.

[0426] 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).

[0427] 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.

[0428] 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.

[0429] 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.

[0430] 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.

[0431] 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.

[0432] 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".

[0433] This invention is a system designed to solve the project management challenges faced by small and medium-sized enterprises and project teams. This system centrally manages project information, enabling efficient task addition and management, progress analysis, and automated report generation.

[0434] The system's main components consist of a database, a user interface, and a server program. Users input project information via a terminal, and the server stores this information in the database. This allows for centralized information management and facilitates information sharing among stakeholders.

[0435] When a user creates a new project or task, they input the information through an interface on their terminal. The server receives this input and updates the data for the corresponding project. The server also monitors the progress and deadlines of specific tasks and sends notifications to the user as needed.

[0436] Furthermore, the server periodically analyzes the project's progress and calculates the progress rate. Based on this information, the server automatically generates reports that include project progress and task status. These reports are provided to the user via their terminal, helping them to understand the overall picture of the project.

[0437] As a concrete example, let's say a user creates a "website development project." In this case, the user uses a terminal to input the project name, deadline, participating members, etc. The server stores this as project information in the database. Later, the user adds "design complete" as a task related to the project. The server updates the task list and monitors the task's deadline.

[0438] In this way, this system simplifies task management and visualizes progress in projects, thereby supporting efficient project operation.

[0439] The following describes the processing flow.

[0440] Step 1:

[0441] The user uses their device to begin creating a new project. They enter details such as the project name, deadline, and participating members.

[0442] Step 2:

[0443] The terminal sends the entered project information to the server. The server stores the received information in a database and generates a project ID. This ID is used for subsequent management.

[0444] Step 3:

[0445] When a user wants to add a task to a specific project via their device, they enter the task name, due date, and assignee.

[0446] Step 4:

[0447] When the server receives input, it uses the corresponding project ID to add a new task to the project's task list. The database is also updated.

[0448] Step 5:

[0449] The server monitors changes to the task list and periodically checks the progress of tasks. It sends notifications to users when tasks are completed or when deadlines are approaching.

[0450] Step 6:

[0451] The user requests a report on their device to check the progress.

[0452] Step 7:

[0453] The server analyzes data from the entire project and calculates the percentage of completed tasks, among other things. Based on this, it automatically generates a report summarizing the progress.

[0454] Step 8:

[0455] The server sends the generated report to the terminal, allowing the user to view detailed project progress information.

[0456] Step 9:

[0457] Users can continue project management as needed, such as adding new tasks or updating the status of existing tasks.

[0458] In this way, the system supports everything from project information registration to progress monitoring and report generation, enabling efficient project management.

[0459] (Example 1)

[0460] 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."

[0461] In project management, small organizations and teams face a wide range of problems. Information is often scattered, making management cumbersome and frequently resulting in an inability to properly track task progress and deadlines. Furthermore, inadequate resource management can lead to project delays and cost overruns. Additionally, spending excessive time on progress reporting is inefficient and hinders rapid decision-making.

[0462] 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.

[0463] In this invention, the server includes means for receiving project information and storing it in a data set; means for adding tasks related to the project and managing a task list; means for updating the progress of tasks and analyzing that progress; means for providing reports to user terminals; and means for periodically monitoring the project progress and issuing warnings to users. This enables more efficient project management, task visibility, resource optimization, and rapid decision-making.

[0464] "Project information" refers to basic information related to the project, such as its name, start date, end date, participating members, and objectives.

[0465] A "data set" is a storage area used to centrally manage information, and includes databases.

[0466] "Work" refers to individual tasks or operations performed within a project.

[0467] A "task list" is a list of all tasks and activities related to a project.

[0468] A "report" is a document that summarizes the project's progress, task status, and other relevant information.

[0469] A "user terminal" is a device that a user uses to access and operate the project system.

[0470] "Progress status" refers to information indicating the degree to which tasks have been completed or the progress toward achieving project goals.

[0471] A "warning" is a notification sent to a user when work is not progressing as planned or when a task deadline is approaching.

[0472] "Resources" refer to the means, such as personnel, materials, or time, necessary to carry out a project.

[0473] This invention is a system for effectively managing projects. This system primarily consists of servers, terminals, and data sets, and aims to improve the efficiency of information aggregation and processing.

[0474] Hardware and software:

[0475] The server can be a high-performance cloud server or an on-premises server machine, and the data set uses a database management system such as MySQL or PostgreSQL. Terminals include computers and mobile devices that users directly interact with, and these have a web browser or dedicated application installed.

[0476] Data processing and computation:

[0477] Users input project information and tasks using a terminal and send the information to the server through the interface. Upon receiving this input information, the server performs a data storage process on the data set and executes an algorithm to analyze the progress of tasks in real time. Scripting languages ​​such as Python and JavaScript can be used for this process.

[0478] The server utilizes a generative AI model to aggregate data and automatically generate reports for users. This AI model enables efficient and highly accurate analysis of progress data. It also optimizes resource data to support efficient resource management.

[0479] Specific example:

[0480] For example, if a user registers a new project called "Website Development Project" from their terminal, they enter information such as the project name, start date, and participating members. The server then saves this information to a data set, and the basic project information is organized. Subsequently, if the user adds tasks such as "Design Completion" and sets deadlines, the server can monitor these tasks, evaluate their progress, and issue warnings to the user as needed.

[0481] Example of a prompt:

[0482] When using a generative AI model, a prompt message such as "Please prepare an updated project progress report for the next meeting" can be used.

[0483] This invention aims to support efficient project management by enabling users to quickly grasp the overall picture of a project through centralized information management.

[0484] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0485] Step 1:

[0486] Users input project and task information through the terminal's user interface. Specifically, they enter the project name, start date, end date, participating members, task details, and deadlines. The entered information is sent from the terminal to the server, thereby collecting basic project information.

