system

Abstract

We provide the system. [Solution] A means of obtaining issue information from multiple data sources and storing it in an integrated database, A means for analyzing problem information using natural language processing, classifying its categories, and assigning priorities to them, A means to automatically check the progress of tasks and generate alerts for stagnation or expiration, A means for generating reminders and update notifications and sending them to users and administrators, A system that provides a user interface and includes means for displaying detailed information and progress on issues.

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 as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a conventional issue management system, it is difficult to appropriately integrate information from a plurality of data sources, and thus a great deal of effort is required to grasp the progress and priority of each issue. In addition, since the progress management of issues is often performed manually, there is a high possibility of missing the timing to perform important tasks or leaving risks unaddressed. Furthermore, since the management of reminders and notifications is also complicated, there is a problem that important information cannot be quickly conveyed to the person in charge or the administrator.

Means for Solving the Problems

[0005] This invention provides a system that acquires issue information from multiple data sources and stores it in an integrated database. It also analyzes the issue information using generative artificial intelligence, automatically categorizing and prioritizing it. Furthermore, it includes means for automatically checking and updating the progress of issues and generating alerts for stagnation or expiration. By automatically generating and sending reminders and update notifications to responsible parties and administrators, it enables rapid information sharing. Additionally, it supports efficient issue management by providing an intuitive, real-time display of detailed issue information and progress through a user interface.

[0006] A "data source" is the source of information used to obtain problem information, and can be in various formats, including CSV files, Excel spreadsheets, and APIs.

[0007] An "integrated database" is a data storage system that centralizes and manages information obtained from multiple data sources.

[0008] "Natural language processing" refers to the technology of analyzing the language that humans normally use and extracting useful information from text data.

[0009] A "generative artificial intelligence model" refers to AI technology that can learn from large amounts of text data and automatically generate new information.

[0010] "Categorization" is the process of classifying information or data based on specific criteria or common attributes.

[0011] "Priority" is an indicator that shows the order and necessity of addressing each issue based on its importance and urgency.

[0012] "Progress status" refers to information that indicates the degree of completion or progress of an issue or task.

[0013] An "alert" is a system notification that alerts users to specific situations or conditions when they occur.

[0014] A "reminder" is a function that notifies you in advance that you need to take a specific action.

[0015] "Automatic generation" refers to the process by which a system autonomously creates information and data without requiring manual intervention.

[0016] "User interface" refers to the display screens and operating methods that users use when interacting with a system.

[0017] "Real-time" refers to a process where information is updated and processed instantaneously, and reflected immediately. [Brief explanation of the drawing]

[0018] [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]Shows an emotion map to which a plurality of emotions are mapped. [Figure 10] Shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Modes for Carrying Out the Invention

[0019] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0020] First, the terms used in the following description will be described.

[0021] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of a plurality of types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0022] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0023] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0024] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0025] 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."

[0026] [First Embodiment]

[0027] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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".

[0039] This invention is a system for improving efficiency and accuracy in task management, and is implemented in the following form.

[0040] 1. Data Integration

[0041] Server: Retrieves issue information from multiple data sources and stores it in a unified database. This prevents information dispersion and organizes it in an easily accessible format.

[0042] 2. Implementation of natural language processing

[0043] Server: The acquired issue information is processed using a generative artificial intelligence model to perform natural language processing. This automatically categorizes the issues and assigns them priorities based on urgency and importance.

[0044] 3. Automated progress management

[0045] Server: Regularly updates the progress of each task and issues alerts if there are delays or deadlines are approaching, enabling timely follow-up.

[0046] 4. Generating reminders and notifications

[0047] Server: Automatically generates necessary reminders and progress check notifications based on the priority and progress of the tasks. These notifications are sent to the terminals of the responsible users and administrators, ensuring smooth information dissemination.

[0048] 5. Providing a user interface

[0049] Terminal: Displays an interface that allows users to view task details and progress. The interface is designed for intuitive operation and displays task status in real time.

[0050] Specific example

[0051] User (assigned person): Access the system from their device and check the newly assigned task. Verify that the category and priority are automatically set, and then update the progress.

[0052] Server: If the assigned person does not check the task for a certain period of time, or if the deadline is approaching, it will automatically generate a reminder and notify the user's terminal.

[0053] User (Administrator): Administrators can view the overall progress of multiple projects from the dashboard. If stagnation is observed, they can provide appropriate instructions and support the team's progress.

[0054] This invention makes it possible to reduce the complexities associated with conventional issue management and support rapid and accurate decision-making regarding issues.

[0055] The following describes the processing flow.

[0056] Step 1:

[0057] The server collects data from multiple issue tracking data sources. This includes importing CSV files and retrieving data via APIs. The collected data is then formatted and stored in an integrated database.

[0058] Step 2:

[0059] The server retrieves issue data from the integrated database and applies a natural language processing engine. This process classifies each issue into the appropriate category based on its content, and automatically sets priorities based on urgency and importance.

[0060] Step 3:

[0061] The server periodically evaluates and updates the progress of tasks. This evaluation is based on progress reports in the database and data from external systems. If progress is stalled or the deadline is approaching, an alert is generated.

[0062] Step 4:

[0063] The server automatically generates reminders and notifications for assignees and administrators based on the priority and progress of the issues. These are delivered as emails and chat messages to facilitate timely follow-up.

[0064] Step 5:

[0065] The terminal provides users with an interface that displays a list of tasks, details, and progress. The information is updated in real time, allowing users to instantly check the latest task status.

[0066] Step 6:

[0067] Users can update their assignment progress through the device interface. They can also add comments about the assignment and customize reminder content as needed.

[0068] (Example 1)

[0069] 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."

[0070] In modern information management, there is a need to effectively integrate data from multiple sources and organize that information quickly and accurately. Furthermore, it is necessary to improve the efficiency and accuracy of information management operations by automating progress monitoring and timely notifications. Traditional methods have faced challenges such as information dispersion and delays, increasing the burden on administrators and users.

[0071] 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.

[0072] In this invention, the server includes means for acquiring information from multiple sources and storing it on a centrally managed recording medium; means for analyzing the information using natural language processing, classifying it, and prioritizing it; and means for automatically monitoring progress and generating warnings in case of stagnation or missed submission deadlines. This enables centralized management and classification of information, prioritization, and automation of progress monitoring and notifications.

[0073] An "information source" refers to an external supplier or system used to obtain data.

[0074] A "centralized storage medium" refers to a single database or storage system used to aggregate and store acquired information.

[0075] "Natural language processing" is a technology that enables computers to understand and analyze human language.

[0076] "Classification" is the process of dividing information into specific categories or groups.

[0077] Prioritization refers to determining the order in which to respond based on the importance and urgency of information and issues.

[0078] "Progress status" refers to the state of how far a particular task or project has progressed.

[0079] A "warning" is a notification intended to inform people in advance of a problem or risk.

[0080] "User interface" refers to the screens and operating environments that users use when interacting with a system.

[0081] This system is designed to improve the efficiency and accuracy of issue management. Specifically, the server plays a primary role in processing information through automated means.

[0082] The server first retrieves data from numerous sources. These sources include email systems and project management tools, and are typically accessed via APIs. The retrieved data is then stored on a centrally managed storage medium on the server, namely a database.

[0083] For natural language processing, a generative AI model is used. The server analyzes the issue information, automatically classifies the issues based on their content, and then prioritizes them considering urgency and importance. This process allows users to tackle issues with evidence-based priorities.

[0084] For monitoring progress, the server periodically checks the database and updates the status of ongoing tasks. If no progress is observed or the submission deadline is approaching, a warning is generated in advance and an alert is sent to the user or administrator through the notification system.

[0085] The terminal provides a user interface, displaying an intuitive interface that allows users to easily operate it. This enables users to view progress and details of issues in real time, allowing for efficient decision-making.

[0086] As a concrete example, a user (assigned person) can log into the system from their device and check newly assigned tasks. Seeing that the generation AI model has automatically set the task category and priority, the user can update the progress status on the spot.

[0087] An example of a prompt message is, "Please review the category and priority of the newly assigned task and update its progress." This allows the system to manage task progress in a timely manner and improve the efficiency of daily operations.

[0088] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0089] Step 1:

[0090] The server retrieves issue information from external systems and tools. Inputs include email and project management tool data via APIs. After verifying data integrity and eliminating duplication, it stores the data on a centrally managed storage medium. This step plays a crucial role in aggregating dispersed information and establishing a foundation for centralized management.

[0091] Step 2:

[0092] The server inputs issue information stored on a centrally managed storage medium into a generating AI model. Natural language processing technology is used to analyze the content of the issues. Based on this analysis, the issues are automatically categorized and assigned priorities according to their importance and urgency. The categorized issue information is then stored in a database as output. This enables real-time decision-making.

[0093] Step 3:

[0094] The server continuously monitors the status of issues in the database. It receives updated progress information as input. The server evaluates progress based on pre-configured criteria and generates warnings if there is stagnation or approaching deadlines. As output, the generated warnings are pushed to the assigned personnel's terminals via the notification system. This allows users to take necessary actions in a timely manner.

[0095] Step 4:

[0096] The terminal provides a user interface, displaying categorized issues and their priorities. The input is real-time issue information sent from the server. Users utilize this interface to update their progress and send new information to the database. The output reflects the latest progress, clearly indicating the next steps to take.

[0097] (Application Example 1)

[0098] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0099] Activity management in modern logistics centers faces challenges due to its reliance on manual processes for monitoring progress and prioritizing tasks, resulting in inefficiencies. Furthermore, insufficient timely notification to workers makes it difficult to manage delays and missed deadlines. As a result, the smooth operation of logistics is hindered, leading to a decline in overall productivity.

[0100] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0101] In this invention, the server includes means for acquiring activity information from multiple information sources and storing it in an integrated information storage device; means for analyzing the activity information using natural language processing, classifying it, and assigning priorities thereto; means for automatically checking the progress and generating warnings for delays or expirations; and means for displaying activity progress information in real time and presenting alerts according to priority through an application installed on a visual display device. This enables improved efficiency in activity management and smoother operations through timely warning issuance in logistics centers.

[0102] "Information sources" refer to the various input devices and databases that serve as the basis for acquiring data.

[0103] "Activity information" refers to operational information, including the content, progress, and priority of each task within the logistics center.

[0104] An "information storage device" refers to hardware or software for centrally storing and managing acquired data.

[0105] "Natural language processing" is a technology that uses computers to analyze human language and perform information classification and semantic analysis.

[0106] A "visual display device" is a device that visually presents information or notifications to a user, and includes smart glasses and displays.

[0107] This invention is a system for improving the efficiency of activity management in a logistics center. The server acquires activity information from multiple sources and stores this information in an integrated information storage device. This makes it possible to centrally manage data related to operations within the center.

[0108] Next, the server analyzes the activity information using natural language processing techniques, classifies it, and assigns priorities. This process utilizes a generative AI model, which analyzes the activity descriptions in detail to evaluate the importance of each task.

[0109] Furthermore, the server automatically monitors the progress of each activity and generates warnings if there are delays or expirations. These warnings and notifications are presented to the user in real time through an application installed on a visual display device. Visual display devices such as smart glasses allow users to intuitively receive information according to priority.

[0110] As a concrete example, consider the inspection process for newly arrived goods. Users can check high-priority tasks through smart glasses and take immediate action. Furthermore, if progress is insufficient, the device will display a notification such as "Inventory deadline is approaching," thereby improving work efficiency.

[0111] An example of a prompt is, "Provide an AI model to manage new incoming products as a specific task and re-evaluate their priority." This allows the generated AI model to efficiently classify and prioritize activity information.

[0112] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0113] Step 1:

[0114] The server acquires activity information from multiple sources. Inputs include real-time data from various databases and external systems. The server collects this data and stores it in an integrated data storage system. It checks for duplicates and missing data and performs cleansing to create a clean and unified dataset.

