system

Abstract

We provide the system. [Solution] A means to collect information from multiple communication tools, An analysis means for extracting relevant work instructions from the collected information, A generation means for automatically generating tasks based on the extracted information, A management means for managing the generated tasks and tracking their progress, A system including a notification mechanism for generating reminders for the aforementioned managed tasks based on deadlines and priorities.

Description

Technical Field

[0004] , , ,

[0005] , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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] <00,00025>In modern remote work environments, it has become common for individuals and teams to use multiple communication tools. As a result, there is a problem that information dispersion and overlooking of work instructions are likely to occur. Due to this, busy businesspersons and project managers are facing the problem of task omissions and the complexity of progress management. It is required to solve this problem and improve the work efficiency of users.

Means for Solving the Problems

[0005] This invention provides a means for automatically collecting information from multiple communication tools and analyzing work instructions using natural language processing. Furthermore, it has a function to automatically generate tasks based on the extracted information and manage the progress of those tasks in real time. It also provides a means for achieving efficient task management by notifying task reminders based on deadlines and priorities.

[0006] A "communication tool" is a general term for applications and systems that allow users to share information and exchange messages with each other.

[0007] "Means of collecting information" refers to a process that has the function of automatically acquiring and storing data from communication tools.

[0008] "Analysis means" refers to the process of using technologies and algorithms to break down collected information and process it in order to understand its meaning.

[0009] A "generation method" is a process that has the function of automatically creating new data or tasks based on analyzed information.

[0010] "Management means" refers to systems and methods for tracking the progress of generated tasks and data and reflecting updates.

[0011] "Notification method" refers to the process of sending messages to users to inform them of reminders or updates.

[0012] "Natural language processing" is a technology that enables computers to understand, interpret, and generate human language.

[0013] A "reminder" is a notification that alerts a user to a specific task or event.

[0014] "Priority" refers to the criteria used to determine the order in which tasks or work are performed, based on their importance and urgency.

[0015] The "means for real-time update" is a technology or function that immediately reflects new information or changes to the entire system when they occur.

Brief Explanation of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] [[ID=1​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiment for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0020] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, a 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 disks (e.g., hard disks), or magnetic tapes, etc.

[0022] 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).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This invention provides a system for managing multiple communication tools used daily by users and supporting efficient task management. This system consists of three elements: a server, a terminal, and a user, and operates as follows.

[0038] First, the server periodically collects necessary information from various communication tools such as email, chat, and phone calls. The server accesses the data using the APIs and protocols of each tool and automatically stores the information.

[0039] Next, the server analyzes the collected data using natural language processing technology. Specifically, the server extracts "task-related instructions" from email bodies and chat messages to identify relevant work instructions.

[0040] Subsequently, the terminal receives the analysis information sent from the server and automatically generates tasks using a generation AI. These generated tasks are then accompanied by metadata such as deadlines and priorities. The terminal displays a list of tasks to the user and provides guidance on how to proceed with the work according to the situation.

[0041] Users can view tasks generated via their devices and set their priorities. By selecting tasks according to their own importance and managing their progress, users can improve their work productivity.

[0042] Furthermore, the server automatically generates reminders based on the deadlines and priorities of tasks set by the user. These reminders are sent to the user's device at the specified date and time, helping to prevent tasks from being overlooked or forgotten.

[0043] Finally, the task progress is updated by the user via their device and sent to the server. The server can then update the information in real time and provide feedback to the user. In this way, the present invention enhances individual work efficiency and enables centralized management of information from multiple tools.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The server collects user data at scheduled times through APIs or protocols of various communication tools. It uses IMAP and POP3 for email servers and accesses various APIs for chat tools to obtain data.

[0047] Step 2:

[0048] The server analyzes the collected data using natural language processing algorithms to identify keywords and phrases related to the task. For example, it extracts messages containing words such as "deadline," "request," and "by XX."

[0049] Step 3:

[0050] The terminal receives analysis results from the server and uses AI to automatically generate tasks based on those results. The generated tasks are then given metadata such as due dates and priorities, and registered in the task management system.

[0051] Step 4:

[0052] Users can review automatically generated tasks through their device and adjust priorities and deadlines as needed. They can also evaluate the importance of tasks and set their execution order.

[0053] Step 5:

[0054] The server creates a reminder schedule based on the set due dates and priorities, and sends notifications to the user's device at the appropriate time. Reminders are sent via email, push notifications, etc.

[0055] Step 6:

[0056] Users update task progress on their devices and report the status of completed and ongoing tasks to the server. The server then displays task progress in real time based on this information.

[0057] Step 7:

[0058] The server provides feedback based on user progress and suggests new action plans as needed, enabling users to maintain an optimal workflow.

[0059] (Example 1)

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

[0061] In today's diverse digital communication landscape, users receive vast amounts of information from various sources, making it challenging to centrally manage this information and efficiently handle tasks. In particular, the fact that information spans multiple platforms increases the risk of overlooking important tasks or delaying high-priority work. Solutions to this problem are urgently needed.

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

[0063] In this invention, the server includes a device means capable of collecting data from multiple digital communication media, an analysis device means for identifying relevant work instructions from the collected data, a generation device for automatically generating work instructions based on the identified information, and a device means for adding deadlines and importance as metadata. This enables the user to efficiently manage information from multiple communication media and prevent tasks from being overlooked or prioritizing incorrectly.

[0064] "Multiple digital communication media" refers to multiple communication platforms that send and receive information in digital format, such as email, chat, and voice calls.

[0065] A "data-collecting device" refers to hardware or software that has the function of automatically acquiring necessary information from a designated digital communication medium and storing or analyzing it.

[0066] An "analysis device" refers to a device that uses natural language processing and other analysis techniques on collected data to identify specific information and interpret its meaning.

[0067] A "generation device" refers to a device that automatically creates tasks for the user to perform based on information identified by an analysis device, and sets additional attributes such as deadlines and importance levels as needed.

[0068] A "management device" refers to a device that organizes generated tasks, tracks and updates their progress, and enables users to visualize and operate them.

[0069] A "reminder generation and notification device" refers to a device that automatically creates and sends messages to users at the appropriate time, taking into account the deadlines and priorities of the tasks being managed, to inform them of the need to perform the task.

[0070] This invention provides a system that enables users to efficiently manage multiple digital communication media and assists in the generation and execution of tasks. This system primarily consists of a server, terminals, and a user interface, with each element working in coordination.

[0071] The server first collects data from numerous digital communication media. This includes software for retrieving information via APIs from email and chat services. For example, it uses the email protocol for email and a specific messaging protocol for chat. The server then analyzes the collected data using natural language processing techniques to identify instructions and information relevant to the task. A text analysis engine is used for this analysis to extract important keywords and phrases.

[0072] The terminal is responsible for automatically generating tasks using a generation AI model based on analysis data sent from the server. Specifically, generation modules implemented in programming languages ​​such as JavaScript (registered trademark) and Python are used. These modules add metadata such as deadlines and priorities to the generated tasks and display them in a user-friendly format.

[0073] Users can view tasks displayed via their device and prioritize them according to their importance. The user interface is designed for intuitive operation, allowing for drag-and-drop task management using a mouse or touch device. This enables users to organize tasks in real time and achieve efficient work operations.

[0074] Furthermore, the server generates reminders based on the managed task information and sends notifications to the user's device at the specified time. This notification function prevents users from forgetting tasks by informing them of deadlines. The notification function is implemented using programs such as Java® or Ruby, and the notification format can be selected from options such as email or app notifications.

[0075] A concrete example is a case where a project manager uses this system to integrate email and chat instructions received from different team members for task management. An example of a prompt message to the generating AI model would be, "Extract task instructions from the following chat history and set the deadline and importance level."

[0076] Thus, the present invention improves user work efficiency by centrally managing information from various communication media and providing task creation, management, and notification functions.

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] The server is responsible for collecting data from digital communication media. First, it retrieves unread messages and notifications through APIs for email and chat services. The input data to be collected includes authentication information and query data for each service. The output generates a list of unprocessed messages and notification data, which then becomes the input data for the next processing step.

[0080] Step 2:

[0081] The server applies natural language processing techniques to the collected data for analysis. Using the collected text data as input, it performs document structure analysis and key phrase extraction, outputting text relevant to work instructions as a result. For example, specific instructions such as "Complete the draft of the project proposal by next week" might be extracted.

[0082] Step 3:

[0083] The terminal receives data analyzed by the server. Based on the instruction data received as input, it uses a generative AI model to automatically generate specific tasks. Prompt text is entered into the generative AI model, and a task with a specific deadline and priority is obtained as output. In this process, a "Project Proposal Draft Creation Task" is generated with attributes such as a deadline of "Next Monday" and a priority of "High".

[0084] Step 4:

[0085] The terminal displays the generated tasks to the user. Through the user interface, it provides a task list and calendar view, making it easy for the user to check them. The displayed tasks are the output, and the user looks at this output to decide on their next action.

[0086] Step 5:

[0087] Users view tasks displayed on their device and prioritize them based on their schedule and importance. They provide feedback to the device regarding task deadlines and progress, and a new priority list is generated. This allows users to focus on individual tasks and complete them efficiently.

[0088] Step 6:

[0089] The server generates reminders based on the task information set by the user. As the deadline approaches, it outputs the reminder as a notification message and sends it to the user's device. This ensures that the progress of all tasks is reliably managed and reduces the risk of missing important deadlines.

[0090] Step 7:

[0091] The progress status is updated when the user completes a task. By entering a completion report into the terminal and sending it to the server, the task status is updated in real time. This then outputs the next task to the user, supporting the creation of a new plan.

[0092] (Application Example 1)

[0093] 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."

[0094] In modern households, communication is active through multiple means of information exchange, leading to a variety of tasks and schedules. However, efficiently organizing these tasks and carrying them out collaboratively within the family is often difficult. Prioritizing individual tasks and transitioning to physical execution also remain challenges. In particular, for elderly people and busy families, there is a need for automated systems to smoothly manage and execute daily tasks.

[0095] 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.

[0096] In this invention, the server includes means for collecting information from multiple information exchange means, analysis means for extracting relevant work instructions from the collected information, and control means for efficiently managing tasks occurring within the home and physically executing those tasks using a device with autonomous driving capabilities. This enables efficient management of tasks within the home, automation of prioritization, and physical execution of tasks by a device with autonomous driving capabilities.

