system

Abstract

We provide the system. [Solution] Means for automatically collecting information, Means for analyzing and organizing collected information, Means for visualizing project progress, A means of identifying where tasks are stalled, Means for generating and presenting notifications, 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, and includes 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, since information is dispersed and personalized, it is difficult to grasp the progress situation. Due to this problem, there are problems such as project delays and missed responses, and it is difficult to perform efficient management. Furthermore, it takes a lot of time to confirm the progress situation and identify the locations where tasks are stagnant, which causes inefficiency in business.

Means for Solving the Problems

[0005] This invention achieves centralized information management by using means for automatically collecting information and means for analyzing and organizing the collected information. Furthermore, it enables real-time situation monitoring by providing means for visualizing project progress and identifying areas where tasks are stalled. In addition, it solves the aforementioned problems by providing a function to immediately transmit important information to relevant parties through means for generating and presenting notifications.

[0006] "Means for automatically collecting information" refers to a mechanism that actively acquires specified data from email or messaging applications and stores it within the system.

[0007] "Means for analyzing and organizing collected information" refers to a system that analyzes acquired data, classifies it based on project names and task types, and structures it to make it easier to manipulate.

[0008] A "means of visualizing project progress" refers to a system that visually displays organized data, making it easy to see what stage a project is currently in.

[0009] "Methods for identifying task bottlenecks" refers to a system that uses progress data to detect tasks that are behind schedule or experiencing problems, and notifies the administrator.

[0010] "Means for generating and presenting notifications" refers to a mechanism that has the function of informing relevant parties via email or message about important progress or events detected by the system. [Brief explanation of the drawing]

[0011] [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] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, let's explain the terminology used in the following explanation.

[0014] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0015] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0017] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0018] 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."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] 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.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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".

[0032] This invention is a system for streamlining project management, comprising a series of functions for automatically collecting, analyzing, visualizing, and notifying information. The embodiments for carrying out the invention are described below.

[0033] The system's main components are a server, terminals, and users. First, the server automatically collects data using APIs for email and messaging applications. Users add a dedicated CC address to all project-related communications, allowing the server to receive conversations and progress information and store it in a database.

[0034] Next, the server analyzes the collected data and organizes it based on project name and task type. This process utilizes a generative AI model to extract and organize important information from the text data. This organized data is later used to visualize project progress.

[0035] On the device, users can visualize project progress through a dashboard. Here, task progress is displayed in graphs and charts for an at-a-glance view. This allows users to grasp the overall project status in real time and make quick decisions.

[0036] Furthermore, the server analyzes progress data to identify stalled tasks and areas where problems are occurring. This allows project managers to quickly understand the challenges they face and take appropriate action. For identified problem areas, the server generates alerts and notifies users via their terminals. These notifications are automatically sent via email or messaging tools, serving as a means of instantly communicating important information.

[0037] As a concrete example, when a new project is launched, the user adds CC addresses to all relevant emails, and the server automatically collects the information. The collected data is analyzed, and the project's progress is visualized on a dashboard. Through this dashboard, the user can check the status in real time and adjust project management as needed. The server identifies problem areas and provides immediate notifications, enabling a rapid response. In this way, the system prevents project management from becoming dependent on individuals and enables efficient work execution.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The server monitors all emails containing the specified CC address for information gathering and aggregates the data upon receipt. It also uses Slack and Zoom APIs to retrieve logs from relevant channels and meetings. This ensures that all project-related communications are stored in the database.

[0041] Step 2:

[0042] The server converts the collected data into text format and, if necessary, converts attachments into a parseable format. Using a generative AI model, it extracts project names, task types, deadlines, etc., from the content of emails and messages, and then classifies and organizes the data based on that information.

[0043] Step 3:

[0044] The server analyzes project progress based on organized data. Specifically, it calculates the number of completed, in-progress, and delayed tasks and clarifies their status. This integrates the project's current status into the database.

[0045] Step 4:

[0046] The terminal retrieves progress data from the server and visualizes it on a dashboard. Using graphs and charts, it allows users to understand the overall project status at a glance. It also provides visual indicators for comparison with other sections and projects.

[0047] Step 5:

[0048] The server further analyzes the progress data to identify bottlenecks and potential problems in tasks. Based on specified criteria, it generates alerts for high-risk or delayed tasks.

[0049] Step 6:

[0050] The device receives notifications sent from the server and displays them to the user. The user can check the notifications on the device, view detailed information on the dashboard, and make quick decisions. In addition, the user can reallocate tasks and adjust resource management as needed based on feedback from the system.

[0051] (Example 1)

[0052] 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."

[0053] In project management, problems arise where task progress is stalled due to communication breakdowns and insufficient information organization. Furthermore, a lack of appropriate means to grasp progress in real time and respond quickly is a challenge.

[0054] 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.

[0055] In this invention, the server includes computing means for automatically collecting information, computing means for analyzing the collected information using a generating AI model and organizing it based on relevant elements, and computing means for identifying task stagnation and clarifying problem areas. This enables efficient management of project progress and rapid, information-based decision-making.

[0056] "Computer means for automatically collecting information" refers to a device that has the function of automatically collecting and storing all communication data related to a project via a dedicated address or API.

[0057] "Computer means for analyzing using a generative AI model and organizing based on relevant elements" refers to a device that uses AI technology to analyze collected text data, extract important information such as project names and task types, and organize it.

[0058] "A computing device that provides an interface for visually displaying project progress" refers to a device that has the function of displaying the project progress in graphs and charts based on organized data, allowing users to check the project progress in real time.

[0059] "Computational means for identifying task stagnation and clarifying problem areas" refers to a device that analyzes progress data to quickly detect and identify project problems such as delays and resource shortages.

[0060] "A means of communication for notifying users of issues and providing them with information" refers to a device that has the function of notifying users of identified problems or important information via email or messaging tools.

[0061] This invention is an information processing system for streamlining project management. Its main components are a server, terminals, and users.

[0062] The server automatically collects relevant data using email and messaging application APIs. By setting a dedicated CC address for all project-related communications, the server aggregates data in real time and stores it in a database. This process utilizes specific communication protocols to efficiently extract data.

[0063] Subsequently, the server analyzes the collected data using a generative AI model. This model extracts important elements from the text data using natural language processing techniques and organizes the information according to project name and task type. This analysis enables centralized management of project progress within the system.

[0064] The analyzed data is visualized on a dashboard located on the terminal. Users can see the project progress at a glance in graphs and charts, enabling quick decision-making. The terminal interface is designed for intuitive operation and provides real-time updated information.

[0065] Furthermore, the server identifies pending tasks and problem areas and generates alerts. These alerts are sent to users based on specified notification methods. Specifically, notifications are sent via email or messaging tools to support the rapid resolution of issues.

[0066] As a concrete example, when a user starts a new project, they enter their CC address in all related emails. The server uses this information to collect data and visualize the progress on a dashboard. For example, if the user enters "I want to check the progress of the new project," the latest progress information will be displayed. In this way, work can be carried out efficiently while maintaining consistency in project management.

[0067] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0068] Step 1:

[0069] The server automatically collects data from email and messaging applications. The CC addresses set by the user are used as input. The server uses these CC addresses to retrieve communication data in real time and store it in a database. This ensures that all project-related communications are collected.

[0070] Step 2:

[0071] The server analyzes the collected data. At this stage, it utilizes a generative AI model to scrutinize the text data received as input. As part of the data processing, natural language processing techniques are used to extract and organize important information from the text, such as project names, task types, and assigned personnel. The output is organized structured data.

[0072] Step 3:

[0073] The terminal visualizes organized data on a dashboard. It receives organized data from the server as input. The terminal generates graphs and charts, allowing users to understand the project's progress at a glance. The output is a dashboard visualizing the progress of tasks.

[0074] Step 4:

[0075] The server uses progress data for further analysis to identify stalled tasks and potential problem areas. It uses pre-processed data before visualization as input. The server performs calculations to identify problems and generates alerts for those problem areas. The output is a warning message regarding the identified problems.

[0076] Step 5:

[0077] The server notifies the user of the generated alerts. The server sends notifications via email or messaging tools. The input is the warning message generated as an alert. The output is the notification information the user receives, which helps them take prompt action.

[0078] Step 6:

[0079] Users can use their devices to check project progress and notifications received from the server on a dashboard. This allows users to understand the situation and revise project plans and task assignments as needed. The inputs are the dashboard and notifications, and the output is faster decision-making and implementation of countermeasures for problems.

[0080] (Application Example 1)

[0081] 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."

[0082] In manufacturing environments, efficient project management and real-time progress tracking of the manufacturing process are crucial. However, if the mechanisms for quickly acquiring and analyzing information are insufficient, delays in tasks and process anomalies may go unnoticed for too long, potentially leading to decreased productivity and an increase in defective products. In such situations, a system is needed that efficiently manages the entire manufacturing process and quickly detects and addresses anomalies.

[0083] 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.

[0084] In this invention, the server includes means for automatically collecting information, means for analyzing and organizing the collected information, means for visualizing the progress of the project, means for identifying areas where tasks are stalled, means for generating and presenting notifications, means for managing the progress of the manufacturing process in real time and detecting abnormal conditions, and means for sending alerts to a central system when an abnormal condition is detected. This enables more efficient overall project management at the manufacturing site and allows for quicker response through early detection of abnormalities.

[0085] "Means of automatically collecting information" refers to a system that automatically acquires necessary data from manufacturing processes and related tasks and converts it into a format usable within the system.

[0086] "Means of analyzing and organizing collected information" refers to methods of analyzing acquired data and arranging it into an orderly form based on specific items or criteria.

[0087] "Means for visualizing project progress" refers to a function that allows the progress of the manufacturing process and the status of tasks to be displayed in an intuitive format using graphs, charts, and other visual aids.

[0088] "Methods for identifying areas where tasks are stalled" refers to mechanisms for detecting parts of a project where progress is behind schedule or where work is stagnating.

[0089] "Means of generating and presenting notifications" refers to a system that generates alerts or messages based on specific conditions and informs the user of them.

[0090] "Means for managing the progress of the manufacturing process in real time and detecting abnormal conditions" refers to technologies that constantly monitor the progress of each process on the manufacturing line and quickly recognize when it deviates from the specified range.

[0091] "A means of sending an alert to a central system when an abnormal condition is detected" refers to a method of issuing an alarm to a central management system when an abnormality is detected, and quickly and accurately conveying that information.

[0092] The system for implementing this invention consists of key components including a server, terminals, and users. The server automatically acquires data from sensors and PLCs (Programmable Logic Controllers) to collect various data during the manufacturing process. This enables real-time data collection.

[0093] The server uses Flask to build an API and analyzes the received data using data analysis libraries such as Pandas. This analysis process organizes the progress of each stage and allows for the rapid identification of task bottlenecks and anomalies. The analyzed data is visualized using Matplotlib and displayed as graphs and charts on the terminal's dashboard.

[0094] The terminal allows users to view the overall project picture and the progress of specific tasks in real time. For example, if an anomaly occurs in the bolt tightening process on the manufacturing line, the dashboard will immediately display the situation visually, allowing users to quickly understand the situation.

[0095] Furthermore, by utilizing the generative AI model, it is possible to design prompt messages that support countermeasures when an anomaly is detected. For example, if a prompt message such as "Please suggest countermeasures in case of an anomaly" is input to the AI ​​model for an assembly process of a vehicle part where an anomaly has occurred, the system will suggest appropriate countermeasures.

[0096] When a bottleneck or anomaly is detected, the server automatically generates an alert notification using the SMTP protocol and sends it directly to the user via email. This notification allows the user to immediately decide on a course of action, enabling efficient project management.

[0097] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0098] Step 1:

[0099] The server automatically collects data from sensors and PLCs (Programmable Logic Controllers) installed on the manufacturing line. Inputs include data from sensors and PLCs. By receiving this data in real time via an API, the server is ready to monitor the status of the manufacturing process.

[0100] Step 2:

[0101] The server analyzes and organizes the collected data using the Pandas library. The input is the raw data collected in step 1, and the output is a well-structured dataset that identifies the progress status and presence or absence of anomalies for each process. This analysis makes any delays or problems in specific processes explicit in the data.