[0115] Step 2:

[0116] The server performs natural language processing on integrated activity information using a generative AI model. The input is clean activity data. The server inputs prompt sentences into the generative AI model, which analyzes the activity descriptions to categorize and prioritize them. This results in structured data that assesses the importance and urgency of each activity.

[0117] Step 3:

[0118] The server automatically monitors the progress of each activity. It receives activity data with assigned priorities as input. The server periodically retrieves progress from the database and analyzes its status. If delays are detected or deadlines are approaching, it generates warnings and prepares a data structure to notify the application.

[0119] Step 4:

[0120] The terminal displays priority-based warnings and progress information to the user in real time via a visual display device (e.g., smart glasses). The input is warning data sent from a server. The terminal visually presents this information through a user interface, enabling the user to respond quickly. For example, a specific notification such as "Inventory deadline is approaching" might be displayed.

[0121] Step 5:

[0122] Users respond based on the warnings and information presented. Input includes notifications and progress information received via the terminal. Based on this information, users prioritize activities and update their progress on the terminal as needed. This ensures efficient operations within the logistics center.

[0123] 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.

[0124] This invention integrates an emotion engine into a task management system, adjusting task priorities and notifications based on the user's emotional state. This reduces the user's psychological burden and enables effective task management.

[0125] 1. Data acquisition and integration

[0126] Server: Collects issue information from multiple data sources and stores it in a unified database. This includes retrieving data from CSV files, APIs, etc., and standardizing the format to ensure consistency.

[0127] 2. Natural Language Processing and Prioritization

[0128] Server: Implements a natural language processing engine to automatically determine categories and priorities from task data. Utilizes a generative artificial intelligence model to analyze task descriptions and assign appropriate priorities.

[0129] 3. Utilizing the Emotional Engine

[0130] Device: Equipped with an emotion engine that recognizes user emotions in real time. This engine evaluates how users are reacting to issues and influences priority and notification content.

[0131] 4. Automated progress management and alert generation

[0132] Server: Regularly checks the progress of tasks and updates them automatically. Generates alerts for stalled tasks or tasks with approaching deadlines and notifies relevant parties.

[0133] 5. Adjust reminders and notifications

[0134] Server: Dynamically adjusts the content of reminders and notifications based on the evaluation results of the emotion engine. For example, if the user is feeling stressed, it may reduce the frequency of notifications.

[0135] 6. Providing a User Interface

[0136] Terminal: Displays an interface to the user that reflects task progress and emotions. The interface is intuitive and can change color scheme and layout according to the emotional state.

[0137] Specific example

[0138] User (Responsible Person): A device that senses the user's emotional state ensures that even urgent tasks are notified while maintaining low psychological stress. The emotion engine recognizes when the user is in a calm state and appropriately notifies them of high-priority tasks.

[0139] User (Administrator): When an administrator oversees multiple projects, they utilize the results of the emotion engine analysis to monitor the work status and psychological burden of team members. They then provide appropriate support as needed.

[0140] This invention, by combining an emotion engine, enables task management that takes into account the user's psychological state, thereby supporting efficient business operations.

[0141] The following describes the processing flow.

[0142] Step 1:

[0143] The server collects issue information from various data sources and retrieves data via CSV file reading or APIs. The retrieved data is then formatted and stored in an integrated database.

[0144] Step 2:

[0145] The server processes issue information from the integrated database and performs natural language processing using generative artificial intelligence. This automatically determines the issue category and priority, and saves the results to the database.

[0146] Step 3:

[0147] The device recognizes the user's emotional state in real time using an emotion engine. It analyzes the user's facial expressions and voice tone through the device's camera and microphone to evaluate their emotional state.

[0148] Step 4:

[0149] The server periodically checks the progress of tasks and updates the database. It generates alerts for tasks that are behind schedule or nearing their deadlines. When doing so, it also takes into account the user's emotional state and adjusts the content of the alerts accordingly.

[0150] Step 5:

[0151] The server dynamically adjusts the content of reminders and notifications based on the evaluation results of the emotion engine. For example, if the user is feeling stressed, the tone of the notification may be made gentler or the frequency reduced.

[0152] Step 6:

[0153] The device displays the user's progress on tasks in accordance with their emotions. Furthermore, the user interface design changes according to the user's emotional state, taking care to reduce psychological burden.

[0154] Step 7:

[0155] Users can check detailed information and progress of tasks through their devices and update their progress as needed. The devices send user input data to the server, which is then reflected in the integrated database.

[0156] Step 8:

[0157] Users (administrators) can monitor the emotional state of the entire team on a management dashboard and adjust team issues or provide support to members as needed. They utilize data from the emotional engine to maintain an appropriate management system.

[0158] (Example 2)

[0159] 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".

[0160] Traditional task management systems fail to consider the emotional state of users, leading to problems such as increased stress and decreased efficiency. Furthermore, the fixed task priorities and notification content prevent the implementation of management optimized for individual users.

[0161] 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.

[0162] This invention includes a server that recognizes the user's emotional state in real time and dynamically adjusts task priorities and notification content based on that state; a server that collects data from multiple sources and stores it in an integrated data storage medium; and a server that analyzes task information using natural language processing, classifies it, and prioritizes it. This reduces the user's psychological burden and enables efficient task management.

[0163] "User" refers to an individual or group that uses the system and is the entity that provides information about their emotional state to the system.

[0164] "Emotional state" refers to data that indicates the user's psychological and emotional condition, and measures the user's stress level and emotional stability.

[0165] "Task priority" refers to the order in which tasks are prioritized, determined by evaluating the importance and urgency of each issue. It involves determining the order in which tasks are processed based on specific criteria.

[0166] "Notification content" refers to the specific message or alert content of information sent to users or administrators, providing information about task progress and necessary actions.

[0167] "Data storage medium" refers to a device or system for temporarily or permanently storing information, including online or offline data storage.

[0168] "Natural language processing" is a technology that allows computers to understand, analyze, and generate human natural language, and is used to categorize and prioritize problem information.

[0169] An "interface" refers to the visual or physical elements that allow a user to interact with a system, designed to display detailed information and progress on a task.

[0170] This invention constructs a system that recognizes the user's emotional state in real time and reflects it in issue management. The server collects information from multiple data sources and stores it on an integrated data storage medium. Specifically, the software used includes a database management system and a natural language processing engine. For natural language processing, a generative AI model is utilized to analyze the issue information.

[0171] The device incorporates an emotion engine that recognizes the user's emotional state, analyzing facial expressions and tone of voice using hardware such as the camera and microphone. This data is sent to a server and used to prioritize tasks and adjust notification content. For example, if the user is tired, the device reduces the frequency of notifications and provides information in a more relaxed tone.

[0172] As a concrete example, when a user adds a task stating, "I need to create a proposal for a new product by next week," this description is input as a prompt into the AI ​​model. If the user's emotional state is calm, the device will notify them of the task as usual and adjust its priority. However, if the system determines that the user is stressed, it will reduce the notification frequency.

[0173] An example of a prompt to input into the generating AI model is the text, "The user has asked you to analyze the task priority for the task 'Create a proposal for a new product'." This method enables the system to manage tasks flexibly and efficiently in accordance with the user's psychological state.

[0174] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0175] Step 1:

[0176] The server retrieves task data from external sources using APIs or CSV files. Inputs include task names, deadlines, and descriptions, and output is data in a standardized format. This data is stored on an integrated data storage medium within the server.

[0177] Step 2:

[0178] The server passes the stored task data to a natural language processing engine, which then analyzes it using a generative AI model. Specifically, the task description serves as the input information, and the output is a categorized list of tasks with their priorities determined. This process automatically evaluates the importance and urgency of each task.

[0179] Step 3:

[0180] The device uses a camera and microphone to collect facial and voice data in order to acquire user emotion data. Inputs include real-time video and audio, which are analyzed by an emotion engine to obtain the user's emotional state as output. This data is then sent to the server.

[0181] Step 4:

[0182] The server uses emotional data to dynamically adjust task priorities and notification content. Input information includes the user's emotional state and task list, while output is an adjusted task list and notification frequency and content. During this process, if the user is experiencing stress, specific adjustments are made, such as reducing the notification frequency.

[0183] Step 5:

[0184] The device provides the user with an interface based on the adjusted data. This includes displaying progress and task lists. The input is updated data from the server, and the output is visual information provided to the user. Here, the interface's color scheme and design are automatically adjusted according to the user's emotional state.

[0185] Step 6:

[0186] Users can view tasks through their devices and add new tasks as needed. When new task information is entered, it is sent back to the server, updating the entire task management cycle. This ensures that users always have access to the latest information.

[0187] (Application Example 2)

[0188] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0189] In modern urban environments, residents are often overwhelmed by a constant stream of notifications and information, leading to stress. This problem stems from the fact that notifications and information from urban services are sent at times that do not take into account the user's emotional state. A system is needed that reduces user stress and enables more effective information delivery.

[0190] 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. In this invention, the server includes means for acquiring issue information from multiple data sources and storing it in an integrated information warehouse, means for analyzing the issue information using natural language processing, classifying its type and assigning its importance, and means for detecting the user's emotional state and adjusting the notification frequency and content. As a result, notifications are sent at the optimal timing according to the user's emotional state, reducing stress on residents and enabling effective information management.

[0191] A "data source" is a data provision system that serves as the basis for acquiring information.

[0192] An "information warehouse" is a database system for integrating and managing a wide variety of data.

[0193] "Natural language processing" is a technology that enables computers to understand and process human language.

[0194] "Type" refers to a category used to classify data or information.

[0195] "Importance" is an indicator that shows the priority of data and information.

[0196] "Emotional state" refers to the result of an assessment of the user's psychological and emotional condition.

[0197] "Notification frequency" refers to the time interval or number of times notifications are sent to the user.

[0198] "Information management" is the process of efficiently acquiring, classifying, storing, and providing information.

[0199] This invention aims to build a system that uses smartphones to send notifications to urban residents at the optimal time. The server first acquires issue information from multiple data sources and stores it in an integrated information warehouse. This allows for centralized management of data provided from various urban services.

[0200] Next, using natural language processing technology, the server analyzes the task information, classifies its type, and assigns importance levels. In this step, a generative intelligence model is used to analyze the task descriptions and enable priority adjustments based on the user's emotional state.

[0201] The device (smartphone) collects the user's biometric data in real time and evaluates their emotional state. For this purpose, the device is equipped with a heart rate sensor and a temperature sensor. Emotional analysis is performed using the TENSORFLOW® library, and the frequency and content of notifications are dynamically adjusted according to the emotional state.

[0202] For example, if the emotion analysis engine determines that the user is relaxed, low-priority notifications will be sent, while if it detects that the user is stressed, the frequency and content of notifications will be limited.

[0203] An example of a prompt message would be, "Your emotional state for today has been recorded. Please optimize when you receive your next notification. Emotional state: Relaxed; High priority task: Road construction notification." This allows for flexible information delivery tailored to each user's individual situation.

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

[0205] Step 1:

[0206] The server retrieves issue information from multiple data sources. Input data is provided via APIs and CSV files. The data is integrated and stored in an information repository. This process involves data format conversion and organization, and the data is consolidated into a unified database.

[0207] Step 2:

[0208] The server performs natural language processing on the stored task information. Integrated task information is provided as input. A generative AI model is used to analyze the content of the tasks, classify them by type, and assign them importance levels. The output is the classified task information and the importance level assigned to each task.

[0209] Step 3:

[0210] The device acquires the user's biometric data and evaluates their emotional state. In this step, heart rate, temperature, and activity information are collected from the smartphone's sensors as input. Emotional analysis is performed using TensorFlow, and the emotional state is output as numerical data.