[0487] Step 2:

[0488] The server analyzes the received project information and stores it in a data set. For storage in the database, a data management system such as MySQL is used, ensuring centralized information management. Based on the input, project and task data structures are generated and recorded in the database. This process establishes a well-organized and easily accessible information state.

[0489] Step 3:

[0490] When a user wants to add a new task to a project, they use a terminal to enter the task name and related information. The server adds the task information received from the terminal to the existing project data and updates the task list. During this process, alerts are also set based on the task's priority and schedule.

[0491] Step 4:

[0492] The server periodically analyzes the project's progress from the data set, generating progress percentages and lists of incomplete tasks. This analysis uses scripts such as Python to process the data using statistical methods. The output progress information is essential for understanding the overall project status.

[0493] Step 5:

[0494] The server utilizes a generative AI model to automatically generate reports based on analyzed progress information. The reports show the project status, including graphs and charts. It's also possible to create customized reports that highlight specific information requested by the user based on prompts. Accuracy and relevance of the reports are ensured at this stage.

[0495] Step 6:

[0496] Finally, the server sends the generated report to the terminal, where the user can view it and grasp the overall picture of the project. The report is provided as a PDF file or through a web-based interface. The user can check the project's health and adjust its direction as needed.

[0497] (Application Example 1)

[0498] 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."

[0499] In project management at home or in small groups, there are challenges in visualizing progress, effectively dividing tasks, and checking progress in real time. As a result, delays in work and inappropriate resource allocation occur, leading to decreased project efficiency.

[0500] 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.

[0501] In this invention, the server includes means for receiving project information and storing the project information in a data set, means for adding tasks related to the project and managing a list of tasks, and means for updating the progress of tasks and analyzing that progress. This enables real-time monitoring of project progress and efficient task management within a home or small group.

[0502] "Information" refers to data and metadata related to projects and tasks, which are the objects that the system manages and analyzes.

[0503] A "data collection" is a digital storage system for systematically storing and managing information.

[0504] A "task" is a specific unit of work or activity that needs to be performed within a project.

[0505] A "task list" is a list used to systematically manage all tasks related to a project.

[0506] "Progress" is an indicator that shows how far along a task or project is in relation to the plan.

[0507] "Analysis" is the process of thoroughly analyzing progress and resource data to identify patterns and trends.

[0508] A "report" is a document that organizes the progress and work information of a project and provides it to the relevant parties.

[0509] "Prediction" is the process of estimating future progress or conditions based on existing data.

[0510] An "alert" is a notification that draws attention to a user when certain conditions are met.

[0511] The system for implementing this invention consists of a server for managing projects, terminals for users to input information, and a data set for storing data. This system is built using Python programming and the Django framework. PostgreSQL is used as the database to achieve highly efficient data management.

[0512] The server stores project and work information entered by users through their terminals into a data set. In this process, the information is stored in a structured format, making it easily accessible and analyzable. The server also uses machine learning libraries such as Scikit-learn to predict progress based on past project data. This allows for the generation of alerts to prevent delays and decreased efficiency. For notifications, the Twilio API is utilized to send real-time alerts to users as push notifications.

[0513] For example, if a user is managing a "home renovation project," they would use their smartphone to enter the project name and work deadlines. The server stores this information in a database and continuously monitors whether the project is progressing according to plan. If delays are anticipated, alerts can be sent to all family members' smartphones to prompt immediate action.

[0514] Examples of prompts for a generative AI model include the following:

[0515] "Please give me ideas for designing an app that optimizes project management within the home. This app would allow users to easily manage projects and check task progress in real time."

[0516] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0517] Step 1:

[0518] Users enter project information and related work details via a terminal. This input includes the project name, deadline, and work description. The data entered by the user is sent to a data set in a structured format.

[0519] Step 2:

[0520] The server stores the received project information in a data set. During this process, data validation is performed as needed to check for any duplication or inconsistencies with existing project data. After validation is complete, the data is securely stored.

[0521] Step 3:

[0522] The server analyzes progress using existing data within the data set. Here, Scikit-learn is used to run a machine learning model that predicts progress based on previous project data. The analysis results are output as a percentage indicating the expected progress.

[0523] Step 4:

[0524] Based on the progress forecast, the server uses the Twilio API to send alerts to the user if there are delays or risks. These notifications appear as push notifications on the device, enabling quick action.

[0525] Step 5:

[0526] The user can review the received alerts and modify the project plan as needed. If additional work or reconfigurations are required, the user will re-enter the updated information from the terminal. The updated information will then be returned to step 2, where it will be stored in the data set and progress analysis will be performed again.

[0527] 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.

[0528] This invention integrates an emotion engine into a project management system to understand user emotions and optimize feedback and project management based on those emotions. In addition to the basic functions of project management, this system provides new insights using user emotion data.

[0529] The system consists of a user interface, server, database, and emotion engine. Users input project and task information via a terminal, which the server receives and stores in the database. In addition to conventional task management functions, the emotion engine analyzes the user's emotional data, which is used to improve task management and communication.

[0530] When a user enters information about a project, the device collects not only the user's input but also data related to their emotions. This includes, for example, facial recognition for expression analysis and voice tone analysis. The emotion engine analyzes this data in real time to estimate the user's stress levels, satisfaction levels, and other factors.

[0531] The server evaluates progress and task assignments based on the collected emotional data, and makes adjustments as needed. It also generates feedback for users regarding project progress and provides suggestions for maintaining motivation and reducing stress.

[0532] For example, if the emotion engine detects high stress levels while a user is conducting an important project review, the server will either notify the user of an appropriate break or re-evaluate task priorities to reduce the stress load.

[0533] Thus, this system, which incorporates an emotion engine, enables project management that takes the user's psychological state into account, thereby improving the overall efficiency and success rate of projects.

[0534] The following describes the processing flow.

