system

Abstract

We provide the system. [Solution] A data transformation means for integrating and standardizing the collected information, A means of analyzing problems using natural language processing technology based on standardized information, A means to identify the category of the problem from the analyzed information and evaluate its importance, A scheduling mechanism for automatically generating and sending notifications to users based on evaluations, A data management system for tracking progress and updating status, Based on the analysis results, the machine work device automatically adjusts and executes tasks according to priority, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In conventional issue management, due to the variety of management forms collected from each department, information is dispersed, making it difficult to grasp the overall picture and taking a great deal of time for progress confirmation. There is also a risk that high-priority issues may be overlooked or important opportunities may be lost. As a result, there are problems in that efficient business execution is difficult and quality improvement is hindered.

Means for Solving the Problems

[0005] This invention provides a data transformation means for integrating and standardizing collected issue information. Furthermore, it includes means for analyzing the standardized information using natural language processing technology to identify issue categories and evaluate their importance. This provides a schedule management means that automatically determines the processing priority of issues and automatically generates and sends appropriate notifications to the user. The invention also provides a system that includes means for building a feedback loop that dynamically reflects progress information from the user and optimizes the process, thereby aiming to solve the above problems.

[0006] "Collected information" refers to data and management sheets related to the issues gathered from each department.

[0007] "Data conversion means" refers to the processes and functions used to convert diverse data formats into a standard format in order to unify them.

[0008] "Standardized information" refers to issue-related information that has been unified into a common format through data conversion means.

[0009] "Natural language processing technology" refers to machine learning techniques and algorithms that use generative AI to analyze human language and understand its meaning.

[0010] "Identifying categories" refers to the process of classifying the analyzed information about a problem to determine which department or field it relates to.

[0011] "Assessing importance" refers to analyzing how much a particular issue impacts business operations and determining its priority.

[0012] "Automatically generating notifications" refers to the program automatically creating reminders and notification messages containing necessary information based on set conditions.

[0013] "Schedule management tools" refer to systems and functions that manage plans and timelines for efficiently handling tasks based on priority and timeframes.

[0014] A "feedback loop" refers to a cyclical process of receiving progress information and feedback from users and optimizing the system based on that information. [Brief explanation of the drawing]

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

Mode for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0018] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), and the like.

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

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

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

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

[0025] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0032] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] The system of this invention is built around three main elements: a server, a terminal, and a user, with the aim of improving the efficiency of digital issue management. This system centrally manages issue management data collected from each department and performs automatic analysis using natural language processing technology to categorize issues, assess their importance, and set priorities.

[0037] The server integrates issue information received from each department. The integrated information is standardized through data transformation and analyzed using natural language processing technology. Based on the analysis results, the server categorizes the issues and evaluates their importance. Next, based on the evaluated information, it automatically generates reminders and progress check notifications for users and sends them according to a schedule.

[0038] The terminal accepts input from users and provides an interface for registering tasks and reporting progress. Users can easily register tasks via the terminal and report their progress in real time.

[0039] For example, if a technical department registers an issue stating that "the server's storage capacity is about to reach its limit," the terminal sends the information to the server. The server analyzes this information, categorizes it as "IT infrastructure management," and assigns it high priority to prevent major disruptions. Based on this, the server sends a reminder to the responsible technician via email or internal chat, emphasizing the need for immediate action. Once the technician completes the task and submits a progress report from their terminal, the server processes the information and notifies the administrator of the completion.

[0040] Thus, the system based on the present invention highly automates the issue management process and supports efficient business operations.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] Users input issue information managed by their respective departments and send it to the server as a digital management sheet via their terminals. The issue information includes an overview of the issue, the person responsible, and the deadline.

[0044] Step 2:

[0045] The server stores the received task information in a database and uses data conversion tools to standardize the format. This allows for centralized management of data in various formats.

[0046] Step 3:

[0047] The server applies natural language processing techniques to standardized task data to perform text analysis. It understands the theme and intent of the tasks and identifies related categories.

[0048] Step 4:

[0049] The server evaluates the importance of the analyzed information and prioritizes each issue. The evaluation is based on the urgency of the issue and its impact on business operations.

[0050] Step 5:

[0051] Based on instructions from the server, the terminal automatically creates reminders for users (assigned personnel and administrators) and sends notifications according to the specified schedule. The notifications include details of the issue and the deadline for action.

[0052] Step 6:

[0053] Users input the progress of their tasks via their devices and report it to the server. This updates the task status in real time, and the latest information is reported to the administrator.

[0054] Step 7:

[0055] The server analyzes the received progress information and optimizes the entire system through a feedback loop to improve efficiency. This enables improvements in future task handling processes.

[0056] (Example 1)

[0057] 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."

[0058] The problem that this invention aims to solve is the inefficiency of digital issue management in companies and organizations. Specifically, this includes the amount of manual work involved in managing issue information provided by each department, the fragmentation of information, and the difficulty in appropriately prioritizing. These problems can lead to delays in responses or the overlooking of important issues.

[0059] 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.

[0060] In this invention, the server includes processing means for integrating task information from multiple sources and converting it into a unified format, analysis means for analyzing the unified information using natural language processing technology and classifying and evaluating tasks, and notification generation and transmission means for informing users via electronic communication based on the analysis results. This enables efficient aggregation, analysis, and rapid response to task information.

[0061] "Issue information" refers to data that includes details about problems and tasks that occur within a company or organization.

[0062] "Integration" is the process of combining data from different sources into a single, cohesive form.

[0063] A "unified format" refers to a standardized form obtained by converting data from different formats into a common standard.

[0064] "Processing means" refers to functions or software modules used to perform data conversion and analysis.

[0065] "Natural language processing technology" is a technology that enables computers to understand and analyze human language, and is particularly used for processing text data.

[0066] A "mission" refers to an individual task or project carried out in accordance with a specific objective.

[0067] Classification is the process of grouping information based on specific criteria.

[0068] "Evaluation" is the act of judging the importance and priority of information.

[0069] "Analysis results" refer to the output data after processing information obtained using natural language processing technology.

[0070] "Notification generation" refers to the automatic creation of messages to inform users about issues.

[0071] "Transmission method" refers to the method or system used to deliver generated notifications to users via email, chat, etc.

[0072] This invention is a system that combines three elements—a server, a terminal, and a user—to improve the efficiency of task management. The embodiments thereof are described below.

[0073] The server receives issue information sent from each department and plays a role in data integration. The server aggregates the information via API and converts the data into a unified format using the 'pandas' library. Next, the server analyzes the data using natural language processing technology. It utilizes natural language processing libraries such as "spaCy" and "NLTK" to categorize and prioritize issues. Based on the analysis results, the server automatically generates reminders and notifications using "smtplib" and sends them to relevant parties via email or the company's internal chat via the "Slack API".

[0074] The terminal provides an interface for users to register tasks and report their progress. Information entered by the user is sent to the server via the terminal and reflected in the database in real time. This operation can be performed intuitively using a web browser or a dedicated application.

[0075] As a concrete example, the manufacturing department registers a task: "We need to finish the prototype of the new product by next month." The server receives this task information sent from the terminal, and through an analysis process, classifies it into the category of "Manufacturing Schedule Management" and assigns a priority. Depending on the importance level, the server sends an appropriate reminder to the person in charge.

[0076] An example of a prompt message for a generative AI model is: "Receive new challenges from the technical department, analyze them, categorize them accordingly, and set priorities."

[0077] Thus, the present invention streamlines operations and facilitates communication within organizations through automated problem analysis and notification.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] The terminal receives new task information from the user as input. The user enters information such as task details, deadline, and responsible person into a form. This input data is formatted correctly on the terminal and prepared for transmission to the server.

[0081] Step 2:

[0082] The server receives task information sent from the terminal. The received data is saved to an internal database via an API. The input here is task data from the terminal, and the output is a formatted database entry. The Python "pandas" library is used for data cleaning and conversion to a unified format.

[0083] Step 3:

[0084] The server applies natural language processing techniques to the stored task information. The input is formatted task data, and the output is the analyzed task category and evaluation information. The data is tokenized and keywords are extracted using the "spaCy" library. Through this process, tasks are classified into specific categories, and their importance and priority are automatically determined.

[0085] Step 4:

[0086] The server generates reminders and notifications based on the analysis results. The input is the analyzed category and priority information, and the output is a notification message. Using Python's "smtplib" and the "Slack API," the generated notifications are sent to the appropriate users via email or chat applications. In this step, users receive information regarding specific action plans and schedules.

[0087] Step 5:

[0088] The terminal provides the user with an interface to report the progress of an assignment. When the user updates the progress, that information is immediately sent to the server via the terminal. It receives new progress data as input and converts it into a format that can be sent to the server.

[0089] Step 6:

[0090] The server receives progress information from the terminal and updates the database. The input is the new progress data, and the output is the updated database state. This allows administrators to monitor progress in real time and take action as needed.

[0091] (Application Example 1)

[0092] 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."

[0093] In manufacturing, failure to respond quickly to abnormalities or problems can lead to production line stagnation and decreased production efficiency. Furthermore, accurately assessing the importance and priority of problems is difficult, and precise instructions must be communicated to the machinery. In this context, a system that automates problem analysis and processing is necessary.

[0094] 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.

[0095] In this invention, the server includes data transformation means for integrating and standardizing collected information, means for analyzing problems using natural language processing technology based on the standardized information, and means for identifying problem categories from the analyzed information and evaluating their importance. This enables the machine work device to automatically adjust and execute tasks according to priority.

[0096] "Integrating collected information" refers to the process of centralizing data obtained from multiple sources and making it analyzable as a whole.

[0097] A "data conversion method for standardization" is a function that converts data expressed in various formats into a unified format.

[0098] "Methods for analyzing problems using natural language processing technology" refers to the function of deciphering information related to a problem and extracting its meaning using technology that allows computers to understand and analyze human language data.

[0099] "A means of identifying the category of issues and evaluating their importance" refers to a function that classifies the analyzed issues into specific groups and determines the priority level of each issue.

[0100] "Means by which machine work devices automatically adjust and execute tasks according to priority" refers to a function in a manufacturing environment where equipment receives work instructions based on the priority of tasks and automatically performs actions accordingly.

[0101] The server integrates data collected from the manufacturing floor and converts it into a standardized format. This makes it possible to handle diverse data obtained from different devices and sensors in a unified manner. Next, the server analyzes this data using natural language processing technology and automatically evaluates the category and importance of the issues. Python's "Natural Language Toolkit (NLTK)" can be used for this analysis. Based on this information, the server automatically sends work instructions to the machine work equipment according to priority.

[0102] The terminal provides an interface for users to register progress reports and new issues at the manufacturing site. Through this interface, users can input information and report it to the server in real time. For example, if a user reports a delay in parts supply on a particular manufacturing line, this is sent to the server as an issue.

[0103] Specific reminders and notifications to users are automatically generated and sent via the company's internal communication platform. Software such as Slack and Microsoft Teams® can be used.

[0104] This system streamlines issue management in the manufacturing process and enables machinery and equipment to perform tasks quickly and appropriately. For example, based on a prompt message such as, "An anomaly has been detected on the new production line. Please enter the details of this anomaly and the necessary countermeasures into the issue management system," discovered issues are quickly communicated and processed.

[0105] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0106] Step 1:

[0107] The terminal allows users to input manufacturing site challenges and progress information into an interface. This input data is sent to the server as strings or numerical information. The server converts the received data into a standardized format using data conversion means. This allows data in different formats to be unified into a unified structure.

[0108] Step 2:

[0109] The server analyzes standardized data using natural language processing techniques. This analysis utilizes the Natural Language Toolkit (NLTK) or similar libraries to analyze the content of the input task. The analysis results determine the task's category and importance. This process extracts information from text data and maps it to categories.

[0110] Step 3:

[0111] The server determines priorities based on category and importance from the analyzed issue information. This process involves algorithmic prioritization based on pre-defined priority criteria. Once priorities are determined, the data is used to generate work instructions for machine work equipment.