[0211] Step 4:

[0212] The server adjusts the content and frequency of notifications based on the user's emotional state. Inputs include priority information for the tasks obtained in the previous stage and the user's emotional state. Data calculations optimize the notification schedule, resulting in the adjusted notification settings as output.

[0213] Step 5:

[0214] The device sends pre-configured notifications to the user. The input is the configured notification settings, and the output is the user receiving notifications at the appropriate time. Using the smartphone's notification function, the notification content is dynamically displayed according to the user's emotional state.

[0215] Step 6:

[0216] The user selects the necessary action based on the notification received from their device. In this step, the user reviews the information displayed in the notification and decides on the next step. The output is the user's action, which is fed back into the next data collection cycle.

[0217] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0218] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0219] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0220] [Second Embodiment]

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

[0222] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0223] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0224] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0225] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0226] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0227] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0228] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0229] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0230] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0231] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0232] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0233] This invention is a system for improving efficiency and accuracy in task management, and is implemented in the following form.

[0234] 1. Data Integration

[0235] Server: Retrieves issue information from multiple data sources and stores it in a unified database. This prevents information dispersion and organizes it in an easily accessible format.

[0236] 2. Implementation of natural language processing

[0237] Server: The acquired issue information is processed using a generative artificial intelligence model to perform natural language processing. This automatically categorizes the issues and assigns them priorities based on urgency and importance.

[0238] 3. Automated progress management

[0239] Server: Regularly updates the progress of each task and issues alerts if there are delays or deadlines are approaching, enabling timely follow-up.

[0240] 4. Generating reminders and notifications

[0241] Server: Automatically generates necessary reminders and progress check notifications based on the priority and progress of the tasks. These notifications are sent to the terminals of the responsible users and administrators, ensuring smooth information dissemination.

[0242] 5. Providing a user interface

[0243] Terminal: Displays an interface that allows users to view task details and progress. The interface is designed for intuitive operation and displays task status in real time.

[0244] Specific example

[0245] User (assigned person): Access the system from their device and check the newly assigned task. Verify that the category and priority are automatically set, and then update the progress.

[0246] Server: If the assigned person does not check the task for a certain period of time, or if the deadline is approaching, it will automatically generate a reminder and notify the user's terminal.

[0247] User (Administrator): Administrators can view the overall progress of multiple projects from the dashboard. If stagnation is observed, they can provide appropriate instructions and support the team's progress.

[0248] This invention makes it possible to reduce the complexities associated with conventional issue management and support rapid and accurate decision-making regarding issues.

[0249] The following describes the processing flow.

[0250] Step 1:

[0251] The server collects data from multiple issue tracking data sources. This includes importing CSV files and retrieving data via APIs. The collected data is then formatted and stored in an integrated database.

[0252] Step 2:

[0253] The server retrieves issue data from the integrated database and applies a natural language processing engine. This process classifies each issue into the appropriate category based on its content, and automatically sets priorities based on urgency and importance.

[0254] Step 3:

[0255] The server periodically evaluates and updates the progress of tasks. This evaluation is based on progress reports in the database and data from external systems. If progress is stalled or the deadline is approaching, an alert is generated.

[0256] Step 4:

[0257] The server automatically generates reminders and notifications for assignees and administrators based on the priority and progress of the issues. These are delivered as emails and chat messages to facilitate timely follow-up.

[0258] Step 5:

[0259] The terminal provides users with an interface that displays a list of tasks, details, and progress. The information is updated in real time, allowing users to instantly check the latest task status.

[0260] Step 6:

[0261] Users can update their assignment progress through the device interface. They can also add comments about the assignment and customize reminder content as needed.

[0262] (Example 1)

[0263] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0264] In modern information management, there is a need to effectively integrate data from multiple sources and organize that information quickly and accurately. Furthermore, it is necessary to improve the efficiency and accuracy of information management operations by automating progress monitoring and timely notifications. Traditional methods have faced challenges such as information dispersion and delays, increasing the burden on administrators and users.

[0265] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0266] In this invention, the server includes means for acquiring information from multiple sources and storing it on a centrally managed recording medium; means for analyzing the information using natural language processing, classifying it, and prioritizing it; and means for automatically monitoring progress and generating warnings in case of stagnation or missed submission deadlines. This enables centralized management and classification of information, prioritization, and automation of progress monitoring and notifications.

[0267] An "information source" refers to an external supplier or system used to obtain data.

[0268] A "centralized storage medium" refers to a single database or storage system used to aggregate and store acquired information.

[0269] "Natural language processing" is a technology that enables computers to understand and analyze human language.

[0270] "Classification" is the process of dividing information into specific categories or groups.

[0271] Prioritization refers to determining the order in which to respond based on the importance and urgency of information and issues.

[0272] "Progress status" refers to the state of how far a particular task or project has progressed.

[0273] A "warning" is a notification intended to inform people in advance of a problem or risk.

[0274] "User interface" refers to the screens and operating environments that users use when interacting with a system.

[0275] This system is designed to improve the efficiency and accuracy of issue management. Specifically, the server plays a primary role in processing information through automated means.

[0276] The server first retrieves data from numerous sources. These sources include email systems and project management tools, and are typically accessed via APIs. The retrieved data is then stored on a centrally managed storage medium on the server, namely a database.

[0277] For natural language processing, a generative AI model is used. The server analyzes the issue information, automatically classifies the issues based on their content, and then prioritizes them considering urgency and importance. This process allows users to tackle issues with evidence-based priorities.

[0278] For monitoring progress, the server periodically checks the database and updates the status of ongoing tasks. If no progress is observed or the submission deadline is approaching, a warning is generated in advance and an alert is sent to the user or administrator through the notification system.

[0279] The terminal provides a user interface, displaying an intuitive interface that allows users to easily operate it. This enables users to view progress and details of issues in real time, allowing for efficient decision-making.

[0280] As a concrete example, a user (assigned person) can log into the system from their device and check newly assigned tasks. Seeing that the generation AI model has automatically set the task category and priority, the user can update the progress status on the spot.

[0281] An example of a prompt message is, "Please review the category and priority of the newly assigned task and update its progress." This allows the system to manage task progress in a timely manner and improve the efficiency of daily operations.

[0282] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0283] Step 1:

[0284] The server obtains issue information from external systems and tools. The inputs are data from emails via APIs and project management tools. After verifying data consistency and eliminating duplicates, it stores this in a centrally managed recording medium. In this step, it plays the role of aggregating scattered information and preparing a foundation for unified management.

[0285] Step 2:

[0286] The server inputs the issue information stored in the centrally managed recording medium into the generative AI model. It uses natural language processing technology for data processing to analyze the content of the issues. Based on this analysis, the issues are automatically categorized and assigned priorities according to their importance and urgency. As output, the classified issue information is stored in the database. This enables real-time decision-making.

[0287] Step 3:

[0288] The server continuously monitors the issue status in the database. As input, it receives updated progress information. The server evaluates the progress based on pre-set criteria and generates warnings if there are stagnations or approaching deadlines. As output, the generated warnings are pushed to the terminals of the responsible persons through the notification system. This enables users to take timely necessary actions.

[0289] Step 4:

[0290] The terminal provides a user interface and displays the categorized issues and their priorities. The input is the real-time issue information sent from the server. The user uses this interface to update the progress status and send new information to the database. As output, the latest progress information is reflected and the next actions to be taken become clear.

[0291] (Application Example 1)

[0292] 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."

[0293] Activity management in modern logistics centers faces challenges due to its reliance on manual processes for monitoring progress and prioritizing tasks, resulting in inefficiencies. Furthermore, insufficient timely notification to workers makes it difficult to manage delays and missed deadlines. As a result, the smooth operation of logistics is hindered, leading to a decline in overall productivity.

[0294] 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.

[0295] In this invention, the server includes means for acquiring activity information from multiple information sources and storing it in an integrated information storage device; means for analyzing the activity information using natural language processing, classifying it, and assigning priorities thereto; means for automatically checking the progress and generating warnings for delays or expirations; and means for displaying activity progress information in real time and presenting alerts according to priority through an application installed on a visual display device. This enables improved efficiency in activity management and smoother operations through timely warning issuance in logistics centers.

[0296] "Information sources" refer to the various input devices and databases that serve as the basis for acquiring data.

[0297] "Activity information" refers to operational information, including the content, progress, and priority of each task within the logistics center.

[0298] An "information storage device" refers to hardware or software for centrally storing and managing acquired data.

[0299] "Natural language processing" is a technology that uses computers to analyze human language and perform information classification and semantic analysis.

[0300] A "visual display device" is a device that visually presents information or notifications to a user, and includes smart glasses and displays.

[0301] This invention is a system for improving the efficiency of activity management in a logistics center. The server acquires activity information from multiple sources and stores this information in an integrated information storage device. This makes it possible to centrally manage data related to operations within the center.

[0302] Next, the server analyzes the activity information using natural language processing techniques, classifies it, and assigns priorities. This process utilizes a generative AI model, which analyzes the activity descriptions in detail to evaluate the importance of each task.

[0303] Furthermore, the server automatically monitors the progress of each activity and generates warnings if there are delays or expirations. These warnings and notifications are presented to the user in real time through an application installed on a visual display device. Visual display devices such as smart glasses allow users to intuitively receive information according to priority.

[0304] As a concrete example, consider the inspection process for newly arrived goods. Users can check high-priority tasks through smart glasses and take immediate action. Furthermore, if progress is insufficient, the device will display a notification such as "Inventory deadline is approaching," thereby improving work efficiency.

[0305] An example of a prompt is, "Provide an AI model to manage new incoming products as a specific task and re-evaluate their priority." This allows the generated AI model to efficiently classify and prioritize activity information.

[0306] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0307] Step 1:

[0308] The server obtains activity information from multiple information sources. As input, there is real-time data from various databases and external systems. The server collects this data and stores it in an integrated information storage device. By checking for data duplication and missing data and performing cleansing, a clean and unified dataset is created.

[0309] Step 2:

[0310] The server performs natural language processing on the integrated activity information using a generative AI model. As input, there is clean activity information data. The server inputs a prompt sentence into the generative AI model, analyzes the description of the activity, and categorizes and prioritizes it. As a result, structured data in which the importance and urgency of each activity are evaluated is obtained.

[0311] Step 3:

[0312] The server automatically checks the progress of each activity. As input, there is activity data with priorities assigned. The server periodically obtains the progress status from the database and analyzes its progress state. If a delay is observed or the deadline is approaching, a warning is generated and a data structure for notifying the application is prepared.

[0313] Step 4:

[0314] The terminal displays warnings and progress information according to priorities in real time to the user via a visual display device (e.g., smart glasses). As input, there is warning data sent from the server. The terminal visually presents this information through the user interface so that the user can respond quickly. For example, a specific notification such as "The inventory count is approaching the deadline" is displayed.

[0315] Step 5:

[0316] Users respond based on the warnings and information presented. Input includes notifications and progress information received via the terminal. Based on this information, users prioritize activities and update their progress on the terminal as needed. This ensures efficient operations within the logistics center.

[0317] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0318] This invention integrates an emotion engine into a task management system, adjusting task priorities and notifications based on the user's emotional state. This reduces the user's psychological burden and enables effective task management.

[0319] 1. Data acquisition and integration

[0320] Server: Collects issue information from multiple data sources and stores it in a unified database. This includes retrieving data from CSV files, APIs, etc., and standardizing the format to ensure consistency.

[0321] 2. Natural Language Processing and Prioritization

[0322] Server: Implements a natural language processing engine to automatically determine categories and priorities from task data. Utilizes a generative artificial intelligence model to analyze task descriptions and assign appropriate priorities.

[0323] 3. Utilizing the Emotional Engine

[0324] Device: Equipped with an emotion engine that recognizes user emotions in real time. This engine evaluates how users are reacting to issues and influences priority and notification content.