[0535] Step 1:

[0536] Users input project and task information using their devices. Simultaneously, emotion-related data is collected. This data collection utilizes facial recognition for expression analysis and voice tone analysis.

[0537] Step 2:

[0538] The terminal sends collected project information and emotional data to the server. The server stores this information in a database. This process accumulates basic project information and the user's emotional state.

[0539] Step 3:

[0540] The server uses an emotion engine to analyze emotional data in real time. This allows it to estimate the user's emotional state, such as stress levels and satisfaction levels.

[0541] Step 4:

[0542] If the emotion engine determines that the user's stress level is high, the server will generate appropriate feedback. For example, it might suggest improvements to time management or suggest taking short breaks.

[0543] Step 5:

[0544] The server receives feedback generated by the user through their device and selects the necessary actions. It then records these selections to help improve the system for future use.

[0545] Step 6:

[0546] As the project progresses, the server continuously analyzes sentiment data and prioritizes tasks and reallocates resources as needed. This process optimizes the project's efficiency.

[0547] Step 7:

[0548] Users view reports on their devices that reflect project progress and analysis results based on sentiment data. These reports provide suggestions for improving project management and maintaining the user's emotional well-being.

[0549] In this way, the system closely links project information and sentiment data, enabling continuous optimization.

[0550] (Example 2)

[0551] 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."

[0552] In project management, it's not enough to simply manage the progress of work items; efficient and flexible management that takes into account the emotions of users is required. However, conventional methods have lacked sufficient feedback and adjustments based on the psychological state and emotions of users, which has sometimes led to decreased project efficiency and success rates. This problem needs to be solved.

[0553] 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.

[0554] In this invention, the server includes means for receiving project information and storing it in a memory device, means for collecting and analyzing user emotional data using an emotional analysis device, and means for adjusting work items and evaluating progress based on the analyzed emotional data. This makes it possible to manage projects while taking user emotions into consideration, thereby improving the overall efficiency and success rate of the project.

[0555] "Project information" refers to all data related to the plans, progress, resources, and schedules associated with a specific activity or task.

[0556] A "storage device" is a physical or virtual device used to store and manage data.

[0557] A "work item" refers to an individual task or activity that is performed within a project.

[0558] A "work item list" is a list that encompasses all work items managed within a project.

[0559] An "emotion analysis device" is a device or software that uses facial recognition, voice analysis, or other methods to determine a user's emotional state.

[0560] "User sentiment data" refers to data that shows information related to the user's psychological state and emotions.

[0561] "Analyzed emotional data" refers to data processed by an emotional analysis device, which specifically evaluates the user's emotional state.

[0562] "Resource management" refers to management methods for effectively allocating and utilizing the resources needed within a project.

[0563] "Resource data" refers to data that includes information related to the resources required for a project, such as personnel, equipment, time, and budget.

[0564] This invention provides a system for considering user emotions in project management. This system consists of a terminal, server, storage device, and emotion analysis device, and effectively manages project information and user emotion data to optimize project progress.

[0565] Users input project details and individual work items using a terminal, and emotional data is collected during this process. For this purpose, the terminal is equipped with a camera and microphone, and facial recognition and voice analysis software are used to analyze the user's expressions and voice. This data is sent in real time to an emotion analysis device, where the user's emotional state is interpreted.

[0566] The server receives this input data and analyzed sentiment data and stores it in its memory. The server also uses a generative AI model to automatically generate feedback for the user from the accumulated data. This feedback includes advice to maintain motivation and suggestions for stress reduction, and is communicated to the user along with project progress information.

[0567] For example, if a high level of stress is detected while a user is conducting a project review, the server will notify them to "take a few minutes' relaxation break." This notification is based on a prompt generated by a generative AI model. An example of a prompt would be, "Please describe the emotions the user is experiencing during the project. In particular, please advise on what measures should be taken if high levels of stress are detected."

[0568] In this way, the system enables advanced project management based on the user's psychological state, aiming to improve operational efficiency and increase the success rate of projects.

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

[0570] Step 1:

[0571] The user uses a terminal to input project information and work items. The terminal receives the user's input data and saves its contents to its storage device. This input includes the project name, deadline, and specific task list. The output is digital data of the project information. In addition, the terminal prepares to collect data for facial recognition and voice analysis.

[0572] Step 2:

[0573] The device uses facial recognition to capture the user's facial expressions and voice analysis to record their tone of voice. This emotion-related data is transmitted in real time to an emotion analysis device. The input consists of facial image and voice data, and the output is initial data indicating the user's emotions. The device also filters out ambient noise during this process to improve analysis accuracy.

[0574] Step 3:

[0575] The emotion analysis device analyzes collected facial recognition data and voice data to determine the user's emotional state. Specifically, it uses various algorithms to quantify and evaluate stress levels and satisfaction levels. The input is emotion-related data obtained in the previous step, and the output is structured emotional information. The emotion analysis device learns similar patterns during the analysis process to improve the accuracy of its judgments.

[0576] Step 4:

[0577] The server integrates analyzed sentiment data and project data to generate optimal feedback for the user. This step utilizes a generative AI model to create prompts tailored to the user's situation. Inputs are sentiment information and project progress data sent from the sentiment analyzer, while output is an appropriate feedback message for the user. Based on this feedback, the server automatically performs tasks such as resetting work item priorities.

[0578] Step 5:

[0579] The server sends a generated feedback message to the terminal, notifying the user. The user receives the notification on the terminal and can take action based on the feedback. The notification may include specific instructions, such as "Take a break." The input is the generated prompt, and the output is the notification displayed on the user's screen. The terminal manages the notification history for later reference.

[0580] (Application Example 2)

[0581] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0582] In modern project management, progress is often managed without considering the psychological state of workers, leading to challenges such as decreased worker motivation and stress, which can impact work efficiency and safety. Furthermore, traditional monitoring systems lack the ability to adjust for workers' emotions, making it difficult to achieve project optimization.