[0112] Step 4:

[0113] The server generates specific work instructions for machine workpieces based on prioritized issues. These instructions are sent to the workpieces and executed automatically. For example, if there is a delay in parts supply, an immediate replenishment instruction is sent to the machine. The instructions include notifications to the necessary equipment via the factory's communication infrastructure.

[0114] Step 5:

[0115] The terminal notifies the user of analysis results and instructions from the server. For example, reminders informing users of the current status of ongoing tasks and issues are automatically sent through the enterprise messaging platform. This allows users to stay informed of the latest status in real time.

[0116] 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.

[0117] The system of this invention not only improves the efficiency of issue management but also provides a more personalized management experience by recognizing and reflecting user emotions. The system mainly consists of the interaction of a server, terminals, and users, and improves the accuracy of issue management by incorporating an emotion engine.

[0118] The server integrates issue information collected from each department into a database and standardizes it using data transformation techniques. The standardized information is then analyzed using natural language processing technology, and issues are automatically categorized and their importance is assessed. An emotion engine is then incorporated to adjust the priority of issues by taking the user's emotional state into account in the analysis results. For example, if a user is under high stress, the system can change the priority of non-urgent issues, such as postponing them.

[0119] The terminal provides an interface for registering tasks, reporting progress, and inputting the user's emotional state. Through the terminal, users can record their emotions through simple input or facial recognition technology.

[0120] As a concrete example, consider a scenario where a project manager is facing multiple urgent issues. When the user registers emotional information along with the issues via their device, the server analyzes this information and considers the user's current stress level when prioritizing the issues. If necessary, the timing of notifications is adjusted to mitigate psychological burden.

[0121] This system goes beyond simple data-centric issue management; it also takes user emotions into account, resulting in intuitive and user-friendly operation. This enables more effective business operations that consider human factors in issue processing.

[0122] The following describes the processing flow.

[0123] Step 1:

[0124] Users input their emotional state along with task information via their device. This emotional state is recorded using a questionnaire-style selection process and facial recognition technology.

[0125] Step 2:

[0126] The terminal receives input from the user and sends compiled issue information and emotional data to the server. This data includes issue details, emotional state, and assignee information.

[0127] Step 3:

[0128] The server stores the received task information in a database and standardizes the information using a data conversion mechanism. This standardization allows for centralized management of data in various input formats.

[0129] Step 4:

[0130] The server analyzes the problem content using natural language processing technology based on standardized information. This analysis identifies the problem category and assesses its importance.

[0131] Step 5:

[0132] The server uses an emotion engine to integrate the user's emotional state into the analysis results. This allows the importance and priority of the evaluated tasks to be adjusted based on emotional factors. For example, if the user is in a high-stress state, non-urgent tasks will be postponed.

[0133] Step 6:

[0134] The device automatically generates and sends notifications to the user based on instructions from the server. The content and timing of the notifications are customized according to the user's emotions.

[0135] Step 7:

[0136] Users report their progress on tasks in real time via their devices, and the server incorporates this information to update the database. The updated information is then optimized through a system-wide feedback loop.

[0137] Step 8:

[0138] The server analyzes progress reports and sentiment data to generate insights for improving system operations. These analysis results can be used to optimize future notification schedules and prioritization.

[0139] (Example 2)

[0140] 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".

[0141] The present invention aims to achieve more personalized task management in a task management system, taking into account the user's emotional state, in contrast to conventional data-centric approaches. The goal is to reduce the user's mental burden and enable more efficient work operations.

[0142] 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.

[0143] In this invention, the server includes information transformation means for integrating and standardizing collected information, means for analyzing the information using natural language processing technology based on the standardized information, and means for identifying the type of information from the analyzed information and evaluating the importance of that information. This enables dynamic task management and reduction of mental burden in accordance with the user's emotional state.

[0144] "Collected information" refers to data on issues and progress obtained from each department and business process.

[0145] "Information transformation means for standardization" refers to a process or technology for organizing data provided in different formats into a consistent format.

[0146] "Natural language analysis technology" is a technique for analyzing text data and extracting useful information, and is also known as natural language processing (NLP).

[0147] "Identifying the type of information" refers to the process of analyzing collected information and classifying it into the category to which each piece of information belongs.

[0148] "Assessing the importance of information" refers to the process of numerically or hierarchically evaluating the impact and urgency of information and determining its priority.

[0149] "Time management means" refers to a technology or process that has a scheduling function to send notifications to users at the right time.

[0150] "Emotional analysis means" refers to a technology or method for analyzing a user's emotions and capturing that state as data.

[0151] "Dynamic prioritization" refers to the process of constantly changing the order in which information is processed, taking into account the user's emotional information to keep it up-to-date.

[0152] "Data management means" refers to the means of recording, tracking, and updating data to maintain the consistency and accuracy of information.

[0153] A "feedback loop" refers to a process of periodically reviewing data and conditions within a system and making improvements or optimizations as needed.

[0154] The system of this invention efficiently manages collected information and realizes dynamic task management that also takes into account the user's emotional state. Cooperation between the server, terminal, and user is essential for implementing this invention.

[0155] The server functions as a central hub for managing information centrally. First, the server collects issue information from each department and project team and stores it in an integrated database. Next, it standardizes the information using data transformation methods, and then utilizes Google® Cloud Natural Language API and other APIs as a platform for analyzing the information using natural language processing technology. Furthermore, it incorporates sentiment analysis engines such as IBM Watson® to adjust the priority of issues while considering the user's emotional state. In this process, data transformation using ETL (Extract, Transform, Load) technology is performed, and the analysis results are fed back in real time.

[0156] The device provides an interface for users to easily input information and receive feedback. This interface incorporates facial recognition technology using the Microsoft Face API and a pull-down menu that quantifies emotional states, simplifying the input of emotional states. Users can intuitively input emotional states by operating the device, making further interaction easier. This intuitive design encourages active user participation in the system.

[0157] As a concrete example, consider a scenario where a project manager is facing an urgent issue. This user inputs emotional information, such as "high stress levels," along with the issue via their device. The server analyzes this information in real time and sends notifications to the user with dynamically adjusted priorities based on their emotional state. These notifications aim to improve both work efficiency and reduce psychological burden.

[0158] By using a generative AI model, prompts are generated that provide specific and appropriate feedback and suggestions based on the user's emotional and problem information. An example of a prompt might be: "Let's say you have many tasks in progress and are feeling a little stressed. When a new task is added in this situation, consider how this system would prioritize the task and alleviate your stress." This makes it easier for users to visualize specific usage scenarios.

[0159] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0160] Step 1:

[0161] The server collects issue information provided in various formats from each department and project team. The input is issue data, and the output is issue information in a unified format. An ETL process is used as the data transformation method to convert the data into JSON or XML format. This ensures that the information is stored in the database in a consistent data format.

[0162] Step 2:

[0163] The server applies natural language processing techniques to standardized information. Specifically, the server uses the Google Cloud Natural Language API to analyze text data of assignments. The input is standardized assignment information, and the output is assignment information with categorized and importance levels. The assignments are classified by category, and the importance level of each category is evaluated using a machine learning algorithm.

[0164] Step 3:

[0165] The server analyzes the user's emotional information using an emotion analysis tool. The input is emotional state data obtained from the user via their terminal, and the output is the analyzed emotional information. The IBM Watson emotion analysis API is used to capture the user's emotional state as numerical data. This information is used to adjust the priority of tasks.

[0166] Step 4:

[0167] The terminal provides users with an interface for registering tasks and inputting emotions. Specifically, users can select their emotional state from a pull-down menu or input it as text on the terminal. Input consists of the user's task and emotion data, while output is standardized data sent to the server.

[0168] Step 5:

[0169] The server dynamically adjusts the priority of issues based on all collected information and generates and sends notifications optimized for the user. Input is analyzed issue information and sentiment information, and output is a list of issues with revised priorities. A scheduling mechanism is used to generate notifications according to priority and adjust notification timing.

[0170] Step 6:

[0171] The user reviews the list of tasks presented through their terminal and submits feedback as needed. The input is the notified list of tasks, and the output is the user's feedback information. This feedback is then used by the server to inform the next task evaluation process.

[0172] (Application Example 2)

[0173] 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".

[0174] In today's work environment, the impact of employees' emotional states on work efficiency and safety cannot be ignored. However, conventional task management systems are data-centric and fail to take into account the emotional state of workers, resulting in problems such as workload imbalances and a higher likelihood of human error. To solve these problems, there is a need for a system that can recognize emotional states in real time and dynamically adjust task priorities and work schedules.

[0175] 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.

[0176] In this invention, the server includes data transformation means for integrating and standardizing collected information, means for analyzing tasks using natural language processing technology based on the standardized information, and emotion recognition means for detecting the user's emotional state and analyzing the data to adjust the priority of tasks. This enables personalized task management that takes into account the user's emotional state, leading to appropriate workload distribution and improved work efficiency.

[0177] A "data conversion means" is a method that has the function of integrating collected information and converting it into a unified format.

[0178] "Natural language processing technology" refers to the technology that enables computers to understand and analyze human language.

[0179] An "emotion recognition method" is a method for detecting a user's emotional state, collecting data on it, and analyzing it.

[0180] "Prioritizing tasks" refers to the process of dynamically determining the importance and implementation order of tasks based on analyzed data.

[0181] "Personalized issue management" is a management method that performs adaptive issue processing according to the emotional state and work environment of individual users.

[0182] The system of this invention enables personalized task management that takes into account the user's emotional state. The system consists of three components: a server, a terminal, and a user.

[0183] The server integrates the collected information and converts it into a unified format using data transformation tools. Furthermore, it analyzes the standardized information using natural language processing techniques to identify the category and importance of the issues. Based on this analysis, the server detects the user's emotional state using emotion recognition tools and adjusts the priority of the issues using that data. For emotion recognition, wearable devices such as smart glasses or headband-type devices are used, and the data they transmit is analyzed.

[0184] The terminal provides an interface for registering tasks and reporting progress. When users input their emotional state, they can also send emotional data to a server via the terminal. The process includes registering emotional states through facial recognition technology and simplified input, enabling flexible data entry.

[0185] By registering emotional information with the system, users can receive adjustments to reduce their physical or psychological burden. For example, if a factory worker wears smart glasses and their stress level changes in real time, the server can adjust the priority and schedule of robotic tasks based on that data.

[0186] As a concrete example, consider robot operation on a factory production line. This robot can adjust its operating speed according to the worker's stress level and take adaptive actions such as temporarily easing its movements when the worker feels excessively fatigued. An example of input to the generative AI model is as follows:

[0187] "The robots used in the project need to analyze workers' stress levels in real time and adjust task schedules based on that data. Consider how the robots will utilize this information to adjust priorities."

[0188] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0189] Step 1:

[0190] The device receives task registration information and emotional state from the user. Task information is entered as text data, and emotional state is acquired through facial recognition via a wearable device or as simple input data. This forms the basis of the user's current task content and emotional data.

[0191] Step 2:

[0192] The server standardizes the task information received from the terminal using a data conversion mechanism and analyzes it using natural language processing technology. The analysis identifies the task category and importance level from the input text data. As a result, preliminary information for the priority level of each task is generated.

[0193] Step 3:

[0194] The server analyzes emotional data acquired from wearable devices using emotion recognition tools. In this analysis, a generative AI model evaluates the emotional data based on the user's emotional state (stress, fatigue, etc.) and readjusts the priority of tasks. At this time, the generative AI model is used to make specific adjustments, such as lowering the priority of certain tasks when stress levels are high.

[0195] Step 4:

[0196] The server generates a finalized schedule with adjusted priorities and notifies the user via their terminal. The notification includes the order and recommended timing for completing tasks, helping the user work with minimal psychological and physical burden.

[0197] Step 5:

[0198] When a user enters progress information into their terminal, it is sent back to the server, which then updates the plan to include this data. This enables real-time schedule optimization. Based on the progress data, future work plans are automatically revised, resulting in adaptive task management.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] [Second Embodiment]

[0203] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0204] 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.