[0097] "Information exchange means" refers to the means and technologies for sending and receiving information between individuals or devices.

[0098] "Work instructions" refer to specific instructions or commands for performing a particular task.

[0099] An "analysis tool" is a device that analyzes collected information and extracts necessary data and instructions from it.

[0100] A "device with autonomous driving capabilities" refers to a device that can automatically move around and perform tasks based on a pre-set program or instructions.

[0101] "Control means" refers to the technologies and functions used to instruct and manage the operation of equipment and devices.

[0102] To implement this invention, it is necessary to construct a system centered on three elements: a server, a terminal, and a user. In this system, the server collects data through multiple information exchange means, analyzes it, and extracts work instructions. Natural language processing technology is used to analyze the collected data, and a generative AI model is used to extract particularly important information.

[0103] The server also automatically generates tasks based on extracted work instructions and adds metadata such as deadlines and priorities. For this purpose, the server utilizes cloud-based databases and scheduling software. Specific examples of such software include "spaCy" and "NLTK" for natural language processing, and "PostgreSQL" for data management.

[0104] The terminal provides a user interface, allowing users to view tasks and set priorities as needed. This enables users to plan their day based on generated tasks and manage their progress in real time. Smartphones and tablets are used as terminal devices, and information is displayed by application software.

[0105] Users update their work progress via their devices and send this information to the server. The server then updates the information in real time, generates reminders as needed, and notifies the user, preventing delays and missed tasks. Reminder notifications are sent using the device's push notification function.

[0106] For example, when a user sends a message to the system such as "Prepare for household chores," that information is transmitted to a device with autonomous driving capabilities, which then automatically performs tasks such as cleaning and arranging items. An example of a prompt message for the generated AI model is: "Extract priority household tasks from the following chat and set the steps to perform them. Example: 'Clean,' 'Create a shopping list,' 'Tidy up the room.'"

[0107] In this way, by centrally managing information across servers, terminals, and users, and enabling the automation of tasks, this system can significantly improve the efficiency of daily life.

[0108] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0109] Step 1:

[0110] The server collects data from multiple means of information exchange. This collection is done via APIs, retrieving data from email, chat, and other digital communications. The input is this digital communication data, and the server collects raw data based on it.

[0111] Step 2:

[0112] The server analyzes the collected data using natural language processing technology to extract work instructions. The input is the raw data obtained in step 1, and tasks and important information are extracted through natural language processing. A generative AI model assists this process to identify specific work instructions.

[0113] Step 3:

[0114] The server automatically generates tasks based on the extracted work instructions and adds metadata. The acquired information is processed by scheduling software, which adds metadata such as deadlines and priorities. The output is a completed task list.

[0115] Step 4:

[0116] The terminal displays the generated tasks in a user interface. The input is a task list sent from the server, which the user uses to check the task status. The terminal also provides options for setting priorities.

[0117] Step 5:

[0118] Users use a terminal to set task priorities and update progress. User actions become input, and the configured information is sent to the server via the terminal. The output is an updated task list.

[0119] Step 6:

[0120] The server uses its storage device to generate a reminder based on the updated information and notifies the device. The server then uses this information to send a push notification to the device indicating the next task to be performed. This ensures the user doesn't forget to complete the task.

[0121] 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.

[0122] This invention provides a system that, in addition to the function of efficiently managing tasks by collecting information from multiple communication tools, is equipped with an emotion engine that recognizes the user's emotions and uses them to assist in task management. This system is composed of a server, terminals, and users, and operates as follows.

[0123] First, the server collects information from sources such as email, chat, and phone calls. The server retrieves and stores data through the APIs and protocols of each tool. The server also uses natural language processing technology to analyze the data and extract work instructions related to the task. Based on this extracted information, the server automatically generates tasks and sends them to the terminal.

[0124] The terminal presents the user with a list of tasks based on the analysis information received from the server and the tasks generated. The terminal is equipped with an emotion engine that analyzes the user's messages and input to recognize their emotional state. For example, it reads phrases such as "tired" or "in a hurry" included in the user's input, as well as negative / positive emotions from the message. Based on the analysis results of this emotion engine, it is possible to dynamically adjust task priorities and reminder settings.

[0125] Users can review generated tasks through their device and manually set priorities. However, the system also takes the user's emotional state into consideration and automatically rearranges tasks and adjusts their importance, thus reducing the user's burden.

[0126] The server can also receive progress information and provide feedback based on emotional states. For example, if a user is feeling stressed, it may reduce the frequency of reminders or send encouraging messages indicating that tasks are progressing smoothly. Specifically, if a user expresses an emotion such as "busy," the emotion engine will recognize this and emphasize reminders to draw attention to tasks with approaching deadlines.

[0127] In this way, the present invention enables flexible and personalized task management that takes user emotions into consideration, providing a less stressful and more efficient work environment.

[0128] The following describes the processing flow.

[0129] Step 1:

[0130] The server collects user data at scheduled times through APIs or protocols of various communication tools. This data includes emails, chat messages, call logs, and more.

[0131] Step 2:

[0132] The server analyzes the collected data using natural language processing algorithms to identify information related to work instructions and tasks. In particular, it extracts sentences containing keywords such as "deadline," "request," and "confirmation," and breaks down the content of the instructions.

[0133] Step 3:

[0134] The terminal uses AI generation based on analysis results sent from the server to automatically generate tasks. At that time, metadata such as due dates and initial priorities are set for the generated tasks.

[0135] Step 4:

[0136] The device uses its built-in emotion engine to recognize the user's emotions. It analyzes the user's interaction history and message content to identify emotions such as "feeling stressed" or "being in a positive state."

[0137] Step 5:

[0138] Based on the analysis results of the emotion engine, the device dynamically adjusts task priorities. For example, if a user indicates high stress levels, it reduces the burden by prioritizing and displaying tasks that require immediate attention.

[0139] Step 6:

[0140] Users can review the task list displayed on their device and manually adjust priorities and deadlines as needed. Feedback on the system's suggestions will be used to improve future adjustments.

[0141] Step 7:

[0142] The server monitors the progress of tasks updated by users and updates the database in a timely manner. The progress is reflected in real time, and the user is given feedback on the current status.

[0143] Step 8:

[0144] Based on the emotional state recognized by the emotion engine, the server sends encouraging messages and relaxation suggestions to the user to support the maintenance of a comfortable work environment.

[0145] Through this series of processes, the present invention provides users with personalized task management and emotionally sensitive work support.

[0146] (Example 2)

[0147] 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".

[0148] Current task management systems do not dynamically adjust priorities while taking into account the user's emotional state, and therefore do not adequately contribute to work efficiency or stress reduction. For this reason, there is a need to provide a personalized management method that reflects the user's emotions, from information gathering to task item generation and notifications.

[0149] 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.

[0150] In this invention, the server includes a device capable of collecting information from multiple communication technologies, an analysis means for extracting relevant work commands from the collected information, and a means for evaluating the user's emotions using emotion analysis technology and dynamically adjusting the priority of work items based on that evaluation. This enables flexible and effective work management based on the user's emotions.

[0151] "Communication technology" refers to all technologies used to send and receive information.

[0152] "Device" refers to a component of hardware or software used to perform a specific function or process.

[0153] "Analysis means" refers to devices and technologies used to analyze information and extract relevant data.

[0154] "Natural language processing technology" refers to the technology used to process human language using computers, enabling the analysis and understanding of text.

[0155] A "generation device" refers to a device that generates specific results or items based on input information.

[0156] A "control device" refers to a device used to operate and manage the entire system.

[0157] A "warning device" refers to a device that notifies the user when certain conditions are met.

[0158] "Emotional analysis technology" refers to technology that analyzes and recognizes a user's emotional state from text and audio data.

[0159] "Feedback" refers to the reactions and evaluations received by users or devices based on the system's output.

[0160] This invention is a system centered on servers, terminals, and users, thereby enabling task management that takes user emotions into consideration. The system components include an information gathering device, analysis means, generation device, control device, warning device, and emotion analysis technology.

[0161] The server acquires information from multiple communication technologies using information gathering devices. This information includes electronic messages, voice recordings, and digital communication logs. After acquisition, the server uses analysis tools to analyze the information using natural language processing technology and extract business instructions and related data. In this process, a specific generative AI model is utilized to interpret complex contexts and identify appropriate data.

[0162] The terminal manages work items automatically generated by a generator based on the analyzed information, using a control device. The terminal displays a task list to the user, which is dynamically changed according to the user's emotional state. Sentiment analysis technology evaluates the user's input text and voice, and uses this as a basis for adjusting the priority of work items. Specifically, it analyzes keywords such as "busy" and "tired" to automatically adjust reminder settings and notification frequencies.

[0163] Users can review these tasks and manually prioritize them, but the workload is reduced through automatic adjustments based on emotions. A feedback function sends user emotional responses to the server in real time, optimizing the overall system operation.

[0164] As a concrete example, here is an example of a prompt message:

[0165] "If a user is worried about a task due tomorrow, the sentiment engine will detect this and set a reminder to prioritize notifications."

[0166] In this way, the present invention enables individually customized task management while taking into account the user's feelings, providing an efficient and less stressful work environment.

[0167] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0168] Step 1:

[0169] The server collects information through multiple communication technologies. Inputs include email, chat messages, and voice call logs. The server uses APIs for each communication technology to retrieve data and store it in a database. The output is raw data awaiting analysis.

[0170] Step 2:

[0171] The server processes the collected raw data using analytical tools. Input data includes text messages and audio data. Using natural language processing techniques, a generative AI model syntactically analyzes the messages and extracts work instructions and emotional states. The output provides task candidates along with the analysis results. For example, if an emotional expression such as "busy" is extracted, time-related tasks will be highlighted.

[0172] Step 3:

[0173] The server generates work items based on the analysis results. Using the analysis results as input, the generation device automatically determines the task content, deadline, and priority, creating a task list. The output is a detailed task list. This list is sent to the terminal and prepared for user access.

[0174] Step 4:

[0175] The terminal displays the task list received from the server. It receives the task list as input and presents it in a user-friendly interface. The terminal utilizes sentiment analysis technology to analyze the user's input and responses, dynamically adjusting the priority of the task list. The output presents a task list optimized for the user.

[0176] Step 5:

[0177] Users view their task list on their terminal and manually adjust priorities as needed. When users input information (e.g., "finished" or "will do later") into their terminal, the task list is updated accordingly. User feedback is sent to the server to help optimize system operation. In this way, the final work progress output is obtained.