[0583] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0584] In this invention, the server includes means for receiving project information and storing the project information in information storage, means for adding tasks related to the project and managing a list of tasks, and means for analyzing user emotional data and generating work adjustment proposals based on the user's psychological state. This makes it possible to adjust tasks while taking into account the emotional state of the workers, thereby improving the safety and efficiency of the project.

[0585] A "project" is a set of work processes that are planned to achieve a specific goal and include multiple tasks.

[0586] "Information storage" refers to a medium or system for storing project information and related data.

[0587] "Work" refers to specific tasks or tasks related to a project.

[0588] "Methods for managing a list of tasks" refer to methods and tools for organizing all tasks related to a project and tracking their progress.

[0589] "Emotional data" refers to information that indicates the user's psychological state, and is obtained from facial expressions, voice tone, and other similar data.

[0590] "Psychological state" refers to the emotional and mental health condition that a user experiences in a particular situation or under specific circumstances.

[0591] "Means for generating work adjustment proposals" refer to methods and tools for analyzing user emotional data and revising work content and schedules accordingly.

[0592] This invention is a system that understands the emotional state of workers in a factory environment and improves work efficiency and safety based on that understanding. The server first receives project information and stores it in information storage. This information includes the project name, deadline, and related tasks.

[0593] Next, the terminal monitors input from the worker and collects emotional data using facial recognition and speech analysis technologies. This uses sensors equipped with high-precision cameras and microphones. A facial recognition system using OpenCV and a speech recognition system equipped with the Google Speech-to-Text API process this data in real time to infer the worker's emotional state.

[0594] The server uses an emotion engine to evaluate the analysis results and generates suggested adjustments to the work based on the worker's stress level and satisfaction level. These adjustments may include pausing work, recommending breaks, or changing work priorities. This allows for efficient production work while maintaining the worker's psychological well-being.

[0595] For example, if a worker on a production line exhibits persistent stress, the system recommends temporarily suspending work and taking a break. If the emotional state does not improve, a manager will be notified, and further action will be considered.

[0596] An example of a prompt for a generating AI model is, "Analyze worker stress levels from camera feeds on the manufacturing line and suggest work adjustments if stress is detected." This prompt guides how the AI ​​interprets the data and generates countermeasures.

[0597] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0598] Step 1:

[0599] The terminal receives project information.

[0600] The user enters the project name, deadline, related tasks, etc., and the terminal sends this information to the information storage. The input data is formatted as a string and converted into a format that can be stored in the information storage as output.

[0601] Step 2:

[0602] The server stores project information in its information storage.

[0603] The server stores project information received from the terminal in a database. It converts the entered project information into an appropriate data structure and stores it in the database for efficient access.

[0604] Step 3:

[0605] The device collects emotional data from the workers.

[0606] While the user is working, the device uses a high-precision camera and microphone to collect facial expressions and audio data. This data is input as image and audio files and processed in real time.

[0607] Step 4:

[0608] The device analyzes emotional data.

[0609] The device uses OpenCV for facial recognition and the Google Speech-to-Text API for speech analysis. By extracting facial features from input image data and analyzing the tone of the speech data, it infers the user's emotional state and outputs an estimated emotion value.

[0610] Step 5:

[0611] The server generates work adjustment proposals based on emotional data.

[0612] The server uses an emotion engine to evaluate the analysis results and generates work adjustment suggestions based on the worker's stress level and satisfaction level. It takes estimated emotions as input and determines and outputs the necessary adjustment actions (such as pausing work or recommending a break).

[0613] Step 6:

[0614] The server notifies the user of the proposed adjustments it has generated.

[0615] The server notifies the user of the generated adjustment proposal via the terminal. The terminal visualizes it for the user, provides feedback to adjust the work content and schedule, and presents the adjustment proposal.

[0616] 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.

[0617] 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.

[0618] 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.

[0619] [Fourth Embodiment]

[0620] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0621] 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.

[0622] 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).

[0623] 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.

[0624] 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.

[0625] 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).

[0626] 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.

[0627] 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.

[0628] 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.

[0629] 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.

[0630] 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.

[0631] 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.

[0632] 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".

[0633] This invention is a system designed to solve the project management challenges faced by small and medium-sized enterprises and project teams. This system centrally manages project information, enabling efficient task addition and management, progress analysis, and automated report generation.

[0634] The system's main components consist of a database, a user interface, and a server program. Users input project information via a terminal, and the server stores this information in the database. This allows for centralized information management and facilitates information sharing among stakeholders.

[0635] When a user creates a new project or task, they input the information through an interface on their terminal. The server receives this input and updates the data for the corresponding project. The server also monitors the progress and deadlines of specific tasks and sends notifications to the user as needed.

[0636] Furthermore, the server periodically analyzes the project's progress and calculates the progress rate. Based on this information, the server automatically generates reports that include project progress and task status. These reports are provided to the user via their terminal, helping them to understand the overall picture of the project.

[0637] As a concrete example, let's say a user creates a "website development project." In this case, the user uses a terminal to input the project name, deadline, participating members, etc. The server stores this as project information in the database. Later, the user adds "design complete" as a task related to the project. The server updates the task list and monitors the task's deadline.

[0638] In this way, this system simplifies task management and visualizes progress in projects, thereby supporting efficient project operation.

[0639] The following describes the processing flow.

[0640] Step 1:

[0641] The user uses their device to begin creating a new project. They enter details such as the project name, deadline, and participating members.

[0642] Step 2:

[0643] The terminal sends the entered project information to the server. The server stores the received information in a database and generates a project ID. This ID is used for subsequent management.

[0644] Step 3:

[0645] When a user wants to add a task to a specific project via their device, they enter the task name, due date, and assignee.

[0646] Step 4:

[0647] When the server receives input, it uses the corresponding project ID to add a new task to the project's task list. The database is also updated.