[0205] 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).

[0206] 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.

[0207] 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.

[0208] 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).

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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".

[0215] The system of this invention is built around three main elements: a server, a terminal, and a user, with the aim of improving the efficiency of digital issue management. This system centrally manages issue management data collected from each department and performs automatic analysis using natural language processing technology to categorize issues, assess their importance, and set priorities.

[0216] The server integrates issue information received from each department. The integrated information is standardized through data transformation and analyzed using natural language processing technology. Based on the analysis results, the server categorizes the issues and evaluates their importance. Next, based on the evaluated information, it automatically generates reminders and progress check notifications for users and sends them according to a schedule.

[0217] The terminal accepts input from users and provides an interface for registering tasks and reporting progress. Users can easily register tasks via the terminal and report their progress in real time.

[0218] For example, if a technical department registers an issue stating that "the server's storage capacity is about to reach its limit," the terminal sends the information to the server. The server analyzes this information, categorizes it as "IT infrastructure management," and assigns it high priority to prevent major disruptions. Based on this, the server sends a reminder to the responsible technician via email or internal chat, emphasizing the need for immediate action. Once the technician completes the task and submits a progress report from their terminal, the server processes the information and notifies the administrator of the completion.

[0219] Thus, the system based on the present invention highly automates the issue management process and supports efficient business operations.

[0220] The following describes the processing flow.

[0221] Step 1:

[0222] Users input issue information managed by their respective departments and send it to the server as a digital management sheet via their terminals. The issue information includes an overview of the issue, the person responsible, and the deadline.

[0223] Step 2:

[0224] The server stores the received task information in a database and uses data conversion tools to standardize the format. This allows for centralized management of data in various formats.

[0225] Step 3:

[0226] The server applies natural language processing techniques to standardized task data to perform text analysis. It understands the theme and intent of the tasks and identifies related categories.

[0227] Step 4:

[0228] The server evaluates the importance of the analyzed information and prioritizes each issue. The evaluation is based on the urgency of the issue and its impact on business operations.

[0229] Step 5:

[0230] Based on instructions from the server, the terminal automatically creates reminders for users (assigned personnel and administrators) and sends notifications according to the specified schedule. The notifications include details of the issue and the deadline for action.

[0231] Step 6:

[0232] Users input the progress of their tasks via their devices and report it to the server. This updates the task status in real time, and the latest information is reported to the administrator.

[0233] Step 7:

[0234] The server analyzes the received progress information and optimizes the entire system through a feedback loop to improve efficiency. This enables improvements in future task handling processes.

[0235] (Example 1)

[0236] 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."

[0237] The problem that this invention aims to solve is the inefficiency of digital issue management in companies and organizations. Specifically, this includes the amount of manual work involved in managing issue information provided by each department, the fragmentation of information, and the difficulty in appropriately prioritizing. These problems can lead to delays in responses or the overlooking of important issues.

[0238] 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.

[0239] In this invention, the server includes processing means for integrating task information from multiple sources and converting it into a unified format, analysis means for analyzing the unified information using natural language processing technology and classifying and evaluating tasks, and notification generation and transmission means for informing users via electronic communication based on the analysis results. This enables efficient aggregation, analysis, and rapid response to task information.

[0240] "Issue information" refers to data that includes details about problems and tasks that occur within a company or organization.

[0241] "Integration" is the process of combining data from different sources into a single, cohesive form.

[0242] A "unified format" refers to a standardized form obtained by converting data from different formats into a common standard.

[0243] "Processing means" refers to functions or software modules used to perform data conversion and analysis.

[0244] "Natural language processing technology" is a technology that enables computers to understand and analyze human language, and is particularly used for processing text data.

[0245] A "mission" refers to an individual task or project carried out in accordance with a specific objective.

[0246] Classification is the process of grouping information based on specific criteria.

[0247] "Evaluation" is the act of judging the importance and priority of information.

[0248] "Analysis results" refer to the output data after processing information obtained using natural language processing technology.

[0249] "Notification generation" refers to the automatic creation of messages to inform users about issues.

[0250] "Transmission method" refers to the method or system used to deliver generated notifications to users via email, chat, etc.

[0251] This invention is a system that combines three elements—a server, a terminal, and a user—to improve the efficiency of task management. The embodiments thereof are described below.

[0252] The server receives issue information sent from each department and plays a role in data integration. The server aggregates the information via API and converts the data into a unified format using the 'pandas' library. Next, the server analyzes the data using natural language processing technology. It utilizes natural language processing libraries such as "spaCy" and "NLTK" to categorize and prioritize issues. Based on the analysis results, the server automatically generates reminders and notifications using "smtplib" and sends them to relevant parties via email or the company's internal chat via the "Slack API".

[0253] The terminal provides an interface for users to register tasks and report their progress. Information entered by the user is sent to the server via the terminal and reflected in the database in real time. This operation can be performed intuitively using a web browser or a dedicated application.

[0254] As a concrete example, the manufacturing department registers a task: "We need to finish the prototype of the new product by next month." The server receives this task information sent from the terminal, and through an analysis process, classifies it into the category of "Manufacturing Schedule Management" and assigns a priority. Depending on the importance level, the server sends an appropriate reminder to the person in charge.

[0255] An example of a prompt message for a generative AI model is: "Receive new challenges from the technical department, analyze them, categorize them accordingly, and set priorities."

[0256] Thus, the present invention streamlines operations and facilitates communication within organizations through automated problem analysis and notification.

[0257] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0258] Step 1:

[0259] The terminal receives new task information from the user as input. The user enters information such as task details, deadline, and responsible person into a form. This input data is formatted correctly on the terminal and prepared for transmission to the server.

[0260] Step 2:

[0261] The server receives task information sent from the terminal. The received data is saved to an internal database via an API. The input here is task data from the terminal, and the output is a formatted database entry. The Python "pandas" library is used for data cleaning and conversion to a unified format.

[0262] Step 3:

[0263] The server applies natural language processing techniques to the stored task information. The input is formatted task data, and the output is the analyzed task category and evaluation information. The data is tokenized and keywords are extracted using the "spaCy" library. Through this process, tasks are classified into specific categories, and their importance and priority are automatically determined.

[0264] Step 4:

[0265] The server generates reminders and notifications based on the analysis results. The input is the analyzed category and priority information, and the output is a notification message. Using Python's "smtplib" and the "Slack API," the generated notifications are sent to the appropriate users via email or chat applications. In this step, users receive information regarding specific action plans and schedules.

[0266] Step 5:

[0267] The terminal provides the user with an interface to report the progress of an assignment. When the user updates the progress, that information is immediately sent to the server via the terminal. It receives new progress data as input and converts it into a format that can be sent to the server.

[0268] Step 6:

[0269] The server receives progress information from the terminal and updates the database. The input is the new progress data, and the output is the updated database state. This allows administrators to monitor progress in real time and take action as needed.

[0270] (Application Example 1)

[0271] 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."

[0272] In manufacturing, failure to respond quickly to abnormalities or problems can lead to production line stagnation and decreased production efficiency. Furthermore, accurately assessing the importance and priority of problems is difficult, and precise instructions must be communicated to the machinery. In this context, a system that automates problem analysis and processing is necessary.

[0273] 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.

[0274] In this invention, the server includes data transformation means for integrating and standardizing collected information, means for analyzing problems using natural language processing technology based on the standardized information, and means for identifying problem categories from the analyzed information and evaluating their importance. This enables the machine work device to automatically adjust and execute tasks according to priority.

[0275] "Integrating collected information" refers to the process of centralizing data obtained from multiple sources and making it analyzable as a whole.

[0276] A "data conversion method for standardization" is a function that converts data expressed in various formats into a unified format.

[0277] "Methods for analyzing problems using natural language processing technology" refers to the function of deciphering information related to a problem and extracting its meaning using technology that allows computers to understand and analyze human language data.

[0278] "A means of identifying the category of issues and evaluating their importance" refers to a function that classifies the analyzed issues into specific groups and determines the priority level of each issue.

[0279] "Means by which machine work devices automatically adjust and execute tasks according to priority" refers to a function in a manufacturing environment where equipment receives work instructions based on the priority of tasks and automatically performs actions accordingly.

[0280] The server integrates data collected from the manufacturing floor and converts it into a standardized format. This makes it possible to handle diverse data obtained from different devices and sensors in a unified manner. Next, the server analyzes this data using natural language processing technology and automatically evaluates the category and importance of the issues. Python's "Natural Language Toolkit (NLTK)" can be used for this analysis. Based on this information, the server automatically sends work instructions to the machine work equipment according to priority.

[0281] The terminal provides an interface for users to register progress reports and new issues at the manufacturing site. Through this interface, users can input information and report it to the server in real time. For example, if a user reports a delay in parts supply on a particular manufacturing line, this is sent to the server as an issue.

[0282] Specific reminders and notifications to users are automatically generated and sent via the company's internal communication platform. Software such as Slack or Microsoft Teams can be used.

[0283] By using this system, issue management at the manufacturing site is streamlined, enabling the mechanical working device to perform operations quickly and appropriately. As a specific example, based on a prompt such as "An abnormality in the new production line has been detected. Please enter the details of this abnormality and the necessary countermeasures below into the issue management system.", the discovered issues are quickly communicated and processed.

[0284] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0285] Step 1:

[0286] The user inputs manufacturing site issues and progress information into the interface via the terminal. This input data is sent to the server as character string or numerical information. The server converts the received data into a standardized format using data conversion means. This enables different forms of data to have a unified structure.

[0287] Step 2:

[0288] The server analyzes the standardized data using natural language processing technology. In this analysis, libraries such as "Natural Language Toolkit (NLTK)" or similar are used to analyze the content of the input issues. As a result of the analysis, the category and importance of the issues are determined. In this process, information is extracted from the text data and mapped to categories.

[0289] Step 3:

[0290] The server determines the priority based on the category and importance from the analyzed issue information. In this process, the priority is algorithmically assigned based on pre-set priority criteria. Once the priority is determined, the data is used to generate work instructions for the mechanical working device.

[0291] Step 4:

[0292] The server generates specific work instructions for machine workpieces based on prioritized issues. These instructions are sent to the workpieces and executed automatically. For example, if there is a delay in parts supply, an immediate replenishment instruction is sent to the machine. The instructions include notifications to the necessary equipment via the factory's communication infrastructure.

[0293] Step 5:

[0294] The terminal notifies the user of analysis results and instructions from the server. For example, reminders informing users of the current status of ongoing tasks and issues are automatically sent through the enterprise messaging platform. This allows users to stay informed of the latest status in real time.

[0295] 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.

[0296] The system of this invention not only improves the efficiency of issue management but also provides a more personalized management experience by recognizing and reflecting user emotions. The system mainly consists of the interaction of a server, terminals, and users, and improves the accuracy of issue management by incorporating an emotion engine.

[0297] The server integrates issue information collected from each department into a database and standardizes it using data transformation techniques. The standardized information is then analyzed using natural language processing technology, and issues are automatically categorized and their importance is assessed. An emotion engine is then incorporated to adjust the priority of issues by taking the user's emotional state into account in the analysis results. For example, if a user is under high stress, the system can change the priority of non-urgent issues, such as postponing them.

[0298] The terminal provides an interface for accepting task registration, progress reports, and input of the user's emotional state. The user can record emotions through simple emotion input or face recognition technology via the terminal.

[0299] As a specific example, consider the case where a project manager is facing multiple urgent tasks. When the user registers emotion information along with the task via the terminal, the server analyzes the information and takes into account the user's current stress level in determining the task priority. If necessary, the notification timing is adjusted and considerations are made to reduce the psychological burden.

[0300] With this system, intuitive and user-friendly operation is achieved by taking into account not only simple data-centered task management but also the user's emotions. This enables more effective business operation considering the human factor in task processing.

[0301] The processing flow is described below.