[0102] Step 3:

[0103] The server visualizes the analysis results using Matplotlib. The input is the analyzed data prepared in step 2, and the output generates visually easy-to-understand graphs and charts. This visualized information is prepared to be displayed on the user's device as an easy-to-understand dashboard.

[0104] Step 4:

[0105] On the terminal, users can check project progress and identify any anomalies through a dashboard. The input is visualization data provided by the server, and the output is the information the user obtains from viewing the dashboard. Here, the user grasps the real-time project status and decides on the next action.

[0106] Step 5:

[0107] The server sends an alert to the user via email using the SMTP protocol when it detects an anomaly exceeding a specific threshold. The input is the identified anomaly information, and the output is the alert email sent to the user. This notification allows the user to immediately respond to the situation and improve the process.

[0108] Step 6:

[0109] The server uses a generative AI model to generate prompt messages that suggest countermeasures for anomalies and bottlenecks. Input includes the nature of the anomaly and historical data, and output is a prompt message suggesting appropriate action. This allows users to efficiently manage projects and make quick decisions.

[0110] 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.

[0111] This invention is a system that optimizes project management based on the user's emotional state by combining an emotion engine with a project management system. The system components include a server, terminals, and users.

[0112] First, the server automatically collects information from email and messaging application APIs. It retrieves communication data via a dedicated CC address and stores it in a database. In parallel, the server uses an emotion engine to analyze the emotions from text created by users and generates emotion data.

[0113] Next, the server analyzes the collected information to identify project progress and areas where tasks are stalled. The analyzed data, along with sentiment data, is visualized on a dashboard, allowing users to understand the overall status of the project at a glance. Sentiment data serves as supplementary information to understand the project's state.

[0114] On the device, users can view this information in real time, and the content and timing of notifications are adjusted according to their emotional data. For example, users experiencing high stress levels will receive notifications in a calmer tone, designed to help them focus on important tasks.

[0115] For example, if a user expresses dissatisfaction during project progress, the server recognizes this emotion using its emotion engine and re-evaluates the task's priority. Emotional data serves as an indicator for reviewing progress from an emotional perspective and taking possible support measures.

[0116] As described above, this invention complements project management, which tends to be highly dependent on individuals, and enables efficient management that takes user emotions into consideration. By introducing an emotion engine, project management becomes a more flexible and human-centered system, making project success more certain.

[0117] The following describes the processing flow.

[0118] Step 1:

[0119] The server collects information through email and messaging application APIs and stores it in a database. When a user adds a CC address to project-related emails, the server automatically retrieves the information as a trigger.

[0120] Step 2:

[0121] The server uses a generative AI model to analyze the collected text data. During this process, it extracts attributes such as project name, task type, and deadline, and then organizes and tags the data.

[0122] Step 3:

[0123] The server uses an emotion engine to analyze user input text and identify the user's emotional state. For example, it assigns an emotion category such as negative or positive emotion.

[0124] Step 4:

[0125] The device displays analysis results and sentiment data retrieved from the server on a dashboard. Along with the visualized project progress, sentiment feedback for each task is also displayed, allowing the user to make judgments based on this information.

[0126] Step 5:

[0127] Based on the analyzed progress and sentiment data, the server identifies tasks that are stalled or require reprioritization. It adjusts the priority of specific tasks according to the sentiment data, supporting the smooth progress of the project.

[0128] Step 6:

[0129] The device receives notifications from the server and sends them to the user. Based on sentiment data, it adjusts the tone of the notification message and provides additional recommended actions as needed.

[0130] Step 7:

[0131] Users review information on the dashboard, assess task progress while taking sentiment data into account, and make adjustments as needed. This allows users to manage projects more flexibly.

[0132] (Example 2)

[0133] 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".

[0134] In project management, management methods that do not consider the emotional state of team members lead to decreased work efficiency, task delays, and lower team morale. Therefore, there is a need for more humane and emotionally sensitive project management systems.

[0135] 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.

[0136] In this invention, the server includes means for automatically collecting information, means for generating emotional data using an emotion engine, and means for adjusting the content and timing of notifications. This makes it possible to efficiently manage project progress from an emotional perspective and improve the motivation of team members.

[0137] "Means for automatically collecting information" refers to a function that automatically acquires data from project-related electronic communications using a program and stores it in a database.

[0138] "Means for analyzing and organizing collected information" refers to functions that use algorithms to analyze data stored on a server and organize it into a form that is easy for humans to understand.

[0139] "Means of visualizing project progress" refers to functions that present the progress of a project and the status of tasks in a visual format, thereby allowing users to understand the information at a glance.

[0140] "Means for identifying areas where tasks are stalled" refers to a function that automatically identifies tasks within a project that are experiencing stagnation and provides information to facilitate improvement.

[0141] "Means of generating emotional data using an emotion engine" refers to a function that analyzes a user's text communication to evaluate their emotions and generates the results as data.

[0142] "Means for adjusting the content and timing of notifications" refers to a function that controls the delivery of notifications based on user sentiment data, ensuring they are delivered with appropriate content and timing.

[0143] "Means of visualizing analyzed emotional data on a dashboard" refers to a function that visually displays the generated emotional data on a dashboard, clearly showing the emotional state of the project.

[0144] This invention is a system that enhances management efficiency and human consideration by taking into account the emotional state of users in project management. The system mainly consists of servers, terminals, and users.

[0145] The server first automatically collects information through APIs of email and messaging applications. This typically involves using the Gmail API or the APIs of messaging platforms. The server monitors these tools, retrieves communication data via dedicated CC addresses, and stores it in a database.

[0146] Next, the server uses an emotion engine to analyze the user's text to determine their emotions. This involves using emotion analysis technologies such as IBM Watson® or Google® Cloud Natural Language API. This generates emotion scores—positive, negative, or neutral—from the text data provided by the user.

[0147] The generated sentiment data is integrated with progress data collected from project management software. This allows the server to analyze the current status and bottlenecks of each task in the project. This data is visualized on a dashboard, allowing users to grasp the overall status of the project at a glance.

[0148] On the device, users can view this information in real time, and the content and timing of notifications are adjusted based on emotional data. For example, if the emotional engine determines that the user is stressed, the server can adjust the tone of notifications to be more calming, encouraging focus on important tasks.

[0149] For example, if a user expresses dissatisfaction during a project, the server recognizes this emotion using its emotion engine, re-evaluates the task's priority, and provides feedback to both the user and the project manager. Emotional data serves as an indicator for considering project progress from an emotional perspective and making necessary adjustments.

[0150] An example of a prompt would be: "How can I determine a team member's stress level from their recent messages and identify the member who is experiencing the most stress?"

[0151] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0152] Step 1:

[0153] The server collects information using APIs for email and messaging applications. It receives data as input from Gmail and messaging platform APIs, and stores the communication data in a database via a dedicated CC address. Specifically, the server calls APIs based on a predetermined schedule or trigger to retrieve new emails and messages.

[0154] Step 2:

[0155] The server analyzes the collected text data using an emotion engine. The input here is the text data of emails and messages stored in step 1. The emotion engine (e.g., IBM Watson, Google Cloud Natural Language API) processes this text and outputs an emotion score. Specifically, the server calls the emotion engine's API to determine the emotional state of each message.

[0156] Step 3:

[0157] The server retrieves progress data from project management software and integrates it with sentiment data. The inputs used are progress data from project management tools (e.g., JIRA, Trello) and sentiment scores obtained in step 2. The server aggregates the data and outputs the progress and sentiment score for each task. Specifically, this involves calling a progress API and executing an algorithm to match the sentiment data.

[0158] Step 4:

[0159] The server visualizes the aggregated data on a dashboard. Inputs include task progress data and sentiment scores integrated in step 3. The server visually represents this data, outputting it as graphs and charts. The server utilizes data visualization tools to ensure users can understand the overall project status at a glance.

[0160] Step 5:

[0161] The device delivers notifications to the user based on project status and sentiment data. Input includes the visualization data generated in step 4. The device adjusts the content and timing of notifications according to the user's stress level, outputting optimized notifications. Specifically, it uses the device's notification function to ensure the user can access important information.

[0162] (Application Example 2)

[0163] 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 device 14 will be referred to as the "terminal."

[0164] In modern manufacturing environments, human emotions and conditions significantly impact productivity and quality. However, a challenge lies in the lack of a system that can grasp these emotions and conditions in real time and respond quickly. In particular, there is a need to reduce worker fatigue and stress on production lines and provide an appropriately adjusted work environment.

[0165] 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.

[0166] In this invention, the server includes means for automatically collecting information, means for analyzing and organizing the collected information, and means for analyzing the user's emotional state. This makes it possible to grasp the emotional state of workers in real time and appropriately adjust the production process.

[0167] "Means of automatically collecting information" refers to technical methods for obtaining data from external data sources such as email and messaging applications.

[0168] "Means for analyzing and organizing collected information" refers to technical methods that structure acquired data, extract necessary information, and convert it into a usable format.

[0169] "Methods for visualizing project progress" refer to technical methods that display the overall progress of a project in an easy-to-understand manner, allowing users to intuitively grasp its status.

[0170] "Methods for identifying areas where tasks are stalled" refers to technical methods that provide the necessary information to recognize areas where progress in a project is stagnating and to take appropriate countermeasures.

[0171] "Means for analyzing a user's emotional state" refers to technical methods for extracting emotions from a user's text-based communication and quantifying or classifying those emotions to understand their state.

[0172] "Means for adjusting work plans based on analyzed emotional data" refers to technical methods for dynamically changing production and task schedules according to the user's emotional state.

[0173] "Means of generating and presenting notifications" refers to technical methods that automatically create messages and deliver them at the appropriate time in order to convey appropriate information to users.

[0174] To realize this invention, a system incorporating an emotion engine will be constructed within the project management system. The system will mainly consist of a server, terminals, and users.

[0175] The server is responsible for automatically collecting information. Specifically, it uses APIs from mail servers and messaging applications to retrieve email content and chat history. This information is stored in the server's database using frameworks such as Flask and Django, in various data formats.

[0176] The collected information is analyzed on the server. Here, natural language processing libraries such as TextBlob and NLTK are used to analyze emotions from text data, quantifying or categorizing them. This allows for an understanding of the user's emotional state and the generation of emotion data based on that understanding.

[0177] Based on the analyzed emotion data, the server automatically adjusts the work plan. The robot's operation schedule and task priorities change according to the user's emotions, improving the efficiency of the production line.

[0178] The device provides an interface that allows users to view emotional data and project progress in real time. This interface enables users to understand their own emotional state and flexibly adjust their work schedule as needed.

[0179] For example, if a worker on a factory production line gives feedback saying, "I'm a little tired today...", the server will perceive this as "fatigue" and adjust the workload of the adjacent robot to increase it. As a result, the worker's burden is reduced. An example of a prompt message might be, "Based on the worker's message: 'I'm a little tired today,' please tell me how to adjust the robot's operation."

[0180] In this way, the entire system works in coordination, enabling more flexible and human-centered project management.

[0181] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0182] Step 1:

[0183] The server automatically collects information from mail servers and messaging applications via APIs. Inputs include email and chat data. This data is stored in the server's database. Output is text data stored in storage.

[0184] Step 2:

[0185] The server analyzes stored text data to generate sentiment data. In this process, natural language processing libraries such as TextBlob and NLTK are used to quantify or categorize the sentiment. The input is text data retrieved from the database, and the output is numerical sentiment data and categorical information. This allows for an understanding of the user's emotional state.

[0186] Step 3:

[0187] The server adjusts the work plan based on the analyzed sentiment data. Specifically, it uses sentiment data to modify robots and task schedules within the project management system. The input is sentiment data, and the output is the adjusted work plan and robot action instructions. The generative AI model used here proposes action patterns based on prompt statements.

[0188] Step 4:

[0189] The terminal presents the user with the adjusted work plan and sentiment data obtained from the server in real time. Inputs include adjustment information and sentiment data from the server. Outputs include screen display information and notifications provided to the user. Specifically, it provides visual progress displays on the user interface.

[0190] Step 5:

[0191] The user reviews the visualized sentiment data and work plan via a terminal and adjusts their work schedule as needed. The input is information obtained from the terminal. The output is the work schedule adjusted by the user. Specifically, the user makes schedule changes, and these changes are then incorporated back into the system.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] [Second Embodiment]

[0196] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0197] 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.