[0325] 4. Automated progress management and alert generation

[0326] Server: Regularly checks the progress of tasks and updates them automatically. Generates alerts for stalled tasks or tasks with approaching deadlines and notifies relevant parties.

[0327] 5. Adjust reminders and notifications

[0328] Server: Dynamically adjusts the content of reminders and notifications based on the evaluation results of the emotion engine. For example, if the user is feeling stressed, it may reduce the frequency of notifications.

[0329] 6. Providing a User Interface

[0330] Terminal: Displays an interface to the user that reflects task progress and emotions. The interface is intuitive and can change color scheme and layout according to the emotional state.

[0331] Specific example

[0332] User (Responsible Person): A device that senses the user's emotional state ensures that even urgent tasks are notified while maintaining low psychological stress. The emotion engine recognizes when the user is in a calm state and appropriately notifies them of high-priority tasks.

[0333] User (Administrator): When an administrator oversees multiple projects, they utilize the results of the emotion engine analysis to monitor the work status and psychological burden of team members. They then provide appropriate support as needed.

[0334] This invention, by combining an emotion engine, enables task management that takes into account the user's psychological state, thereby supporting efficient business operations.

[0335] The following describes the processing flow.

[0336] Step 1:

[0337] The server collects issue information from various data sources and retrieves data via CSV file reading or APIs. The retrieved data is then formatted and stored in an integrated database.

[0338] Step 2:

[0339] The server processes issue information from the integrated database and performs natural language processing using generative artificial intelligence. This automatically determines the issue category and priority, and saves the results to the database.

[0340] Step 3:

[0341] The device recognizes the user's emotional state in real time using an emotion engine. It analyzes the user's facial expressions and voice tone through the device's camera and microphone to evaluate their emotional state.

[0342] Step 4:

[0343] The server periodically checks the progress of tasks and updates the database. It generates alerts for tasks that are behind schedule or nearing their deadlines. When doing so, it also takes into account the user's emotional state and adjusts the content of the alerts accordingly.

[0344] Step 5:

[0345] The server dynamically adjusts the content of reminders and notifications based on the evaluation results of the emotion engine. For example, if the user is feeling stressed, the tone of the notification may be made gentler or the frequency reduced.

[0346] Step 6:

[0347] The device displays the user's progress on tasks in accordance with their emotions. Furthermore, the user interface design changes according to the user's emotional state, taking care to reduce psychological burden.

[0348] Step 7:

[0349] Users can check detailed information and progress of tasks through their devices and update their progress as needed. The devices send user input data to the server, which is then reflected in the integrated database.

[0350] Step 8:

[0351] Users (administrators) can monitor the emotional state of the entire team on a management dashboard and adjust team issues or provide support to members as needed. They utilize data from the emotional engine to maintain an appropriate management system.

[0352] (Example 2)

[0353] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0354] Traditional task management systems fail to consider the emotional state of users, leading to problems such as increased stress and decreased efficiency. Furthermore, the fixed task priorities and notification content prevent the implementation of management optimized for individual users.

[0355] 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.

[0356] This invention includes a server that recognizes the user's emotional state in real time and dynamically adjusts task priorities and notification content based on that state; a server that collects data from multiple sources and stores it in an integrated data storage medium; and a server that analyzes task information using natural language processing, classifies it, and prioritizes it. This reduces the user's psychological burden and enables efficient task management.

[0357] "User" refers to an individual or group that uses the system and is the entity that provides information about their emotional state to the system.

[0358] "Emotional state" refers to data that indicates the user's psychological and emotional condition, and measures the user's stress level and emotional stability.

[0359] "Task priority" refers to the order in which tasks are prioritized, determined by evaluating the importance and urgency of each issue. It involves determining the order in which tasks are processed based on specific criteria.

[0360] "Notification content" refers to the specific message or alert content of information sent to users or administrators, providing information about task progress and necessary actions.

[0361] "Data storage medium" refers to a device or system for temporarily or permanently storing information, including online or offline data storage.

[0362] "Natural language processing" is a technology that allows computers to understand, analyze, and generate human natural language, and is used to categorize and prioritize problem information.

[0363] An "interface" refers to the visual or physical elements that allow a user to interact with a system, designed to display detailed information and progress on a task.

[0364] This invention constructs a system that recognizes the user's emotional state in real time and reflects it in issue management. The server collects information from multiple data sources and stores it on an integrated data storage medium. Specifically, the software used includes a database management system and a natural language processing engine. For natural language processing, a generative AI model is utilized to analyze the issue information.

[0365] The device incorporates an emotion engine that recognizes the user's emotional state, analyzing facial expressions and tone of voice using hardware such as the camera and microphone. This data is sent to a server and used to prioritize tasks and adjust notification content. For example, if the user is tired, the device reduces the frequency of notifications and provides information in a more relaxed tone.

[0366] As a concrete example, when a user adds a task stating, "I need to create a proposal for a new product by next week," this description is input as a prompt into the AI ​​model. If the user's emotional state is calm, the device will notify them of the task as usual and adjust its priority. However, if the system determines that the user is stressed, it will reduce the notification frequency.

[0367] An example of a prompt to input into the generating AI model is the text, "The user has asked you to analyze the task priority for the task 'Create a proposal for a new product'." This method enables the system to manage tasks flexibly and efficiently in accordance with the user's psychological state.

[0368] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0369] Step 1:

[0370] The server retrieves task data from external sources using APIs or CSV files. Inputs include task names, deadlines, and descriptions, and output is data in a standardized format. This data is stored on an integrated data storage medium within the server.

[0371] Step 2:

[0372] The server passes the stored task data to a natural language processing engine, which then analyzes it using a generative AI model. Specifically, the task description serves as the input information, and the output is a categorized list of tasks with their priorities determined. This process automatically evaluates the importance and urgency of each task.

[0373] Step 3:

[0374] The device uses a camera and microphone to collect facial and voice data in order to acquire user emotion data. Inputs include real-time video and audio, which are analyzed by an emotion engine to obtain the user's emotional state as output. This data is then sent to the server.

[0375] Step 4:

[0376] The server uses emotional data to dynamically adjust task priorities and notification content. Input information includes the user's emotional state and task list, while output is an adjusted task list and notification frequency and content. During this process, if the user is experiencing stress, specific adjustments are made, such as reducing the notification frequency.

[0377] Step 5:

[0378] The device provides the user with an interface based on the adjusted data. This includes displaying progress and task lists. The input is updated data from the server, and the output is visual information provided to the user. Here, the interface's color scheme and design are automatically adjusted according to the user's emotional state.

[0379] Step 6:

[0380] Users can view tasks through their devices and add new tasks as needed. When new task information is entered, it is sent back to the server, updating the entire task management cycle. This ensures that users always have access to the latest information.

[0381] (Application Example 2)

[0382] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0383] In modern urban environments, residents are often overwhelmed by a constant stream of notifications and information, leading to stress. This problem stems from the fact that notifications and information from urban services are sent at times that do not take into account the user's emotional state. A system is needed that reduces user stress and enables more effective information delivery.

[0384] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring issue information from multiple data sources and storing it in an integrated information warehouse, means for analyzing the issue information using natural language processing, classifying its type and assigning its importance, and means for detecting the user's emotional state and adjusting the notification frequency and content. As a result, notifications are sent at the optimal timing according to the user's emotional state, reducing stress on residents and enabling effective information management.

[0385] A "data source" is a data provision system that serves as the basis for acquiring information.

[0386] An "information warehouse" is a database system for integrating and managing a wide variety of data.

[0387] "Natural language processing" is a technology that enables computers to understand and process human language.

[0388] "Type" refers to a category used to classify data or information.

[0389] "Importance" is an indicator that shows the priority of data and information.

[0390] "Emotional state" refers to the result of an assessment of the user's psychological and emotional condition.

[0391] "Notification frequency" refers to the time interval or number of times notifications are sent to the user.

[0392] "Information management" is the process of efficiently acquiring, classifying, storing, and providing information.

[0393] This invention aims to build a system that uses smartphones to send notifications to urban residents at the optimal time. The server first acquires issue information from multiple data sources and stores it in an integrated information warehouse. This allows for centralized management of data provided from various urban services.

[0394] Next, using natural language processing technology, the server analyzes the task information, classifies its type, and assigns importance levels. In this step, a generative intelligence model is used to analyze the task descriptions and enable priority adjustments based on the user's emotional state.

[0395] The device (smartphone) collects the user's biometric data in real time and evaluates their emotional state. For this purpose, the device is equipped with a heart rate sensor and a temperature sensor. Emotional analysis is performed using the TensorFlow library, and the frequency and content of notifications are dynamically adjusted according to the emotional state.

[0396] For example, if the emotion analysis engine determines that the user is relaxed, low-priority notifications will be sent, while if it detects that the user is stressed, the frequency and content of notifications will be limited.

[0397] An example of a prompt message would be, "Your emotional state for today has been recorded. Please optimize when you receive your next notification. Emotional state: Relaxed; High priority task: Road construction notification." This allows for flexible information delivery tailored to each user's individual situation.

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

[0399] Step 1:

[0400] The server retrieves issue information from multiple data sources. Input data is provided via APIs and CSV files. The data is integrated and stored in an information repository. This process involves data format conversion and organization, and the data is consolidated into a unified database.

[0401] Step 2:

[0402] The server performs natural language processing on the stored task information. Integrated task information is provided as input. A generative AI model is used to analyze the content of the tasks, classify them by type, and assign them importance levels. The output is the classified task information and the importance level assigned to each task.

[0403] Step 3:

[0404] The device acquires the user's biometric data and evaluates their emotional state. In this step, heart rate, temperature, and activity information are collected from the smartphone's sensors as input. Emotional analysis is performed using TensorFlow, and the emotional state is output as numerical data.

[0405] Step 4:

[0406] The server adjusts the content and frequency of notifications based on the user's emotional state. Inputs include priority information for the tasks obtained in the previous stage and the user's emotional state. Data calculations optimize the notification schedule, resulting in the adjusted notification settings as output.

[0407] Step 5:

[0408] The device sends pre-configured notifications to the user. The input is the configured notification settings, and the output is the user receiving notifications at the appropriate time. Using the smartphone's notification function, the notification content is dynamically displayed according to the user's emotional state.

[0409] Step 6:

[0410] The user selects the necessary action based on the notification received from their device. In this step, the user reviews the information displayed in the notification and decides on the next step. The output is the user's action, which is fed back into the next data collection cycle.

[0411] 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.

[0412] 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.

[0413] 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.

[0414] [Third Embodiment]

[0415] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0416] 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.

[0417] 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).

[0418] 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.

[0419] 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.

[0420] 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).

[0421] 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.

[0422] 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.

[0423] 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.

[0424] 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.

[0425] 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.

[0426] 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".

[0427] This invention is a system for improving efficiency and accuracy in task management, and is implemented in the following form.

[0428] 1. Data Integration

[0429] Server: Retrieves issue information from multiple data sources and stores it in a unified database. This prevents information dispersion and organizes it in an easily accessible format.

[0430] 2. Implementation of natural language processing

[0431] Server: The acquired issue information is processed using a generative artificial intelligence model to perform natural language processing. This automatically categorizes the issues and assigns them priorities based on urgency and importance.

[0432] 3. Automated progress management

[0433] Server: Regularly updates the progress of each task and issues alerts if there are delays or deadlines are approaching, enabling timely follow-up.

[0434] 4. Generating reminders and notifications

[0435] Server: Automatically generates necessary reminders and progress check notifications based on the priority and progress of the tasks. These notifications are sent to the terminals of the responsible users and administrators, ensuring smooth information dissemination.

[0436] 5. Providing a user interface

[0437] Terminal: Displays an interface that allows users to view task details and progress. The interface is designed for intuitive operation and displays task status in real time.