[0648] Step 5:

[0649] The server monitors changes to the task list and periodically checks the progress of tasks. It sends notifications to users when tasks are completed or when deadlines are approaching.

[0650] Step 6:

[0651] The user requests a report on their device to check the progress.

[0652] Step 7:

[0653] The server analyzes data from the entire project and calculates the percentage of completed tasks, among other things. Based on this, it automatically generates a report summarizing the progress.

[0654] Step 8:

[0655] The server sends the generated report to the terminal, allowing the user to view detailed project progress information.

[0656] Step 9:

[0657] Users can continue project management as needed, such as adding new tasks or updating the status of existing tasks.

[0658] In this way, the system supports everything from project information registration to progress monitoring and report generation, enabling efficient project management.

[0659] (Example 1)

[0660] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0661] In project management, small organizations and teams face a wide range of problems. Information is often scattered, making management cumbersome and frequently resulting in an inability to properly track task progress and deadlines. Furthermore, inadequate resource management can lead to project delays and cost overruns. Additionally, spending excessive time on progress reporting is inefficient and hinders rapid decision-making.

[0662] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0663] In this invention, the server includes means for receiving project information and storing it in a data set; means for adding tasks related to the project and managing a task list; means for updating the progress of tasks and analyzing that progress; means for providing reports to user terminals; and means for periodically monitoring the project progress and issuing warnings to users. This enables more efficient project management, task visibility, resource optimization, and rapid decision-making.

[0664] "Project information" refers to basic information related to the project, such as its name, start date, end date, participating members, and objectives.

[0665] A "data set" is a storage area used to centrally manage information, and includes databases.

[0666] "Work" refers to individual tasks or operations performed within a project.

[0667] A "task list" is a list of all tasks and activities related to a project.

[0668] A "report" is a document that summarizes the project's progress, task status, and other relevant information.

[0669] A "user terminal" is a device that a user uses to access and operate the project system.

[0670] "Progress status" refers to information indicating the degree to which tasks have been completed or the progress toward achieving project goals.

[0671] A "warning" is a notification sent to a user when work is not progressing as planned or when a task deadline is approaching.

[0672] "Resources" refer to the means, such as personnel, materials, or time, necessary to carry out a project.

[0673] This invention is a system for effectively managing projects. This system primarily consists of servers, terminals, and data sets, and aims to improve the efficiency of information aggregation and processing.

[0674] Hardware and software:

[0675] The server can be a high-performance cloud server or an on-premises server machine, and the data set uses a database management system such as MySQL or PostgreSQL. Terminals include computers and mobile devices that users directly interact with, and these have a web browser or dedicated application installed.

[0676] Data processing and computation:

[0677] Users input project information and tasks using a terminal and send the information to the server through the interface. Upon receiving this input information, the server performs a data storage process on the data set and executes an algorithm to analyze the progress of tasks in real time. Scripting languages ​​such as Python and JavaScript can be used for this process.

[0678] The server utilizes a generative AI model to aggregate data and automatically generate reports for users. This AI model enables efficient and highly accurate analysis of progress data. It also optimizes resource data to support efficient resource management.

[0679] Specific example:

[0680] For example, if a user registers a new project called "Website Development Project" from their terminal, they enter information such as the project name, start date, and participating members. The server then saves this information to a data set, and the basic project information is organized. Subsequently, if the user adds tasks such as "Design Completion" and sets deadlines, the server can monitor these tasks, evaluate their progress, and issue warnings to the user as needed.

[0681] Example of a prompt:

[0682] When using a generative AI model, a prompt message such as "Please prepare an updated project progress report for the next meeting" can be used.

[0683] This invention aims to support efficient project management by enabling users to quickly grasp the overall picture of a project through centralized information management.

[0684] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0685] Step 1:

[0686] Users input project and task information through the terminal's user interface. Specifically, they enter the project name, start date, end date, participating members, task details, and deadlines. The entered information is sent from the terminal to the server, thereby collecting basic project information.

[0687] Step 2:

[0688] The server analyzes the received project information and stores it in a data set. For storage in the database, a data management system such as MySQL is used, ensuring centralized information management. Based on the input, project and task data structures are generated and recorded in the database. This process establishes a well-organized and easily accessible information state.

[0689] Step 3:

[0690] When a user wants to add a new task to a project, they use a terminal to enter the task name and related information. The server adds the task information received from the terminal to the existing project data and updates the task list. During this process, alerts are also set based on the task's priority and schedule.

[0691] Step 4:

[0692] The server periodically analyzes the project's progress from the data set, generating progress percentages and lists of incomplete tasks. This analysis uses scripts such as Python to process the data using statistical methods. The output progress information is essential for understanding the overall project status.

[0693] Step 5:

[0694] The server utilizes a generative AI model to automatically generate reports based on analyzed progress information. The reports show the project status, including graphs and charts. It's also possible to create customized reports that highlight specific information requested by the user based on prompts. Accuracy and relevance of the reports are ensured at this stage.

[0695] Step 6:

[0696] Finally, the server sends the generated report to the terminal, where the user can view it and grasp the overall picture of the project. The report is provided as a PDF file or through a web-based interface. The user can check the project's health and adjust its direction as needed.

[0697] (Application Example 1)

[0698] 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".

[0699] In project management at home or in small groups, there are challenges in visualizing progress, effectively dividing tasks, and checking progress in real time. As a result, delays in work and inappropriate resource allocation occur, leading to decreased project efficiency.

[0700] 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.

[0701] In this invention, the server includes means for receiving project information and storing the project information in a data set, means for adding tasks related to the project and managing a list of tasks, and means for updating the progress of tasks and analyzing that progress. This enables real-time monitoring of project progress and efficient task management within a home or small group.

[0702] "Information" refers to data and metadata related to projects and tasks, which are the objects that the system manages and analyzes.

[0703] A "data collection" is a digital storage system for systematically storing and managing information.