[0178] (Application Example 2)

[0179] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0180] In modern factory environments, worker fatigue and stress often affect productivity and safety. Therefore, accurately understanding workers' emotional states and adjusting work instructions accordingly is crucial. However, current systems lack the flexibility to adjust work instructions based on individual workers' emotions.

[0181] 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.

[0182] In this invention, the server includes means for collecting information from multiple data transmission devices, analysis means for extracting relevant activity instructions from the collected information, and means for sensing the emotional state of the worker and flexibly adjusting the work content. This makes it possible to sense the emotional state of the worker in real time and adjust the work accordingly.

[0183] A "data transmission device" is a means of collecting information and transmitting that information between other devices or systems.

[0184] "Activity instructions" refer to specific instructions necessary to perform a particular task or operation.

[0185] "Analysis means" are technical methods for interpreting collected information and deriving useful information or instructions from it.

[0186] A "generation method" is a means of automatically creating new tasks or operations based on analyzed information.

[0187] A "management tool" is a means that has the function of tracking the progress of generated work or tasks and making corrections or adjustments as needed.

[0188] A "notification method" is a means of informing workers of necessary information based on the deadlines and priorities of tasks and work.

[0189] "Emotional state" refers to the psychological state and emotions of workers, and these can be factors that affect work efficiency and safety.

[0190] "Flexible work adjustment" refers to appropriately changing the content and schedule of work in accordance with the emotional state and circumstances of the workers.

[0191] To realize this invention, it is necessary to build a system in which servers, terminals, and users work together.

[0192] First, the server collects information from multiple data transmission devices, such as email servers and chat platforms via the internet. Then, it uses information processing technology to analyze the collected data and extract activity instructions. Natural language processing technology is used for this analysis, with examples including the Python library Spacy and the BERT model. Tasks are automatically generated from the information obtained through the analysis and sent to the terminal.

[0193] The device is equipped with an emotion engine that senses the user's emotional state. This engine identifies the user's emotions through speech and text analysis and dynamically adjusts the task content as needed. This process requires real-time analysis, and a real-time processing framework such as Apache® Kafka is used.

[0194] For example, if a user says "I'm tired," the device recognizes this emotion and notifies the server. This automatically adjusts the task content and schedule, reducing the user's burden.

[0195] Examples of prompts for a generative AI model include the following:

[0196] "We need a program designed to analyze user emotions from voice input and optimize work instructions accordingly. The program should adjust workload and feedback based on specific emotional keywords."

[0197] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0198] Step 1:

[0199] The server collects information from multiple data transmission devices. Specifically, it receives messages and instructions sent by users via email servers and chat platform APIs. The input is text data. This text data is extracted and stored in a database.

[0200] Step 2:

[0201] The server performs natural language processing on the collected text data. Here, Spacy and BERT models are used to analyze and extract useful activity instructions from the data. The input is the text data saved in step 1. The output is the extracted activity instructions. Data processing involves calculating word embeddings and extracting relevant phrases.

[0202] Step 3:

[0203] The server automatically generates tasks based on the extracted activity instructions. The input here is the activity instructions obtained in step 2. The generated work tasks are structured based on priority and deadline. This information is ready to be sent to the terminal. The output is a series of generated work tasks. Data calculations include prioritizing and optimizing each related task.

[0204] Step 4:

[0205] The terminal receives work tasks sent from the server. It then uses an emotion engine to understand the user's emotions in real time. Input is the user's voice or entered text, which is analyzed to identify their emotional state. Output is task priorities and schedules that reflect the user's emotions. Real-time analysis is performed, and the emotional state is extracted through necessary data processing.

[0206] Step 5:

[0207] Users can review the work tasks displayed on the terminal and manually modify them as needed. The input is the task list displayed on the terminal, and the output is the task settings manually adjusted by the user. By flexibly changing the priority and timing of each task at the user's discretion, a more efficient work environment is provided.

[0208] Step 6:

[0209] The server generates and sends feedback to the user based on the user's emotional state and work progress. The input consists of real-time updated emotional state data and work progress data. The output is a feedback message sent to the user. This process utilizes a generative AI model to generate prompts for appropriate encouragement and warnings as needed.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] [Second Embodiment]

[0214] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0215] 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.

[0216] 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).

[0217] 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.

[0218] 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.

[0219] 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).

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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".

[0226] This invention provides a system for managing multiple communication tools used daily by users and supporting efficient task management. This system consists of three elements: a server, a terminal, and a user, and operates as follows.

[0227] First, the server periodically collects necessary information from various communication tools such as email, chat, and phone calls. The server accesses the data using the APIs and protocols of each tool and automatically stores the information.

[0228] Next, the server analyzes the collected data using natural language processing techniques. Specifically, the server extracts "task-related instructions" from email bodies and chat messages to identify relevant work instructions.

[0229] Subsequently, the terminal receives the analysis information sent from the server and automatically generates tasks using a generation AI. These generated tasks are then accompanied by metadata such as deadlines and priorities. The terminal displays a list of tasks to the user and provides guidance on how to proceed with the work according to the situation.

[0230] Users can view tasks generated via their devices and set their priorities. By selecting tasks according to their own importance and managing their progress, users can improve their work productivity.

[0231] Furthermore, the server automatically generates reminders based on the deadlines and priorities of tasks set by the user. These reminders are sent to the user's device at the specified date and time, helping to prevent tasks from being overlooked or forgotten.

[0232] Finally, the task progress is updated by the user via their device and sent to the server. The server can then update the information in real time and provide feedback to the user. In this way, the present invention enhances individual work efficiency and enables centralized management of information from multiple tools.

[0233] The following describes the processing flow.

[0234] Step 1:

[0235] The server collects user data at scheduled times through APIs or protocols of various communication tools. It uses IMAP and POP3 for email servers and accesses various APIs for chat tools to obtain data.

[0236] Step 2:

[0237] The server analyzes the collected data using natural language processing algorithms to identify keywords and phrases related to the task. For example, it extracts messages containing words such as "deadline," "request," and "by XX."

[0238] Step 3:

[0239] The terminal receives analysis results from the server and uses AI to automatically generate tasks based on those results. The generated tasks are then given metadata such as due dates and priorities, and registered in the task management system.

[0240] Step 4:

[0241] Users can review automatically generated tasks through their device and adjust priorities and deadlines as needed. They can also evaluate the importance of tasks and set their execution order.

[0242] Step 5:

[0243] The server creates a reminder schedule based on the set due dates and priorities, and sends notifications to the user's device at the appropriate time. Reminders are sent via email, push notifications, etc.

[0244] Step 6:

[0245] Users update task progress on their devices and report the status of completed and ongoing tasks to the server. The server then displays task progress in real time based on this information.

[0246] Step 7:

[0247] The server provides feedback based on user progress and suggests new action plans as needed, enabling users to maintain an optimal workflow.

[0248] (Example 1)

[0249] 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."

[0250] In today's diverse digital communication landscape, users receive vast amounts of information from various sources, making it challenging to centrally manage this information and efficiently handle tasks. In particular, the fact that information spans multiple platforms increases the risk of overlooking important tasks or delaying high-priority work. Solutions to this problem are urgently needed.

[0251] 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.

[0252] In this invention, the server includes a device means capable of collecting data from multiple digital communication media, an analysis device means for identifying relevant work instructions from the collected data, a generation device for automatically generating work instructions based on the identified information, and a device means for adding deadlines and importance as metadata. This enables the user to efficiently manage information from multiple communication media and prevent tasks from being overlooked or prioritizing incorrectly.

[0253] "Multiple digital communication media" refers to multiple communication platforms that send and receive information in digital format, such as email, chat, and voice calls.

[0254] A "data-collecting device" refers to hardware or software that has the function of automatically acquiring necessary information from a designated digital communication medium and storing or analyzing it.

[0255] An "analysis device" refers to a device that uses natural language processing and other analysis techniques on collected data to identify specific information and interpret its meaning.

[0256] A "generation device" refers to a device that automatically creates tasks for the user to perform based on information identified by an analysis device, and sets additional attributes such as deadlines and importance levels as needed.

[0257] A "management device" refers to a device that organizes generated tasks, tracks and updates their progress, and enables users to visualize and operate them.

[0258] A "reminder generation and notification device" refers to a device that automatically creates and sends messages to users at the appropriate time, taking into account the deadlines and priorities of the tasks being managed, to inform them of the need to perform the task.

[0259] This invention provides a system that enables users to efficiently manage multiple digital communication media and assists in the generation and execution of tasks. This system primarily consists of a server, terminals, and a user interface, with each element working in coordination.

[0260] The server first collects data from numerous digital communication media. This includes software for retrieving information via APIs from email and chat services. For example, it uses the email protocol for email and a specific messaging protocol for chat. The server then analyzes the collected data using natural language processing techniques to identify instructions and information relevant to the task. A text analysis engine is used for this analysis to extract important keywords and phrases.

[0261] The terminal is responsible for automatically generating tasks using a generation AI model based on analysis data sent from the server. Specifically, generation modules implemented in programming languages ​​such as JavaScript and Python are used. These modules add metadata such as deadlines and priorities to the generated tasks and display them in a user-friendly format.

[0262] Users can view tasks displayed via their device and prioritize them according to their importance. The user interface is designed for intuitive operation, allowing for drag-and-drop task management using a mouse or touch device. This enables users to organize tasks in real time and achieve efficient work operations.

[0263] Furthermore, the server generates reminders based on the managed task information and sends notifications to the user's device at the specified time. This notification function prevents users from forgetting tasks by informing them of deadlines. The notification function is implemented using programs such as Java or Ruby, and the notification format can be selected from options such as email or app notifications.

[0264] A concrete example is a case where a project manager uses this system to integrate email and chat instructions received from different team members for task management. An example of a prompt message to the generating AI model would be, "Extract task instructions from the following chat history and set the deadline and importance level."

[0265] Thus, the present invention improves user work efficiency by centrally managing information from various communication media and providing task creation, management, and notification functions.

[0266] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0267] Step 1:

[0268] The server is responsible for collecting data from digital communication media. First, it retrieves unread messages and notifications through APIs for email and chat services. The input data to be collected includes authentication information and query data for each service. The output generates a list of unprocessed messages and notification data, which then becomes the input data for the next processing step.