[0438] Specific example

[0439] User (assigned person): Access the system from their device and check the newly assigned task. Verify that the category and priority are automatically set, and then update the progress.

[0440] Server: If the assigned person does not check the task for a certain period of time, or if the deadline is approaching, it will automatically generate a reminder and notify the user's terminal.

[0441] User (Administrator): Administrators can view the overall progress of multiple projects from the dashboard. If stagnation is observed, they can provide appropriate instructions and support the team's progress.

[0442] This invention makes it possible to reduce the complexities associated with conventional issue management and support rapid and accurate decision-making regarding issues.

[0443] The following describes the processing flow.

[0444] Step 1:

[0445] The server collects data from multiple issue tracking data sources. This includes importing CSV files and retrieving data via APIs. The collected data is then formatted and stored in an integrated database.

[0446] Step 2:

[0447] The server retrieves issue data from the integrated database and applies a natural language processing engine. This process classifies each issue into the appropriate category based on its content, and automatically sets priorities based on urgency and importance.

[0448] Step 3:

[0449] The server periodically evaluates and updates the progress of tasks. This evaluation is based on progress reports in the database and data from external systems. If progress is stalled or the deadline is approaching, an alert is generated.

[0450] Step 4:

[0451] The server automatically generates reminders and notifications for assignees and administrators based on the priority and progress of the issues. These are delivered as emails and chat messages to facilitate timely follow-up.

[0452] Step 5:

[0453] The terminal provides users with an interface that displays a list of tasks, details, and progress. The information is updated in real time, allowing users to instantly check the latest task status.

[0454] Step 6:

[0455] Users can update their assignment progress through the device interface. They can also add comments about the assignment and customize reminder content as needed.

[0456] (Example 1)

[0457] 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."

[0458] In modern information management, there is a need to effectively integrate data from multiple sources and organize that information quickly and accurately. Furthermore, it is necessary to improve the efficiency and accuracy of information management operations by automating progress monitoring and timely notifications. Traditional methods have faced challenges such as information dispersion and delays, increasing the burden on administrators and users.

[0459] 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.

[0460] In this invention, the server includes means for acquiring information from multiple sources and storing it on a centrally managed recording medium; means for analyzing the information using natural language processing, classifying it, and prioritizing it; and means for automatically monitoring progress and generating warnings in case of stagnation or missed submission deadlines. This enables centralized management and classification of information, prioritization, and automation of progress monitoring and notifications.

[0461] An "information source" refers to an external supplier or system used to obtain data.

[0462] A "centralized storage medium" refers to a single database or storage system used to aggregate and store acquired information.

[0463] "Natural language processing" is a technology that enables computers to understand and analyze human language.

[0464] "Classification" is the process of dividing information into specific categories or groups.

[0465] Prioritization refers to determining the order in which to respond based on the importance and urgency of information and issues.

[0466] "Progress status" refers to the state of how far a particular task or project has progressed.

[0467] A "warning" is a notification intended to inform people in advance of a problem or risk.

[0468] "User interface" refers to the screens and operating environments that users use when interacting with a system.

[0469] This system is designed to improve the efficiency and accuracy of issue management. Specifically, the server plays a primary role in processing information through automated means.

[0470] The server first retrieves data from numerous sources. These sources include email systems and project management tools, and are typically accessed via APIs. The retrieved data is then stored on a centrally managed storage medium on the server, namely a database.

[0471] For natural language processing, a generative AI model is used. The server analyzes the issue information, automatically classifies the issues based on their content, and then prioritizes them considering urgency and importance. This process allows users to tackle issues with evidence-based priorities.

[0472] For monitoring progress, the server periodically checks the database and updates the status of ongoing tasks. If no progress is observed or the submission deadline is approaching, a warning is generated in advance and an alert is sent to the user or administrator through the notification system.

[0473] The terminal provides a user interface, displaying an intuitive interface that allows users to easily operate it. This enables users to view progress and details of issues in real time, allowing for efficient decision-making.

[0474] As a concrete example, a user (assigned person) can log into the system from their device and check newly assigned tasks. Seeing that the generation AI model has automatically set the task category and priority, the user can update the progress status on the spot.

[0475] An example of a prompt message is, "Please review the category and priority of the newly assigned task and update its progress." This allows the system to manage task progress in a timely manner and improve the efficiency of daily operations.

[0476] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0477] Step 1:

[0478] The server retrieves issue information from external systems and tools. Inputs include email and project management tool data via APIs. After verifying data integrity and eliminating duplication, it stores the data on a centrally managed storage medium. This step plays a crucial role in aggregating dispersed information and establishing a foundation for centralized management.

[0479] Step 2:

[0480] The server inputs issue information stored on a centrally managed storage medium into a generating AI model. Natural language processing technology is used to analyze the content of the issues. Based on this analysis, the issues are automatically categorized and assigned priorities according to their importance and urgency. The categorized issue information is then stored in a database as output. This enables real-time decision-making.

[0481] Step 3:

[0482] The server continuously monitors the status of issues in the database. It receives updated progress information as input. The server evaluates progress based on pre-configured criteria and generates warnings if there is stagnation or approaching deadlines. As output, the generated warnings are pushed to the assigned personnel's terminals via the notification system. This allows users to take necessary actions in a timely manner.

[0483] Step 4:

[0484] The terminal provides a user interface, displaying categorized issues and their priorities. The input is real-time issue information sent from the server. Users utilize this interface to update their progress and send new information to the database. The output reflects the latest progress, clearly indicating the next steps to take.

[0485] (Application Example 1)

[0486] 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."

[0487] Activity management in modern logistics centers faces challenges due to its reliance on manual processes for monitoring progress and prioritizing tasks, resulting in inefficiencies. Furthermore, insufficient timely notification to workers makes it difficult to manage delays and missed deadlines. As a result, the smooth operation of logistics is hindered, leading to a decline in overall productivity.

[0488] 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.

[0489] In this invention, the server includes means for acquiring activity information from multiple information sources and storing it in an integrated information storage device; means for analyzing the activity information using natural language processing, classifying it, and assigning priorities thereto; means for automatically checking the progress and generating warnings for delays or expirations; and means for displaying activity progress information in real time and presenting alerts according to priority through an application installed on a visual display device. This enables improved efficiency in activity management and smoother operations through timely warning issuance in logistics centers.

[0490] "Information sources" refer to the various input devices and databases that serve as the basis for acquiring data.

[0491] "Activity information" refers to operational information, including the content, progress, and priority of each task within the logistics center.

[0492] An "information storage device" refers to hardware or software for centrally storing and managing acquired data.

[0493] "Natural language processing" is a technology that uses computers to analyze human language and perform information classification and semantic analysis.

[0494] A "visual display device" is a device that visually presents information or notifications to a user, and includes smart glasses and displays.

[0495] This invention is a system for improving the efficiency of activity management in a logistics center. The server acquires activity information from multiple sources and stores this information in an integrated information storage device. This makes it possible to centrally manage data related to operations within the center.

[0496] Next, the server analyzes the activity information using natural language processing techniques, classifies it, and assigns priorities. This process utilizes a generative AI model, which analyzes the activity descriptions in detail to evaluate the importance of each task.

[0497] Furthermore, the server automatically monitors the progress of each activity and generates warnings if there are delays or expirations. These warnings and notifications are presented to the user in real time through an application installed on a visual display device. Visual display devices such as smart glasses allow users to intuitively receive information according to priority.

[0498] As a concrete example, consider the inspection process for newly arrived goods. Users can check high-priority tasks through smart glasses and take immediate action. Furthermore, if progress is insufficient, the device will display a notification such as "Inventory deadline is approaching," thereby improving work efficiency.

[0499] An example of a prompt is, "Provide an AI model to manage new incoming products as a specific task and re-evaluate their priority." This allows the generated AI model to efficiently classify and prioritize activity information.

[0500] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0501] Step 1:

[0502] The server acquires activity information from multiple sources. Inputs include real-time data from various databases and external systems. The server collects this data and stores it in an integrated data storage system. It checks for duplicates and missing data and performs cleansing to create a clean and unified dataset.

[0503] Step 2:

[0504] The server performs natural language processing on integrated activity information using a generative AI model. The input is clean activity data. The server inputs prompt sentences into the generative AI model, which analyzes the activity descriptions to categorize and prioritize them. This results in structured data that assesses the importance and urgency of each activity.

[0505] Step 3:

[0506] The server automatically monitors the progress of each activity. It receives activity data with assigned priorities as input. The server periodically retrieves progress from the database and analyzes its status. If delays are detected or deadlines are approaching, it generates warnings and prepares a data structure to notify the application.

[0507] Step 4:

[0508] The terminal displays priority-based warnings and progress information to the user in real time via a visual display device (e.g., smart glasses). The input is warning data sent from a server. The terminal visually presents this information through a user interface, enabling the user to respond quickly. For example, a specific notification such as "Inventory deadline is approaching" might be displayed.

[0509] Step 5:

[0510] Users respond based on the warnings and information presented. Input includes notifications and progress information received via the terminal. Based on this information, users prioritize activities and update their progress on the terminal as needed. This ensures efficient operations within the logistics center.

[0511] 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.

[0512] This invention integrates an emotion engine into a task management system, adjusting task priorities and notifications based on the user's emotional state. This reduces the user's psychological burden and enables effective task management.

[0513] 1. Data acquisition and integration

[0514] Server: Collects issue information from multiple data sources and stores it in a unified database. This includes retrieving data from CSV files, APIs, etc., and standardizing the format to ensure consistency.

[0515] 2. Natural Language Processing and Prioritization

[0516] Server: Implements a natural language processing engine to automatically determine categories and priorities from task data. Utilizes a generative artificial intelligence model to analyze task descriptions and assign appropriate priorities.

[0517] 3. Utilizing the Emotional Engine

[0518] Device: Equipped with an emotion engine that recognizes user emotions in real time. This engine evaluates how users are reacting to issues and influences priority and notification content.

[0519] 4. Automated progress management and alert generation

[0520] Server: Regularly checks the progress of tasks and updates them automatically. Generates alerts for stalled tasks or tasks with approaching deadlines and notifies relevant parties.

[0521] 5. Adjust reminders and notifications

[0522] Server: Dynamically adjusts the content of reminders and notifications based on the evaluation results of the emotion engine. For example, if the user is feeling stressed, it may reduce the frequency of notifications.

[0523] 6. Providing a User Interface

[0524] Terminal: Displays an interface to the user that reflects task progress and emotions. The interface is intuitive and can change color scheme and layout according to the emotional state.

[0525] Specific example

[0526] User (Responsible Person): A device that senses the user's emotional state ensures that even urgent tasks are notified while maintaining low psychological stress. The emotion engine recognizes when the user is in a calm state and appropriately notifies them of high-priority tasks.

[0527] User (Administrator): When an administrator oversees multiple projects, they utilize the results of the emotion engine analysis to monitor the work status and psychological burden of team members. They then provide appropriate support as needed.

[0528] This invention, by combining an emotion engine, enables task management that takes into account the user's psychological state, thereby supporting efficient business operations.

[0529] The following describes the processing flow.

[0530] Step 1:

[0531] The server collects issue information from various data sources and retrieves data via CSV file reading or APIs. The retrieved data is then formatted and stored in an integrated database.

[0532] Step 2:

[0533] The server processes issue information from the integrated database and performs natural language processing using generative artificial intelligence. This automatically determines the issue category and priority, and saves the results to the database.

[0534] Step 3:

[0535] The device recognizes the user's emotional state in real time using an emotion engine. It analyzes the user's facial expressions and voice tone through the device's camera and microphone to evaluate their emotional state.

[0536] Step 4:

[0537] The server periodically checks the progress of tasks and updates the database. It generates alerts for tasks that are behind schedule or nearing their deadlines. When doing so, it also takes into account the user's emotional state and adjusts the content of the alerts accordingly.