[0198] 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).

[0199] 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.

[0200] 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.

[0201] 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).

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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".

[0208] This invention is a system for streamlining project management, comprising a series of functions for automatically collecting, analyzing, visualizing, and notifying information. The embodiments for carrying out the invention are described below.

[0209] The system's main components are a server, terminals, and users. First, the server automatically collects data using APIs for email and messaging applications. Users add a dedicated CC address to all project-related communications, allowing the server to receive conversations and progress information and store it in a database.

[0210] Next, the server analyzes the collected data and organizes it based on project name and task type. This process utilizes a generative AI model to extract and organize important information from the text data. This organized data is later used to visualize project progress.

[0211] On the device, users can visualize project progress through a dashboard. Here, task progress is displayed in graphs and charts for an at-a-glance view. This allows users to grasp the overall project status in real time and make quick decisions.

[0212] Furthermore, the server analyzes progress data to identify stalled tasks and areas where problems are occurring. This allows project managers to quickly understand the challenges they face and take appropriate action. For identified problem areas, the server generates alerts and notifies users via their terminals. These notifications are automatically sent via email or messaging tools, serving as a means of instantly communicating important information.

[0213] As a concrete example, when a new project is launched, the user adds CC addresses to all relevant emails, and the server automatically collects the information. The collected data is analyzed, and the project's progress is visualized on a dashboard. Through this dashboard, the user can check the status in real time and adjust project management as needed. The server identifies problem areas and provides immediate notifications, enabling a rapid response. In this way, the system prevents project management from becoming dependent on individuals and enables efficient work execution.

[0214] The following describes the processing flow.

[0215] Step 1:

[0216] The server monitors all emails containing the specified CC address for information gathering and aggregates the data upon receipt. It also uses Slack and Zoom APIs to retrieve logs from relevant channels and meetings. This ensures that all project-related communications are stored in the database.

[0217] Step 2:

[0218] The server converts the collected data into text format and, if necessary, converts attachments into a parseable format. Using a generative AI model, it extracts project names, task types, deadlines, etc., from the content of emails and messages, and then classifies and organizes the data based on that information.

[0219] Step 3:

[0220] The server analyzes project progress based on organized data. Specifically, it calculates the number of completed, in-progress, and delayed tasks and clarifies their status. This integrates the project's current status into the database.

[0221] Step 4:

[0222] The terminal retrieves progress data from the server and visualizes it on a dashboard. Using graphs and charts, it allows users to understand the overall project status at a glance. It also provides visual indicators for comparison with other sections and projects.

[0223] Step 5:

[0224] The server further analyzes the progress data to identify bottlenecks and potential problems in tasks. Based on specified criteria, it generates alerts for high-risk or delayed tasks.

[0225] Step 6:

[0226] The device receives notifications sent from the server and displays them to the user. The user can check the notifications on the device, view detailed information on the dashboard, and make quick decisions. In addition, the user can reallocate tasks and adjust resource management as needed based on feedback from the system.

[0227] (Example 1)

[0228] 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."

[0229] In project management, problems arise where task progress is stalled due to communication breakdowns and insufficient information organization. Furthermore, a lack of appropriate means to grasp progress in real time and respond quickly is a challenge.

[0230] 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.

[0231] In this invention, the server includes computing means for automatically collecting information, computing means for analyzing the collected information using a generating AI model and organizing it based on relevant elements, and computing means for identifying task stagnation and clarifying problem areas. This enables efficient management of project progress and rapid, information-based decision-making.

[0232] "Computer means for automatically collecting information" refers to a device that has the function of automatically collecting and storing all communication data related to a project via a dedicated address or API.

[0233] "Computer means for analyzing using a generative AI model and organizing based on relevant elements" refers to a device that uses AI technology to analyze collected text data, extract important information such as project names and task types, and organize it.

[0234] "A computing device that provides an interface for visually displaying project progress" refers to a device that has the function of displaying the project progress in graphs and charts based on organized data, allowing users to check the project progress in real time.

[0235] "Computational means for identifying task stagnation and clarifying problem areas" refers to a device that analyzes progress data to quickly detect and identify project problems such as delays and resource shortages.

[0236] "A means of communication for notifying users of issues and providing them with information" refers to a device that has the function of notifying users of identified problems or important information via email or messaging tools.

[0237] This invention is an information processing system for streamlining project management. Its main components are a server, terminals, and users.

[0238] The server automatically collects relevant data using email and messaging application APIs. By setting a dedicated CC address for all project-related communications, the server aggregates data in real time and stores it in a database. This process utilizes specific communication protocols to efficiently extract data.

[0239] Subsequently, the server analyzes the collected data using a generative AI model. This model extracts important elements from the text data using natural language processing techniques and organizes the information according to project name and task type. This analysis enables centralized management of project progress within the system.

[0240] The analyzed data is visualized on a dashboard located on the terminal. Users can see the project progress at a glance in graphs and charts, enabling quick decision-making. The terminal interface is designed for intuitive operation and provides real-time updated information.

[0241] Furthermore, the server identifies pending tasks and problem areas and generates alerts. These alerts are sent to users based on specified notification methods. Specifically, notifications are sent via email or messaging tools to support the rapid resolution of issues.

[0242] As a concrete example, when a user starts a new project, they enter their CC address in all related emails. The server uses this information to collect data and visualize the progress on a dashboard. For example, if the user enters "I want to check the progress of the new project," the latest progress information will be displayed. In this way, work can be carried out efficiently while maintaining consistency in project management.

[0243] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0244] Step 1:

[0245] The server automatically collects data from email and messaging applications. The CC addresses set by the user are used as input. The server uses these CC addresses to retrieve communication data in real time and store it in a database. This ensures that all project-related communications are collected.

[0246] Step 2:

[0247] The server analyzes the collected data. At this stage, it utilizes a generative AI model to scrutinize the text data received as input. As part of the data processing, natural language processing techniques are used to extract and organize important information from the text, such as project names, task types, and assigned personnel. The output is organized structured data.

[0248] Step 3:

[0249] The terminal visualizes organized data on a dashboard. It receives organized data from the server as input. The terminal generates graphs and charts, allowing users to understand the project's progress at a glance. The output is a dashboard visualizing the progress of tasks.

[0250] Step 4:

[0251] The server uses progress data for further analysis to identify stalled tasks and potential problem areas. It uses pre-processed data before visualization as input. The server performs calculations to identify problems and generates alerts for those problem areas. The output is a warning message regarding the identified problems.

[0252] Step 5:

[0253] The server notifies the user of the generated alerts. The server sends notifications via email or messaging tools. The input is the warning message generated as an alert. The output is the notification information the user receives, which helps them take prompt action.

[0254] Step 6:

[0255] Users can use their devices to check project progress and notifications received from the server on a dashboard. This allows users to understand the situation and revise project plans and task assignments as needed. The inputs are the dashboard and notifications, and the output is faster decision-making and implementation of countermeasures for problems.

[0256] (Application Example 1)

[0257] 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."

[0258] In manufacturing environments, efficient project management and real-time progress tracking of the manufacturing process are crucial. However, if the mechanisms for quickly acquiring and analyzing information are insufficient, delays in tasks and process anomalies may go unnoticed for too long, potentially leading to decreased productivity and an increase in defective products. In such situations, a system is needed that efficiently manages the entire manufacturing process and quickly detects and addresses anomalies.

[0259] 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.

[0260] In this invention, the server includes means for automatically collecting information, means for analyzing and organizing the collected information, means for visualizing the progress of the project, means for identifying areas where tasks are stalled, means for generating and presenting notifications, means for managing the progress of the manufacturing process in real time and detecting abnormal conditions, and means for sending alerts to a central system when an abnormal condition is detected. This enables more efficient overall project management at the manufacturing site and allows for quicker response through early detection of abnormalities.

[0261] "Means of automatically collecting information" refers to a system that automatically acquires necessary data from manufacturing processes and related tasks and converts it into a format usable within the system.

[0262] "Means of analyzing and organizing collected information" refers to methods of analyzing acquired data and arranging it into an orderly form based on specific items or criteria.

[0263] "Means for visualizing project progress" refers to a function that allows the progress of the manufacturing process and the status of tasks to be displayed in an intuitive format using graphs, charts, and other visual aids.

[0264] "Methods for identifying areas where tasks are stalled" refers to mechanisms for detecting parts of a project where progress is behind schedule or where work is stagnating.

[0265] "Means of generating and presenting notifications" refers to a system that generates alerts or messages based on specific conditions and informs the user of them.

[0266] "Means for managing the progress of the manufacturing process in real time and detecting abnormal conditions" refers to technologies that constantly monitor the progress of each process on the manufacturing line and quickly recognize when it deviates from the specified range.

[0267] "A means of sending an alert to a central system when an abnormal condition is detected" refers to a method of issuing an alarm to a central management system when an abnormality is detected, and quickly and accurately conveying that information.

[0268] The system for implementing this invention consists of key components including a server, terminals, and users. The server automatically acquires data from sensors and PLCs (Programmable Logic Controllers) to collect various data during the manufacturing process. This enables real-time data collection.

[0269] The server uses Flask to build an API and analyzes the received data using data analysis libraries such as Pandas. This analysis process organizes the progress of each stage and allows for the rapid identification of task bottlenecks and anomalies. The analyzed data is visualized using Matplotlib and displayed as graphs and charts on the terminal's dashboard.

[0270] The terminal allows users to view the overall project picture and the progress of specific tasks in real time. For example, if an anomaly occurs in the bolt tightening process on the manufacturing line, the dashboard will immediately display the situation visually, allowing users to quickly understand the situation.

[0271] Furthermore, by utilizing the generative AI model, it is possible to design prompt messages that support countermeasures when an anomaly is detected. For example, if a prompt message such as "Please suggest countermeasures in case of an anomaly" is input to the AI ​​model for an assembly process of a vehicle part where an anomaly has occurred, the system will suggest appropriate countermeasures.

[0272] When a bottleneck or anomaly is detected, the server automatically generates an alert notification using the SMTP protocol and sends it directly to the user via email. This notification allows the user to immediately decide on a course of action, enabling efficient project management.

[0273] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0274] Step 1:

[0275] The server automatically collects data from sensors and PLCs (Programmable Logic Controllers) installed on the manufacturing line. Inputs include data from sensors and PLCs. By receiving this data in real time via an API, the server is ready to monitor the status of the manufacturing process.

[0276] Step 2:

[0277] The server analyzes and organizes the collected data using the Pandas library. The input is the original data collected in Step 1, and the output is a well-organized dataset in which the progress status of each process and the presence or absence of abnormalities are identified. Through this analysis, the delays and malfunctions in the work at a specific process are made explicit as data.

[0278] Step 3:

[0279] The server visualizes the analysis results using Matplotlib. The input is the analyzed data prepared in Step 2, and the output is visually easy-to-understand graphs and charts. This visualized information is prepared to be displayed on the terminal as a user-friendly dashboard.

[0280] Step 4:

[0281] On the terminal, the user can check the progress status of the project and the abnormal locations through the dashboard. The input is the visualized data provided by the server, and the output is the information obtained by the user viewing the dashboard. Here, the user grasps the real-time project situation and determines the next action.

[0282] Step 5:

[0283] When the server detects an abnormality exceeding a specific threshold, it sends an alert to the user by email using the SMTP protocol. The input is the identified abnormality information, and the output is the alert email sent to the user. Through this notification, the user can immediately respond to the situation and attempt to improve the process.

[0284] Step 6:

[0285] The server uses a generative AI model to generate prompt texts that propose countermeasures for abnormalities and delays. The inputs include the nature of the abnormalities and past data, and the output is a prompt text that proposes appropriate countermeasures. This enables users to efficiently manage projects and make decisions quickly.

[0286] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion identification model 59 and perform specific processing using the user's emotions.

[0287] This invention is a system that optimizes project management based on the user's emotional state by combining an emotion engine with a project management system. The components of the system include a server, a terminal, and a user.

[0288] First, the server automatically collects information from the APIs of email and messaging applications. It obtains communication data via a dedicated CC address and stores it in a database. In parallel, the server uses an emotion engine to analyze emotions from the text created by the user and generates emotion data.