[0704] A "task" is a specific unit of work or activity that needs to be performed within a project.

[0705] A "task list" is a list used to systematically manage all tasks related to a project.

[0706] "Progress" is an indicator that shows how far along a task or project is in relation to the plan.

[0707] "Analysis" is the process of thoroughly analyzing progress and resource data to identify patterns and trends.

[0708] A "report" is a document that organizes the progress and work information of a project and provides it to the relevant parties.

[0709] "Prediction" is the process of estimating future progress or conditions based on existing data.

[0710] An "alert" is a notification that draws attention to a user when certain conditions are met.

[0711] The system for implementing this invention consists of a server for managing projects, terminals for users to input information, and a data set for storing data. This system is built using Python programming and the Django framework. PostgreSQL is used as the database to achieve highly efficient data management.

[0712] The server stores project and work information entered by users through their terminals into a data set. In this process, the information is stored in a structured format, making it easily accessible and analyzable. The server also uses machine learning libraries such as Scikit-learn to predict progress based on past project data. This allows for the generation of alerts to prevent delays and decreased efficiency. For notifications, the Twilio API is utilized to send real-time alerts to users as push notifications.

[0713] For example, if a user is managing a "home renovation project," they would use their smartphone to enter the project name and work deadlines. The server stores this information in a database and continuously monitors whether the project is progressing according to plan. If delays are anticipated, alerts can be sent to all family members' smartphones to prompt immediate action.

[0714] Examples of prompts for a generative AI model include the following:

[0715] "Please give me ideas for designing an app that optimizes project management within the home. This app would allow users to easily manage projects and check task progress in real time."

[0716] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0717] Step 1:

[0718] Users enter project information and related work details via a terminal. This input includes the project name, deadline, and work description. The data entered by the user is sent to a data set in a structured format.

[0719] Step 2:

[0720] The server stores the received project information in a data set. During this process, data validation is performed as needed to check for any duplication or inconsistencies with existing project data. After validation is complete, the data is securely stored.

[0721] Step 3:

[0722] The server analyzes progress using existing data within the data set. Here, Scikit-learn is used to run a machine learning model that predicts progress based on previous project data. The analysis results are output as a percentage indicating the expected progress.

[0723] Step 4:

[0724] Based on the progress forecast, the server uses the Twilio API to send alerts to the user if there are delays or risks. These notifications appear as push notifications on the device, enabling quick action.

[0725] Step 5:

[0726] The user can review the received alerts and modify the project plan as needed. If additional work or reconfigurations are required, the user will re-enter the updated information from the terminal. The updated information will then be returned to step 2, where it will be stored in the data set and progress analysis will be performed again.

[0727] 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.

[0728] This invention integrates an emotion engine into a project management system to understand user emotions and optimize feedback and project management based on those emotions. In addition to the basic functions of project management, this system provides new insights using user emotion data.

[0729] The system consists of a user interface, server, database, and emotion engine. Users input project and task information via a terminal, which the server receives and stores in the database. In addition to conventional task management functions, the emotion engine analyzes the user's emotional data, which is used to improve task management and communication.

[0730] When a user enters information about a project, the device collects not only the user's input but also data related to their emotions. This includes, for example, facial recognition for expression analysis and voice tone analysis. The emotion engine analyzes this data in real time to estimate the user's stress levels, satisfaction levels, and other factors.

[0731] The server evaluates progress and task assignments based on the collected emotional data, and makes adjustments as needed. It also generates feedback for users regarding project progress and provides suggestions for maintaining motivation and reducing stress.

[0732] For example, if the emotion engine detects high stress levels while a user is conducting an important project review, the server will either notify the user of an appropriate break or re-evaluate task priorities to reduce the stress load.

[0733] Thus, this system, which incorporates an emotion engine, enables project management that takes the user's psychological state into account, thereby improving the overall efficiency and success rate of projects.

[0734] The following describes the processing flow.

[0735] Step 1:

[0736] Users input project and task information using their devices. Simultaneously, emotion-related data is collected. This data collection utilizes facial recognition for expression analysis and voice tone analysis.

[0737] Step 2:

[0738] The terminal sends collected project information and emotional data to the server. The server stores this information in a database. This process accumulates basic project information and the user's emotional state.

[0739] Step 3:

[0740] The server uses an emotion engine to analyze emotional data in real time. This allows it to estimate the user's emotional state, such as stress levels and satisfaction levels.

[0741] Step 4:

[0742] If the emotion engine determines that the user's stress level is high, the server will generate appropriate feedback. For example, it might suggest improvements to time management or suggest taking short breaks.

[0743] Step 5:

[0744] The server receives feedback generated by the user through their device and selects the necessary actions. It then records these selections to help improve the system for future use.

[0745] Step 6:

[0746] As the project progresses, the server continuously analyzes sentiment data and prioritizes tasks and reallocates resources as needed. This process optimizes the project's efficiency.

[0747] Step 7:

[0748] Users view reports on their devices that reflect project progress and analysis results based on sentiment data. These reports provide suggestions for improving project management and maintaining the user's emotional well-being.

[0749] In this way, the system closely links project information and sentiment data, enabling continuous optimization.

[0750] (Example 2)

[0751] 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".

[0752] In project management, it's not enough to simply manage the progress of work items; efficient and flexible management that takes into account the emotions of users is required. However, conventional methods have lacked sufficient feedback and adjustments based on the psychological state and emotions of users, which has sometimes led to decreased project efficiency and success rates. This problem needs to be solved.

[0753] 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.

[0754] In this invention, the server includes means for receiving project information and storing it in a memory device, means for collecting and analyzing user emotional data using an emotional analysis device, and means for adjusting work items and evaluating progress based on the analyzed emotional data. This makes it possible to manage projects while taking user emotions into consideration, thereby improving the overall efficiency and success rate of the project.

[0755] "Project information" refers to all data related to the plans, progress, resources, and schedules associated with a specific activity or task.