[0302] Step 1:

[0303] The user inputs their emotional state together with task information via the terminal. The emotional state is recorded by using questionnaire-style options or face recognition technology.

[0304] Step 2:

[0305] The terminal receives the input from the user and sends the task information and emotion data together to the server. This data includes task details, emotional state, and assignee information.

[0306] Step 3:

[0307] The server saves the received task information in the database and standardizes the information by means of data conversion. This standardization enables unified management of data in various input formats.

[0308] Step 4:

[0309] The server analyzes the problem content using natural language processing technology based on standardized information. This analysis identifies the problem category and assesses its importance.

[0310] Step 5:

[0311] The server uses an emotion engine to integrate the user's emotional state into the analysis results. This allows the importance and priority of the evaluated tasks to be adjusted based on emotional factors. For example, if the user is in a high-stress state, non-urgent tasks will be postponed.

[0312] Step 6:

[0313] The device automatically generates and sends notifications to the user based on instructions from the server. The content and timing of the notifications are customized according to the user's emotions.

[0314] Step 7:

[0315] Users report their progress on tasks in real time via their devices, and the server incorporates this information to update the database. The updated information is then optimized through a system-wide feedback loop.

[0316] Step 8:

[0317] The server analyzes progress reports and sentiment data to generate insights for improving system operations. These analysis results can be used to optimize future notification schedules and prioritization.

[0318] (Example 2)

[0319] 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".

[0320] The present invention aims to achieve more personalized task management in a task management system, taking into account the user's emotional state, in contrast to conventional data-centric approaches. The goal is to reduce the user's mental burden and enable more efficient work operations.

[0321] 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.

[0322] In this invention, the server includes information transformation means for integrating and standardizing collected information, means for analyzing the information using natural language processing technology based on the standardized information, and means for identifying the type of information from the analyzed information and evaluating the importance of that information. This enables dynamic task management and reduction of mental burden in accordance with the user's emotional state.

[0323] "Collected information" refers to data on issues and progress obtained from each department and business process.

[0324] "Information transformation means for standardization" refers to a process or technology for organizing data provided in different formats into a consistent format.

[0325] "Natural language analysis technology" is a technique for analyzing text data and extracting useful information, and is also known as natural language processing (NLP).

[0326] "Identifying the type of information" refers to the process of analyzing collected information and classifying it into the category to which each piece of information belongs.

[0327] "Assessing the importance of information" refers to the process of numerically or hierarchically evaluating the impact and urgency of information and determining its priority.

[0328] "Time management means" refers to a technology or process that has a scheduling function to send notifications to users at the right time.

[0329] "Emotional analysis means" refers to a technology or method for analyzing a user's emotions and capturing that state as data.

[0330] "Dynamic prioritization" refers to the process of constantly changing the order in which information is processed, taking into account the user's emotional information to keep it up-to-date.

[0331] "Data management means" refers to the means of recording, tracking, and updating data to maintain the consistency and accuracy of information.

[0332] A "feedback loop" refers to a process of periodically reviewing data and conditions within a system and making improvements or optimizations as needed.

[0333] The system of this invention efficiently manages collected information and realizes dynamic task management that also takes into account the user's emotional state. Cooperation between the server, terminal, and user is essential for implementing this invention.

[0334] The server functions as a central hub for managing information centrally. First, the server collects issue information from each department and project team and stores it in an integrated database. Next, it standardizes the information using data transformation methods, and then utilizes Google Cloud Natural Language API and other APIs as a platform for analyzing the information using natural language processing technology. Furthermore, it incorporates sentiment analysis engines such as IBM Watson to adjust the priority of issues while considering the user's emotional state. In this process, data transformation using ETL (Extract, Transform, Load) technology is performed, and the analysis results are fed back in real time.

[0335] The device provides an interface for users to easily input information and receive feedback. This interface incorporates facial recognition technology using the Microsoft Face API and a pull-down menu that quantifies emotional states, simplifying the input of emotional states. Users can intuitively input emotional states by operating the device, making further interaction easier. This intuitive design encourages active user participation in the system.

[0336] As a concrete example, consider a scenario where a project manager is facing an urgent issue. This user inputs emotional information, such as "high stress levels," along with the issue via their device. The server analyzes this information in real time and sends notifications to the user with dynamically adjusted priorities based on their emotional state. These notifications aim to improve both work efficiency and reduce psychological burden.

[0337] By using a generative AI model, prompts are generated that provide specific and appropriate feedback and suggestions based on the user's emotional and problem information. An example of a prompt might be: "Let's say you have many tasks in progress and are feeling a little stressed. When a new task is added in this situation, consider how this system would prioritize the task and alleviate your stress." This makes it easier for users to visualize specific usage scenarios.

[0338] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0339] Step 1:

[0340] The server collects issue information provided in various formats from each department and project team. The input is issue data, and the output is issue information in a unified format. An ETL process is used as the data transformation method to convert the data into JSON or XML format. This ensures that the information is stored in the database in a consistent data format.

[0341] Step 2:

[0342] The server applies natural language processing techniques to standardized information. Specifically, the server uses the Google Cloud Natural Language API to analyze text data of assignments. The input is standardized assignment information, and the output is assignment information with categorized and importance levels. The assignments are classified by category, and the importance level of each category is evaluated using a machine learning algorithm.

[0343] Step 3:

[0344] The server analyzes the user's emotional information using an emotion analysis tool. The input is emotional state data obtained from the user via their terminal, and the output is the analyzed emotional information. The IBM Watson emotion analysis API is used to capture the user's emotional state as numerical data. This information is used to adjust the priority of tasks.

[0345] Step 4:

[0346] The terminal provides users with an interface for registering tasks and inputting emotions. Specifically, users can select their emotional state from a pull-down menu or input it as text on the terminal. Input consists of the user's task and emotion data, while output is standardized data sent to the server.

[0347] Step 5:

[0348] The server dynamically adjusts the priority of issues based on all collected information and generates and sends notifications optimized for the user. Input is analyzed issue information and sentiment information, and output is a list of issues with revised priorities. A scheduling mechanism is used to generate notifications according to priority and adjust notification timing.

[0349] Step 6:

[0350] The user reviews the list of tasks presented through their terminal and submits feedback as needed. The input is the notified list of tasks, and the output is the user's feedback information. This feedback is then used by the server to inform the next task evaluation process.

[0351] (Application Example 2)

[0352] 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."

[0353] In today's work environment, the impact of employees' emotional states on work efficiency and safety cannot be ignored. However, conventional task management systems are data-centric and fail to take into account the emotional state of workers, resulting in problems such as workload imbalances and a higher likelihood of human error. To solve these problems, there is a need for a system that can recognize emotional states in real time and dynamically adjust task priorities and work schedules.

[0354] 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.

[0355] In this invention, the server includes data transformation means for integrating and standardizing collected information, means for analyzing tasks using natural language processing technology based on the standardized information, and emotion recognition means for detecting the user's emotional state and analyzing the data to adjust the priority of tasks. This enables personalized task management that takes into account the user's emotional state, leading to appropriate workload distribution and improved work efficiency.

[0356] A "data conversion means" is a method that has the function of integrating collected information and converting it into a unified format.

[0357] "Natural language processing technology" refers to the technology that enables computers to understand and analyze human language.

[0358] An "emotion recognition method" is a method for detecting a user's emotional state, collecting data on it, and analyzing it.

[0359] "Prioritizing tasks" refers to the process of dynamically determining the importance and implementation order of tasks based on analyzed data.

[0360] "Personalized issue management" is a management method that performs adaptive issue processing according to the emotional state and work environment of individual users.

[0361] The system of this invention enables personalized task management that takes into account the user's emotional state. The system consists of three components: a server, a terminal, and a user.

[0362] The server integrates the collected information and converts it into a unified format using data transformation tools. Furthermore, it analyzes the standardized information using natural language processing techniques to identify the category and importance of the issues. Based on this analysis, the server detects the user's emotional state using emotion recognition tools and adjusts the priority of the issues using that data. For emotion recognition, wearable devices such as smart glasses or headband-type devices are used, and the data they transmit is analyzed.

[0363] The terminal provides an interface for registering tasks and reporting progress. When users input their emotional state, they can also send emotional data to a server via the terminal. The process includes registering emotional states through facial recognition technology and simplified input, enabling flexible data entry.

[0364] By registering emotional information with the system, users can receive adjustments to reduce their physical or psychological burden. For example, if a factory worker wears smart glasses and their stress level changes in real time, the server can adjust the priority and schedule of robotic tasks based on that data.

[0365] As a concrete example, consider robot operation on a factory production line. This robot can adjust its operating speed according to the worker's stress level and take adaptive actions such as temporarily easing its movements when the worker feels excessively fatigued. An example of input to the generative AI model is as follows:

[0366] "The robots used in the project need to analyze workers' stress levels in real time and adjust task schedules based on that data. Consider how the robots will utilize this information to adjust priorities."

[0367] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0368] Step 1:

[0369] The device receives task registration information and emotional state from the user. Task information is entered as text data, and emotional state is acquired through facial recognition via a wearable device or as simple input data. This forms the basis of the user's current task content and emotional data.

[0370] Step 2:

[0371] The server standardizes the task information received from the terminal using a data conversion mechanism and analyzes it using natural language processing technology. The analysis identifies the task category and importance level from the input text data. As a result, preliminary information for the priority level of each task is generated.

[0372] Step 3:

[0373] The server analyzes emotional data acquired from wearable devices using emotion recognition tools. In this analysis, a generative AI model evaluates the emotional data based on the user's emotional state (stress, fatigue, etc.) and readjusts the priority of tasks. At this time, the generative AI model is used to make specific adjustments, such as lowering the priority of certain tasks when stress levels are high.

[0374] Step 4:

[0375] The server generates a finalized schedule with adjusted priorities and notifies the user via their terminal. The notification includes the order and recommended timing for completing tasks, helping the user work with minimal psychological and physical burden.

[0376] Step 5:

[0377] When a user enters progress information into their terminal, it is sent back to the server, which then updates the plan to include this data. This enables real-time schedule optimization. Based on the progress data, future work plans are automatically revised, resulting in adaptive task management.

[0378] 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.

[0379] 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.

[0380] 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.

[0381] [Third Embodiment]

[0382] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0383] 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.

[0384] 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).

[0385] 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.

[0386] 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.

[0387] 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).

[0388] 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.

[0389] 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.

[0390] 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.

[0391] 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.

[0392] 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.

[0393] 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".

[0394] The system of this invention is built around three main elements: a server, a terminal, and a user, with the aim of improving the efficiency of digital issue management. This system centrally manages issue management data collected from each department and performs automatic analysis using natural language processing technology to categorize issues, assess their importance, and set priorities.

[0395] The server integrates issue information received from each department. The integrated information is standardized through data transformation and analyzed using natural language processing technology. Based on the analysis results, the server categorizes the issues and evaluates their importance. Next, based on the evaluated information, it automatically generates reminders and progress check notifications for users and sends them according to a schedule.

[0396] The terminal accepts input from users and provides an interface for registering tasks and reporting progress. Users can easily register tasks via the terminal and report their progress in real time.

[0397] For example, if a technical department registers an issue stating that "the server's storage capacity is about to reach its limit," the terminal sends the information to the server. The server analyzes this information, categorizes it as "IT infrastructure management," and assigns it high priority to prevent major disruptions. Based on this, the server sends a reminder to the responsible technician via email or internal chat, emphasizing the need for immediate action. Once the technician completes the task and submits a progress report from their terminal, the server processes the information and notifies the administrator of the completion.

[0398] Thus, the system based on the present invention highly automates the issue management process and supports efficient business operations.

[0399] The following describes the processing flow.

[0400] Step 1:

[0401] Users input issue information managed by their respective departments and send it to the server as a digital management sheet via their terminals. The issue information includes an overview of the issue, the person responsible, and the deadline.

[0402] Step 2:

[0403] The server stores the received task information in a database and uses data conversion tools to standardize the format. This allows for centralized management of data in various formats.