[0289] Next, the server analyzes the collected information to identify the progress of the project and the locations where tasks are delayed. The analyzed data is visualized on a dashboard together with the emotion data, enabling the user to understand the overall situation of the project at a glance. The emotion data functions as supplementary information for understanding the state of the project.

[0290] On the terminal, the user can view this information in real time, and the content and timing of notifications are adjusted according to the emotion data. For example, for users in a high-stress state, notifications are generated in a more gentle tone, allowing them to focus on important tasks.

[0291] For example, if a user expresses dissatisfaction during project progress, the server recognizes this emotion using its emotion engine and re-evaluates the task's priority. Emotional data serves as an indicator for reviewing progress from an emotional perspective and taking possible support measures.

[0292] As described above, this invention complements project management, which tends to be highly dependent on individuals, and enables efficient management that takes user emotions into consideration. By introducing an emotion engine, project management becomes a more flexible and human-centered system, making project success more certain.

[0293] The following describes the processing flow.

[0294] Step 1:

[0295] The server collects information through email and messaging application APIs and stores it in a database. When a user adds a CC address to project-related emails, the server automatically retrieves the information as a trigger.

[0296] Step 2:

[0297] The server uses a generative AI model to analyze the collected text data. During this process, it extracts attributes such as project name, task type, and deadline, and then organizes and tags the data.

[0298] Step 3:

[0299] The server uses an emotion engine to analyze user input text and identify the user's emotional state. For example, it assigns an emotion category such as negative or positive emotion.

[0300] Step 4:

[0301] The terminal displays the analysis results and emotion data obtained from the server on the dashboard. Along with the visualized project progress, emotion feedback for each task is also displayed, and the user judges the situation based on this.

[0302] Step 5:

[0303] Based on the analyzed progress data and emotion data, the server identifies tasks that are stuck or need to have their priorities reset. According to the emotion data, the response priority for the identified tasks is adjusted to support the smooth progress of the project.

[0304] Step 6:

[0305] The terminal receives a notification from the server and sends it to the user. According to the emotion data, the tone of the notification message is adjusted, and additional recommended actions are provided as needed.

[0306] Step 7:

[0307] The user checks the information on the dashboard, evaluates the progress of the tasks taking into account the emotion data, and makes adjustments as needed. As a result, the user can apply project management more flexibly.

[0308] (Example 2)

[0309] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0310] In project management, management methods that do not consider the emotional state of team members have problems such as a decrease in work efficiency, task delays, and a decline in team morale. Therefore, there is a demand for the realization of a more human and emotion - considerate project management system.

[0311] 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.

[0312] In this invention, the server includes means for automatically collecting information, means for generating emotional data using an emotion engine, and means for adjusting the content and timing of notifications. This makes it possible to efficiently manage project progress from an emotional perspective and improve the motivation of team members.

[0313] "Means for automatically collecting information" refers to a function that automatically acquires data from project-related electronic communications using a program and stores it in a database.

[0314] "Means for analyzing and organizing collected information" refers to functions that use algorithms to analyze data stored on a server and organize it into a form that is easy for humans to understand.

[0315] "Means of visualizing project progress" refers to functions that present the progress of a project and the status of tasks in a visual format, thereby allowing users to understand the information at a glance.

[0316] "Means for identifying areas where tasks are stalled" refers to a function that automatically identifies tasks within a project that are experiencing stagnation and provides information to facilitate improvement.

[0317] "Means of generating emotional data using an emotion engine" refers to a function that analyzes a user's text communication to evaluate their emotions and generates the results as data.

[0318] "Means for adjusting the content and timing of notifications" refers to a function that controls the delivery of notifications based on user sentiment data, ensuring they are delivered with appropriate content and timing.

[0319] "Means of visualizing analyzed emotional data on a dashboard" refers to a function that visually displays the generated emotional data on a dashboard, clearly showing the emotional state of the project.

[0320] This invention is a system that enhances management efficiency and human consideration by taking into account the emotional state of users in project management. The system mainly consists of servers, terminals, and users.

[0321] The server first automatically collects information through APIs of email and messaging applications. This typically involves using the Gmail API or the APIs of messaging platforms. The server monitors these tools, retrieves communication data via dedicated CC addresses, and stores it in a database.

[0322] Next, the server uses an emotion engine to analyze the user's text to determine their emotions. This involves using emotion analysis technologies such as IBM Watson or Google Cloud Natural Language API. This generates emotion scores—positive, negative, or neutral—from the text data provided by the user.

[0323] The generated sentiment data is integrated with progress data collected from project management software. This allows the server to analyze the current status and bottlenecks of each task in the project. This data is visualized on a dashboard, allowing users to grasp the overall status of the project at a glance.

[0324] On the device, users can view this information in real time, and the content and timing of notifications are adjusted based on emotional data. For example, if the emotional engine determines that the user is stressed, the server can adjust the tone of notifications to be more calming, encouraging focus on important tasks.

[0325] For example, if a user expresses dissatisfaction during a project, the server recognizes this emotion using its emotion engine, re-evaluates the task's priority, and provides feedback to both the user and the project manager. Emotional data serves as an indicator for considering project progress from an emotional perspective and making necessary adjustments.

[0326] An example of a prompt would be: "How can I determine a team member's stress level from their recent messages and identify the member who is experiencing the most stress?"

[0327] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0328] Step 1:

[0329] The server collects information using APIs for email and messaging applications. It receives data as input from Gmail and messaging platform APIs, and stores the communication data in a database via a dedicated CC address. Specifically, the server calls APIs based on a predetermined schedule or trigger to retrieve new emails and messages.

[0330] Step 2:

[0331] The server analyzes the collected text data using an emotion engine. The input here is the text data of emails and messages stored in step 1. The emotion engine (e.g., IBM Watson, Google Cloud Natural Language API) processes this text and outputs an emotion score. Specifically, the server calls the emotion engine's API to determine the emotional state of each message.

[0332] Step 3:

[0333] The server retrieves progress data from project management software and integrates it with sentiment data. The inputs used are progress data from project management tools (e.g., JIRA, Trello) and sentiment scores obtained in step 2. The server aggregates the data and outputs the progress and sentiment score for each task. Specifically, this involves calling a progress API and executing an algorithm to match the sentiment data.

[0334] Step 4:

[0335] The server visualizes the aggregated data on a dashboard. Inputs include task progress data and sentiment scores integrated in step 3. The server visually represents this data, outputting it as graphs and charts. The server utilizes data visualization tools to ensure users can understand the overall project status at a glance.

[0336] Step 5:

[0337] The device delivers notifications to the user based on project status and sentiment data. Input includes the visualization data generated in step 4. The device adjusts the content and timing of notifications according to the user's stress level, outputting optimized notifications. Specifically, it uses the device's notification function to ensure the user can access important information.

[0338] (Application Example 2)

[0339] 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."

[0340] In modern manufacturing environments, human emotions and conditions significantly impact productivity and quality. However, a challenge lies in the lack of a system that can grasp these emotions and conditions in real time and respond quickly. In particular, there is a need to reduce worker fatigue and stress on production lines and provide an appropriately adjusted work environment.

[0341] 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.

[0342] In this invention, the server includes means for automatically collecting information, means for analyzing and organizing the collected information, and means for analyzing the user's emotional state. This makes it possible to grasp the emotional state of workers in real time and appropriately adjust the production process.

[0343] "Means of automatically collecting information" refers to technical methods for obtaining data from external data sources such as email and messaging applications.

[0344] "Means for analyzing and organizing collected information" refers to technical methods that structure acquired data, extract necessary information, and convert it into a usable format.

[0345] "Methods for visualizing project progress" refer to technical methods that display the overall progress of a project in an easy-to-understand manner, allowing users to intuitively grasp its status.

[0346] "Methods for identifying areas where tasks are stalled" refers to technical methods that provide the necessary information to recognize areas where progress in a project is stagnating and to take appropriate countermeasures.

[0347] "Means for analyzing a user's emotional state" refers to technical methods for extracting emotions from a user's text-based communication and quantifying or classifying those emotions to understand their state.

[0348] "Means for adjusting work plans based on analyzed emotional data" refers to technical methods for dynamically changing production and task schedules according to the user's emotional state.

[0349] "Means of generating and presenting notifications" refers to technical methods that automatically create messages and deliver them at the appropriate time in order to convey appropriate information to users.

[0350] To realize this invention, a system incorporating an emotion engine will be constructed within the project management system. The system will mainly consist of a server, terminals, and users.

[0351] The server is responsible for automatically collecting information. Specifically, it uses APIs from mail servers and messaging applications to retrieve email content and chat history. This information is stored in the server's database using frameworks such as Flask and Django, in various data formats.

[0352] The collected information is analyzed on the server. Here, natural language processing libraries such as TextBlob and NLTK are used to analyze emotions from text data, quantifying or categorizing them. This allows for an understanding of the user's emotional state and the generation of emotion data based on that understanding.

[0353] Based on the analyzed emotion data, the server automatically adjusts the work plan. The robot's operation schedule and task priorities change according to the user's emotions, improving the efficiency of the production line.

[0354] The device provides an interface that allows users to view emotional data and project progress in real time. This interface enables users to understand their own emotional state and flexibly adjust their work schedule as needed.

[0355] For example, if a worker on a factory production line gives feedback saying, "I'm a little tired today...", the server will perceive this as "fatigue" and adjust the workload of the adjacent robot to increase it. As a result, the worker's burden is reduced. An example of a prompt message might be, "Based on the worker's message: 'I'm a little tired today,' please tell me how to adjust the robot's operation."

[0356] In this way, the entire system works in coordination, enabling more flexible and human-centered project management.

[0357] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0358] Step 1:

[0359] The server automatically collects information from mail servers and messaging applications via APIs. Inputs include email and chat data. This data is stored in the server's database. Output is text data stored in storage.

[0360] Step 2:

[0361] The server analyzes stored text data to generate sentiment data. In this process, natural language processing libraries such as TextBlob and NLTK are used to quantify or categorize the sentiment. The input is text data retrieved from the database, and the output is numerical sentiment data and categorical information. This allows for an understanding of the user's emotional state.

[0362] Step 3:

[0363] The server adjusts the work plan based on the analyzed sentiment data. Specifically, it uses sentiment data to modify robots and task schedules within the project management system. The input is sentiment data, and the output is the adjusted work plan and robot action instructions. The generative AI model used here proposes action patterns based on prompt statements.

[0364] Step 4:

[0365] The terminal presents the user with the adjusted work plan and sentiment data obtained from the server in real time. Inputs include adjustment information and sentiment data from the server. Outputs include screen display information and notifications provided to the user. Specifically, it provides visual progress displays on the user interface.

[0366] Step 5:

[0367] The user reviews the visualized sentiment data and work plan via a terminal and adjusts their work schedule as needed. The input is information obtained from the terminal. The output is the work schedule adjusted by the user. Specifically, the user makes schedule changes, and these changes are then incorporated back into the system.

[0368] 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.

[0369] 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.

[0370] 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.

[0371] [Third Embodiment]

[0372] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0373] 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.

[0374] 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).

[0375] 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.

[0376] 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.

[0377] 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).

[0378] 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.

[0379] 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.

[0380] 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.

[0381] 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.

[0382] 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.

[0383] 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".

[0384] This invention is a system for streamlining project management, comprising a series of functions for automatically collecting, analyzing, visualizing, and notifying information. The embodiments for carrying out the invention are described below.

[0385] The system's main components are a server, terminals, and users. First, the server automatically collects data using APIs for email and messaging applications. Users add a dedicated CC address to all project-related communications, allowing the server to receive conversations and progress information and store it in a database.

[0386] Next, the server analyzes the collected data and organizes it based on project name and task type. This process utilizes a generative AI model to extract and organize important information from the text data. This organized data is later used to visualize project progress.

[0387] On the device, users can visualize project progress through a dashboard. Here, task progress is displayed in graphs and charts for an at-a-glance view. This allows users to grasp the overall project status in real time and make quick decisions.

[0388] Furthermore, the server analyzes progress data to identify stalled tasks and areas where problems are occurring. This allows project managers to quickly understand the challenges they face and take appropriate action. For identified problem areas, the server generates alerts and notifies users via their terminals. These notifications are automatically sent via email or messaging tools, serving as a means of instantly communicating important information.