[0269] Step 2:

[0270] The server applies natural language processing techniques to the collected data for analysis. Using the collected text data as input, it performs document structure analysis and key phrase extraction, outputting text relevant to work instructions as a result. For example, specific instructions such as "Complete the draft of the project proposal by next week" might be extracted.

[0271] Step 3:

[0272] The terminal receives data analyzed by the server. Based on the instruction data received as input, it uses a generative AI model to automatically generate specific tasks. Prompt text is entered into the generative AI model, and a task with a specific deadline and priority is obtained as output. In this process, a "Project Proposal Draft Creation Task" is generated with attributes such as a deadline of "Next Monday" and a priority of "High".

[0273] Step 4:

[0274] The terminal displays the generated tasks to the user. Through the user interface, it provides a task list and calendar view, making it easy for the user to check them. The displayed tasks are the output, and the user looks at this output to decide on their next action.

[0275] Step 5:

[0276] Users view tasks displayed on their device and prioritize them based on their schedule and importance. They provide feedback to the device regarding task deadlines and progress, and a new priority list is generated. This allows users to focus on individual tasks and complete them efficiently.

[0277] Step 6:

[0278] The server generates a reminder based on the information of the tasks set by the user. When the deadline approaches, the reminder is output as a notification message and sent to the user's terminal. This ensures the progress of all tasks is managed and reduces the risk of missing important deadlines.

[0279] Step 7:

[0280] When the user completes a task, the progress status is updated. By entering a completion report as input into the terminal and sending it to the server, the status of the task is updated in real time. Thereby, the next task to proceed is output to the user, assisting in new planning.

[0281] (Application Example 1)

[0282] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0283] In a modern home environment, communication by multiple information exchange means is active, and various tasks and schedules are generated accordingly. However, it is often difficult to efficiently organize these tasks and implement them in cooperation within the home. Also, prioritizing individual tasks and the transition to physical implementation remain challenges. In particular, in households with the elderly or those with busy schedules, an automated system for smoothly managing and performing daily tasks is required.

[0284] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0285] In this invention, the server includes means capable of collecting information from a plurality of information exchange means, analysis means for extracting relevant work instructions from the collected information, and control means for efficiently managing the work generated within the home and physically executing the work by means of a device having an autonomous driving function. Thereby, efficient management of the work within the home, automation of priorities, and physical execution of work by means of a device having an autonomous driving function become possible.

[0286] The "information exchange means" refers to means and technologies for transmitting and receiving information between individuals and devices.

[0287] The "work instruction" indicates specific instructions and commands for executing a specific work.

[0288] The "analysis means" has a function of analyzing the collected information and extracting necessary data and instructions therefrom.

[0289] The "device having an autonomous driving function" refers to a device that can automatically move around and execute work based on pre-set programs and instructions.

[0290] The "control means" means technologies and functions for instructing and managing the operations of devices and apparatuses.

[0291] To implement this invention, it is necessary to construct a system centered around three elements: a server, a terminal, and a user.In this system, the server collects data through a plurality of information exchange means, analyzes it, and extracts work instructions. For the analysis of the collected data, natural language analysis technology is utilized, and a generative AI model is used to extract particularly important information.

[0292] The server also automatically generates tasks based on extracted work instructions and adds metadata such as deadlines and priorities. For this purpose, the server utilizes cloud-based databases and scheduling software. Specific examples of such software include "spaCy" and "NLTK" for natural language processing, and "PostgreSQL" for data management.

[0293] The terminal provides a user interface, allowing users to view tasks and set priorities as needed. This enables users to plan their day based on generated tasks and manage their progress in real time. Smartphones and tablets are used as terminal devices, and information is displayed by application software.

[0294] Users update their work progress via their devices and send this information to the server. The server then updates the information in real time, generates reminders as needed, and notifies the user, preventing delays and missed tasks. Reminder notifications are sent using the device's push notification function.

[0295] For example, when a user sends a message to the system such as "Prepare for household chores," that information is transmitted to a device with autonomous driving capabilities, which then automatically performs tasks such as cleaning and arranging items. An example of a prompt message for the generated AI model is: "Extract priority household tasks from the following chat and set the steps to perform them. Example: 'Clean,' 'Create a shopping list,' 'Tidy up the room.'"

[0296] In this way, by centrally managing information across servers, terminals, and users, and enabling the automation of tasks, this system can significantly improve the efficiency of daily life.

[0297] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0298] Step 1:

[0299] The server collects data from multiple information exchange means. This collection is carried out via an API, obtaining data from emails, chats, and other digital communications. The input is these digital communication data, and raw data is collected based on them.

[0300] Step 2:

[0301] The server analyzes the collected data using natural language analysis technology and extracts work instructions. The input is the raw data obtained in Step 1, and tasks and important information are extracted through natural language processing. A generative AI model supports this process to identify specific work instructions.

[0302] Step 3:

[0303] The server automatically generates work based on the extracted work instructions and adds metadata. The obtained information is processed by scheduling software, and metadata such as deadlines and priorities is assigned. The output is a completed work list.

[0304] Step 4:

[0305] The terminal displays the generated work on the user interface. The input is the work list sent from the server, and based on this, the user checks the work status. Here, the terminal provides options for setting priorities.

[0306] Step 5:

[0307] The user uses the terminal to set the priority of the work and update the progress. The user's actions serve as the input, and the information set through the terminal is sent to the server. The output is an updated work list.

[0308] Step 6:

[0309] The server uses its storage device to generate a reminder based on the updated information and notifies the device. The server then uses this information to send a push notification to the device indicating the next task to be performed. This ensures the user doesn't forget to complete the task.

[0310] 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.

[0311] This invention provides a system that, in addition to the function of efficiently managing tasks by collecting information from multiple communication tools, is equipped with an emotion engine that recognizes the user's emotions and uses them to assist in task management. This system is composed of a server, terminals, and users, and operates as follows.

[0312] First, the server collects information from sources such as email, chat, and phone calls. The server retrieves and stores data through the APIs and protocols of each tool. The server also uses natural language processing technology to analyze the data and extract work instructions related to the task. Based on this extracted information, the server automatically generates tasks and sends them to the terminal.

[0313] The terminal presents the user with a list of tasks based on the analysis information received from the server and the tasks generated. The terminal is equipped with an emotion engine that analyzes the user's messages and input to recognize their emotional state. For example, it reads phrases such as "tired" or "in a hurry" included in the user's input, as well as negative / positive emotions from the message. Based on the analysis results of this emotion engine, it is possible to dynamically adjust task priorities and reminder settings.

[0314] Users can review generated tasks through their device and manually set priorities. However, the system also takes the user's emotional state into consideration and automatically rearranges tasks and adjusts their importance, thus reducing the user's burden.

[0315] The server can also receive progress information and provide feedback based on emotional states. For example, if a user is feeling stressed, it may reduce the frequency of reminders or send encouraging messages indicating that tasks are progressing smoothly. Specifically, if a user expresses an emotion such as "busy," the emotion engine will recognize this and emphasize reminders to draw attention to tasks with approaching deadlines.

[0316] In this way, the present invention enables flexible and personalized task management that takes user emotions into consideration, providing a less stressful and more efficient work environment.

[0317] The following describes the processing flow.

[0318] Step 1:

[0319] The server collects user data at scheduled times through APIs or protocols of various communication tools. This data includes emails, chat messages, call logs, and more.

[0320] Step 2:

[0321] The server analyzes the collected data using natural language processing algorithms to identify information related to work instructions and tasks. In particular, it extracts sentences containing keywords such as "deadline," "request," and "confirmation," and breaks down the content of the instructions.

[0322] Step 3:

[0323] The terminal uses AI generation based on analysis results sent from the server to automatically generate tasks. At that time, metadata such as due dates and initial priorities are set for the generated tasks.

[0324] Step 4:

[0325] The device uses its built-in emotion engine to recognize the user's emotions. It analyzes the user's interaction history and message content to identify emotions such as "feeling stressed" or "being in a positive state."

[0326] Step 5:

[0327] Based on the analysis results of the emotion engine, the device dynamically adjusts task priorities. For example, if a user indicates high stress levels, it reduces the burden by prioritizing and displaying tasks that require immediate attention.

[0328] Step 6:

[0329] Users can review the task list displayed on their device and manually adjust priorities and deadlines as needed. Feedback on the system's suggestions will be used to improve future adjustments.

[0330] Step 7:

[0331] The server monitors the progress of tasks updated by users and updates the database in a timely manner. The progress is reflected in real time, and the user is given feedback on the current status.

[0332] Step 8:

[0333] Based on the emotional state recognized by the emotion engine, the server sends encouraging messages and relaxation suggestions to the user to support the maintenance of a comfortable work environment.

[0334] Through this series of processes, the present invention provides users with personalized task management and emotionally sensitive work support.

[0335] (Example 2)

[0336] 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".

[0337] Current task management systems do not dynamically adjust priorities while taking into account the user's emotional state, and therefore do not adequately contribute to work efficiency or stress reduction. For this reason, there is a need to provide a personalized management method that reflects the user's emotions, from information gathering to task item generation and notifications.

[0338] 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.

[0339] In this invention, the server includes a device capable of collecting information from multiple communication technologies, an analysis means for extracting relevant work commands from the collected information, and a means for evaluating the user's emotions using emotion analysis technology and dynamically adjusting the priority of work items based on that evaluation. This enables flexible and effective work management based on the user's emotions.

[0340] "Communication technology" refers to all technologies used to send and receive information.

[0341] "Device" refers to a component of hardware or software used to perform a specific function or process.

[0342] "Analysis means" refers to devices and technologies used to analyze information and extract relevant data.

[0343] "Natural language processing technology" refers to the technology used to process human language using computers, enabling the analysis and understanding of text.

[0344] A "generation device" refers to a device that generates specific results or items based on input information.

[0345] A "control device" refers to a device used to operate and manage the entire system.

[0346] A "warning device" refers to a device that notifies the user when certain conditions are met.

[0347] "Emotional analysis technology" refers to technology that analyzes and recognizes a user's emotional state from text and audio data.

[0348] "Feedback" refers to the reactions and evaluations received by users or devices based on the system's output.

[0349] This invention is a system centered on servers, terminals, and users, thereby enabling task management that takes user emotions into consideration. The system components include an information gathering device, analysis means, generation device, control device, warning device, and emotion analysis technology.