[0404] Step 3:

[0405] The server applies natural language processing techniques to standardized task data to perform text analysis. It understands the theme and intent of the tasks and identifies related categories.

[0406] Step 4:

[0407] The server evaluates the importance of the analyzed information and prioritizes each issue. The evaluation is based on the urgency of the issue and its impact on business operations.

[0408] Step 5:

[0409] Based on instructions from the server, the terminal automatically creates reminders for users (assigned personnel and administrators) and sends notifications according to the specified schedule. The notifications include details of the issue and the deadline for action.

[0410] Step 6:

[0411] Users input the progress of their tasks via their devices and report it to the server. This updates the task status in real time, and the latest information is reported to the administrator.

[0412] Step 7:

[0413] The server analyzes the received progress information and optimizes the entire system through a feedback loop to improve efficiency. This enables improvements in future task handling processes.

[0414] (Example 1)

[0415] 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."

[0416] The problem that this invention aims to solve is the inefficiency of digital issue management in companies and organizations. Specifically, this includes the amount of manual work involved in managing issue information provided by each department, the fragmentation of information, and the difficulty in appropriately prioritizing. These problems can lead to delays in responses or the overlooking of important issues.

[0417] 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.

[0418] In this invention, the server includes processing means for integrating task information from multiple sources and converting it into a unified format, analysis means for analyzing the unified information using natural language processing technology and classifying and evaluating tasks, and notification generation and transmission means for informing users via electronic communication based on the analysis results. This enables efficient aggregation, analysis, and rapid response to task information.

[0419] "Issue information" refers to data that includes details about problems and tasks that occur within a company or organization.

[0420] "Integration" is the process of combining data from different sources into a single, cohesive form.

[0421] A "unified format" refers to a standardized form obtained by converting data from different formats into a common standard.

[0422] "Processing means" refers to functions or software modules used to perform data conversion and analysis.

[0423] "Natural language processing technology" is a technology that enables computers to understand and analyze human language, and is particularly used for processing text data.

[0424] A "mission" refers to an individual task or project carried out in accordance with a specific objective.

[0425] Classification is the process of grouping information based on specific criteria.

[0426] "Evaluation" is the act of judging the importance and priority of information.

[0427] "Analysis results" refer to the output data after processing information obtained using natural language processing technology.

[0428] "Notification generation" refers to the automatic creation of messages to inform users about issues.

[0429] "Transmission method" refers to the method or system used to deliver generated notifications to users via email, chat, etc.

[0430] This invention is a system that combines three elements—a server, a terminal, and a user—to improve the efficiency of task management. The embodiments thereof are described below.

[0431] The server receives issue information sent from each department and plays a role in data integration. The server aggregates the information via API and converts the data into a unified format using the 'pandas' library. Next, the server analyzes the data using natural language processing technology. It utilizes natural language processing libraries such as "spaCy" and "NLTK" to categorize and prioritize issues. Based on the analysis results, the server automatically generates reminders and notifications using "smtplib" and sends them to relevant parties via email or the company's internal chat via the "Slack API".

[0432] The terminal provides an interface for users to register tasks and report their progress. Information entered by the user is sent to the server via the terminal and reflected in the database in real time. This operation can be performed intuitively using a web browser or a dedicated application.

[0433] As a concrete example, the manufacturing department registers a task: "We need to finish the prototype of the new product by next month." The server receives this task information sent from the terminal, and through an analysis process, classifies it into the category of "Manufacturing Schedule Management" and assigns a priority. Depending on the importance level, the server sends an appropriate reminder to the person in charge.

[0434] An example of a prompt message for a generative AI model is: "Receive new challenges from the technical department, analyze them, categorize them accordingly, and set priorities."

[0435] Thus, the present invention streamlines operations and facilitates communication within organizations through automated problem analysis and notification.

[0436] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0437] Step 1:

[0438] The terminal receives new task information from the user as input. The user enters information such as task details, deadline, and responsible person into a form. This input data is formatted correctly on the terminal and prepared for transmission to the server.

[0439] Step 2:

[0440] The server receives task information sent from the terminal. The received data is saved to an internal database via an API. The input here is task data from the terminal, and the output is a formatted database entry. The Python "pandas" library is used for data cleaning and conversion to a unified format.

[0441] Step 3:

[0442] The server applies natural language processing techniques to the stored task information. The input is formatted task data, and the output is the analyzed task category and evaluation information. The data is tokenized and keywords are extracted using the "spaCy" library. Through this process, tasks are classified into specific categories, and their importance and priority are automatically determined.

[0443] Step 4:

[0444] The server generates reminders and notifications based on the analysis results. The input is the analyzed category and priority information, and the output is a notification message. Using Python's "smtplib" and the "Slack API," the generated notifications are sent to the appropriate users via email or chat applications. In this step, users receive information regarding specific action plans and schedules.

[0445] Step 5:

[0446] The terminal provides the user with an interface to report the progress of an assignment. When the user updates the progress, that information is immediately sent to the server via the terminal. It receives new progress data as input and converts it into a format that can be sent to the server.

[0447] Step 6:

[0448] The server receives progress information from the terminal and updates the database. The input is the new progress data, and the output is the updated database state. This allows administrators to monitor progress in real time and take action as needed.

[0449] (Application Example 1)

[0450] 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."

[0451] In manufacturing, failure to respond quickly to abnormalities or problems can lead to production line stagnation and decreased production efficiency. Furthermore, accurately assessing the importance and priority of problems is difficult, and precise instructions must be communicated to the machinery. In this context, a system that automates problem analysis and processing is necessary.

[0452] 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.

[0453] In this invention, the server includes data transformation means for integrating and standardizing collected information, means for analyzing problems using natural language processing technology based on the standardized information, and means for identifying problem categories from the analyzed information and evaluating their importance. This enables the machine work device to automatically adjust and execute tasks according to priority.

[0454] "Integrating collected information" refers to the process of centralizing data obtained from multiple sources and making it analyzable as a whole.

[0455] A "data conversion method for standardization" is a function that converts data expressed in various formats into a unified format.

[0456] "Methods for analyzing problems using natural language processing technology" refers to the function of deciphering information related to a problem and extracting its meaning using technology that allows computers to understand and analyze human language data.

[0457] "A means of identifying the category of issues and evaluating their importance" refers to a function that classifies the analyzed issues into specific groups and determines the priority level of each issue.

[0458] "Means by which machine work devices automatically adjust and execute tasks according to priority" refers to a function in a manufacturing environment where equipment receives work instructions based on the priority of tasks and automatically performs actions accordingly.

[0459] The server integrates data collected from the manufacturing floor and converts it into a standardized format. This makes it possible to handle diverse data obtained from different devices and sensors in a unified manner. Next, the server analyzes this data using natural language processing technology and automatically evaluates the category and importance of the issues. Python's "Natural Language Toolkit (NLTK)" can be used for this analysis. Based on this information, the server automatically sends work instructions to the machine work equipment according to priority.

[0460] The terminal provides an interface for users to register progress reports and new issues at the manufacturing site. Through this interface, users can input information and report it to the server in real time. For example, if a user reports a delay in parts supply on a particular manufacturing line, this is sent to the server as an issue.

[0461] Specific reminders and notifications to users are automatically generated and sent via the company's internal communication platform. Software such as Slack or Microsoft Teams can be used.

[0462] This system streamlines issue management in the manufacturing process and enables machinery and equipment to perform tasks quickly and appropriately. For example, based on a prompt message such as, "An anomaly has been detected on the new production line. Please enter the details of this anomaly and the necessary countermeasures into the issue management system," discovered issues are quickly communicated and processed.

[0463] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0464] Step 1:

[0465] The terminal allows users to input manufacturing site challenges and progress information into an interface. This input data is sent to the server as strings or numerical information. The server converts the received data into a standardized format using data conversion means. This allows data in different formats to be unified into a unified structure.

[0466] Step 2:

[0467] The server analyzes standardized data using natural language processing techniques. This analysis utilizes the Natural Language Toolkit (NLTK) or similar libraries to analyze the content of the input task. The analysis results determine the task's category and importance. This process extracts information from text data and maps it to categories.

[0468] Step 3:

[0469] The server determines priorities based on category and importance from the analyzed issue information. This process involves algorithmic prioritization based on pre-defined priority criteria. Once priorities are determined, the data is used to generate work instructions for machine work equipment.

[0470] Step 4:

[0471] The server generates specific work instructions for machine workpieces based on prioritized issues. These instructions are sent to the workpieces and executed automatically. For example, if there is a delay in parts supply, an immediate replenishment instruction is sent to the machine. The instructions include notifications to the necessary equipment via the factory's communication infrastructure.

[0472] Step 5:

[0473] The terminal notifies the user of analysis results and instructions from the server. For example, reminders informing users of the current status of ongoing tasks and issues are automatically sent through the enterprise messaging platform. This allows users to stay informed of the latest status in real time.

[0474] 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.

[0475] The system of this invention not only improves the efficiency of issue management but also provides a more personalized management experience by recognizing and reflecting user emotions. The system mainly consists of the interaction of a server, terminals, and users, and improves the accuracy of issue management by incorporating an emotion engine.

[0476] The server integrates issue information collected from each department into a database and standardizes it using data transformation techniques. The standardized information is then analyzed using natural language processing technology, and issues are automatically categorized and their importance is assessed. An emotion engine is then incorporated to adjust the priority of issues by taking the user's emotional state into account in the analysis results. For example, if a user is under high stress, the system can change the priority of non-urgent issues, such as postponing them.

[0477] The terminal provides an interface for registering tasks, reporting progress, and inputting the user's emotional state. Through the terminal, users can record their emotions through simple input or facial recognition technology.

[0478] As a concrete example, consider a scenario where a project manager is facing multiple urgent issues. When the user registers emotional information along with the issues via their device, the server analyzes this information and considers the user's current stress level when prioritizing the issues. If necessary, the timing of notifications is adjusted to mitigate psychological burden.

[0479] This system goes beyond simple data-centric issue management; it also takes user emotions into account, resulting in intuitive and user-friendly operation. This enables more effective business operations that consider human factors in issue processing.

[0480] The following describes the processing flow.

[0481] Step 1:

[0482] Users input their emotional state along with task information via their device. This emotional state is recorded using a questionnaire-style selection process and facial recognition technology.

[0483] Step 2:

[0484] The terminal receives input from the user and sends compiled issue information and emotional data to the server. This data includes issue details, emotional state, and assignee information.

[0485] Step 3:

[0486] The server stores the received task information in a database and standardizes the information using a data conversion mechanism. This standardization allows for centralized management of data in various input formats.

[0487] Step 4:

[0488] The server analyzes the problem content using natural language processing technology based on standardized information. This analysis identifies the problem category and assesses its importance.

[0489] Step 5:

[0490] The server uses an emotion engine to integrate the user's emotional state into the analysis results. This allows the importance and priority of the evaluated tasks to be adjusted based on emotional factors. For example, if the user is in a high-stress state, non-urgent tasks will be postponed.

[0491] Step 6:

[0492] The device automatically generates and sends notifications to the user based on instructions from the server. The content and timing of the notifications are customized according to the user's emotions.

[0493] Step 7:

[0494] Users report their progress on tasks in real time via their devices, and the server incorporates this information to update the database. The updated information is then optimized through a system-wide feedback loop.

[0495] Step 8:

[0496] The server analyzes progress reports and sentiment data to generate insights for improving system operations. These analysis results can be used to optimize future notification schedules and prioritization.

[0497] (Example 2)

[0498] 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."

[0499] The present invention aims to achieve more personalized task management in a task management system, taking into account the user's emotional state, in contrast to conventional data-centric approaches. The goal is to reduce the user's mental burden and enable more efficient work operations.

[0500] 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.

[0501] In this invention, the server includes information transformation means for integrating and standardizing collected information, means for analyzing the information using natural language processing technology based on the standardized information, and means for identifying the type of information from the analyzed information and evaluating the importance of that information. This enables dynamic task management and reduction of mental burden in accordance with the user's emotional state.