[0389] As a concrete example, when a new project is launched, the user adds CC addresses to all relevant emails, and the server automatically collects the information. The collected data is analyzed, and the project's progress is visualized on a dashboard. Through this dashboard, the user can check the status in real time and adjust project management as needed. The server identifies problem areas and provides immediate notifications, enabling a rapid response. In this way, the system prevents project management from becoming dependent on individuals and enables efficient work execution.

[0390] The following describes the processing flow.

[0391] Step 1:

[0392] The server monitors all emails containing the specified CC address for information gathering and aggregates the data upon receipt. It also uses Slack and Zoom APIs to retrieve logs from relevant channels and meetings. This ensures that all project-related communications are stored in the database.

[0393] Step 2:

[0394] The server converts the collected data into text format and, if necessary, converts attachments into a parseable format. Using a generative AI model, it extracts project names, task types, deadlines, etc., from the content of emails and messages, and then classifies and organizes the data based on that information.

[0395] Step 3:

[0396] The server analyzes project progress based on organized data. Specifically, it calculates the number of completed, in-progress, and delayed tasks and clarifies their status. This integrates the project's current status into the database.

[0397] Step 4:

[0398] The terminal retrieves progress data from the server and visualizes it on a dashboard. Using graphs and charts, it allows users to understand the overall project status at a glance. It also provides visual indicators for comparison with other sections and projects.

[0399] Step 5:

[0400] The server further analyzes the progress data to identify bottlenecks and potential problems in tasks. Based on specified criteria, it generates alerts for high-risk or delayed tasks.

[0401] Step 6:

[0402] The device receives notifications sent from the server and displays them to the user. The user can check the notifications on the device, view detailed information on the dashboard, and make quick decisions. In addition, the user can reallocate tasks and adjust resource management as needed based on feedback from the system.

[0403] (Example 1)

[0404] 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."

[0405] In project management, problems arise where task progress is stalled due to communication breakdowns and insufficient information organization. Furthermore, a lack of appropriate means to grasp progress in real time and respond quickly is a challenge.

[0406] 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.

[0407] In this invention, the server includes computing means for automatically collecting information, computing means for analyzing the collected information using a generating AI model and organizing it based on relevant elements, and computing means for identifying task stagnation and clarifying problem areas. This enables efficient management of project progress and rapid, information-based decision-making.

[0408] "Computer means for automatically collecting information" refers to a device that has the function of automatically collecting and storing all communication data related to a project via a dedicated address or API.

[0409] "Computer means for analyzing using a generative AI model and organizing based on relevant elements" refers to a device that uses AI technology to analyze collected text data, extract important information such as project names and task types, and organize it.

[0410] "A computing device that provides an interface for visually displaying project progress" refers to a device that has the function of displaying the project progress in graphs and charts based on organized data, allowing users to check the project progress in real time.

[0411] "Computational means for identifying task stagnation and clarifying problem areas" refers to a device that analyzes progress data to quickly detect and identify project problems such as delays and resource shortages.

[0412] "A means of communication for notifying users of issues and providing them with information" refers to a device that has the function of notifying users of identified problems or important information via email or messaging tools.

[0413] This invention is an information processing system for streamlining project management. Its main components are a server, terminals, and users.

[0414] The server automatically collects relevant data using email and messaging application APIs. By setting a dedicated CC address for all project-related communications, the server aggregates data in real time and stores it in a database. This process utilizes specific communication protocols to efficiently extract data.

[0415] Subsequently, the server analyzes the collected data using a generative AI model. This model extracts important elements from the text data using natural language processing techniques and organizes the information according to project name and task type. This analysis enables centralized management of project progress within the system.

[0416] The analyzed data is visualized on a dashboard located on the terminal. Users can see the project progress at a glance in graphs and charts, enabling quick decision-making. The terminal interface is designed for intuitive operation and provides real-time updated information.

[0417] Furthermore, the server identifies pending tasks and problem areas and generates alerts. These alerts are sent to users based on specified notification methods. Specifically, notifications are sent via email or messaging tools to support the rapid resolution of issues.

[0418] As a concrete example, when a user starts a new project, they enter their CC address in all related emails. The server uses this information to collect data and visualize the progress on a dashboard. For example, if the user enters "I want to check the progress of the new project," the latest progress information will be displayed. In this way, work can be carried out efficiently while maintaining consistency in project management.

[0419] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0420] Step 1:

[0421] The server automatically collects data from email and messaging applications. The CC addresses set by the user are used as input. The server uses these CC addresses to retrieve communication data in real time and store it in a database. This ensures that all project-related communications are collected.

[0422] Step 2:

[0423] The server analyzes the collected data. At this stage, it utilizes a generative AI model to scrutinize the text data received as input. As part of the data processing, natural language processing techniques are used to extract and organize important information from the text, such as project names, task types, and assigned personnel. The output is organized structured data.

[0424] Step 3:

[0425] The terminal visualizes organized data on a dashboard. It receives organized data from the server as input. The terminal generates graphs and charts, allowing users to understand the project's progress at a glance. The output is a dashboard visualizing the progress of tasks.

[0426] Step 4:

[0427] The server uses progress data for further analysis to identify stalled tasks and potential problem areas. It uses pre-processed data before visualization as input. The server performs calculations to identify problems and generates alerts for those problem areas. The output is a warning message regarding the identified problems.

[0428] Step 5:

[0429] The server notifies the user of the generated alerts. The server sends notifications via email or messaging tools. The input is the warning message generated as an alert. The output is the notification information the user receives, which helps them take prompt action.

[0430] Step 6:

[0431] Users can use their devices to check project progress and notifications received from the server on a dashboard. This allows users to understand the situation and revise project plans and task assignments as needed. The inputs are the dashboard and notifications, and the output is faster decision-making and implementation of countermeasures for problems.

[0432] (Application Example 1)

[0433] 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."

[0434] In manufacturing environments, efficient project management and real-time progress tracking of the manufacturing process are crucial. However, if the mechanisms for quickly acquiring and analyzing information are insufficient, delays in tasks and process anomalies may go unnoticed for too long, potentially leading to decreased productivity and an increase in defective products. In such situations, a system is needed that efficiently manages the entire manufacturing process and quickly detects and addresses anomalies.

[0435] 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.

[0436] In this invention, the server includes means for automatically collecting information, means for analyzing and organizing the collected information, means for visualizing the progress of the project, means for identifying areas where tasks are stalled, means for generating and presenting notifications, means for managing the progress of the manufacturing process in real time and detecting abnormal conditions, and means for sending alerts to a central system when an abnormal condition is detected. This enables more efficient overall project management at the manufacturing site and allows for quicker response through early detection of abnormalities.

[0437] "Means of automatically collecting information" refers to a system that automatically acquires necessary data from manufacturing processes and related tasks and converts it into a format usable within the system.

[0438] "Means of analyzing and organizing collected information" refers to methods of analyzing acquired data and arranging it into an orderly form based on specific items or criteria.

[0439] "Means for visualizing project progress" refers to a function that allows the progress of the manufacturing process and the status of tasks to be displayed in an intuitive format using graphs, charts, and other visual aids.

[0440] "Methods for identifying areas where tasks are stalled" refers to mechanisms for detecting parts of a project where progress is behind schedule or where work is stagnating.

[0441] "Means of generating and presenting notifications" refers to a system that generates alerts or messages based on specific conditions and informs the user of them.

[0442] "Means for managing the progress of the manufacturing process in real time and detecting abnormal conditions" refers to technologies that constantly monitor the progress of each process on the manufacturing line and quickly recognize when it deviates from the specified range.

[0443] "A means of sending an alert to a central system when an abnormal condition is detected" refers to a method of issuing an alarm to a central management system when an abnormality is detected, and quickly and accurately conveying that information.

[0444] The system for implementing this invention consists of key components including a server, terminals, and users. The server automatically acquires data from sensors and PLCs (Programmable Logic Controllers) to collect various data during the manufacturing process. This enables real-time data collection.

[0445] The server uses Flask to build an API and analyzes the received data using data analysis libraries such as Pandas. This analysis process organizes the progress of each stage and allows for the rapid identification of task bottlenecks and anomalies. The analyzed data is visualized using Matplotlib and displayed as graphs and charts on the terminal's dashboard.

[0446] The terminal allows users to view the overall project picture and the progress of specific tasks in real time. For example, if an anomaly occurs in the bolt tightening process on the manufacturing line, the dashboard will immediately display the situation visually, allowing users to quickly understand the situation.

[0447] Furthermore, by utilizing the generative AI model, it is possible to design prompt messages that support countermeasures when an anomaly is detected. For example, if a prompt message such as "Please suggest countermeasures in case of an anomaly" is input to the AI ​​model for an assembly process of a vehicle part where an anomaly has occurred, the system will suggest appropriate countermeasures.

[0448] When a bottleneck or anomaly is detected, the server automatically generates an alert notification using the SMTP protocol and sends it directly to the user via email. This notification allows the user to immediately decide on a course of action, enabling efficient project management.

[0449] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0450] Step 1:

[0451] The server automatically collects data from sensors and PLCs (Programmable Logic Controllers) installed on the manufacturing line. Inputs include data from sensors and PLCs. By receiving this data in real time via an API, the server is ready to monitor the status of the manufacturing process.

[0452] Step 2:

[0453] The server analyzes and organizes the collected data using the Pandas library. The input is the raw data collected in step 1, and the output is a well-structured dataset that identifies the progress status and presence or absence of anomalies for each process. This analysis makes any delays or problems in specific processes explicit in the data.

[0454] Step 3:

[0455] The server visualizes the analysis results using Matplotlib. The input is the analyzed data prepared in step 2, and the output generates visually easy-to-understand graphs and charts. This visualized information is prepared to be displayed on the user's device as an easy-to-understand dashboard.

[0456] Step 4:

[0457] On the terminal, users can check project progress and identify any anomalies through a dashboard. The input is visualization data provided by the server, and the output is the information the user obtains from viewing the dashboard. Here, the user grasps the real-time project status and decides on the next action.

[0458] Step 5:

[0459] The server sends an alert to the user via email using the SMTP protocol when it detects an anomaly exceeding a specific threshold. The input is the identified anomaly information, and the output is the alert email sent to the user. This notification allows the user to immediately respond to the situation and improve the process.

[0460] Step 6:

[0461] The server uses a generative AI model to generate prompt messages that suggest countermeasures for anomalies and bottlenecks. Input includes the nature of the anomaly and historical data, and output is a prompt message suggesting appropriate action. This allows users to efficiently manage projects and make quick decisions.

[0462] 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.

[0463] This invention is a system that optimizes project management based on the user's emotional state by combining an emotion engine with a project management system. The system components include a server, terminals, and users.

[0464] First, the server automatically collects information from email and messaging application APIs. It retrieves communication data via a dedicated CC address and stores it in a database. In parallel, the server uses an emotion engine to analyze the emotions from text created by users and generates emotion data.

[0465] Next, the server analyzes the collected information to identify project progress and areas where tasks are stalled. The analyzed data, along with sentiment data, is visualized on a dashboard, allowing users to understand the overall status of the project at a glance. Sentiment data serves as supplementary information to understand the project's state.

[0466] On the device, users can view this information in real time, and the content and timing of notifications are adjusted according to their emotional data. For example, users experiencing high stress levels will receive notifications in a calmer tone, designed to help them focus on important tasks.

[0467] For example, if a user expresses dissatisfaction during project progress, the server recognizes this emotion using its emotion engine and re-evaluates the task's priority. Emotional data serves as an indicator for reviewing progress from an emotional perspective and taking possible support measures.

[0468] As described above, this invention complements project management, which tends to be highly dependent on individuals, and enables efficient management that takes user emotions into consideration. By introducing an emotion engine, project management becomes a more flexible and human-centered system, making project success more certain.

[0469] The following describes the processing flow.

[0470] Step 1:

[0471] The server collects information through email and messaging application APIs and stores it in a database. When a user adds a CC address to project-related emails, the server automatically retrieves the information as a trigger.

[0472] Step 2:

[0473] The server uses a generative AI model to analyze the collected text data. During this process, it extracts attributes such as project name, task type, and deadline, and then organizes and tags the data.

[0474] Step 3:

[0475] The server uses an emotion engine to analyze user input text and identify the user's emotional state. For example, it assigns an emotion category such as negative or positive emotion.