[0756] A "storage device" is a physical or virtual device used to store and manage data.

[0757] A "work item" refers to an individual task or activity that is performed within a project.

[0758] A "work item list" is a list that encompasses all work items managed within a project.

[0759] An "emotion analysis device" is a device or software that uses facial recognition, voice analysis, or other methods to determine a user's emotional state.

[0760] "User sentiment data" refers to data that shows information related to the user's psychological state and emotions.

[0761] "Analyzed emotional data" refers to data processed by an emotional analysis device, which specifically evaluates the user's emotional state.

[0762] "Resource management" refers to management methods for effectively allocating and utilizing the resources needed within a project.

[0763] "Resource data" refers to data that includes information related to the resources required for a project, such as personnel, equipment, time, and budget.

[0764] This invention provides a system for considering user emotions in project management. This system consists of a terminal, server, storage device, and emotion analysis device, and effectively manages project information and user emotion data to optimize project progress.

[0765] Users input project details and individual work items using a terminal, and emotional data is collected during this process. For this purpose, the terminal is equipped with a camera and microphone, and facial recognition and voice analysis software are used to analyze the user's expressions and voice. This data is sent in real time to an emotion analysis device, where the user's emotional state is interpreted.

[0766] The server receives this input data and analyzed sentiment data and stores it in its memory. The server also uses a generative AI model to automatically generate feedback for the user from the accumulated data. This feedback includes advice to maintain motivation and suggestions for stress reduction, and is communicated to the user along with project progress information.

[0767] For example, if a high level of stress is detected while a user is conducting a project review, the server will notify them to "take a few minutes' relaxation break." This notification is based on a prompt generated by a generative AI model. An example of a prompt would be, "Please describe the emotions the user is experiencing during the project. In particular, please advise on what measures should be taken if high levels of stress are detected."

[0768] In this way, the system enables advanced project management based on the user's psychological state, aiming to improve operational efficiency and increase the success rate of projects.

[0769] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0770] Step 1:

[0771] The user uses a terminal to input project information and work items. The terminal receives the user's input data and saves its contents to its storage device. This input includes the project name, deadline, and specific task list. The output is digital data of the project information. In addition, the terminal prepares to collect data for facial recognition and voice analysis.

[0772] Step 2:

[0773] The device uses facial recognition to capture the user's facial expressions and voice analysis to record their tone of voice. This emotion-related data is transmitted in real time to an emotion analysis device. The input consists of facial image and voice data, and the output is initial data indicating the user's emotions. The device also filters out ambient noise during this process to improve analysis accuracy.

[0774] Step 3:

[0775] The emotion analysis device analyzes collected facial recognition data and voice data to determine the user's emotional state. Specifically, it uses various algorithms to quantify and evaluate stress levels and satisfaction levels. The input is emotion-related data obtained in the previous step, and the output is structured emotional information. The emotion analysis device learns similar patterns during the analysis process to improve the accuracy of its judgments.

[0776] Step 4:

[0777] The server integrates analyzed sentiment data and project data to generate optimal feedback for the user. This step utilizes a generative AI model to create prompts tailored to the user's situation. Inputs are sentiment information and project progress data sent from the sentiment analyzer, while output is an appropriate feedback message for the user. Based on this feedback, the server automatically performs tasks such as resetting work item priorities.

[0778] Step 5:

[0779] The server sends a generated feedback message to the terminal, notifying the user. The user receives the notification on the terminal and can take action based on the feedback. The notification may include specific instructions, such as "Take a break." The input is the generated prompt, and the output is the notification displayed on the user's screen. The terminal manages the notification history for later reference.

[0780] (Application Example 2)

[0781] 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".

[0782] In modern project management, progress is often managed without considering the psychological state of workers, leading to challenges such as decreased worker motivation and stress, which can impact work efficiency and safety. Furthermore, traditional monitoring systems lack the ability to adjust for workers' emotions, making it difficult to achieve project optimization.

[0783] 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.

[0784] In this invention, the server includes means for receiving project information and storing the project information in information storage, means for adding tasks related to the project and managing a list of tasks, and means for analyzing user emotional data and generating work adjustment proposals based on the user's psychological state. This makes it possible to adjust tasks while taking into account the emotional state of the workers, thereby improving the safety and efficiency of the project.

[0785] A "project" is a set of work processes that are planned to achieve a specific goal and include multiple tasks.

[0786] "Information storage" refers to a medium or system for storing project information and related data.

[0787] "Work" refers to specific tasks or tasks related to a project.

[0788] "Methods for managing a list of tasks" refer to methods and tools for organizing all tasks related to a project and tracking their progress.

[0789] "Emotional data" refers to information that indicates the user's psychological state, and is obtained from facial expressions, voice tone, and other similar data.

[0790] "Psychological state" refers to the emotional and mental health condition that a user experiences in a particular situation or under specific circumstances.

[0791] "Means for generating work adjustment proposals" refer to methods and tools for analyzing user emotional data and revising work content and schedules accordingly.

[0792] This invention is a system that understands the emotional state of workers in a factory environment and improves work efficiency and safety based on that understanding. The server first receives project information and stores it in information storage. This information includes the project name, deadline, and related tasks.

[0793] Next, the terminal monitors input from the worker and collects emotional data using facial recognition and speech analysis technologies. This uses sensors equipped with high-precision cameras and microphones. A facial recognition system using OpenCV and a speech recognition system equipped with the Google Speech-to-Text API process this data in real time to infer the worker's emotional state.

[0794] The server uses an emotion engine to evaluate the analysis results and generates suggested adjustments to the work based on the worker's stress level and satisfaction level. These adjustments may include pausing work, recommending breaks, or changing work priorities. This allows for efficient production work while maintaining the worker's psychological well-being.