[0350] The server acquires information from multiple communication technologies using information gathering devices. This information includes electronic messages, voice recordings, and digital communication logs. After acquisition, the server uses analysis tools to analyze the information using natural language processing technology and extract business instructions and related data. In this process, a specific generative AI model is utilized to interpret complex contexts and identify appropriate data.

[0351] The terminal manages work items automatically generated by a generator based on the analyzed information, using a control device. The terminal displays a task list to the user, which is dynamically changed according to the user's emotional state. Sentiment analysis technology evaluates the user's input text and voice, and uses this as a basis for adjusting the priority of work items. Specifically, it analyzes keywords such as "busy" and "tired" to automatically adjust reminder settings and notification frequencies.

[0352] Users can review these tasks and manually prioritize them, but the workload is reduced through automatic adjustments based on emotions. A feedback function sends user emotional responses to the server in real time, optimizing the overall system operation.

[0353] As a concrete example, here is an example of a prompt message:

[0354] "If a user is worried about a task due tomorrow, the sentiment engine will detect this and set a reminder to prioritize notifications."

[0355] In this way, the present invention enables individually customized task management while taking into account the user's feelings, providing an efficient and less stressful work environment.

[0356] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0357] Step 1:

[0358] The server collects information through multiple communication technologies. Inputs include email, chat messages, and voice call logs. The server uses APIs for each communication technology to retrieve data and store it in a database. The output is raw data awaiting analysis.

[0359] Step 2:

[0360] The server processes the collected raw data using analytical tools. Input data includes text messages and audio data. Using natural language processing techniques, a generative AI model syntactically analyzes the messages and extracts work instructions and emotional states. The output provides task candidates along with the analysis results. For example, if an emotional expression such as "busy" is extracted, time-related tasks will be highlighted.

[0361] Step 3:

[0362] The server generates work items based on the analysis results. Using the analysis results as input, the generation device automatically determines the task content, deadline, and priority, creating a task list. The output is a detailed task list. This list is sent to the terminal and prepared for user access.

[0363] Step 4:

[0364] The terminal displays the task list received from the server. It receives the task list as input and presents it in a user-friendly interface. The terminal utilizes sentiment analysis technology to analyze the user's input and responses, dynamically adjusting the priority of the task list. The output presents a task list optimized for the user.

[0365] Step 5:

[0366] Users view their task list on their terminal and manually adjust priorities as needed. When users input information (e.g., "finished" or "will do later") into their terminal, the task list is updated accordingly. User feedback is sent to the server to help optimize system operation. In this way, the final work progress output is obtained.

[0367] (Application Example 2)

[0368] 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."

[0369] In modern factory environments, worker fatigue and stress often affect productivity and safety. Therefore, accurately understanding workers' emotional states and adjusting work instructions accordingly is crucial. However, current systems lack the flexibility to adjust work instructions based on individual workers' emotions.

[0370] 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.

[0371] In this invention, the server includes means for collecting information from multiple data transmission devices, analysis means for extracting relevant activity instructions from the collected information, and means for sensing the emotional state of the worker and flexibly adjusting the work content. This makes it possible to sense the emotional state of the worker in real time and adjust the work accordingly.

[0372] A "data transmission device" is a means of collecting information and transmitting that information between other devices or systems.

[0373] "Activity instructions" refer to specific instructions necessary to perform a particular task or operation.

[0374] "Analysis means" are technical methods for interpreting collected information and deriving useful information or instructions from it.

[0375] A "generation method" is a means of automatically creating new tasks or operations based on analyzed information.

[0376] A "management tool" is a means that has the function of tracking the progress of generated work or tasks and making corrections or adjustments as needed.

[0377] A "notification method" is a means of informing workers of necessary information based on the deadlines and priorities of tasks and work.

[0378] "Emotional state" refers to the psychological state and emotions of workers, and these can be factors that affect work efficiency and safety.

[0379] "Flexible work adjustment" refers to appropriately changing the content and schedule of work in accordance with the emotional state and circumstances of the workers.

[0380] To realize this invention, it is necessary to build a system in which servers, terminals, and users work together.

[0381] First, the server collects information from multiple data transmission devices, such as email servers and chat platforms via the internet. Then, it uses information processing technology to analyze the collected data and extract activity instructions. Natural language processing technology is used for this analysis, with examples including the Python library Spacy and the BERT model. Tasks are automatically generated from the information obtained through the analysis and sent to the terminal.

[0382] The device is equipped with an emotion engine that senses the user's emotional state. This engine identifies the user's emotions through speech and text analysis and dynamically adjusts the task content as needed. This process requires real-time analysis, and a real-time processing framework such as Apache Kafka is used.

[0383] For example, if a user says "I'm tired," the device recognizes this emotion and notifies the server. This automatically adjusts the task content and schedule, reducing the user's burden.

[0384] Examples of prompts for a generative AI model include the following:

[0385] "We need a program designed to analyze user emotions from voice input and optimize work instructions accordingly. The program should adjust workload and feedback based on specific emotional keywords."

[0386] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0387] Step 1:

[0388] The server collects information from multiple data transmission devices. Specifically, it receives messages and instructions sent by users via email servers and chat platform APIs. The input is text data. This text data is extracted and stored in a database.

[0389] Step 2:

[0390] The server performs natural language processing on the collected text data. Here, Spacy and BERT models are used to analyze and extract useful activity instructions from the data. The input is the text data saved in step 1. The output is the extracted activity instructions. Data processing involves calculating word embeddings and extracting relevant phrases.

[0391] Step 3:

[0392] The server automatically generates tasks based on the extracted activity instructions. The input here is the activity instructions obtained in step 2. The generated work tasks are structured based on priority and deadline. This information is ready to be sent to the terminal. The output is a series of generated work tasks. Data calculations include prioritizing and optimizing each related task.

[0393] Step 4:

[0394] The terminal receives work tasks sent from the server. It then uses an emotion engine to understand the user's emotions in real time. Input is the user's voice or entered text, which is analyzed to identify their emotional state. Output is task priorities and schedules that reflect the user's emotions. Real-time analysis is performed, and the emotional state is extracted through necessary data processing.

[0395] Step 5:

[0396] Users can review the work tasks displayed on the terminal and manually modify them as needed. The input is the task list displayed on the terminal, and the output is the task settings manually adjusted by the user. By flexibly changing the priority and timing of each task at the user's discretion, a more efficient work environment is provided.

[0397] Step 6:

[0398] The server generates and sends feedback to the user based on the user's emotional state and work progress. The input consists of real-time updated emotional state data and work progress data. The output is a feedback message sent to the user. This process utilizes a generative AI model to generate prompts for appropriate encouragement and warnings as needed.

[0399] 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.

[0400] 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.

[0401] 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.

[0402] [Third Embodiment]

[0403] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0404] 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.

[0405] 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).

[0406] 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.

[0407] 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.

[0408] 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).

[0409] 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.

[0410] 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.

[0411] 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.

[0412] 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.

[0413] 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.

[0414] 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".

[0415] This invention provides a system for managing multiple communication tools used daily by users and supporting efficient task management. This system consists of three elements: a server, a terminal, and a user, and operates as follows.

[0416] First, the server periodically collects necessary information from various communication tools such as email, chat, and phone calls. The server accesses the data using the APIs and protocols of each tool and automatically stores the information.

[0417] Next, the server analyzes the collected data using natural language processing techniques. Specifically, the server extracts "task-related instructions" from email bodies and chat messages to identify relevant work instructions.

[0418] Subsequently, the terminal receives the analysis information sent from the server and automatically generates tasks using a generation AI. These generated tasks are then accompanied by metadata such as deadlines and priorities. The terminal displays a list of tasks to the user and provides guidance on how to proceed with the work according to the situation.

[0419] Users can view tasks generated via their devices and set their priorities. By selecting tasks according to their own importance and managing their progress, users can improve their work productivity.

[0420] Furthermore, the server automatically generates reminders based on the deadlines and priorities of tasks set by the user. These reminders are sent to the user's device at the specified date and time, helping to prevent tasks from being overlooked or forgotten.

[0421] Finally, the task progress is updated by the user via their device and sent to the server. The server can then update the information in real time and provide feedback to the user. In this way, the present invention enhances individual work efficiency and enables centralized management of information from multiple tools.

[0422] The following describes the processing flow.

[0423] Step 1:

[0424] The server collects user data at scheduled times through APIs or protocols of various communication tools. It uses IMAP and POP3 for email servers and accesses various APIs for chat tools to obtain data.

[0425] Step 2:

[0426] The server analyzes the collected data using natural language processing algorithms to identify keywords and phrases related to the task. For example, it extracts messages containing words such as "deadline," "request," and "by XX."

[0427] Step 3:

[0428] The terminal receives analysis results from the server and uses AI to automatically generate tasks based on those results. The generated tasks are then given metadata such as due dates and priorities, and registered in the task management system.

[0429] Step 4:

[0430] Users can review automatically generated tasks through their device and adjust priorities and deadlines as needed. They can also evaluate the importance of tasks and set their execution order.

[0431] Step 5:

[0432] The server creates a reminder schedule based on the set due dates and priorities, and sends notifications to the user's device at the appropriate time. Reminders are sent via email, push notifications, etc.

[0433] Step 6:

[0434] Users update task progress on their devices and report the status of completed and ongoing tasks to the server. The server then displays task progress in real time based on this information.

[0435] Step 7:

[0436] The server provides feedback based on user progress and suggests new action plans as needed, enabling users to maintain an optimal workflow.

[0437] (Example 1)

[0438] 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."

[0439] In today's diverse digital communication landscape, users receive vast amounts of information from various sources, making it challenging to centrally manage this information and efficiently handle tasks. In particular, the fact that information spans multiple platforms increases the risk of overlooking important tasks or delaying high-priority work. Solutions to this problem are urgently needed.

[0440] 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.

[0441] In this invention, the server includes a device means capable of collecting data from multiple digital communication media, an analysis device means for identifying relevant work instructions from the collected data, a generation device for automatically generating work instructions based on the identified information, and a device means for adding deadlines and importance as metadata. This enables the user to efficiently manage information from multiple communication media and prevent tasks from being overlooked or prioritizing incorrectly.

[0442] "Multiple digital communication media" refers to multiple communication platforms that send and receive information in digital format, such as email, chat, and voice calls.