[0476] Step 4:

[0477] The device displays analysis results and sentiment data retrieved from the server on a dashboard. Along with the visualized project progress, sentiment feedback for each task is also displayed, allowing the user to make judgments based on this information.

[0478] Step 5:

[0479] Based on the analyzed progress and sentiment data, the server identifies tasks that are stalled or require reprioritization. It adjusts the priority of specific tasks according to the sentiment data, supporting the smooth progress of the project.

[0480] Step 6:

[0481] The device receives notifications from the server and sends them to the user. Based on sentiment data, it adjusts the tone of the notification message and provides additional recommended actions as needed.

[0482] Step 7:

[0483] Users review information on the dashboard, assess task progress while taking sentiment data into account, and make adjustments as needed. This allows users to manage projects more flexibly.

[0484] (Example 2)

[0485] 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."

[0486] In project management, management methods that do not consider the emotional state of team members lead to decreased work efficiency, task delays, and lower team morale. Therefore, there is a need for more humane and emotionally sensitive project management systems.

[0487] 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.

[0488] In this invention, the server includes means for automatically collecting information, means for generating emotional data using an emotion engine, and means for adjusting the content and timing of notifications. This makes it possible to efficiently manage project progress from an emotional perspective and improve the motivation of team members.

[0489] "Means for automatically collecting information" refers to a function that automatically acquires data from project-related electronic communications using a program and stores it in a database.

[0490] "Means for analyzing and organizing collected information" refers to functions that use algorithms to analyze data stored on a server and organize it into a form that is easy for humans to understand.

[0491] "Means of visualizing project progress" refers to functions that present the progress of a project and the status of tasks in a visual format, thereby allowing users to understand the information at a glance.

[0492] "Means for identifying areas where tasks are stalled" refers to a function that automatically identifies tasks within a project that are experiencing stagnation and provides information to facilitate improvement.

[0493] "Means of generating emotional data using an emotion engine" refers to a function that analyzes a user's text communication to evaluate their emotions and generates the results as data.

[0494] "Means for adjusting the content and timing of notifications" refers to a function that controls the delivery of notifications based on user sentiment data, ensuring they are delivered with appropriate content and timing.

[0495] "Means of visualizing analyzed emotional data on a dashboard" refers to a function that visually displays the generated emotional data on a dashboard, clearly showing the emotional state of the project.

[0496] This invention is a system that enhances management efficiency and human consideration by taking into account the emotional state of users in project management. The system mainly consists of servers, terminals, and users.

[0497] The server first automatically collects information through APIs of email and messaging applications. This typically involves using the Gmail API or the APIs of messaging platforms. The server monitors these tools, retrieves communication data via dedicated CC addresses, and stores it in a database.

[0498] Next, the server uses an emotion engine to analyze the user's text to determine their emotions. This involves using emotion analysis technologies such as IBM Watson or Google Cloud Natural Language API. This generates emotion scores—positive, negative, or neutral—from the text data provided by the user.

[0499] The generated sentiment data is integrated with progress data collected from project management software. This allows the server to analyze the current status and bottlenecks of each task in the project. This data is visualized on a dashboard, allowing users to grasp the overall status of the project at a glance.

[0500] On the device, users can view this information in real time, and the content and timing of notifications are adjusted based on emotional data. For example, if the emotional engine determines that the user is stressed, the server can adjust the tone of notifications to be more calming, encouraging focus on important tasks.

[0501] For example, if a user expresses dissatisfaction during a project, the server recognizes this emotion using its emotion engine, re-evaluates the task's priority, and provides feedback to both the user and the project manager. Emotional data serves as an indicator for considering project progress from an emotional perspective and making necessary adjustments.

[0502] An example of a prompt would be: "How can I determine a team member's stress level from their recent messages and identify the member who is experiencing the most stress?"

[0503] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0504] Step 1:

[0505] The server collects information using APIs for email and messaging applications. It receives data as input from Gmail and messaging platform APIs, and stores the communication data in a database via a dedicated CC address. Specifically, the server calls APIs based on a predetermined schedule or trigger to retrieve new emails and messages.

[0506] Step 2:

[0507] The server analyzes the collected text data using an emotion engine. The input here is the text data of emails and messages stored in step 1. The emotion engine (e.g., IBM Watson, Google Cloud Natural Language API) processes this text and outputs an emotion score. Specifically, the server calls the emotion engine's API to determine the emotional state of each message.

[0508] Step 3:

[0509] The server retrieves progress data from project management software and integrates it with sentiment data. The inputs used are progress data from project management tools (e.g., JIRA, Trello) and sentiment scores obtained in step 2. The server aggregates the data and outputs the progress and sentiment score for each task. Specifically, this involves calling a progress API and executing an algorithm to match the sentiment data.

[0510] Step 4:

[0511] The server visualizes the aggregated data on a dashboard. Inputs include task progress data and sentiment scores integrated in step 3. The server visually represents this data, outputting it as graphs and charts. The server utilizes data visualization tools to ensure users can understand the overall project status at a glance.

[0512] Step 5:

[0513] The device delivers notifications to the user based on project status and sentiment data. Input includes the visualization data generated in step 4. The device adjusts the content and timing of notifications according to the user's stress level, outputting optimized notifications. Specifically, it uses the device's notification function to ensure the user can access important information.

[0514] (Application Example 2)

[0515] 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."

[0516] In modern manufacturing environments, human emotions and conditions significantly impact productivity and quality. However, a challenge lies in the lack of a system that can grasp these emotions and conditions in real time and respond quickly. In particular, there is a need to reduce worker fatigue and stress on production lines and provide an appropriately adjusted work environment.

[0517] 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.

[0518] In this invention, the server includes means for automatically collecting information, means for analyzing and organizing the collected information, and means for analyzing the user's emotional state. This makes it possible to grasp the emotional state of workers in real time and appropriately adjust the production process.

[0519] "Means of automatically collecting information" refers to technical methods for obtaining data from external data sources such as email and messaging applications.

[0520] "Means for analyzing and organizing collected information" refers to technical methods that structure acquired data, extract necessary information, and convert it into a usable format.

[0521] "Methods for visualizing project progress" refer to technical methods that display the overall progress of a project in an easy-to-understand manner, allowing users to intuitively grasp its status.

[0522] "Methods for identifying areas where tasks are stalled" refers to technical methods that provide the necessary information to recognize areas where progress in a project is stagnating and to take appropriate countermeasures.

[0523] "Means for analyzing a user's emotional state" refers to technical methods for extracting emotions from a user's text-based communication and quantifying or classifying those emotions to understand their state.

[0524] "Means for adjusting work plans based on analyzed emotional data" refers to technical methods for dynamically changing production and task schedules according to the user's emotional state.

[0525] "Means of generating and presenting notifications" refers to technical methods that automatically create messages and deliver them at the appropriate time in order to convey appropriate information to users.

[0526] To realize this invention, a system incorporating an emotion engine will be constructed within the project management system. The system will mainly consist of a server, terminals, and users.

[0527] The server is responsible for automatically collecting information. Specifically, it uses APIs from mail servers and messaging applications to retrieve email content and chat history. This information is stored in the server's database using frameworks such as Flask and Django, in various data formats.

[0528] The collected information is analyzed on the server. Here, natural language processing libraries such as TextBlob and NLTK are used to analyze emotions from text data, quantifying or categorizing them. This allows for an understanding of the user's emotional state and the generation of emotion data based on that understanding.

[0529] Based on the analyzed emotion data, the server automatically adjusts the work plan. The robot's operation schedule and task priorities change according to the user's emotions, improving the efficiency of the production line.

[0530] The device provides an interface that allows users to view emotional data and project progress in real time. This interface enables users to understand their own emotional state and flexibly adjust their work schedule as needed.

[0531] For example, if a worker on a factory production line gives feedback saying, "I'm a little tired today...", the server will perceive this as "fatigue" and adjust the workload of the adjacent robot to increase it. As a result, the worker's burden is reduced. An example of a prompt message might be, "Based on the worker's message: 'I'm a little tired today,' please tell me how to adjust the robot's operation."

[0532] In this way, the entire system works in coordination, enabling more flexible and human-centered project management.

[0533] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0534] Step 1:

[0535] The server automatically collects information from mail servers and messaging applications via APIs. Inputs include email and chat data. This data is stored in the server's database. Output is text data stored in storage.

[0536] Step 2:

[0537] The server analyzes stored text data to generate sentiment data. In this process, natural language processing libraries such as TextBlob and NLTK are used to quantify or categorize the sentiment. The input is text data retrieved from the database, and the output is numerical sentiment data and categorical information. This allows for an understanding of the user's emotional state.

[0538] Step 3:

[0539] The server adjusts the work plan based on the analyzed sentiment data. Specifically, it uses sentiment data to modify robots and task schedules within the project management system. The input is sentiment data, and the output is the adjusted work plan and robot action instructions. The generative AI model used here proposes action patterns based on prompt statements.

[0540] Step 4:

[0541] The terminal presents the user with the adjusted work plan and sentiment data obtained from the server in real time. Inputs include adjustment information and sentiment data from the server. Outputs include screen display information and notifications provided to the user. Specifically, it provides visual progress displays on the user interface.

[0542] Step 5:

[0543] The user reviews the visualized sentiment data and work plan via a terminal and adjusts their work schedule as needed. The input is information obtained from the terminal. The output is the work schedule adjusted by the user. Specifically, the user makes schedule changes, and these changes are then incorporated back into the system.

[0544] 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.

[0545] 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.

[0546] 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.

[0547] [Fourth Embodiment]

[0548] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0549] 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.

[0550] 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).

[0551] 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.

[0552] 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.

[0553] 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).

[0554] 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.

[0555] 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.

[0556] 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.

[0557] 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.

[0558] 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.

[0559] 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.

[0560] 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".

[0561] This invention is a system for streamlining project management, comprising a series of functions for automatically collecting, analyzing, visualizing, and notifying information. The embodiments for carrying out the invention are described below.

[0562] The system's main components are a server, terminals, and users. First, the server automatically collects data using APIs for email and messaging applications. Users add a dedicated CC address to all project-related communications, allowing the server to receive conversations and progress information and store it in a database.

[0563] Next, the server analyzes the collected data and organizes it based on project name and task type. This process utilizes a generative AI model to extract and organize important information from the text data. This organized data is later used to visualize project progress.

[0564] On the device, users can visualize project progress through a dashboard. Here, task progress is displayed in graphs and charts for an at-a-glance view. This allows users to grasp the overall project status in real time and make quick decisions.

[0565] Furthermore, the server analyzes progress data to identify stalled tasks and areas where problems are occurring. This allows project managers to quickly understand the challenges they face and take appropriate action. For identified problem areas, the server generates alerts and notifies users via their terminals. These notifications are automatically sent via email or messaging tools, serving as a means of instantly communicating important information.

[0566] As a concrete example, when a new project is launched, the user adds CC addresses to all relevant emails, and the server automatically collects the information. The collected data is analyzed, and the project's progress is visualized on a dashboard. Through this dashboard, the user can check the status in real time and adjust project management as needed. The server identifies problem areas and provides immediate notifications, enabling a rapid response. In this way, the system prevents project management from becoming dependent on individuals and enables efficient work execution.

[0567] The following describes the processing flow.

[0568] Step 1:

[0569] The server monitors all emails containing the specified CC address for information gathering and aggregates the data upon receipt. It also uses Slack and Zoom APIs to retrieve logs from relevant channels and meetings. This ensures that all project-related communications are stored in the database.

[0570] Step 2:

[0571] The server converts the collected data into text format and, if necessary, converts attachments into a parseable format. Using a generative AI model, it extracts project names, task types, deadlines, etc., from the content of emails and messages, and then classifies and organizes the data based on that information.

[0572] Step 3:

[0573] The server analyzes project progress based on organized data. Specifically, it calculates the number of completed, in-progress, and delayed tasks and clarifies their status. This integrates the project's current status into the database.

[0574] Step 4:

[0575] The terminal retrieves progress data from the server and visualizes it on a dashboard. Using graphs and charts, it allows users to understand the overall project status at a glance. It also provides visual indicators for comparison with other sections and projects.