[0502] "Collected information" refers to data on issues and progress obtained from each department and business process.

[0503] "Information transformation means for standardization" refers to a process or technology for organizing data provided in different formats into a consistent format.

[0504] "Natural language analysis technology" is a technique for analyzing text data and extracting useful information, and is also known as natural language processing (NLP).

[0505] "Identifying the type of information" refers to the process of analyzing collected information and classifying it into the category to which each piece of information belongs.

[0506] "Assessing the importance of information" refers to the process of numerically or hierarchically evaluating the impact and urgency of information and determining its priority.

[0507] "Time management means" refers to a technology or process that has a scheduling function to send notifications to users at the right time.

[0508] "Emotional analysis means" refers to a technology or method for analyzing a user's emotions and capturing that state as data.

[0509] "Dynamic prioritization" refers to the process of constantly changing the order in which information is processed, taking into account the user's emotional information to keep it up-to-date.

[0510] "Data management means" refers to the means of recording, tracking, and updating data to maintain the consistency and accuracy of information.

[0511] A "feedback loop" refers to a process of periodically reviewing data and conditions within a system and making improvements or optimizations as needed.

[0512] The system of this invention efficiently manages collected information and realizes dynamic task management that also takes into account the user's emotional state. Cooperation between the server, terminal, and user is essential for implementing this invention.

[0513] The server functions as a central hub for managing information centrally. First, the server collects issue information from each department and project team and stores it in an integrated database. Next, it standardizes the information using data transformation methods, and then utilizes Google Cloud Natural Language API and other APIs as a platform for analyzing the information using natural language processing technology. Furthermore, it incorporates sentiment analysis engines such as IBM Watson to adjust the priority of issues while considering the user's emotional state. In this process, data transformation using ETL (Extract, Transform, Load) technology is performed, and the analysis results are fed back in real time.

[0514] The device provides an interface for users to easily input information and receive feedback. This interface incorporates facial recognition technology using the Microsoft Face API and a pull-down menu that quantifies emotional states, simplifying the input of emotional states. Users can intuitively input emotional states by operating the device, making further interaction easier. This intuitive design encourages active user participation in the system.

[0515] As a concrete example, consider a scenario where a project manager is facing an urgent issue. This user inputs emotional information, such as "high stress levels," along with the issue via their device. The server analyzes this information in real time and sends notifications to the user with dynamically adjusted priorities based on their emotional state. These notifications aim to improve both work efficiency and reduce psychological burden.

[0516] By using a generative AI model, prompts are generated that provide specific and appropriate feedback and suggestions based on the user's emotional and problem information. An example of a prompt might be: "Let's say you have many tasks in progress and are feeling a little stressed. When a new task is added in this situation, consider how this system would prioritize the task and alleviate your stress." This makes it easier for users to visualize specific usage scenarios.

[0517] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0518] Step 1:

[0519] The server collects issue information provided in various formats from each department and project team. The input is issue data, and the output is issue information in a unified format. An ETL process is used as the data transformation method to convert the data into JSON or XML format. This ensures that the information is stored in the database in a consistent data format.

[0520] Step 2:

[0521] The server applies natural language processing techniques to standardized information. Specifically, the server uses the Google Cloud Natural Language API to analyze text data of assignments. The input is standardized assignment information, and the output is assignment information with categorized and importance levels. The assignments are classified by category, and the importance level of each category is evaluated using a machine learning algorithm.

[0522] Step 3:

[0523] The server analyzes the user's emotional information using an emotion analysis tool. The input is emotional state data obtained from the user via their terminal, and the output is the analyzed emotional information. The IBM Watson emotion analysis API is used to capture the user's emotional state as numerical data. This information is used to adjust the priority of tasks.

[0524] Step 4:

[0525] The terminal provides users with an interface for registering tasks and inputting emotions. Specifically, users can select their emotional state from a pull-down menu or input it as text on the terminal. Input consists of the user's task and emotion data, while output is standardized data sent to the server.

[0526] Step 5:

[0527] The server dynamically adjusts the priority of issues based on all collected information and generates and sends notifications optimized for the user. Input is analyzed issue information and sentiment information, and output is a list of issues with revised priorities. A scheduling mechanism is used to generate notifications according to priority and adjust notification timing.

[0528] Step 6:

[0529] The user reviews the list of tasks presented through their terminal and submits feedback as needed. The input is the notified list of tasks, and the output is the user's feedback information. This feedback is then used by the server to inform the next task evaluation process.

[0530] (Application Example 2)

[0531] 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."

[0532] In today's work environment, the impact of employees' emotional states on work efficiency and safety cannot be ignored. However, conventional task management systems are data-centric and fail to take into account the emotional state of workers, resulting in problems such as workload imbalances and a higher likelihood of human error. To solve these problems, there is a need for a system that can recognize emotional states in real time and dynamically adjust task priorities and work schedules.

[0533] 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.

[0534] In this invention, the server includes data transformation means for integrating and standardizing collected information, means for analyzing tasks using natural language processing technology based on the standardized information, and emotion recognition means for detecting the user's emotional state and analyzing the data to adjust the priority of tasks. This enables personalized task management that takes into account the user's emotional state, leading to appropriate workload distribution and improved work efficiency.

[0535] A "data conversion means" is a method that has the function of integrating collected information and converting it into a unified format.

[0536] "Natural language processing technology" refers to the technology that enables computers to understand and analyze human language.

[0537] An "emotion recognition method" is a method for detecting a user's emotional state, collecting data on it, and analyzing it.

[0538] "Prioritizing tasks" refers to the process of dynamically determining the importance and implementation order of tasks based on analyzed data.

[0539] "Personalized issue management" is a management method that performs adaptive issue processing according to the emotional state and work environment of individual users.

[0540] The system of this invention enables personalized task management that takes into account the user's emotional state. The system consists of three components: a server, a terminal, and a user.

[0541] The server integrates the collected information and converts it into a unified format using data transformation tools. Furthermore, it analyzes the standardized information using natural language processing techniques to identify the category and importance of the issues. Based on this analysis, the server detects the user's emotional state using emotion recognition tools and adjusts the priority of the issues using that data. For emotion recognition, wearable devices such as smart glasses or headband-type devices are used, and the data they transmit is analyzed.

[0542] The terminal provides an interface for registering tasks and reporting progress. When users input their emotional state, they can also send emotional data to a server via the terminal. The process includes registering emotional states through facial recognition technology and simplified input, enabling flexible data entry.

[0543] By registering emotional information with the system, users can receive adjustments to reduce their physical or psychological burden. For example, if a factory worker wears smart glasses and their stress level changes in real time, the server can adjust the priority and schedule of robotic tasks based on that data.

[0544] As a concrete example, consider robot operation on a factory production line. This robot can adjust its operating speed according to the worker's stress level and take adaptive actions such as temporarily easing its movements when the worker feels excessively fatigued. An example of input to the generative AI model is as follows:

[0545] "The robots used in the project need to analyze workers' stress levels in real time and adjust task schedules based on that data. Consider how the robots will utilize this information to adjust priorities."

[0546] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0547] Step 1:

[0548] The device receives task registration information and emotional state from the user. Task information is entered as text data, and emotional state is acquired through facial recognition via a wearable device or as simple input data. This forms the basis of the user's current task content and emotional data.

[0549] Step 2:

[0550] The server standardizes the task information received from the terminal using a data conversion mechanism and analyzes it using natural language processing technology. The analysis identifies the task category and importance level from the input text data. As a result, preliminary information for the priority level of each task is generated.

[0551] Step 3:

[0552] The server analyzes emotional data acquired from wearable devices using emotion recognition tools. In this analysis, a generative AI model evaluates the emotional data based on the user's emotional state (stress, fatigue, etc.) and readjusts the priority of tasks. At this time, the generative AI model is used to make specific adjustments, such as lowering the priority of certain tasks when stress levels are high.

[0553] Step 4:

[0554] The server generates a finalized schedule with adjusted priorities and notifies the user via their terminal. The notification includes the order and recommended timing for completing tasks, helping the user work with minimal psychological and physical burden.

[0555] Step 5:

[0556] When a user enters progress information into their terminal, it is sent back to the server, which then updates the plan to include this data. This enables real-time schedule optimization. Based on the progress data, future work plans are automatically revised, resulting in adaptive task management.

[0557] 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.

[0558] 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.

[0559] 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.

[0560] [Fourth Embodiment]

[0561] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0562] 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.

[0563] 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).

[0564] 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.

[0565] 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.

[0566] 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).

[0567] 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.

[0568] 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.

[0569] 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.

[0570] 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.

[0571] 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.

[0572] 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.

[0573] 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".

[0574] The system of this invention is built around three main elements: a server, a terminal, and a user, with the aim of improving the efficiency of digital issue management. This system centrally manages issue management data collected from each department and performs automatic analysis using natural language processing technology to categorize issues, assess their importance, and set priorities.

[0575] The server integrates issue information received from each department. The integrated information is standardized through data transformation and analyzed using natural language processing technology. Based on the analysis results, the server categorizes the issues and evaluates their importance. Next, based on the evaluated information, it automatically generates reminders and progress check notifications for users and sends them according to a schedule.

[0576] The terminal accepts input from users and provides an interface for registering tasks and reporting progress. Users can easily register tasks via the terminal and report their progress in real time.

[0577] For example, if a technical department registers an issue stating that "the server's storage capacity is about to reach its limit," the terminal sends the information to the server. The server analyzes this information, categorizes it as "IT infrastructure management," and assigns it high priority to prevent major disruptions. Based on this, the server sends a reminder to the responsible technician via email or internal chat, emphasizing the need for immediate action. Once the technician completes the task and submits a progress report from their terminal, the server processes the information and notifies the administrator of the completion.

[0578] Thus, the system based on the present invention highly automates the issue management process and supports efficient business operations.

[0579] The following describes the processing flow.

[0580] Step 1:

[0581] Users input issue information managed by their respective departments and send it to the server as a digital management sheet via their terminals. The issue information includes an overview of the issue, the person responsible, and the deadline.

[0582] Step 2:

[0583] The server stores the received task information in a database and uses data conversion tools to standardize the format. This allows for centralized management of data in various formats.

[0584] Step 3:

[0585] The server applies natural language processing techniques to standardized task data to perform text analysis. It understands the theme and intent of the tasks and identifies related categories.

[0586] Step 4:

[0587] The server evaluates the importance of the analyzed information and prioritizes each issue. The evaluation is based on the urgency of the issue and its impact on business operations.

[0588] Step 5:

[0589] Based on instructions from the server, the terminal automatically creates reminders for users (assigned personnel and administrators) and sends notifications according to the specified schedule. The notifications include details of the issue and the deadline for action.

[0590] Step 6:

[0591] Users input the progress of their tasks via their devices and report it to the server. This updates the task status in real time, and the latest information is reported to the administrator.

[0592] Step 7:

[0593] The server analyzes the received progress information and optimizes the entire system through a feedback loop to improve efficiency. This enables improvements in future task handling processes.

[0594] (Example 1)

[0595] 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".

[0596] The problem that this invention aims to solve is the inefficiency of digital issue management in companies and organizations. Specifically, this includes the amount of manual work involved in managing issue information provided by each department, the fragmentation of information, and the difficulty in appropriately prioritizing. These problems can lead to delays in responses or the overlooking of important issues.

[0597] 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.

[0598] In this invention, the server includes processing means for integrating task information from multiple sources and converting it into a unified format, analysis means for analyzing the unified information using natural language processing technology and classifying and evaluating tasks, and notification generation and transmission means for informing users via electronic communication based on the analysis results. This enables efficient aggregation, analysis, and rapid response to task information.

[0599] "Issue information" refers to data that includes details about problems and tasks that occur within a company or organization.

[0600] "Integration" is the process of combining data from different sources into a single, cohesive form.

[0601] A "unified format" refers to a standardized form obtained by converting data from different formats into a common standard.