[0443] A "data-collecting device" refers to hardware or software that has the function of automatically acquiring necessary information from a designated digital communication medium and storing or analyzing it.

[0444] An "analysis device" refers to a device that uses natural language processing and other analysis techniques on collected data to identify specific information and interpret its meaning.

[0445] A "generation device" refers to a device that automatically creates tasks for the user to perform based on information identified by an analysis device, and sets additional attributes such as deadlines and importance levels as needed.

[0446] A "management device" refers to a device that organizes generated tasks, tracks and updates their progress, and enables users to visualize and operate them.

[0447] A "reminder generation and notification device" refers to a device that automatically creates and sends messages to users at the appropriate time, taking into account the deadlines and priorities of the tasks being managed, to inform them of the need to perform the task.

[0448] This invention provides a system that enables users to efficiently manage multiple digital communication media and assists in the generation and execution of tasks. This system primarily consists of a server, terminals, and a user interface, with each element working in coordination.

[0449] The server first collects data from numerous digital communication media. This includes software for retrieving information via APIs from email and chat services. For example, it uses the email protocol for email and a specific messaging protocol for chat. The server then analyzes the collected data using natural language processing techniques to identify instructions and information relevant to the task. A text analysis engine is used for this analysis to extract important keywords and phrases.

[0450] The terminal is responsible for automatically generating tasks using a generation AI model based on analysis data sent from the server. Specifically, generation modules implemented in programming languages ​​such as JavaScript and Python are used. These modules add metadata such as deadlines and priorities to the generated tasks and display them in a user-friendly format.

[0451] Users can view tasks displayed via their device and prioritize them according to their importance. The user interface is designed for intuitive operation, allowing for drag-and-drop task management using a mouse or touch device. This enables users to organize tasks in real time and achieve efficient work operations.

[0452] Furthermore, the server generates reminders based on the managed task information and sends notifications to the user's device at the specified time. This notification function prevents users from forgetting tasks by informing them of deadlines. The notification function is implemented using programs such as Java or Ruby, and the notification format can be selected from options such as email or app notifications.

[0453] A concrete example is a case where a project manager uses this system to integrate email and chat instructions received from different team members for task management. An example of a prompt message to the generating AI model would be, "Extract task instructions from the following chat history and set the deadline and importance level."

[0454] Thus, the present invention improves user work efficiency by centrally managing information from various communication media and providing task creation, management, and notification functions.

[0455] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0456] Step 1:

[0457] The server is responsible for collecting data from digital communication media. First, it retrieves unread messages and notifications through APIs for email and chat services. The input data to be collected includes authentication information and query data for each service. The output generates a list of unprocessed messages and notification data, which then becomes the input data for the next processing step.

[0458] Step 2:

[0459] The server applies natural language processing techniques to the collected data for analysis. Using the collected text data as input, it performs document structure analysis and key phrase extraction, outputting text relevant to work instructions as a result. For example, specific instructions such as "Complete the draft of the project proposal by next week" might be extracted.

[0460] Step 3:

[0461] The terminal receives data analyzed by the server. Based on the instruction data received as input, it uses a generative AI model to automatically generate specific tasks. Prompt text is entered into the generative AI model, and a task with a specific deadline and priority is obtained as output. In this process, a "Project Proposal Draft Creation Task" is generated with attributes such as a deadline of "Next Monday" and a priority of "High".

[0462] Step 4:

[0463] The terminal displays the generated tasks to the user. Through the user interface, it provides a task list and calendar view, making it easy for the user to check them. The displayed tasks are the output, and the user looks at this output to decide on their next action.

[0464] Step 5:

[0465] Users view tasks displayed on their device and prioritize them based on their schedule and importance. They provide feedback to the device regarding task deadlines and progress, and a new priority list is generated. This allows users to focus on individual tasks and complete them efficiently.

[0466] Step 6:

[0467] The server generates reminders based on the task information set by the user. As the deadline approaches, it outputs the reminder as a notification message and sends it to the user's device. This ensures that the progress of all tasks is reliably managed and reduces the risk of missing important deadlines.

[0468] Step 7:

[0469] The progress status is updated when the user completes a task. By entering a completion report into the terminal and sending it to the server, the task status is updated in real time. This then outputs the next task to the user, supporting the creation of a new plan.

[0470] (Application Example 1)

[0471] 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."

[0472] In modern households, communication is active through multiple means of information exchange, leading to a variety of tasks and schedules. However, efficiently organizing these tasks and carrying them out collaboratively within the family is often difficult. Prioritizing individual tasks and transitioning to physical execution also remain challenges. In particular, for elderly people and busy families, there is a need for automated systems to smoothly manage and execute daily tasks.

[0473] 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.

[0474] In this invention, the server includes means for collecting information from multiple information exchange means, analysis means for extracting relevant work instructions from the collected information, and control means for efficiently managing tasks occurring within the home and physically executing those tasks using a device with autonomous driving capabilities. This enables efficient management of tasks within the home, automation of prioritization, and physical execution of tasks by a device with autonomous driving capabilities.

[0475] "Information exchange means" refers to the means and technologies for sending and receiving information between individuals or devices.

[0476] "Work instructions" refer to specific instructions or commands for performing a particular task.

[0477] An "analysis tool" is a device that analyzes collected information and extracts necessary data and instructions from it.

[0478] A "device with autonomous driving capabilities" refers to a device that can automatically move around and perform tasks based on a pre-set program or instructions.

[0479] "Control means" refers to the technology and functions used to instruct and manage the operation of equipment and devices.

[0480] To implement this invention, it is necessary to construct a system centered on three elements: a server, a terminal, and a user. In this system, the server collects data through multiple information exchange means, analyzes it, and extracts work instructions. Natural language processing technology is used to analyze the collected data, and a generative AI model is used to extract particularly important information.

[0481] The server also automatically generates tasks based on extracted work instructions and adds metadata such as deadlines and priorities. For this purpose, the server utilizes cloud-based databases and scheduling software. Specific examples of such software include "spaCy" and "NLTK" for natural language processing, and "PostgreSQL" for data management.

[0482] The terminal provides a user interface, allowing users to view tasks and set priorities as needed. This enables users to plan their day based on generated tasks and manage their progress in real time. Smartphones and tablets are used as terminal devices, and information is displayed by application software.

[0483] Users update their work progress via their devices and send this information to the server. The server then updates the information in real time, generates reminders as needed, and notifies the user, preventing delays and missed tasks. Reminder notifications are sent using the device's push notification function.

[0484] For example, when a user sends a message to the system such as "Prepare for household chores," that information is transmitted to a device with autonomous driving capabilities, which then automatically performs tasks such as cleaning and arranging items. An example of a prompt message for the generated AI model is: "Extract priority household tasks from the following chat and set the steps to perform them. Example: 'Clean,' 'Create a shopping list,' 'Tidy up the room.'"

[0485] In this way, by centrally managing information across servers, terminals, and users, and enabling the automation of tasks, this system can significantly improve the efficiency of daily life.

[0486] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0487] Step 1:

[0488] The server collects data from multiple means of information exchange. This collection is done via APIs, retrieving data from email, chat, and other digital communications. The input is this digital communication data, and the server collects raw data based on it.

[0489] Step 2:

[0490] The server analyzes the collected data using natural language processing technology to extract work instructions. The input is the raw data obtained in step 1, and tasks and important information are extracted through natural language processing. A generative AI model assists this process to identify specific work instructions.

[0491] Step 3:

[0492] The server automatically generates tasks based on the extracted work instructions and adds metadata. The acquired information is processed by scheduling software, which adds metadata such as deadlines and priorities. The output is a completed task list.

[0493] Step 4:

[0494] The terminal displays the generated tasks in a user interface. The input is a task list sent from the server, which the user uses to check the task status. The terminal also provides options for setting priorities.

[0495] Step 5:

[0496] Users use a terminal to set task priorities and update progress. User actions become input, and the configured information is sent to the server via the terminal. The output is an updated task list.

[0497] Step 6:

[0498] The server uses its storage device to generate a reminder based on the updated information and notifies the device. The server then uses this information to send a push notification to the device indicating the next task to be performed. This ensures the user doesn't forget to complete the task.

[0499] 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.

[0500] This invention provides a system that, in addition to the function of efficiently managing tasks by collecting information from multiple communication tools, is equipped with an emotion engine that recognizes the user's emotions and uses them to assist in task management. This system is composed of a server, terminals, and users, and operates as follows.

[0501] First, the server collects information from sources such as email, chat, and phone calls. The server retrieves and stores data through the APIs and protocols of each tool. The server also uses natural language processing technology to analyze the data and extract work instructions related to the task. Based on this extracted information, the server automatically generates tasks and sends them to the terminal.

[0502] The terminal presents the user with a list of tasks based on the analysis information received from the server and the tasks generated. The terminal is equipped with an emotion engine that analyzes the user's messages and input to recognize their emotional state. For example, it reads phrases such as "tired" or "in a hurry" included in the user's input, as well as negative / positive emotions from the message. Based on the analysis results of this emotion engine, it is possible to dynamically adjust task priorities and reminder settings.

[0503] Users can review generated tasks through their device and manually set priorities. However, the system also takes the user's emotional state into consideration and automatically rearranges tasks and adjusts their importance, thus reducing the user's burden.

[0504] The server can also receive progress information and provide feedback based on emotional states. For example, if a user is feeling stressed, it may reduce the frequency of reminders or send encouraging messages indicating that tasks are progressing smoothly. Specifically, if a user expresses an emotion such as "busy," the emotion engine will recognize this and emphasize reminders to draw attention to tasks with approaching deadlines.

[0505] In this way, the present invention enables flexible and personalized task management that takes user emotions into consideration, providing a less stressful and more efficient work environment.

[0506] The following describes the processing flow.

[0507] Step 1:

[0508] The server collects user data at scheduled times through APIs or protocols of various communication tools. This data includes emails, chat messages, call logs, and more.

[0509] Step 2:

[0510] The server analyzes the collected data using natural language processing algorithms to identify information related to work instructions and tasks. In particular, it extracts sentences containing keywords such as "deadline," "request," and "confirmation," and breaks down the content of the instructions.

[0511] Step 3:

[0512] The terminal uses AI generation based on analysis results sent from the server to automatically generate tasks. At that time, metadata such as due dates and initial priorities are set for the generated tasks.