[0576] Step 5:

[0577] The server further analyzes the progress data to identify bottlenecks and potential problems in tasks. Based on specified criteria, it generates alerts for high-risk or delayed tasks.

[0578] Step 6:

[0579] The device receives notifications sent from the server and displays them to the user. The user can check the notifications on the device, view detailed information on the dashboard, and make quick decisions. In addition, the user can reallocate tasks and adjust resource management as needed based on feedback from the system.

[0580] (Example 1)

[0581] 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".

[0582] In project management, problems arise where task progress is stalled due to communication breakdowns and insufficient information organization. Furthermore, a lack of appropriate means to grasp progress in real time and respond quickly is a challenge.

[0583] 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.

[0584] In this invention, the server includes computing means for automatically collecting information, computing means for analyzing the collected information using a generating AI model and organizing it based on relevant elements, and computing means for identifying task stagnation and clarifying problem areas. This enables efficient management of project progress and rapid, information-based decision-making.

[0585] "Computer means for automatically collecting information" refers to a device that has the function of automatically collecting and storing all communication data related to a project via a dedicated address or API.

[0586] "Computer means for analyzing using a generative AI model and organizing based on relevant elements" refers to a device that uses AI technology to analyze collected text data, extract important information such as project names and task types, and organize it.

[0587] "A computing device that provides an interface for visually displaying project progress" refers to a device that has the function of displaying the project progress in graphs and charts based on organized data, allowing users to check the project progress in real time.

[0588] "Computational means for identifying task stagnation and clarifying problem areas" refers to a device that analyzes progress data to quickly detect and identify project problems such as delays and resource shortages.

[0589] "A means of communication for notifying users of issues and providing them with information" refers to a device that has the function of notifying users of identified problems or important information via email or messaging tools.

[0590] This invention is an information processing system for streamlining project management. Its main components are a server, terminals, and users.

[0591] The server automatically collects relevant data using email and messaging application APIs. By setting a dedicated CC address for all project-related communications, the server aggregates data in real time and stores it in a database. This process utilizes specific communication protocols to efficiently extract data.

[0592] Subsequently, the server analyzes the collected data using a generative AI model. This model extracts important elements from the text data using natural language processing techniques and organizes the information according to project name and task type. This analysis enables centralized management of project progress within the system.

[0593] The analyzed data is visualized on a dashboard located on the terminal. Users can see the project progress at a glance in graphs and charts, enabling quick decision-making. The terminal interface is designed for intuitive operation and provides real-time updated information.

[0594] Furthermore, the server identifies pending tasks and problem areas and generates alerts. These alerts are sent to users based on specified notification methods. Specifically, notifications are sent via email or messaging tools to support the rapid resolution of issues.

[0595] As a concrete example, when a user starts a new project, they enter their CC address in all related emails. The server uses this information to collect data and visualize the progress on a dashboard. For example, if the user enters "I want to check the progress of the new project," the latest progress information will be displayed. In this way, work can be carried out efficiently while maintaining consistency in project management.

[0596] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0597] Step 1:

[0598] The server automatically collects data from email and messaging applications. The CC addresses set by the user are used as input. The server uses these CC addresses to retrieve communication data in real time and store it in a database. This ensures that all project-related communications are collected.

[0599] Step 2:

[0600] The server analyzes the collected data. At this stage, it utilizes a generative AI model to scrutinize the text data received as input. As part of the data processing, natural language processing techniques are used to extract and organize important information from the text, such as project names, task types, and assigned personnel. The output is organized structured data.

[0601] Step 3:

[0602] The terminal visualizes organized data on a dashboard. It receives organized data from the server as input. The terminal generates graphs and charts, allowing users to understand the project's progress at a glance. The output is a dashboard visualizing the progress of tasks.

[0603] Step 4:

[0604] The server uses progress data for further analysis to identify stalled tasks and potential problem areas. It uses pre-processed data before visualization as input. The server performs calculations to identify problems and generates alerts for those problem areas. The output is a warning message regarding the identified problems.

[0605] Step 5:

[0606] The server notifies the user of the generated alerts. The server sends notifications via email or messaging tools. The input is the warning message generated as an alert. The output is the notification information the user receives, which helps them take prompt action.

[0607] Step 6:

[0608] Users can use their devices to check project progress and notifications received from the server on a dashboard. This allows users to understand the situation and revise project plans and task assignments as needed. The inputs are the dashboard and notifications, and the output is faster decision-making and implementation of countermeasures for problems.

[0609] (Application Example 1)

[0610] 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".

[0611] In manufacturing environments, efficient project management and real-time progress tracking of the manufacturing process are crucial. However, if the mechanisms for quickly acquiring and analyzing information are insufficient, delays in tasks and process anomalies may go unnoticed for too long, potentially leading to decreased productivity and an increase in defective products. In such situations, a system is needed that efficiently manages the entire manufacturing process and quickly detects and addresses anomalies.

[0612] 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.

[0613] In this invention, the server includes means for automatically collecting information, means for analyzing and organizing the collected information, means for visualizing the progress of the project, means for identifying areas where tasks are stalled, means for generating and presenting notifications, means for managing the progress of the manufacturing process in real time and detecting abnormal conditions, and means for sending alerts to a central system when an abnormal condition is detected. This enables more efficient overall project management at the manufacturing site and allows for quicker response through early detection of abnormalities.

[0614] "Means of automatically collecting information" refers to a system that automatically acquires necessary data from manufacturing processes and related tasks and converts it into a format usable within the system.

[0615] "Means of analyzing and organizing collected information" refers to methods of analyzing acquired data and arranging it into an orderly form based on specific items or criteria.

[0616] "Means for visualizing project progress" refers to a function that allows the progress of the manufacturing process and the status of tasks to be displayed in an intuitive format using graphs, charts, and other visual aids.

[0617] "Methods for identifying areas where tasks are stalled" refers to mechanisms for detecting parts of a project where progress is behind schedule or where work is stagnating.

[0618] "Means of generating and presenting notifications" refers to a system that generates alerts or messages based on specific conditions and informs the user of them.

[0619] "Means for managing the progress of the manufacturing process in real time and detecting abnormal conditions" refers to technologies that constantly monitor the progress of each process on the manufacturing line and quickly recognize when it deviates from the specified range.

[0620] "A means of sending an alert to a central system when an abnormal condition is detected" refers to a method of issuing an alarm to a central management system when an abnormality is detected, and quickly and accurately conveying that information.

[0621] The system for implementing this invention consists of key components including a server, terminals, and users. The server automatically acquires data from sensors and PLCs (Programmable Logic Controllers) to collect various data during the manufacturing process. This enables real-time data collection.

[0622] The server uses Flask to build an API and analyzes the received data using data analysis libraries such as Pandas. This analysis process organizes the progress of each stage and allows for the rapid identification of task bottlenecks and anomalies. The analyzed data is visualized using Matplotlib and displayed as graphs and charts on the terminal's dashboard.

[0623] The terminal allows users to view the overall project picture and the progress of specific tasks in real time. For example, if an anomaly occurs in the bolt tightening process on the manufacturing line, the dashboard will immediately display the situation visually, allowing users to quickly understand the situation.

[0624] Furthermore, by utilizing the generative AI model, it is possible to design prompt messages that support countermeasures when an anomaly is detected. For example, if a prompt message such as "Please suggest countermeasures in case of an anomaly" is input to the AI ​​model for an assembly process of a vehicle part where an anomaly has occurred, the system will suggest appropriate countermeasures.

[0625] When a bottleneck or anomaly is detected, the server automatically generates an alert notification using the SMTP protocol and sends it directly to the user via email. This notification allows the user to immediately decide on a course of action, enabling efficient project management.

[0626] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0627] Step 1:

[0628] The server automatically collects data from sensors and PLCs (Programmable Logic Controllers) installed on the manufacturing line. Inputs include data from sensors and PLCs. By receiving this data in real time via an API, the server is ready to monitor the status of the manufacturing process.

[0629] Step 2:

[0630] The server analyzes and organizes the collected data using the Pandas library. The input is the raw data collected in step 1, and the output is a well-structured dataset that identifies the progress status and presence or absence of anomalies for each process. This analysis makes any delays or problems in specific processes explicit in the data.

[0631] Step 3:

[0632] The server visualizes the analysis results using Matplotlib. The input is the analyzed data prepared in step 2, and the output generates visually easy-to-understand graphs and charts. This visualized information is prepared to be displayed on the user's device as an easy-to-understand dashboard.

[0633] Step 4:

[0634] On the terminal, users can check project progress and identify any anomalies through a dashboard. The input is visualization data provided by the server, and the output is the information the user obtains from viewing the dashboard. Here, the user grasps the real-time project status and decides on the next action.

[0635] Step 5:

[0636] The server sends an alert to the user via email using the SMTP protocol when it detects an anomaly exceeding a specific threshold. The input is the identified anomaly information, and the output is the alert email sent to the user. This notification allows the user to immediately respond to the situation and improve the process.

[0637] Step 6:

[0638] The server uses a generative AI model to generate prompt messages that suggest countermeasures for anomalies and bottlenecks. Input includes the nature of the anomaly and historical data, and output is a prompt message suggesting appropriate action. This allows users to efficiently manage projects and make quick decisions.

[0639] 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.

[0640] This invention is a system that optimizes project management based on the user's emotional state by combining an emotion engine with a project management system. The system components include a server, terminals, and users.

[0641] First, the server automatically collects information from email and messaging application APIs. It retrieves communication data via a dedicated CC address and stores it in a database. In parallel, the server uses an emotion engine to analyze the emotions from text created by users and generates emotion data.

[0642] Next, the server analyzes the collected information to identify project progress and areas where tasks are stalled. The analyzed data, along with sentiment data, is visualized on a dashboard, allowing users to understand the overall status of the project at a glance. Sentiment data serves as supplementary information to understand the project's state.

[0643] On the device, users can view this information in real time, and the content and timing of notifications are adjusted according to their emotional data. For example, users experiencing high stress levels will receive notifications in a calmer tone, designed to help them focus on important tasks.

[0644] For example, if a user expresses dissatisfaction during project progress, the server recognizes this emotion using its emotion engine and re-evaluates the task's priority. Emotional data serves as an indicator for reviewing progress from an emotional perspective and taking possible support measures.

[0645] As described above, this invention complements project management, which tends to be highly dependent on individuals, and enables efficient management that takes user emotions into consideration. By introducing an emotion engine, project management becomes a more flexible and human-centered system, making project success more certain.

[0646] The following describes the processing flow.

[0647] Step 1:

[0648] The server collects information through email and messaging application APIs and stores it in a database. When a user adds a CC address to project-related emails, the server automatically retrieves the information as a trigger.

[0649] Step 2:

[0650] The server uses a generative AI model to analyze the collected text data. During this process, it extracts attributes such as project name, task type, and deadline, and then organizes and tags the data.

[0651] Step 3:

[0652] The server uses an emotion engine to analyze user input text and identify the user's emotional state. For example, it assigns an emotion category such as negative or positive emotion.

[0653] Step 4:

[0654] The device displays analysis results and sentiment data retrieved from the server on a dashboard. Along with the visualized project progress, sentiment feedback for each task is also displayed, allowing the user to make judgments based on this information.

[0655] Step 5:

[0656] Based on the analyzed progress and sentiment data, the server identifies tasks that are stalled or require reprioritization. It adjusts the priority of specific tasks according to the sentiment data, supporting the smooth progress of the project.

[0657] Step 6:

[0658] The device receives notifications from the server and sends them to the user. Based on sentiment data, it adjusts the tone of the notification message and provides additional recommended actions as needed.

[0659] Step 7:

[0660] Users review information on the dashboard, assess task progress while taking sentiment data into account, and make adjustments as needed. This allows users to manage projects more flexibly.

[0661] (Example 2)

[0662] 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".

[0663] In project management, management methods that do not consider the emotional state of team members lead to decreased work efficiency, task delays, and lower team morale. Therefore, there is a need for more humane and emotionally sensitive project management systems.

[0664] 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.

[0665] In this invention, the server includes means for automatically collecting information, means for generating emotional data using an emotion engine, and means for adjusting the content and timing of notifications. This makes it possible to efficiently manage project progress from an emotional perspective and improve the motivation of team members.

[0666] "Means for automatically collecting information" refers to a function that automatically acquires data from project-related electronic communications using a program and stores it in a database.