[0538] Step 5:

[0539] The server dynamically adjusts the content of reminders and notifications based on the evaluation results of the emotion engine. For example, if the user is feeling stressed, the tone of the notification may be made gentler or the frequency reduced.

[0540] Step 6:

[0541] The device displays the user's progress on tasks in accordance with their emotions. Furthermore, the user interface design changes according to the user's emotional state, taking care to reduce psychological burden.

[0542] Step 7:

[0543] Users can check detailed information and progress of tasks through their devices and update their progress as needed. The devices send user input data to the server, which is then reflected in the integrated database.

[0544] Step 8:

[0545] Users (administrators) can monitor the emotional state of the entire team on a management dashboard and adjust team issues or provide support to members as needed. They utilize data from the emotional engine to maintain an appropriate management system.

[0546] (Example 2)

[0547] 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."

[0548] Traditional task management systems fail to consider the emotional state of users, leading to problems such as increased stress and decreased efficiency. Furthermore, the fixed task priorities and notification content prevent the implementation of management optimized for individual users.

[0549] 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.

[0550] This invention includes a server that recognizes the user's emotional state in real time and dynamically adjusts task priorities and notification content based on that state; a server that collects data from multiple sources and stores it in an integrated data storage medium; and a server that analyzes task information using natural language processing, classifies it, and prioritizes it. This reduces the user's psychological burden and enables efficient task management.

[0551] "User" refers to an individual or group that uses the system and is the entity that provides information about their emotional state to the system.

[0552] "Emotional state" refers to data that indicates the user's psychological and emotional condition, and measures the user's stress level and emotional stability.

[0553] "Task priority" refers to the order in which tasks are prioritized, determined by evaluating the importance and urgency of each issue. It involves determining the order in which tasks are processed based on specific criteria.

[0554] "Notification content" refers to the specific message or alert content of information sent to users or administrators, providing information about task progress and necessary actions.

[0555] "Data storage medium" refers to a device or system for temporarily or permanently storing information, including online or offline data storage.

[0556] "Natural language processing" is a technology that allows computers to understand, analyze, and generate human natural language, and is used to categorize and prioritize problem information.

[0557] An "interface" refers to the visual or physical elements that allow a user to interact with a system, designed to display detailed information and progress on a task.

[0558] This invention constructs a system that recognizes the user's emotional state in real time and reflects it in issue management. The server collects information from multiple data sources and stores it on an integrated data storage medium. Specifically, the software used includes a database management system and a natural language processing engine. For natural language processing, a generative AI model is utilized to analyze the issue information.

[0559] The device incorporates an emotion engine that recognizes the user's emotional state, analyzing facial expressions and tone of voice using hardware such as the camera and microphone. This data is sent to a server and used to prioritize tasks and adjust notification content. For example, if the user is tired, the device reduces the frequency of notifications and provides information in a more relaxed tone.

[0560] As a concrete example, when a user adds a task stating, "I need to create a proposal for a new product by next week," this description is input as a prompt into the AI ​​model. If the user's emotional state is calm, the device will notify them of the task as usual and adjust its priority. However, if the system determines that the user is stressed, it will reduce the notification frequency.

[0561] An example of a prompt to input into the generating AI model is the text, "The user has asked you to analyze the task priority for the task 'Create a proposal for a new product'." This method enables the system to manage tasks flexibly and efficiently in accordance with the user's psychological state.

[0562] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0563] Step 1:

[0564] The server retrieves task data from external sources using APIs or CSV files. Inputs include task names, deadlines, and descriptions, and output is data in a standardized format. This data is stored on an integrated data storage medium within the server.

[0565] Step 2:

[0566] The server passes the stored task data to a natural language processing engine, which then analyzes it using a generative AI model. Specifically, the task description serves as the input information, and the output is a categorized list of tasks with their priorities determined. This process automatically evaluates the importance and urgency of each task.

[0567] Step 3:

[0568] The device uses a camera and microphone to collect facial and voice data in order to acquire user emotion data. Inputs include real-time video and audio, which are analyzed by an emotion engine to obtain the user's emotional state as output. This data is then sent to the server.

[0569] Step 4:

[0570] The server uses emotional data to dynamically adjust task priorities and notification content. Input information includes the user's emotional state and task list, while output is an adjusted task list and notification frequency and content. During this process, if the user is experiencing stress, specific adjustments are made, such as reducing the notification frequency.

[0571] Step 5:

[0572] The device provides the user with an interface based on the adjusted data. This includes displaying progress and task lists. The input is updated data from the server, and the output is visual information provided to the user. Here, the interface's color scheme and design are automatically adjusted according to the user's emotional state.

[0573] Step 6:

[0574] Users can view tasks through their devices and add new tasks as needed. When new task information is entered, it is sent back to the server, updating the entire task management cycle. This ensures that users always have access to the latest information.

[0575] (Application Example 2)

[0576] 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."

[0577] In modern urban environments, residents are often overwhelmed by a constant stream of notifications and information, leading to stress. This problem stems from the fact that notifications and information from urban services are sent at times that do not take into account the user's emotional state. A system is needed that reduces user stress and enables more effective information delivery.

[0578] 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. In this invention, the server includes means for acquiring issue information from multiple data sources and storing it in an integrated information warehouse, means for analyzing the issue information using natural language processing, classifying its type and assigning its importance, and means for detecting the user's emotional state and adjusting the notification frequency and content. As a result, notifications are sent at the optimal timing according to the user's emotional state, reducing stress on residents and enabling effective information management.

[0579] A "data source" is a data provision system that serves as the basis for acquiring information.

[0580] An "information warehouse" is a database system for integrating and managing a wide variety of data.

[0581] "Natural language processing" is a technology that enables computers to understand and process human language.

[0582] "Type" refers to a category used to classify data or information.

[0583] "Importance" is an indicator that shows the priority of data and information.

[0584] "Emotional state" refers to the result of an assessment of the user's psychological and emotional condition.

[0585] "Notification frequency" refers to the time interval or number of times notifications are sent to the user.

[0586] "Information management" is the process of efficiently acquiring, classifying, storing, and providing information.

[0587] This invention aims to build a system that uses smartphones to send notifications to urban residents at the optimal time. The server first acquires issue information from multiple data sources and stores it in an integrated information warehouse. This allows for centralized management of data provided from various urban services.

[0588] Next, using natural language processing technology, the server analyzes the task information, classifies its type, and assigns importance levels. In this step, a generative intelligence model is used to analyze the task descriptions and enable priority adjustments based on the user's emotional state.

[0589] The device (smartphone) collects the user's biometric data in real time and evaluates their emotional state. For this purpose, the device is equipped with a heart rate sensor and a temperature sensor. Emotional analysis is performed using the TensorFlow library, and the frequency and content of notifications are dynamically adjusted according to the emotional state.

[0590] For example, if the emotion analysis engine determines that the user is relaxed, low-priority notifications will be sent, while if it detects that the user is stressed, the frequency and content of notifications will be limited.

[0591] An example of a prompt message would be, "Your emotional state for today has been recorded. Please optimize when you receive your next notification. Emotional state: Relaxed; High priority task: Road construction notification." This allows for flexible information delivery tailored to each user's individual situation.

[0592] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0593] Step 1:

[0594] The server retrieves issue information from multiple data sources. Input data is provided via APIs and CSV files. The data is integrated and stored in an information repository. This process involves data format conversion and organization, and the data is consolidated into a unified database.

[0595] Step 2:

[0596] The server performs natural language processing on the stored task information. Integrated task information is provided as input. A generative AI model is used to analyze the content of the tasks, classify them by type, and assign them importance levels. The output is the classified task information and the importance level assigned to each task.

[0597] Step 3:

[0598] The device acquires the user's biometric data and evaluates their emotional state. In this step, heart rate, temperature, and activity information are collected from the smartphone's sensors as input. Emotional analysis is performed using TensorFlow, and the emotional state is output as numerical data.

[0599] Step 4:

[0600] The server adjusts the content and frequency of notifications based on the user's emotional state. Inputs include priority information for the tasks obtained in the previous stage and the user's emotional state. Data calculations optimize the notification schedule, resulting in the adjusted notification settings as output.

[0601] Step 5:

[0602] The device sends pre-configured notifications to the user. The input is the configured notification settings, and the output is the user receiving notifications at the appropriate time. Using the smartphone's notification function, the notification content is dynamically displayed according to the user's emotional state.

[0603] Step 6:

[0604] The user selects the necessary action based on the notification received from their device. In this step, the user reviews the information displayed in the notification and decides on the next step. The output is the user's action, which is fed back into the next data collection cycle.

[0605] 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.

[0606] 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.

[0607] 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.

[0608] [Fourth Embodiment]

[0609] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0610] 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.

[0611] 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).

[0612] 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.

[0613] 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.

[0614] 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).

[0615] 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.

[0616] 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.

[0617] 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.

[0618] 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.

[0619] 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.

[0620] 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.

[0621] 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".

[0622] This invention is a system for improving efficiency and accuracy in task management, and is implemented in the following form.

[0623] 1. Data Integration

[0624] Server: Retrieves issue information from multiple data sources and stores it in a unified database. This prevents information dispersion and organizes it in an easily accessible format.

[0625] 2. Implementation of natural language processing

[0626] Server: The acquired issue information is processed using a generative artificial intelligence model to perform natural language processing. This automatically categorizes the issues and assigns them priorities based on urgency and importance.

[0627] 3. Automated progress management

[0628] Server: Regularly updates the progress of each task and issues alerts if there are delays or deadlines are approaching, enabling timely follow-up.

[0629] 4. Generating reminders and notifications

[0630] Server: Automatically generates necessary reminders and progress check notifications based on the priority and progress of the tasks. These notifications are sent to the terminals of the responsible users and administrators, ensuring smooth information dissemination.

[0631] 5. Providing a user interface

[0632] Terminal: Displays an interface that allows users to view task details and progress. The interface is designed for intuitive operation and displays task status in real time.

[0633] Specific example

[0634] User (assigned person): Access the system from their device and check the newly assigned task. Verify that the category and priority are automatically set, and then update the progress.

[0635] Server: If the assigned person does not check the task for a certain period of time, or if the deadline is approaching, it will automatically generate a reminder and notify the user's terminal.

[0636] User (Administrator): Administrators can view the overall progress of multiple projects from the dashboard. If stagnation is observed, they can provide appropriate instructions and support the team's progress.

[0637] This invention makes it possible to reduce the complexities associated with conventional issue management and support rapid and accurate decision-making regarding issues.

[0638] The following describes the processing flow.

[0639] Step 1:

[0640] The server collects data from multiple issue tracking data sources. This includes importing CSV files and retrieving data via APIs. The collected data is then formatted and stored in an integrated database.

[0641] Step 2:

[0642] The server retrieves issue data from the integrated database and applies a natural language processing engine. This process classifies each issue into the appropriate category based on its content, and automatically sets priorities based on urgency and importance.

[0643] Step 3:

[0644] The server periodically evaluates and updates the progress of tasks. This evaluation is based on progress reports in the database and data from external systems. If progress is stalled or the deadline is approaching, an alert is generated.

[0645] Step 4:

[0646] The server automatically generates reminders and notifications for assignees and administrators based on the priority and progress of the issues. These are delivered as emails and chat messages to facilitate timely follow-up.

[0647] Step 5:

[0648] The terminal provides users with an interface that displays a list of tasks, details, and progress. The information is updated in real time, allowing users to instantly check the latest task status.

[0649] Step 6:

[0650] Users can update their assignment progress through the device interface. They can also add comments about the assignment and customize reminder content as needed.

[0651] (Example 1)

[0652] 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".