[0795] For example, if a worker on a production line exhibits persistent stress, the system recommends temporarily suspending work and taking a break. If the emotional state does not improve, a manager will be notified, and further action will be considered.

[0796] An example of a prompt for a generating AI model is, "Analyze worker stress levels from camera feeds on the manufacturing line and suggest work adjustments if stress is detected." This prompt guides how the AI ​​interprets the data and generates countermeasures.

[0797] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0798] Step 1:

[0799] The terminal receives project information.

[0800] The user enters the project name, deadline, related tasks, etc., and the terminal sends this information to the information storage. The input data is formatted as a string and converted into a format that can be stored in the information storage as output.

[0801] Step 2:

[0802] The server stores project information in its information storage.

[0803] The server stores project information received from the terminal in a database. It converts the entered project information into an appropriate data structure and stores it in the database for efficient access.

[0804] Step 3:

[0805] The device collects emotional data from the workers.

[0806] While the user is working, the device uses a high-precision camera and microphone to collect facial expressions and audio data. This data is input as image and audio files and processed in real time.

[0807] Step 4:

[0808] The device analyzes emotional data.

[0809] The device uses OpenCV for facial recognition and the Google Speech-to-Text API for speech analysis. By extracting facial features from input image data and analyzing the tone of the speech data, it infers the user's emotional state and outputs an estimated emotion value.

[0810] Step 5:

[0811] The server generates work adjustment proposals based on emotional data.

[0812] The server uses an emotion engine to evaluate the analysis results and generates work adjustment suggestions based on the worker's stress level and satisfaction level. It takes estimated emotions as input and determines and outputs the necessary adjustment actions (such as pausing work or recommending a break).

[0813] Step 6:

[0814] The server notifies the user of the proposed adjustments it has generated.

[0815] The server notifies the user of the generated adjustment proposal via the terminal. The terminal visualizes it for the user, provides feedback to adjust the work content and schedule, and presents the adjustment proposal.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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."

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] The following is further disclosed regarding the embodiments described above.

[0838] (Claim 1)

[0839] A means of receiving project information and storing project information in a database,

[0840] A means of adding tasks related to a project and managing the task list,

[0841] A means to update the progress of a task and analyze that progress,

[0842] A method for automatically generating reports based on project progress and task information,

[0843] A system that includes this.

[0844] (Claim 2)

[0845] The system according to claim 1, further comprising means for monitoring the progress of a task in real time and notifying the user.

[0846] (Claim 3)

[0847] The system according to claim 1, further comprising means for analyzing resource data within a project in order to optimize project resource management.

[0848] "Example 1"

[0849] (Claim 1)

[0850] A means for receiving project information and storing the project information in a data set,

[0851] A means to add tasks related to the project and manage the task list,

[0852] A means to update the progress status of work and analyze that progress,

[0853] A method for automatically generating reports based on project progress and business information,

[0854] A means of providing the report to the user terminal,

[0855] A means to regularly monitor project progress and issue warnings to users,

[0856] A system that includes this.

[0857] (Claim 2)

[0858] The system according to claim 1, further comprising means for monitoring the progress of work in real time and notifying participants.

[0859] (Claim 3)

[0860] To optimize project resource management, we need a means to analyze resource data within the project,

[0861] The system according to claim 1, further comprising means for assisting in the customization of progress reports using a generative AI model.

[0862] "Application Example 1"

[0863] (Claim 1)

[0864] A means for receiving project information and storing the project information in a data set,

[0865] A means of adding tasks related to a project and managing the task list,

[0866] A means to update the progress of the work and analyze that progress,

[0867] A method for automatically generating reports based on project progress and work information,

[0868] A means of predicting progress from existing data and generating alerts,

[0869] A system that includes this.

[0870] (Claim 2)

[0871] The system according to claim 1, further comprising means for monitoring the progress of work in real time and notifying the user.

[0872] (Claim 3)

[0873] The system according to claim 1, further comprising means for analyzing resource data within a project in order to optimize the resource management of the project.

[0874] "Example 2 of combining an emotion engine"

[0875] (Claim 1)

[0876] A means for receiving project information and storing the project information in a storage device,

[0877] A means of adding work items related to a project and managing the list of work items,

[0878] A means to update the progress status of work items and analyze that progress,

[0879] A means for collecting and analyzing user emotional data using an emotion analysis device,

[0880] A means of adjusting work items and evaluating progress based on analyzed emotional data,

[0881] A method for automatically generating documents based on project progress and work item information,

[0882] A system that includes this.

[0883] (Claim 2)

[0884] The system according to claim 1, further comprising means for monitoring the progress of work items and user sentiment data in real time and notifying the user.

[0885] (Claim 3)

[0886] The system according to claim 1, further comprising means for analyzing resource data within a project and adjusting resources based on user sentiment data in order to optimize project resource management.

[0887] "Application example 2 when combining with an emotional engine"

[0888] (Claim 1)

[0889] A means for receiving project information and storing the project information in information storage,

[0890] A means to add tasks related to a project and manage the task list,

[0891] A means to update the progress of work and analyze that progress,

[0892] A means for analyzing user emotional data and generating work adjustment proposals based on psychological state,

[0893] A method for automatically generating reports based on project progress and work information,

[0894] A system that includes this.

[0895] (Claim 2)

[0896] The system according to claim 1, further comprising means for monitoring the progress of work in real time and notifying the user.

[0897] (Claim 3)

[0898] The system according to claim 1, further comprising means for analyzing resource data within a project in order to optimize the resource management of the project. [Explanation of symbols]

[0899] 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 receiving project information and storing project information in a database, A means of adding tasks related to a project and managing the task list, A means to update the progress of a task and analyze that progress, A method for automatically generating reports based on project progress and task information, A system that includes this.

2. The system according to claim 1, further comprising means for monitoring the progress of a task in real time and notifying the user.

3. The system according to claim 1, further comprising means for analyzing resource data within a project in order to optimize project resource management.

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