[0602] "Processing means" refers to functions or software modules used to perform data conversion and analysis.

[0603] "Natural language processing technology" is a technology that enables computers to understand and analyze human language, and is particularly used for processing text data.

[0604] A "mission" refers to an individual task or project carried out in accordance with a specific objective.

[0605] Classification is the process of grouping information based on specific criteria.

[0606] "Evaluation" is the act of judging the importance and priority of information.

[0607] "Analysis results" refer to the output data after processing information obtained using natural language processing technology.

[0608] "Notification generation" refers to the automatic creation of messages to inform users about issues.

[0609] "Transmission method" refers to the method or system used to deliver generated notifications to users via email, chat, etc.

[0610] This invention is a system that combines three elements—a server, a terminal, and a user—to improve the efficiency of task management. The embodiments thereof are described below.

[0611] The server receives issue information sent from each department and plays a role in data integration. The server aggregates the information via API and converts the data into a unified format using the 'pandas' library. Next, the server analyzes the data using natural language processing technology. It utilizes natural language processing libraries such as "spaCy" and "NLTK" to categorize and prioritize issues. Based on the analysis results, the server automatically generates reminders and notifications using "smtplib" and sends them to relevant parties via email or the company's internal chat via the "Slack API".

[0612] The terminal provides an interface for users to register tasks and report their progress. Information entered by the user is sent to the server via the terminal and reflected in the database in real time. This operation can be performed intuitively using a web browser or a dedicated application.

[0613] As a concrete example, the manufacturing department registers a task: "We need to finish the prototype of the new product by next month." The server receives this task information sent from the terminal, and through an analysis process, classifies it into the category of "Manufacturing Schedule Management" and assigns a priority. Depending on the importance level, the server sends an appropriate reminder to the person in charge.

[0614] An example of a prompt message for a generative AI model is: "Receive new challenges from the technical department, analyze them, categorize them accordingly, and set priorities."

[0615] Thus, the present invention streamlines operations and facilitates communication within organizations through automated problem analysis and notification.

[0616] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0617] Step 1:

[0618] The terminal receives new task information from the user as input. The user enters information such as task details, deadline, and responsible person into a form. This input data is formatted correctly on the terminal and prepared for transmission to the server.

[0619] Step 2:

[0620] The server receives task information sent from the terminal. The received data is saved to an internal database via an API. The input here is task data from the terminal, and the output is a formatted database entry. The Python "pandas" library is used for data cleaning and conversion to a unified format.

[0621] Step 3:

[0622] The server applies natural language processing techniques to the stored task information. The input is formatted task data, and the output is the analyzed task category and evaluation information. The data is tokenized and keywords are extracted using the "spaCy" library. Through this process, tasks are classified into specific categories, and their importance and priority are automatically determined.

[0623] Step 4:

[0624] The server generates reminders and notifications based on the analysis results. The input is the analyzed category and priority information, and the output is a notification message. Using Python's "smtplib" and the "Slack API," the generated notifications are sent to the appropriate users via email or chat applications. In this step, users receive information regarding specific action plans and schedules.

[0625] Step 5:

[0626] The terminal provides the user with an interface to report the progress of an assignment. When the user updates the progress, that information is immediately sent to the server via the terminal. It receives new progress data as input and converts it into a format that can be sent to the server.

[0627] Step 6:

[0628] The server receives progress information from the terminal and updates the database. The input is the new progress data, and the output is the updated database state. This allows administrators to monitor progress in real time and take action as needed.

[0629] (Application Example 1)

[0630] 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".

[0631] In manufacturing, failure to respond quickly to abnormalities or problems can lead to production line stagnation and decreased production efficiency. Furthermore, accurately assessing the importance and priority of problems is difficult, and precise instructions must be communicated to the machinery. In this context, a system that automates problem analysis and processing is necessary.

[0632] 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.

[0633] In this invention, the server includes data transformation means for integrating and standardizing collected information, means for analyzing problems using natural language processing technology based on the standardized information, and means for identifying problem categories from the analyzed information and evaluating their importance. This enables the machine work device to automatically adjust and execute tasks according to priority.

[0634] "Integrating collected information" refers to the process of centralizing data obtained from multiple sources and making it analyzable as a whole.

[0635] A "data conversion method for standardization" is a function that converts data expressed in various formats into a unified format.

[0636] "Methods for analyzing problems using natural language processing technology" refers to the function of deciphering information related to a problem and extracting its meaning using technology that allows computers to understand and analyze human language data.

[0637] "A means of identifying the category of issues and evaluating their importance" refers to a function that classifies the analyzed issues into specific groups and determines the priority level of each issue.

[0638] "Means by which machine work devices automatically adjust and execute tasks according to priority" refers to a function in a manufacturing environment where equipment receives work instructions based on the priority of tasks and automatically performs actions accordingly.

[0639] The server integrates data collected from the manufacturing floor and converts it into a standardized format. This makes it possible to handle diverse data obtained from different devices and sensors in a unified manner. Next, the server analyzes this data using natural language processing technology and automatically evaluates the category and importance of the issues. Python's "Natural Language Toolkit (NLTK)" can be used for this analysis. Based on this information, the server automatically sends work instructions to the machine work equipment according to priority.

[0640] The terminal provides an interface for users to register progress reports and new issues at the manufacturing site. Through this interface, users can input information and report it to the server in real time. For example, if a user reports a delay in parts supply on a particular manufacturing line, this is sent to the server as an issue.

[0641] Specific reminders and notifications to users are automatically generated and sent via the company's internal communication platform. Software such as Slack or Microsoft Teams can be used.

[0642] This system streamlines issue management in the manufacturing process and enables machinery and equipment to perform tasks quickly and appropriately. For example, based on a prompt message such as, "An anomaly has been detected on the new production line. Please enter the details of this anomaly and the necessary countermeasures into the issue management system," discovered issues are quickly communicated and processed.

[0643] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0644] Step 1:

[0645] The terminal allows users to input manufacturing site challenges and progress information into an interface. This input data is sent to the server as strings or numerical information. The server converts the received data into a standardized format using data conversion means. This allows data in different formats to be unified into a unified structure.

[0646] Step 2:

[0647] The server analyzes standardized data using natural language processing techniques. This analysis utilizes the Natural Language Toolkit (NLTK) or similar libraries to analyze the content of the input task. The analysis results determine the task's category and importance. This process extracts information from text data and maps it to categories.

[0648] Step 3:

[0649] The server determines priorities based on category and importance from the analyzed issue information. This process involves algorithmic prioritization based on pre-defined priority criteria. Once priorities are determined, the data is used to generate work instructions for machine work equipment.

[0650] Step 4:

[0651] The server generates specific work instructions for machine workpieces based on prioritized issues. These instructions are sent to the workpieces and executed automatically. For example, if there is a delay in parts supply, an immediate replenishment instruction is sent to the machine. The instructions include notifications to the necessary equipment via the factory's communication infrastructure.

[0652] Step 5:

[0653] The terminal notifies the user of analysis results and instructions from the server. For example, reminders informing users of the current status of ongoing tasks and issues are automatically sent through the enterprise messaging platform. This allows users to stay informed of the latest status in real time.

[0654] 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.

[0655] The system of this invention not only improves the efficiency of issue management but also provides a more personalized management experience by recognizing and reflecting user emotions. The system mainly consists of the interaction of a server, terminals, and users, and improves the accuracy of issue management by incorporating an emotion engine.

[0656] The server integrates issue information collected from each department into a database and standardizes it using data transformation techniques. The standardized information is then analyzed using natural language processing technology, and issues are automatically categorized and their importance is assessed. An emotion engine is then incorporated to adjust the priority of issues by taking the user's emotional state into account in the analysis results. For example, if a user is under high stress, the system can change the priority of non-urgent issues, such as postponing them.

[0657] The terminal provides an interface for registering tasks, reporting progress, and inputting the user's emotional state. Through the terminal, users can record their emotions through simple input or facial recognition technology.

[0658] As a concrete example, consider a scenario where a project manager is facing multiple urgent issues. When the user registers emotional information along with the issues via their device, the server analyzes this information and considers the user's current stress level when prioritizing the issues. If necessary, the timing of notifications is adjusted to mitigate psychological burden.

[0659] This system goes beyond simple data-centric issue management; it also takes user emotions into account, resulting in intuitive and user-friendly operation. This enables more effective business operations that consider human factors in issue processing.

[0660] The following describes the processing flow.

[0661] Step 1:

[0662] Users input their emotional state along with task information via their device. This emotional state is recorded using a questionnaire-style selection process and facial recognition technology.

[0663] Step 2:

[0664] The terminal receives input from the user and sends compiled issue information and emotional data to the server. This data includes issue details, emotional state, and assignee information.

[0665] Step 3:

[0666] The server stores the received task information in a database and standardizes the information using a data conversion mechanism. This standardization allows for centralized management of data in various input formats.

[0667] Step 4:

[0668] The server analyzes the problem content using natural language processing technology based on standardized information. This analysis identifies the problem category and assesses its importance.

[0669] Step 5:

[0670] The server uses an emotion engine to integrate the user's emotional state into the analysis results. This allows the importance and priority of the evaluated tasks to be adjusted based on emotional factors. For example, if the user is in a high-stress state, non-urgent tasks will be postponed.

[0671] Step 6:

[0672] The device automatically generates and sends notifications to the user based on instructions from the server. The content and timing of the notifications are customized according to the user's emotions.

[0673] Step 7:

[0674] Users report their progress on tasks in real time via their devices, and the server incorporates this information to update the database. The updated information is then optimized through a system-wide feedback loop.

[0675] Step 8:

[0676] The server analyzes progress reports and sentiment data to generate insights for improving system operations. These analysis results can be used to optimize future notification schedules and prioritization.

[0677] (Example 2)

[0678] 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".

[0679] The present invention aims to achieve more personalized task management in a task management system, taking into account the user's emotional state, in contrast to conventional data-centric approaches. The goal is to reduce the user's mental burden and enable more efficient work operations.

[0680] 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.

[0681] In this invention, the server includes information transformation means for integrating and standardizing collected information, means for analyzing the information using natural language processing technology based on the standardized information, and means for identifying the type of information from the analyzed information and evaluating the importance of that information. This enables dynamic task management and reduction of mental burden in accordance with the user's emotional state.

[0682] "Collected information" refers to data on issues and progress obtained from each department and business process.

[0683] "Information transformation means for standardization" refers to a process or technology for organizing data provided in different formats into a consistent format.

[0684] "Natural language analysis technology" is a technique for analyzing text data and extracting useful information, and is also known as natural language processing (NLP).

[0685] "Identifying the type of information" refers to the process of analyzing collected information and classifying it into the category to which each piece of information belongs.

[0686] "Assessing the importance of information" refers to the process of numerically or hierarchically evaluating the impact and urgency of information and determining its priority.

[0687] "Time management means" refers to a technology or process that has a scheduling function to send notifications to users at the right time.

[0688] "Emotional analysis means" refers to a technology or method for analyzing a user's emotions and capturing that state as data.

[0689] "Dynamic prioritization" refers to the process of constantly changing the order in which information is processed, taking into account the user's emotional information to keep it up-to-date.

[0690] "Data management means" refers to the means of recording, tracking, and updating data to maintain the consistency and accuracy of information.

[0691] A "feedback loop" refers to a process of periodically reviewing data and conditions within a system and making improvements or optimizations as needed.

[0692] The system of this invention efficiently manages collected information and realizes dynamic task management that also takes into account the user's emotional state. Cooperation between the server, terminal, and user is essential for implementing this invention.

[0693] The server functions as a central hub for managing information centrally. First, the server collects issue information from each department and project team and stores it in an integrated database. Next, it standardizes the information using data transformation methods, and then utilizes Google Cloud Natural Language API and other APIs as a platform for analyzing the information using natural language processing technology. Furthermore, it incorporates sentiment analysis engines such as IBM Watson to adjust the priority of issues while considering the user's emotional state. In this process, data transformation using ETL (Extract, Transform, Load) technology is performed, and the analysis results are fed back in real time.