[0653] In modern information management, there is a need to effectively integrate data from multiple sources and organize that information quickly and accurately. Furthermore, it is necessary to improve the efficiency and accuracy of information management operations by automating progress monitoring and timely notifications. Traditional methods have faced challenges such as information dispersion and delays, increasing the burden on administrators and users.

[0654] 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.

[0655] In this invention, the server includes means for acquiring information from multiple sources and storing it on a centrally managed recording medium; means for analyzing the information using natural language processing, classifying it, and prioritizing it; and means for automatically monitoring progress and generating warnings in case of stagnation or missed submission deadlines. This enables centralized management and classification of information, prioritization, and automation of progress monitoring and notifications.

[0656] An "information source" refers to an external supplier or system used to obtain data.

[0657] A "centralized storage medium" refers to a single database or storage system used to aggregate and store acquired information.

[0658] "Natural language processing" is a technology that enables computers to understand and analyze human language.

[0659] "Classification" is the process of dividing information into specific categories or groups.

[0660] Prioritization refers to determining the order in which to respond based on the importance and urgency of information and issues.

[0661] "Progress status" refers to the state of how far a particular task or project has progressed.

[0662] A "warning" is a notification intended to inform people in advance of a problem or risk.

[0663] "User interface" refers to the screens and operating environments that users use when interacting with a system.

[0664] This system is designed to improve the efficiency and accuracy of issue management. Specifically, the server plays a primary role in processing information through automated means.

[0665] The server first retrieves data from numerous sources. These sources include email systems and project management tools, and are typically accessed via APIs. The retrieved data is then stored on a centrally managed storage medium on the server, namely a database.

[0666] For natural language processing, a generative AI model is used. The server analyzes the issue information, automatically classifies the issues based on their content, and then prioritizes them considering urgency and importance. This process allows users to tackle issues with evidence-based priorities.

[0667] For monitoring progress, the server periodically checks the database and updates the status of ongoing tasks. If no progress is observed or the submission deadline is approaching, a warning is generated in advance and an alert is sent to the user or administrator through the notification system.

[0668] The terminal provides a user interface, displaying an intuitive interface that allows users to easily operate it. This enables users to view progress and details of issues in real time, allowing for efficient decision-making.

[0669] As a concrete example, a user (assigned person) can log into the system from their device and check newly assigned tasks. Seeing that the generation AI model has automatically set the task category and priority, the user can update the progress status on the spot.

[0670] An example of a prompt message is, "Please review the category and priority of the newly assigned task and update its progress." This allows the system to manage task progress in a timely manner and improve the efficiency of daily operations.

[0671] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0672] Step 1:

[0673] The server retrieves issue information from external systems and tools. Inputs include email and project management tool data via APIs. After verifying data integrity and eliminating duplication, it stores the data on a centrally managed storage medium. This step plays a crucial role in aggregating dispersed information and establishing a foundation for centralized management.

[0674] Step 2:

[0675] The server inputs issue information stored on a centrally managed storage medium into a generating AI model. Natural language processing technology is used to analyze the content of the issues. Based on this analysis, the issues are automatically categorized and assigned priorities according to their importance and urgency. The categorized issue information is then stored in a database as output. This enables real-time decision-making.

[0676] Step 3:

[0677] The server continuously monitors the status of issues in the database. It receives updated progress information as input. The server evaluates progress based on pre-configured criteria and generates warnings if there is stagnation or approaching deadlines. As output, the generated warnings are pushed to the assigned personnel's terminals via the notification system. This allows users to take necessary actions in a timely manner.

[0678] Step 4:

[0679] The terminal provides a user interface, displaying categorized issues and their priorities. The input is real-time issue information sent from the server. Users utilize this interface to update their progress and send new information to the database. The output reflects the latest progress, clearly indicating the next steps to take.

[0680] (Application Example 1)

[0681] 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".

[0682] Activity management in modern logistics centers faces challenges due to its reliance on manual processes for monitoring progress and prioritizing tasks, resulting in inefficiencies. Furthermore, insufficient timely notification to workers makes it difficult to manage delays and missed deadlines. As a result, the smooth operation of logistics is hindered, leading to a decline in overall productivity.

[0683] 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.

[0684] In this invention, the server includes means for acquiring activity information from multiple information sources and storing it in an integrated information storage device; means for analyzing the activity information using natural language processing, classifying it, and assigning priorities thereto; means for automatically checking the progress and generating warnings for delays or expirations; and means for displaying activity progress information in real time and presenting alerts according to priority through an application installed on a visual display device. This enables improved efficiency in activity management and smoother operations through timely warning issuance in logistics centers.

[0685] "Information sources" refer to the various input devices and databases that serve as the basis for acquiring data.

[0686] "Activity information" refers to operational information, including the content, progress, and priority of each task within the logistics center.

[0687] An "information storage device" refers to hardware or software for centrally storing and managing acquired data.

[0688] "Natural language processing" is a technology that uses computers to analyze human language and perform information classification and semantic analysis.

[0689] A "visual display device" is a device that visually presents information or notifications to a user, and includes smart glasses and displays.

[0690] This invention is a system for improving the efficiency of activity management in a logistics center. The server acquires activity information from multiple sources and stores this information in an integrated information storage device. This makes it possible to centrally manage data related to operations within the center.

[0691] Next, the server analyzes the activity information using natural language processing techniques, classifies it, and assigns priorities. This process utilizes a generative AI model, which analyzes the activity descriptions in detail to evaluate the importance of each task.

[0692] Furthermore, the server automatically monitors the progress of each activity and generates warnings if there are delays or expirations. These warnings and notifications are presented to the user in real time through an application installed on a visual display device. Visual display devices such as smart glasses allow users to intuitively receive information according to priority.

[0693] As a concrete example, consider the inspection process for newly arrived goods. Users can check high-priority tasks through smart glasses and take immediate action. Furthermore, if progress is insufficient, the device will display a notification such as "Inventory deadline is approaching," thereby improving work efficiency.

[0694] An example of a prompt is, "Provide an AI model to manage new incoming products as a specific task and re-evaluate their priority." This allows the generated AI model to efficiently classify and prioritize activity information.

[0695] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0696] Step 1:

[0697] The server acquires activity information from multiple sources. Inputs include real-time data from various databases and external systems. The server collects this data and stores it in an integrated data storage system. It checks for duplicates and missing data and performs cleansing to create a clean and unified dataset.

[0698] Step 2:

[0699] The server performs natural language processing on integrated activity information using a generative AI model. The input is clean activity data. The server inputs prompt sentences into the generative AI model, which analyzes the activity descriptions to categorize and prioritize them. This results in structured data that assesses the importance and urgency of each activity.

[0700] Step 3:

[0701] The server automatically monitors the progress of each activity. It receives activity data with assigned priorities as input. The server periodically retrieves progress from the database and analyzes its status. If delays are detected or deadlines are approaching, it generates warnings and prepares a data structure to notify the application.

[0702] Step 4:

[0703] The terminal displays priority-based warnings and progress information to the user in real time via a visual display device (e.g., smart glasses). The input is warning data sent from a server. The terminal visually presents this information through a user interface, enabling the user to respond quickly. For example, a specific notification such as "Inventory deadline is approaching" might be displayed.

[0704] Step 5:

[0705] Users respond based on the warnings and information presented. Input includes notifications and progress information received via the terminal. Based on this information, users prioritize activities and update their progress on the terminal as needed. This ensures efficient operations within the logistics center.

[0706] 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.

[0707] This invention integrates an emotion engine into a task management system, adjusting task priorities and notifications based on the user's emotional state. This reduces the user's psychological burden and enables effective task management.

[0708] 1. Data acquisition and integration

[0709] Server: Collects issue information from multiple data sources and stores it in a unified database. This includes retrieving data from CSV files, APIs, etc., and standardizing the format to ensure consistency.

[0710] 2. Natural Language Processing and Prioritization

[0711] Server: Implements a natural language processing engine to automatically determine categories and priorities from task data. Utilizes a generative artificial intelligence model to analyze task descriptions and assign appropriate priorities.

[0712] 3. Utilizing the Emotional Engine

[0713] Device: Equipped with an emotion engine that recognizes user emotions in real time. This engine evaluates how users are reacting to issues and influences priority and notification content.

[0714] 4. Automated progress management and alert generation

[0715] Server: Regularly checks the progress of tasks and updates them automatically. Generates alerts for stalled tasks or tasks with approaching deadlines and notifies relevant parties.

[0716] 5. Adjust reminders and notifications

[0717] Server: Dynamically adjusts the content of reminders and notifications based on the evaluation results of the emotion engine. For example, if the user is feeling stressed, it may reduce the frequency of notifications.

[0718] 6. Providing a User Interface

[0719] Terminal: Displays an interface to the user that reflects task progress and emotions. The interface is intuitive and can change color scheme and layout according to the emotional state.

[0720] Specific example

[0721] User (Responsible Person): A device that senses the user's emotional state ensures that even urgent tasks are notified while maintaining low psychological stress. The emotion engine recognizes when the user is in a calm state and appropriately notifies them of high-priority tasks.

[0722] User (Administrator): When an administrator oversees multiple projects, they utilize the results of the emotion engine analysis to monitor the work status and psychological burden of team members. They then provide appropriate support as needed.

[0723] This invention, by combining an emotion engine, enables task management that takes into account the user's psychological state, thereby supporting efficient business operations.

[0724] The following describes the processing flow.

[0725] Step 1:

[0726] The server collects issue information from various data sources and retrieves data via CSV file reading or APIs. The retrieved data is then formatted and stored in an integrated database.

[0727] Step 2:

[0728] The server processes issue information from the integrated database and performs natural language processing using generative artificial intelligence. This automatically determines the issue category and priority, and saves the results to the database.

[0729] Step 3:

[0730] The device recognizes the user's emotional state in real time using an emotion engine. It analyzes the user's facial expressions and voice tone through the device's camera and microphone to evaluate their emotional state.

[0731] Step 4:

[0732] The server periodically checks the progress of tasks and updates the database. It generates alerts for tasks that are behind schedule or nearing their deadlines. When doing so, it also takes into account the user's emotional state and adjusts the content of the alerts accordingly.

[0733] Step 5:

[0734] The server dynamically adjusts the content of reminders and notifications based on the evaluation results of the emotion engine. For example, if the user is feeling stressed, the tone of the notification may be made gentler or the frequency reduced.

[0735] Step 6:

[0736] The device displays the user's progress on tasks in accordance with their emotions. Furthermore, the user interface design changes according to the user's emotional state, taking care to reduce psychological burden.

[0737] Step 7:

[0738] Users can check detailed information and progress of tasks through their devices and update their progress as needed. The devices send user input data to the server, which is then reflected in the integrated database.

[0739] Step 8:

[0740] Users (administrators) can monitor the emotional state of the entire team on a management dashboard and adjust team issues or provide support to members as needed. They utilize data from the emotional engine to maintain an appropriate management system.

[0741] (Example 2)

[0742] 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".

[0743] Traditional task management systems fail to consider the emotional state of users, leading to problems such as increased stress and decreased efficiency. Furthermore, the fixed task priorities and notification content prevent the implementation of management optimized for individual users.

[0744] 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.

[0745] This invention includes a server that recognizes the user's emotional state in real time and dynamically adjusts task priorities and notification content based on that state; a server that collects data from multiple sources and stores it in an integrated data storage medium; and a server that analyzes task information using natural language processing, classifies it, and prioritizes it. This reduces the user's psychological burden and enables efficient task management.

[0746] "User" refers to an individual or group that uses the system and is the entity that provides information about their emotional state to the system.

[0747] "Emotional state" refers to data that indicates the user's psychological and emotional condition, and measures the user's stress level and emotional stability.

[0748] "Task priority" refers to the order in which tasks are prioritized, determined by evaluating the importance and urgency of each issue. It involves determining the order in which tasks are processed based on specific criteria.