[0513] Step 4:

[0514] The device uses its built-in emotion engine to recognize the user's emotions. It analyzes the user's interaction history and message content to identify emotions such as "feeling stressed" or "being in a positive state."

[0515] Step 5:

[0516] Based on the analysis results of the emotion engine, the device dynamically adjusts task priorities. For example, if a user indicates high stress levels, it reduces the burden by prioritizing and displaying tasks that require immediate attention.

[0517] Step 6:

[0518] Users can review the task list displayed on their device and manually adjust priorities and deadlines as needed. Feedback on the system's suggestions will be used to improve future adjustments.

[0519] Step 7:

[0520] The server monitors the progress of tasks updated by users and updates the database in a timely manner. The progress is reflected in real time, and the user is given feedback on the current status.

[0521] Step 8:

[0522] Based on the emotional state recognized by the emotion engine, the server sends encouraging messages and relaxation suggestions to the user to support the maintenance of a comfortable work environment.

[0523] Through this series of processes, the present invention provides users with personalized task management and emotionally sensitive work support.

[0524] (Example 2)

[0525] 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."

[0526] Current task management systems do not dynamically adjust priorities while taking into account the user's emotional state, and therefore do not adequately contribute to work efficiency or stress reduction. For this reason, there is a need to provide a personalized management method that reflects the user's emotions, from information gathering to task item generation and notifications.

[0527] 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.

[0528] In this invention, the server includes a device capable of collecting information from multiple communication technologies, an analysis means for extracting relevant work commands from the collected information, and a means for evaluating the user's emotions using emotion analysis technology and dynamically adjusting the priority of work items based on that evaluation. This enables flexible and effective work management based on the user's emotions.

[0529] "Communication technology" refers to all technologies used to send and receive information.

[0530] "Device" refers to a component of hardware or software used to perform a specific function or process.

[0531] "Analysis means" refers to devices and technologies used to analyze information and extract relevant data.

[0532] "Natural language processing technology" refers to the technology used to process human language using computers, enabling the analysis and understanding of text.

[0533] A "generation device" refers to a device that generates specific results or items based on input information.

[0534] A "control device" refers to a device used to operate and manage the entire system.

[0535] A "warning device" refers to a device that notifies the user when certain conditions are met.

[0536] "Emotional analysis technology" refers to technology that analyzes and recognizes a user's emotional state from text and audio data.

[0537] "Feedback" refers to the reactions and evaluations received by users or devices based on the system's output.

[0538] This invention is a system centered on servers, terminals, and users, thereby enabling task management that takes user emotions into consideration. The system components include an information gathering device, analysis means, generation device, control device, warning device, and emotion analysis technology.

[0539] The server acquires information from multiple communication technologies using information gathering devices. This information includes electronic messages, voice recordings, and digital communication logs. After acquisition, the server uses analysis tools to analyze the information using natural language processing technology and extract business instructions and related data. In this process, a specific generative AI model is utilized to interpret complex contexts and identify appropriate data.

[0540] The terminal manages work items automatically generated by a generator based on the analyzed information, using a control device. The terminal displays a task list to the user, which is dynamically changed according to the user's emotional state. Sentiment analysis technology evaluates the user's input text and voice, and uses this as a basis for adjusting the priority of work items. Specifically, it analyzes keywords such as "busy" and "tired" to automatically adjust reminder settings and notification frequencies.

[0541] Users can review these tasks and manually prioritize them, but the workload is reduced through automatic adjustments based on emotions. A feedback function sends user emotional responses to the server in real time, optimizing the overall system operation.

[0542] As a concrete example, here is an example of a prompt message:

[0543] "If a user is worried about a task due tomorrow, the sentiment engine will detect this and set a reminder to prioritize notifications."

[0544] In this way, the present invention enables individually customized task management while taking into account the user's feelings, providing an efficient and less stressful work environment.

[0545] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0546] Step 1:

[0547] The server collects information through multiple communication technologies. Inputs include email, chat messages, and voice call logs. The server uses APIs for each communication technology to retrieve data and store it in a database. The output is raw data awaiting analysis.

[0548] Step 2:

[0549] The server processes the collected raw data using analytical tools. Input data includes text messages and audio data. Using natural language processing techniques, a generative AI model syntactically analyzes the messages and extracts work instructions and emotional states. The output provides task candidates along with the analysis results. For example, if an emotional expression such as "busy" is extracted, time-related tasks will be highlighted.

[0550] Step 3:

[0551] The server generates work items based on the analysis results. Using the analysis results as input, the generation device automatically determines the task content, deadline, and priority, creating a task list. The output is a detailed task list. This list is sent to the terminal and prepared for user access.

[0552] Step 4:

[0553] The terminal displays the task list received from the server. It receives the task list as input and presents it in a user-friendly interface. The terminal utilizes sentiment analysis technology to analyze the user's input and responses, dynamically adjusting the priority of the task list. The output presents a task list optimized for the user.

[0554] Step 5:

[0555] Users view their task list on their terminal and manually adjust priorities as needed. When users input information (e.g., "finished" or "will do later") into their terminal, the task list is updated accordingly. User feedback is sent to the server to help optimize system operation. In this way, the final work progress output is obtained.

[0556] (Application Example 2)

[0557] 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."

[0558] In modern factory environments, worker fatigue and stress often affect productivity and safety. Therefore, accurately understanding workers' emotional states and adjusting work instructions accordingly is crucial. However, current systems lack the flexibility to adjust work instructions based on individual workers' emotions.

[0559] 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.

[0560] In this invention, the server includes means for collecting information from multiple data transmission devices, analysis means for extracting relevant activity instructions from the collected information, and means for sensing the emotional state of the worker and flexibly adjusting the work content. This makes it possible to sense the emotional state of the worker in real time and adjust the work accordingly.

[0561] A "data transmission device" is a means of collecting information and transmitting that information between other devices or systems.

[0562] "Activity instructions" refer to specific instructions necessary to perform a particular task or operation.

[0563] "Analysis means" are technical methods for interpreting collected information and deriving useful information or instructions from it.

[0564] A "generation method" is a means of automatically creating new tasks or operations based on analyzed information.

[0565] A "management tool" is a means that has the function of tracking the progress of generated work or tasks and making corrections or adjustments as needed.

[0566] A "notification method" is a means of informing workers of necessary information based on the deadlines and priorities of tasks and work.

[0567] "Emotional state" refers to the psychological state and emotions of workers, and these can be factors that affect work efficiency and safety.

[0568] "Flexible work adjustment" refers to appropriately changing the content and schedule of work in accordance with the emotional state and circumstances of the workers.

[0569] To realize this invention, it is necessary to build a system in which servers, terminals, and users work together.

[0570] First, the server collects information from multiple data transmission devices, such as email servers and chat platforms via the internet. Then, it uses information processing technology to analyze the collected data and extract activity instructions. Natural language processing technology is used for this analysis, with examples including the Python library Spacy and the BERT model. Tasks are automatically generated from the information obtained through the analysis and sent to the terminal.

[0571] The device is equipped with an emotion engine that senses the user's emotional state. This engine identifies the user's emotions through speech and text analysis and dynamically adjusts the task content as needed. This process requires real-time analysis, and a real-time processing framework such as Apache Kafka is used.

[0572] For example, if a user says "I'm tired," the device recognizes this emotion and notifies the server. This automatically adjusts the task content and schedule, reducing the user's burden.

[0573] Examples of prompts for a generative AI model include the following:

[0574] "We need a program designed to analyze user emotions from voice input and optimize work instructions accordingly. The program should adjust workload and feedback based on specific emotional keywords."

[0575] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0576] Step 1:

[0577] The server collects information from multiple data transmission devices. Specifically, it receives messages and instructions sent by users via email servers and chat platform APIs. The input is text data. This text data is extracted and stored in a database.

[0578] Step 2:

[0579] The server performs natural language processing on the collected text data. Here, Spacy and BERT models are used to analyze and extract useful activity instructions from the data. The input is the text data saved in step 1. The output is the extracted activity instructions. Data processing involves calculating word embeddings and extracting relevant phrases.

[0580] Step 3:

[0581] The server automatically generates tasks based on the extracted activity instructions. The input here is the activity instructions obtained in step 2. The generated work tasks are structured based on priority and deadline. This information is ready to be sent to the terminal. The output is a series of generated work tasks. Data calculations include prioritizing and optimizing each related task.

[0582] Step 4:

[0583] The terminal receives work tasks sent from the server. It then uses an emotion engine to understand the user's emotions in real time. Input is the user's voice or entered text, which is analyzed to identify their emotional state. Output is task priorities and schedules that reflect the user's emotions. Real-time analysis is performed, and the emotional state is extracted through necessary data processing.

[0584] Step 5:

[0585] Users can review the work tasks displayed on the terminal and manually modify them as needed. The input is the task list displayed on the terminal, and the output is the task settings manually adjusted by the user. By flexibly changing the priority and timing of each task at the user's discretion, a more efficient work environment is provided.

[0586] Step 6:

[0587] The server generates and sends feedback to the user based on the user's emotional state and work progress. The input consists of real-time updated emotional state data and work progress data. The output is a feedback message sent to the user. This process utilizes a generative AI model to generate prompts for appropriate encouragement and warnings as needed.

[0588] 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.

[0589] 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.

[0590] 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.

[0591] [Fourth Embodiment]

[0592] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0593] 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.

[0594] 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).

[0595] 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.

[0596] 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.

[0597] 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).

[0598] 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.

[0599] 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.

[0600] 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.

[0601] 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.

[0602] 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.

[0603] 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.

[0604] 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".

[0605] This invention provides a system for managing multiple communication tools used daily by users and supporting efficient task management. This system consists of three elements: a server, a terminal, and a user, and operates as follows.

[0606] First, the server periodically collects necessary information from various communication tools such as email, chat, and phone calls. The server accesses the data using the APIs and protocols of each tool and automatically stores the information.

[0607] Next, the server analyzes the collected data using natural language processing techniques. Specifically, the server extracts "task-related instructions" from email bodies and chat messages to identify relevant work instructions.

[0608] Subsequently, the terminal receives the analysis information sent from the server and automatically generates tasks using a generation AI. These generated tasks are then accompanied by metadata such as deadlines and priorities. The terminal displays a list of tasks to the user and provides guidance on how to proceed with the work according to the situation.

[0609] Users can view tasks generated via their devices and set their priorities. By selecting tasks according to their own importance and managing their progress, users can improve their work productivity.