[0694] The device provides an interface for users to easily input information and receive feedback. This interface incorporates facial recognition technology using the Microsoft Face API and a pull-down menu that quantifies emotional states, simplifying the input of emotional states. Users can intuitively input emotional states by operating the device, making further interaction easier. This intuitive design encourages active user participation in the system.

[0695] As a concrete example, consider a scenario where a project manager is facing an urgent issue. This user inputs emotional information, such as "high stress levels," along with the issue via their device. The server analyzes this information in real time and sends notifications to the user with dynamically adjusted priorities based on their emotional state. These notifications aim to improve both work efficiency and reduce psychological burden.

[0696] By using a generative AI model, prompts are generated that provide specific and appropriate feedback and suggestions based on the user's emotional and problem information. An example of a prompt might be: "Let's say you have many tasks in progress and are feeling a little stressed. When a new task is added in this situation, consider how this system would prioritize the task and alleviate your stress." This makes it easier for users to visualize specific usage scenarios.

[0697] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0698] Step 1:

[0699] The server collects issue information provided in various formats from each department and project team. The input is issue data, and the output is issue information in a unified format. An ETL process is used as the data transformation method to convert the data into JSON or XML format. This ensures that the information is stored in the database in a consistent data format.

[0700] Step 2:

[0701] The server applies natural language processing techniques to standardized information. Specifically, the server uses the Google Cloud Natural Language API to analyze text data of assignments. The input is standardized assignment information, and the output is assignment information with categorized and importance levels. The assignments are classified by category, and the importance level of each category is evaluated using a machine learning algorithm.

[0702] Step 3:

[0703] The server analyzes the user's emotional information using an emotion analysis tool. The input is emotional state data obtained from the user via their terminal, and the output is the analyzed emotional information. The IBM Watson emotion analysis API is used to capture the user's emotional state as numerical data. This information is used to adjust the priority of tasks.

[0704] Step 4:

[0705] The terminal provides users with an interface for registering tasks and inputting emotions. Specifically, users can select their emotional state from a pull-down menu or input it as text on the terminal. Input consists of the user's task and emotion data, while output is standardized data sent to the server.

[0706] Step 5:

[0707] The server dynamically adjusts the priority of issues based on all collected information and generates and sends notifications optimized for the user. Input is analyzed issue information and sentiment information, and output is a list of issues with revised priorities. A scheduling mechanism is used to generate notifications according to priority and adjust notification timing.

[0708] Step 6:

[0709] The user reviews the list of tasks presented through their terminal and submits feedback as needed. The input is the notified list of tasks, and the output is the user's feedback information. This feedback is then used by the server to inform the next task evaluation process.

[0710] (Application Example 2)

[0711] 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".

[0712] In today's work environment, the impact of employees' emotional states on work efficiency and safety cannot be ignored. However, conventional task management systems are data-centric and fail to take into account the emotional state of workers, resulting in problems such as workload imbalances and a higher likelihood of human error. To solve these problems, there is a need for a system that can recognize emotional states in real time and dynamically adjust task priorities and work schedules.

[0713] 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.

[0714] In this invention, the server includes data transformation means for integrating and standardizing collected information, means for analyzing tasks using natural language processing technology based on the standardized information, and emotion recognition means for detecting the user's emotional state and analyzing the data to adjust the priority of tasks. This enables personalized task management that takes into account the user's emotional state, leading to appropriate workload distribution and improved work efficiency.

[0715] A "data conversion means" is a method that has the function of integrating collected information and converting it into a unified format.

[0716] "Natural language processing technology" refers to the technology that enables computers to understand and analyze human language.

[0717] An "emotion recognition method" is a method for detecting a user's emotional state, collecting data on it, and analyzing it.

[0718] "Prioritizing tasks" refers to the process of dynamically determining the importance and implementation order of tasks based on analyzed data.

[0719] "Personalized issue management" is a management method that performs adaptive issue processing according to the emotional state and work environment of individual users.

[0720] The system of this invention enables personalized task management that takes into account the user's emotional state. The system consists of three components: a server, a terminal, and a user.

[0721] The server integrates the collected information and converts it into a unified format using data transformation tools. Furthermore, it analyzes the standardized information using natural language processing techniques to identify the category and importance of the issues. Based on this analysis, the server detects the user's emotional state using emotion recognition tools and adjusts the priority of the issues using that data. For emotion recognition, wearable devices such as smart glasses or headband-type devices are used, and the data they transmit is analyzed.

[0722] The terminal provides an interface for registering tasks and reporting progress. When users input their emotional state, they can also send emotional data to a server via the terminal. The process includes registering emotional states through facial recognition technology and simplified input, enabling flexible data entry.

[0723] By registering emotional information with the system, users can receive adjustments to reduce their physical or psychological burden. For example, if a factory worker wears smart glasses and their stress level changes in real time, the server can adjust the priority and schedule of robotic tasks based on that data.

[0724] As a concrete example, consider robot operation on a factory production line. This robot can adjust its operating speed according to the worker's stress level and take adaptive actions such as temporarily easing its movements when the worker feels excessively fatigued. An example of input to the generative AI model is as follows:

[0725] "The robots used in the project need to analyze workers' stress levels in real time and adjust task schedules based on that data. Consider how the robots will utilize this information to adjust priorities."

[0726] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0727] Step 1:

[0728] The device receives task registration information and emotional state from the user. Task information is entered as text data, and emotional state is acquired through facial recognition via a wearable device or as simple input data. This forms the basis of the user's current task content and emotional data.

[0729] Step 2:

[0730] The server standardizes the task information received from the terminal using a data conversion mechanism and analyzes it using natural language processing technology. The analysis identifies the task category and importance level from the input text data. As a result, preliminary information for the priority level of each task is generated.

[0731] Step 3:

[0732] The server analyzes emotional data acquired from wearable devices using emotion recognition tools. In this analysis, a generative AI model evaluates the emotional data based on the user's emotional state (stress, fatigue, etc.) and readjusts the priority of tasks. At this time, the generative AI model is used to make specific adjustments, such as lowering the priority of certain tasks when stress levels are high.

[0733] Step 4:

[0734] The server generates a finalized schedule with adjusted priorities and notifies the user via their terminal. The notification includes the order and recommended timing for completing tasks, helping the user work with minimal psychological and physical burden.

[0735] Step 5:

[0736] When a user enters progress information into their terminal, it is sent back to the server, which then updates the plan to include this data. This enables real-time schedule optimization. Based on the progress data, future work plans are automatically revised, resulting in adaptive task management.

[0737] 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.

[0738] 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.

[0739] 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.

[0740] 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.

[0741] 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.

[0742] 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.

[0743] 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.

[0744] 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.

[0745] 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."

[0746] 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.

[0747] 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.

[0748] 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.

[0749] 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.

[0750] 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.

[0751] 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.

[0752] 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.

[0753] 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.

[0754] 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.

[0755] 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.

[0756] 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.

[0757] 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.

[0758] The following is further disclosed regarding the embodiments described above.

[0759] (Claim 1)

[0760] A data transformation means for integrating and standardizing the collected information,

[0761] A means of analyzing problems using natural language processing technology based on standardized information,

[0762] A means to identify the category of the problem from the analyzed information and evaluate its importance,

[0763] A scheduling mechanism for automatically generating and sending notifications to users based on evaluations,

[0764] A data management system for tracking progress and updating status,

[0765] A system that includes this.

[0766] (Claim 2)

[0767] The system according to claim 1, comprising means for automatically determining the priority of task processing based on categories and importance identified by analysis.

[0768] (Claim 3)

[0769] The system according to claim 1, comprising means for dynamically reflecting user-registered progress information and constructing a feedback loop for optimization.

[0770] "Example 1"

[0771] (Claim 1)

[0772] A processing means for integrating problem information from multiple sources and converting it into a unified format,

[0773] An analytical means for analyzing unified information using natural language processing technology, and for classifying and evaluating tasks,

[0774] A means for generating and transmitting notifications to inform users via electronic communication based on the analysis results,

[0775] A dynamic data management system for updating user registration details and progress information, and for tracking the progress of operations.

[0776] A system that includes this.

[0777] (Claim 2)

[0778] The system according to claim 1, comprising a processing means for automatically determining the processing priority of tasks from the analysis results.

[0779] (Claim 3)

[0780] The system according to claim 1, comprising processing means for constructing an information feedback mechanism that immediately reflects ongoing work information from users and optimizes it for completion.

[0781] "Application Example 1"

[0782] (Claim 1)

[0783] A data transformation means for integrating and standardizing the collected information,

[0784] A means of analyzing problems using natural language processing technology based on standardized information,

[0785] A means to identify the category of the problem from the analyzed information and evaluate its importance,

[0786] A scheduling mechanism for automatically generating and sending notifications to users based on evaluations,

[0787] A data management system for tracking progress and updating status,

[0788] Based on the analysis results, the machine work device automatically adjusts and executes tasks according to priority,

[0789] A system that includes this.

[0790] (Claim 2)

[0791] The system according to claim 1, which includes means for automatically determining the priority of task processing based on categories and importance identified by analysis, and for transmitting instructions to a machine work device.

[0792] (Claim 3)

[0793] The system according to claim 1, which includes means for dynamically reflecting user-registered progress information and building a feedback loop for optimization, and for monitoring the work results of a machine work device.

[0794] "Example 2 of combining an emotion engine"

[0795] (Claim 1)

[0796] Information transformation means for integrating collected information and standardizing it,

[0797] A means of analyzing information using natural language processing technology based on standardized information,

[0798] A means for identifying the type of information from the analyzed information and evaluating its importance,

[0799] A time management system for automatically generating and sending notifications to users based on evaluations,

[0800] An emotion analysis method that acquires user emotional information and adds that emotional state to the analysis results,

[0801] A means of dynamically adjusting the priority of information according to emotional state,

[0802] A data management system for tracking progress and updating status,

[0803] A system that includes this.

[0804] (Claim 2)

[0805] The system according to claim 1, comprising means for automatically determining the priority of information processing based on the type and importance identified by analysis and the emotional state of the user.

[0806] (Claim 3)

[0807] The system according to claim 1, comprising means for constructing a feedback loop for dynamically reflecting and optimizing progress information and emotional states registered by users.

[0808] "Application example 2 when combining with an emotional engine"

[0809] (Claim 1)

[0810] A data transformation means for integrating and standardizing the collected information,

[0811] A means of analyzing problems using natural language processing technology based on standardized information,

[0812] A means to identify the category of the problem from the analyzed information and evaluate its importance,

[0813] A scheduling mechanism for automatically generating and sending notifications to users based on evaluations,

[0814] A data management system for tracking progress and updating status,

[0815] An emotion recognition method for detecting the user's emotional state, analyzing that data, and adjusting the priority of tasks,

[0816] A system that includes this.

[0817] (Claim 2)

[0818] The system according to claim 1, comprising means for automatically determining the priority of task processing based on categories and importance identified by analysis, and adjusting it while taking into account the user's emotional state.

[0819] (Claim 3)

[0820] The system according to claim 1, which includes means for dynamically reflecting user-registered progress information, building a feedback loop for optimization, and further performing priority adjustments based on sentiment data in the process. [Explanation of Symbols]

[0821] 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 data transformation means for integrating and standardizing the collected information, A means of analyzing problems using natural language processing technology based on standardized information, A means to identify the category of the problem from the analyzed information and evaluate its importance, A scheduling mechanism for automatically generating and sending notifications to users based on evaluations, A data management system for tracking progress and updating status, Based on the analysis results, the machine work device automatically adjusts and executes tasks according to priority, A system that includes this.

2. The system according to claim 1, which includes means for automatically determining the priority of task processing based on the categories and importance identified by analysis, and for transmitting instructions to a machine work device.

3. The system according to claim 1, which includes means for dynamically reflecting user-registered progress information and constructing a feedback loop for optimization, and for monitoring the work results of a machine work device.

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