[0667] "Means for analyzing and organizing collected information" refers to functions that use algorithms to analyze data stored on a server and organize it into a form that is easy for humans to understand.

[0668] "Means of visualizing project progress" refers to functions that present the progress of a project and the status of tasks in a visual format, thereby allowing users to understand the information at a glance.

[0669] "Means for identifying areas where tasks are stalled" refers to a function that automatically identifies tasks within a project that are experiencing stagnation and provides information to facilitate improvement.

[0670] "Means of generating emotional data using an emotion engine" refers to a function that analyzes a user's text communication to evaluate their emotions and generates the results as data.

[0671] "Means for adjusting the content and timing of notifications" refers to a function that controls the delivery of notifications based on user sentiment data, ensuring they are delivered with appropriate content and timing.

[0672] "Means of visualizing analyzed emotional data on a dashboard" refers to a function that visually displays the generated emotional data on a dashboard, clearly showing the emotional state of the project.

[0673] This invention is a system that enhances management efficiency and human consideration by taking into account the emotional state of users in project management. The system mainly consists of servers, terminals, and users.

[0674] The server first automatically collects information through APIs of email and messaging applications. This typically involves using the Gmail API or the APIs of messaging platforms. The server monitors these tools, retrieves communication data via dedicated CC addresses, and stores it in a database.

[0675] Next, the server uses an emotion engine to analyze the user's text to determine their emotions. This involves using emotion analysis technologies such as IBM Watson or Google Cloud Natural Language API. This generates emotion scores—positive, negative, or neutral—from the text data provided by the user.

[0676] The generated sentiment data is integrated with progress data collected from project management software. This allows the server to analyze the current status and bottlenecks of each task in the project. This data is visualized on a dashboard, allowing users to grasp the overall status of the project at a glance.

[0677] On the device, users can view this information in real time, and the content and timing of notifications are adjusted based on emotional data. For example, if the emotional engine determines that the user is stressed, the server can adjust the tone of notifications to be more calming, encouraging focus on important tasks.

[0678] For example, if a user expresses dissatisfaction during a project, the server recognizes this emotion using its emotion engine, re-evaluates the task's priority, and provides feedback to both the user and the project manager. Emotional data serves as an indicator for considering project progress from an emotional perspective and making necessary adjustments.

[0679] An example of a prompt would be: "How can I determine a team member's stress level from their recent messages and identify the member who is experiencing the most stress?"

[0680] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0681] Step 1:

[0682] The server collects information using APIs for email and messaging applications. It receives data as input from Gmail and messaging platform APIs, and stores the communication data in a database via a dedicated CC address. Specifically, the server calls APIs based on a predetermined schedule or trigger to retrieve new emails and messages.

[0683] Step 2:

[0684] The server analyzes the collected text data using an emotion engine. The input here is the text data of emails and messages stored in step 1. The emotion engine (e.g., IBM Watson, Google Cloud Natural Language API) processes this text and outputs an emotion score. Specifically, the server calls the emotion engine's API to determine the emotional state of each message.

[0685] Step 3:

[0686] The server retrieves progress data from project management software and integrates it with sentiment data. The inputs used are progress data from project management tools (e.g., JIRA, Trello) and sentiment scores obtained in step 2. The server aggregates the data and outputs the progress and sentiment score for each task. Specifically, this involves calling a progress API and executing an algorithm to match the sentiment data.

[0687] Step 4:

[0688] The server visualizes the aggregated data on a dashboard. Inputs include task progress data and sentiment scores integrated in step 3. The server visually represents this data, outputting it as graphs and charts. The server utilizes data visualization tools to ensure users can understand the overall project status at a glance.

[0689] Step 5:

[0690] The device delivers notifications to the user based on project status and sentiment data. Input includes the visualization data generated in step 4. The device adjusts the content and timing of notifications according to the user's stress level, outputting optimized notifications. Specifically, it uses the device's notification function to ensure the user can access important information.

[0691] (Application Example 2)

[0692] 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".

[0693] In modern manufacturing environments, human emotions and conditions significantly impact productivity and quality. However, a challenge lies in the lack of a system that can grasp these emotions and conditions in real time and respond quickly. In particular, there is a need to reduce worker fatigue and stress on production lines and provide an appropriately adjusted work environment.

[0694] 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.

[0695] In this invention, the server includes means for automatically collecting information, means for analyzing and organizing the collected information, and means for analyzing the user's emotional state. This makes it possible to grasp the emotional state of workers in real time and appropriately adjust the production process.

[0696] "Means of automatically collecting information" refers to technical methods for obtaining data from external data sources such as email and messaging applications.

[0697] "Means for analyzing and organizing collected information" refers to technical methods that structure acquired data, extract necessary information, and convert it into a usable format.

[0698] "Methods for visualizing project progress" refer to technical methods that display the overall progress of a project in an easy-to-understand manner, allowing users to intuitively grasp its status.

[0699] "Methods for identifying areas where tasks are stalled" refers to technical methods that provide the necessary information to recognize areas where progress in a project is stagnating and to take appropriate countermeasures.

[0700] "Means for analyzing a user's emotional state" refers to technical methods for extracting emotions from a user's text-based communication and quantifying or classifying those emotions to understand their state.

[0701] "Means for adjusting work plans based on analyzed emotional data" refers to technical methods for dynamically changing production and task schedules according to the user's emotional state.

[0702] "Means of generating and presenting notifications" refers to technical methods that automatically create messages and deliver them at the appropriate time in order to convey appropriate information to users.

[0703] To realize this invention, a system incorporating an emotion engine will be constructed within the project management system. The system will mainly consist of a server, terminals, and users.

[0704] The server is responsible for automatically collecting information. Specifically, it uses APIs from mail servers and messaging applications to retrieve email content and chat history. This information is stored in the server's database using frameworks such as Flask and Django, in various data formats.

[0705] The collected information is analyzed on the server. Here, natural language processing libraries such as TextBlob and NLTK are used to analyze emotions from text data, quantifying or categorizing them. This allows for an understanding of the user's emotional state and the generation of emotion data based on that understanding.

[0706] Based on the analyzed emotion data, the server automatically adjusts the work plan. The robot's operation schedule and task priorities change according to the user's emotions, improving the efficiency of the production line.

[0707] The device provides an interface that allows users to view emotional data and project progress in real time. This interface enables users to understand their own emotional state and flexibly adjust their work schedule as needed.

[0708] For example, if a worker on a factory production line gives feedback saying, "I'm a little tired today...", the server will perceive this as "fatigue" and adjust the workload of the adjacent robot to increase it. As a result, the worker's burden is reduced. An example of a prompt message might be, "Based on the worker's message: 'I'm a little tired today,' please tell me how to adjust the robot's operation."

[0709] In this way, the entire system works in coordination, enabling more flexible and human-centered project management.

[0710] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0711] Step 1:

[0712] The server automatically collects information from mail servers and messaging applications via APIs. Inputs include email and chat data. This data is stored in the server's database. Output is text data stored in storage.

[0713] Step 2:

[0714] The server analyzes stored text data to generate sentiment data. In this process, natural language processing libraries such as TextBlob and NLTK are used to quantify or categorize the sentiment. The input is text data retrieved from the database, and the output is numerical sentiment data and categorical information. This allows for an understanding of the user's emotional state.

[0715] Step 3:

[0716] The server adjusts the work plan based on the analyzed sentiment data. Specifically, it uses sentiment data to modify robots and task schedules within the project management system. The input is sentiment data, and the output is the adjusted work plan and robot action instructions. The generative AI model used here proposes action patterns based on prompt statements.

[0717] Step 4:

[0718] The terminal presents the user with the adjusted work plan and sentiment data obtained from the server in real time. Inputs include adjustment information and sentiment data from the server. Outputs include screen display information and notifications provided to the user. Specifically, it provides visual progress displays on the user interface.

[0719] Step 5:

[0720] The user reviews the visualized sentiment data and work plan via a terminal and adjusts their work schedule as needed. The input is information obtained from the terminal. The output is the work schedule adjusted by the user. Specifically, the user makes schedule changes, and these changes are then incorporated back into the system.

[0721] 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.

[0722] 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.

[0723] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0724] 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.

[0725] 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.

[0726] 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.

[0727] 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.

[0728] 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.

[0729] 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."

[0730] 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.

[0731] 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.

[0732] 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.

[0733] 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.

[0734] 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.

[0735] 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.

[0736] 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.

[0737] 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.

[0738] 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.

[0739] 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.

[0740] 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.

[0741] 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 as being incorporated by reference.

[0742] The following is further disclosed regarding the embodiments described above.

[0743] (Claim 1)

[0744] Means for automatically collecting information,

[0745] Means for analyzing and organizing collected information,

[0746] Means for visualizing project progress,

[0747] A means of identifying where tasks are stalled,

[0748] Means for generating and presenting notifications,

[0749] A system that includes this.

[0750] (Claim 2)

[0751] The system according to claim 1, comprising means for monitoring communication tools for collecting information.

[0752] (Claim 3)

[0753] The system according to claim 1, comprising means for setting priorities based on analyzed information.

[0754] "Example 1"

[0755] (Claim 1)

[0756] A computing means for automatically collecting information,

[0757] A computing means that analyzes the collected information using a generative AI model and organizes it based on related elements,

[0758] A computing means that provides an interface for visually displaying the progress of a project,

[0759] A computing tool to identify task stagnation and clarify problem areas,

[0760] A means of communication for notifying users of issues and providing them with information,

[0761] A system that includes this.

[0762] (Claim 2)

[0763] The system according to claim 1, comprising a computer for monitoring communication sources for collecting information and for extracting relevant information.

[0764] (Claim 3)

[0765] The system according to claim 1, comprising a computing means for dynamically setting project priorities based on analyzed information.

[0766] "Application Example 1"

[0767] (Claim 1)

[0768] Means for automatically collecting information,

[0769] Means for analyzing and organizing collected information,

[0770] Means for visualizing project progress,

[0771] A means of identifying where tasks are stalled,

[0772] Means for generating and presenting notifications,

[0773] A means for managing the progress of the manufacturing process in real time and detecting abnormal conditions,

[0774] A means of sending an alert to the central system when an abnormal condition is detected,

[0775] A system that includes this.

[0776] (Claim 2)

[0777] The system according to claim 1, comprising means for monitoring means for communication means for collecting information.

[0778] (Claim 3)

[0779] The system according to claim 1, comprising means for setting priorities and assisting in process adjustments based on analyzed information.

[0780] "Example 2 of combining an emotion engine"

[0781] (Claim 1)

[0782] Means for automatically collecting information,

[0783] Means for analyzing and organizing collected information,

[0784] Means for visualizing project progress,

[0785] A means of identifying where tasks are stalled,

[0786] A means of generating emotional data using an emotion engine,

[0787] Means to adjust the content and timing of notifications,

[0788] A means of visualizing the analyzed emotional data on a dashboard,

[0789] A system that includes this.

[0790] (Claim 2)

[0791] The system according to claim 1, comprising means for monitoring communication tools for collecting information.

[0792] (Claim 3)

[0793] The system according to claim 1, comprising means for setting priorities and optimizing notifications based on analyzed information and sentiment data.

[0794] "Application example 2 of combining emotional engines"

[0795] (Claim 1)

[0796] Means for automatically collecting information,

[0797] Means for analyzing and organizing collected information,

[0798] Means for visualizing project progress,

[0799] A means of identifying where tasks are stalled,

[0800] A means of analyzing the user's emotional state,

[0801] A means of adjusting the work plan based on the analyzed emotional data,

[0802] Means for generating and presenting notifications,

[0803] A system that includes this.

[0804] (Claim 2)

[0805] The system according to claim 1, further comprising means for monitoring a device that manipulates data for collecting information.

[0806] (Claim 3)

[0807] The system according to claim 1, comprising means for setting priorities based on analyzed information and sentiment data. [Explanation of Symbols]

[0808] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means for automatically collecting information, Means for analyzing and organizing collected information, Means for visualizing project progress, A means of identifying where tasks are stalled, Means for generating and presenting notifications, A system that includes this.

2. The system according to claim 1, comprising means for monitoring a communication tool for collecting information.

3. The system according to claim 1, comprising means for setting priorities based on analyzed information.

Images