[0749] "Notification content" refers to the specific message or alert content of information sent to users or administrators, providing information about task progress and necessary actions.

[0750] "Data storage medium" refers to a device or system for temporarily or permanently storing information, including online or offline data storage.

[0751] "Natural language processing" is a technology that allows computers to understand, analyze, and generate human natural language, and is used to categorize and prioritize problem information.

[0752] An "interface" refers to the visual or physical elements that allow a user to interact with a system, designed to display detailed information and progress on a task.

[0753] This invention constructs a system that recognizes the user's emotional state in real time and reflects it in issue management. The server collects information from multiple data sources and stores it on an integrated data storage medium. Specifically, the software used includes a database management system and a natural language processing engine. For natural language processing, a generative AI model is utilized to analyze the issue information.

[0754] The device incorporates an emotion engine that recognizes the user's emotional state, analyzing facial expressions and tone of voice using hardware such as the camera and microphone. This data is sent to a server and used to prioritize tasks and adjust notification content. For example, if the user is tired, the device reduces the frequency of notifications and provides information in a more relaxed tone.

[0755] As a concrete example, when a user adds a task stating, "I need to create a proposal for a new product by next week," this description is input as a prompt into the AI ​​model. If the user's emotional state is calm, the device will notify them of the task as usual and adjust its priority. However, if the system determines that the user is stressed, it will reduce the notification frequency.

[0756] An example of a prompt to input into the generating AI model is the text, "The user has asked you to analyze the task priority for the task 'Create a proposal for a new product'." This method enables the system to manage tasks flexibly and efficiently in accordance with the user's psychological state.

[0757] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0758] Step 1:

[0759] The server retrieves task data from external sources using APIs or CSV files. Inputs include task names, deadlines, and descriptions, and output is data in a standardized format. This data is stored on an integrated data storage medium within the server.

[0760] Step 2:

[0761] The server passes the stored task data to a natural language processing engine, which then analyzes it using a generative AI model. Specifically, the task description serves as the input information, and the output is a categorized list of tasks with their priorities determined. This process automatically evaluates the importance and urgency of each task.

[0762] Step 3:

[0763] The device uses a camera and microphone to collect facial and voice data in order to acquire user emotion data. Inputs include real-time video and audio, which are analyzed by an emotion engine to obtain the user's emotional state as output. This data is then sent to the server.

[0764] Step 4:

[0765] The server uses emotional data to dynamically adjust task priorities and notification content. Input information includes the user's emotional state and task list, while output is an adjusted task list and notification frequency and content. During this process, if the user is experiencing stress, specific adjustments are made, such as reducing the notification frequency.

[0766] Step 5:

[0767] The device provides the user with an interface based on the adjusted data. This includes displaying progress and task lists. The input is updated data from the server, and the output is visual information provided to the user. Here, the interface's color scheme and design are automatically adjusted according to the user's emotional state.

[0768] Step 6:

[0769] Users can view tasks through their devices and add new tasks as needed. When new task information is entered, it is sent back to the server, updating the entire task management cycle. This ensures that users always have access to the latest information.

[0770] (Application Example 2)

[0771] 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".

[0772] In modern urban environments, residents are often overwhelmed by a constant stream of notifications and information, leading to stress. This problem stems from the fact that notifications and information from urban services are sent at times that do not take into account the user's emotional state. A system is needed that reduces user stress and enables more effective information delivery.

[0773] 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. In this invention, the server includes means for acquiring issue information from multiple data sources and storing it in an integrated information warehouse, means for analyzing the issue information using natural language processing, classifying its type and assigning its importance, and means for detecting the user's emotional state and adjusting the notification frequency and content. As a result, notifications are sent at the optimal timing according to the user's emotional state, reducing stress on residents and enabling effective information management.

[0774] A "data source" is a data provision system that serves as the basis for acquiring information.

[0775] An "information warehouse" is a database system for integrating and managing a wide variety of data.

[0776] "Natural language processing" is a technology that enables computers to understand and process human language.

[0777] "Type" refers to a category used to classify data or information.

[0778] "Importance" is an indicator that shows the priority of data and information.

[0779] "Emotional state" refers to the result of an assessment of the user's psychological and emotional condition.

[0780] "Notification frequency" refers to the time interval or number of times notifications are sent to the user.

[0781] "Information management" is the process of efficiently acquiring, classifying, storing, and providing information.

[0782] This invention aims to build a system that uses smartphones to send notifications to urban residents at the optimal time. The server first acquires issue information from multiple data sources and stores it in an integrated information warehouse. This allows for centralized management of data provided from various urban services.

[0783] Next, using natural language processing technology, the server analyzes the task information, classifies its type, and assigns importance levels. In this step, a generative intelligence model is used to analyze the task descriptions and enable priority adjustments based on the user's emotional state.

[0784] The device (smartphone) collects the user's biometric data in real time and evaluates their emotional state. For this purpose, the device is equipped with a heart rate sensor and a temperature sensor. Emotional analysis is performed using the TensorFlow library, and the frequency and content of notifications are dynamically adjusted according to the emotional state.

[0785] For example, if the emotion analysis engine determines that the user is relaxed, low-priority notifications will be sent, while if it detects that the user is stressed, the frequency and content of notifications will be limited.

[0786] An example of a prompt message would be, "Your emotional state for today has been recorded. Please optimize when you receive your next notification. Emotional state: Relaxed; High priority task: Road construction notification." This allows for flexible information delivery tailored to each user's individual situation.

[0787] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0788] Step 1:

[0789] The server retrieves issue information from multiple data sources. Input data is provided via APIs and CSV files. The data is integrated and stored in an information repository. This process involves data format conversion and organization, and the data is consolidated into a unified database.

[0790] Step 2:

[0791] The server performs natural language processing on the stored task information. Integrated task information is provided as input. A generative AI model is used to analyze the content of the tasks, classify them by type, and assign them importance levels. The output is the classified task information and the importance level assigned to each task.

[0792] Step 3:

[0793] The device acquires the user's biometric data and evaluates their emotional state. In this step, heart rate, temperature, and activity information are collected from the smartphone's sensors as input. Emotional analysis is performed using TensorFlow, and the emotional state is output as numerical data.

[0794] Step 4:

[0795] The server adjusts the content and frequency of notifications based on the user's emotional state. Inputs include priority information for the tasks obtained in the previous stage and the user's emotional state. Data calculations optimize the notification schedule, resulting in the adjusted notification settings as output.

[0796] Step 5:

[0797] The device sends pre-configured notifications to the user. The input is the configured notification settings, and the output is the user receiving notifications at the appropriate time. Using the smartphone's notification function, the notification content is dynamically displayed according to the user's emotional state.

[0798] Step 6:

[0799] The user selects the necessary action based on the notification received from their device. In this step, the user reviews the information displayed in the notification and decides on the next step. The output is the user's action, which is fed back into the next data collection cycle.

[0800] 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.

[0801] 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.

[0802] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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."

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0821] The following is further disclosed regarding the embodiments described above.

[0822] (Claim 1)

[0823] A means of obtaining issue information from multiple data sources and storing it in an integrated database,

[0824] A means for analyzing problem information using natural language processing, classifying its categories, and assigning priorities to them,

[0825] A means to automatically check the progress of tasks and generate alerts for stagnation or expiration,

[0826] A means for generating reminders and update notifications and sending them to users and administrators,

[0827] A system that provides a user interface and includes means for displaying detailed information and progress on issues.

[0828] (Claim 2)

[0829] The system according to claim 1, which implements a generative artificial intelligence model for categorizing and prioritizing problem information and analyzes the problem description text.

[0830] (Claim 3)

[0831] The system according to claim 1, which updates and displays information in real time according to the progress and priority in the user interface.

[0832] "Example 1"

[0833] (Claim 1)

[0834] A means of acquiring information from multiple sources and storing it on a centrally managed recording medium,

[0835] A means for analyzing information using natural language processing, classifying it, and prioritizing it,

[0836] A means of automatically monitoring progress and generating warnings in case of stagnation or missed submission deadlines,

[0837] A means for generating reminder and update notifications and sending them to users and administrators,

[0838] A system that provides a user interface and includes means for displaying detailed information and progress.

[0839] (Claim 2)

[0840] The system according to claim 1, which implements a generating intelligent processing model for classifying and prioritizing information, and analyzes the descriptive text of the information.

[0841] (Claim 3)

[0842] The system according to claim 1, which displays information according to progress and priority in an immediate update in the user interface.

[0843] "Application Example 1"

[0844] (Claim 1)

[0845] A means for acquiring activity information from multiple sources and storing it in an integrated information storage device,

[0846] A means for analyzing activity information using natural language processing, classifying it, and assigning priorities to it,

[0847] A means for automatically checking the progress of activities and generating warnings for delays or expirations,

[0848] Means for generating notifications and update information and sending them to operators and administrators,

[0849] A user screen is provided, along with a means to display detailed information and progress of activities.

[0850] A means of displaying activity progress information in real time and providing alerts according to priority through an application installed on a visual display device,

[0851] A system that includes this.

[0852] (Claim 2)

[0853] The system according to claim 1, which implements a generative artificial intelligence model for classifying and prioritizing activity information and analyzes the description of the activity.

[0854] (Claim 3)

[0855] The system according to claim 1, which updates and displays information on the user screen in real time according to the progress and priority, and immediately notifies the user via a visual display device.

[0856] "Example 2 of combining an emotion engine"

[0857] (Claim 1)

[0858] A means of recognizing the user's emotional state in real time and dynamically adjusting task priority and notification content based on that,

[0859] A means for collecting data from multiple sources and storing it in an integrated data storage medium,

[0860] A method for analyzing problem information using natural language processing, classifying it, and prioritizing it,

[0861] A means to automatically check the progress of tasks and generate warnings for stagnation or exceeding deadlines,

[0862] A means of generating notifications and update information and sending them to users and administrators,

[0863] A system that provides an interface and includes means for displaying detailed information and progress on a task.

[0864] (Claim 2)

[0865] The system according to claim 1, which adjusts the frequency and content of notifications based on the user's emotional information.

[0866] (Claim 3)

[0867] The system according to claim 1, which adjusts the color scheme and layout of the interface in real time according to the user's emotional state.

[0868] "Application example 2 when combining with an emotional engine"

[0869] (Claim 1)

[0870] A means of acquiring problem information from multiple data sources and storing it in an integrated information warehouse,

[0871] A means for analyzing problem information using natural language processing, classifying its type, and assigning importance levels to it,

[0872] A means for automatically checking the progress of a task and generating alerts for stagnation or exceeding deadlines,

[0873] Means for generating reminder and update notifications and sending them to users and supervisors,

[0874] A means to detect the user's emotional state and adjust the frequency and content of notifications,

[0875] A means of dynamically changing the color scheme and layout of the interface according to the user's emotional state,

[0876] A system that provides a user interface and includes means for displaying detailed information and progress on tasks.

[0877] (Claim 2)

[0878] The system according to claim 1, which implements a generative intelligence model for categorizing and prioritizing task information, analyzes the task description, and dynamically adjusts the priority based on the user's emotional state.

[0879] (Claim 3)

[0880] The system according to claim 1, which updates and displays information in real time at the user interface according to the progress, the user's emotional state, and priority. [Explanation of symbols]

[0881] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of obtaining issue information from multiple data sources and storing it in an integrated database, A means for analyzing problem information using natural language processing, classifying its categories, and assigning priorities to them, A means to automatically check the progress of tasks and generate alerts for stagnation or expiration, A means for generating reminders and update notifications and sending them to users and administrators, A system that provides a user interface and includes means for displaying detailed information and progress on issues.

2. The system according to claim 1, which implements a generative artificial intelligence model for categorizing and prioritizing problem information and analyzes the problem description text.

3. The system according to claim 1, which updates and displays information in real time according to the progress and priority in the user interface.

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