[0610] Furthermore, the server automatically generates reminders based on the deadlines and priorities of tasks set by the user. These reminders are sent to the user's device at the specified date and time, helping to prevent tasks from being overlooked or forgotten.

[0611] Finally, the task progress is updated by the user via their device and sent to the server. The server can then update the information in real time and provide feedback to the user. In this way, the present invention enhances individual work efficiency and enables centralized management of information from multiple tools.

[0612] The following describes the processing flow.

[0613] Step 1:

[0614] The server collects user data at scheduled times through APIs or protocols of various communication tools. It uses IMAP and POP3 for email servers and accesses various APIs for chat tools to obtain data.

[0615] Step 2:

[0616] The server analyzes the collected data using natural language processing algorithms to identify keywords and phrases related to the task. For example, it extracts messages containing words such as "deadline," "request," and "by XX."

[0617] Step 3:

[0618] The terminal receives analysis results from the server and uses AI to automatically generate tasks based on those results. The generated tasks are then given metadata such as due dates and priorities, and registered in the task management system.

[0619] Step 4:

[0620] Users can review automatically generated tasks through their device and adjust priorities and deadlines as needed. They can also evaluate the importance of tasks and set their execution order.

[0621] Step 5:

[0622] The server creates a reminder schedule based on the set due dates and priorities, and sends notifications to the user's device at the appropriate time. Reminders are sent via email, push notifications, etc.

[0623] Step 6:

[0624] Users update task progress on their devices and report the status of completed and ongoing tasks to the server. The server then displays task progress in real time based on this information.

[0625] Step 7:

[0626] The server provides feedback based on user progress and suggests new action plans as needed, enabling users to maintain an optimal workflow.

[0627] (Example 1)

[0628] 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".

[0629] In today's diverse digital communication landscape, users receive vast amounts of information from various sources, making it challenging to centrally manage this information and efficiently handle tasks. In particular, the fact that information spans multiple platforms increases the risk of overlooking important tasks or delaying high-priority work. Solutions to this problem are urgently needed.

[0630] 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.

[0631] In this invention, the server includes a device means capable of collecting data from multiple digital communication media, an analysis device means for identifying relevant work instructions from the collected data, a generation device for automatically generating work instructions based on the identified information, and a device means for adding deadlines and importance as metadata. This enables the user to efficiently manage information from multiple communication media and prevent tasks from being overlooked or prioritizing incorrectly.

[0632] "Multiple digital communication media" refers to multiple communication platforms that send and receive information in digital format, such as email, chat, and voice calls.

[0633] A "data-collecting device" refers to hardware or software that has the function of automatically acquiring necessary information from a designated digital communication medium and storing or analyzing it.

[0634] An "analysis device" refers to a device that uses natural language processing and other analysis techniques on collected data to identify specific information and interpret its meaning.

[0635] A "generation device" refers to a device that automatically creates tasks for the user to perform based on information identified by an analysis device, and sets additional attributes such as deadlines and importance levels as needed.

[0636] A "management device" refers to a device that organizes generated tasks, tracks and updates their progress, and enables users to visualize and operate them.

[0637] A "reminder generation and notification device" refers to a device that automatically creates and sends messages to users at the appropriate time, taking into account the deadlines and priorities of the tasks being managed, to inform them of the need to perform the task.

[0638] This invention provides a system that enables users to efficiently manage multiple digital communication media and assists in the generation and execution of tasks. This system primarily consists of a server, terminals, and a user interface, with each element working in coordination.

[0639] The server first collects data from numerous digital communication media. This includes software for retrieving information via APIs from email and chat services. For example, it uses the email protocol for email and a specific messaging protocol for chat. The server then analyzes the collected data using natural language processing techniques to identify instructions and information relevant to the task. A text analysis engine is used for this analysis to extract important keywords and phrases.

[0640] The terminal is responsible for automatically generating tasks using a generation AI model based on analysis data sent from the server. Specifically, generation modules implemented in programming languages ​​such as JavaScript and Python are used. These modules add metadata such as deadlines and priorities to the generated tasks and display them in a user-friendly format.

[0641] Users can view tasks displayed via their device and prioritize them according to their importance. The user interface is designed for intuitive operation, allowing for drag-and-drop task management using a mouse or touch device. This enables users to organize tasks in real time and achieve efficient work operations.

[0642] Furthermore, the server generates reminders based on the managed task information and sends notifications to the user's device at the specified time. This notification function prevents users from forgetting tasks by informing them of deadlines. The notification function is implemented using programs such as Java or Ruby, and the notification format can be selected from options such as email or app notifications.

[0643] A concrete example is a case where a project manager uses this system to integrate email and chat instructions received from different team members for task management. An example of a prompt message to the generating AI model would be, "Extract task instructions from the following chat history and set the deadline and importance level."

[0644] Thus, the present invention improves user work efficiency by centrally managing information from various communication media and providing task creation, management, and notification functions.

[0645] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0646] Step 1:

[0647] The server is responsible for collecting data from digital communication media. First, it retrieves unread messages and notifications through APIs for email and chat services. The input data to be collected includes authentication information and query data for each service. The output generates a list of unprocessed messages and notification data, which then becomes the input data for the next processing step.

[0648] Step 2:

[0649] The server applies natural language processing techniques to the collected data for analysis. Using the collected text data as input, it performs document structure analysis and key phrase extraction, outputting text relevant to work instructions as a result. For example, specific instructions such as "Complete the draft of the project proposal by next week" might be extracted.

[0650] Step 3:

[0651] The terminal receives data analyzed by the server. Based on the instruction data received as input, it uses a generative AI model to automatically generate specific tasks. Prompt text is entered into the generative AI model, and a task with a specific deadline and priority is obtained as output. In this process, a "Project Proposal Draft Creation Task" is generated with attributes such as a deadline of "Next Monday" and a priority of "High".

[0652] Step 4:

[0653] The terminal displays the generated tasks to the user. Through the user interface, it provides a task list and calendar view, making it easy for the user to check them. The displayed tasks are the output, and the user looks at this output to decide on their next action.

[0654] Step 5:

[0655] Users view tasks displayed on their device and prioritize them based on their schedule and importance. They provide feedback to the device regarding task deadlines and progress, and a new priority list is generated. This allows users to focus on individual tasks and complete them efficiently.

[0656] Step 6:

[0657] The server generates reminders based on the task information set by the user. As the deadline approaches, it outputs the reminder as a notification message and sends it to the user's device. This ensures that the progress of all tasks is reliably managed and reduces the risk of missing important deadlines.

[0658] Step 7:

[0659] The progress status is updated when the user completes a task. By entering a completion report into the terminal and sending it to the server, the task status is updated in real time. This then outputs the next task to the user, supporting the creation of a new plan.

[0660] (Application Example 1)

[0661] 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".

[0662] In modern households, communication is active through multiple means of information exchange, leading to a variety of tasks and schedules. However, efficiently organizing these tasks and carrying them out collaboratively within the family is often difficult. Prioritizing individual tasks and transitioning to physical execution also remain challenges. In particular, for elderly people and busy families, there is a need for automated systems to smoothly manage and execute daily tasks.

[0663] 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.

[0664] In this invention, the server includes means for collecting information from multiple information exchange means, analysis means for extracting relevant work instructions from the collected information, and control means for efficiently managing tasks occurring within the home and physically executing those tasks using a device with autonomous driving capabilities. This enables efficient management of tasks within the home, automation of prioritization, and physical execution of tasks by a device with autonomous driving capabilities.

[0665] "Information exchange means" refers to the means and technologies for sending and receiving information between individuals or devices.

[0666] "Work instructions" refer to specific instructions or commands for performing a particular task.

[0667] An "analysis tool" is a device that analyzes collected information and extracts necessary data and instructions from it.

[0668] A "device with autonomous driving capabilities" refers to a device that can automatically move around and perform tasks based on a pre-set program or instructions.

[0669] "Control means" refers to the technology and functions used to instruct and manage the operation of equipment and devices.

[0670] To implement this invention, it is necessary to construct a system centered on three elements: a server, a terminal, and a user. In this system, the server collects data through multiple information exchange means, analyzes it, and extracts work instructions. Natural language processing technology is used to analyze the collected data, and a generative AI model is used to extract particularly important information.

[0671] The server also automatically generates tasks based on extracted work instructions and adds metadata such as deadlines and priorities. For this purpose, the server utilizes cloud-based databases and scheduling software. Specific examples of such software include "spaCy" and "NLTK" for natural language processing, and "PostgreSQL" for data management.

[0672] The terminal provides a user interface, allowing users to view tasks and set priorities as needed. This enables users to plan their day based on generated tasks and manage their progress in real time. Smartphones and tablets are used as terminal devices, and information is displayed by application software.

[0673] Users update their work progress via their devices and send this information to the server. The server then updates the information in real time, generates reminders as needed, and notifies the user, preventing delays and missed tasks. Reminder notifications are sent using the device's push notification function.

[0674] For example, when a user sends a message to the system such as "Prepare for household chores," that information is transmitted to a device with autonomous driving capabilities, which then automatically performs tasks such as cleaning and arranging items. An example of a prompt message for the generated AI model is: "Extract priority household tasks from the following chat and set the steps to perform them. Example: 'Clean,' 'Create a shopping list,' 'Tidy up the room.'"

[0675] In this way, by centrally managing information across servers, terminals, and users, and enabling the automation of tasks, this system can significantly improve the efficiency of daily life.

[0676] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0677] Step 1:

[0678] The server collects data from multiple means of information exchange. This collection is done via APIs, retrieving data from email, chat, and other digital communications. The input is this digital communication data, and the server collects raw data based on it.

[0679] Step 2:

[0680] The server analyzes the collected data using natural language processing technology to extract work instructions. The input is the raw data obtained in step 1, and tasks and important information are extracted through natural language processing. A generative AI model assists this process to identify specific work instructions.

[0681] Step 3:

[0682] The server automatically generates tasks based on the extracted work instructions and adds metadata. The acquired information is processed by scheduling software, which adds metadata such as deadlines and priorities. The output is a completed task list.

[0683] Step 4:

[0684] The terminal displays the generated tasks in a user interface. The input is a task list sent from the server, which the user uses to check the task status. The terminal also provides options for setting priorities.

[0685] Step 5:

[0686] Users use a terminal to set task priorities and update progress. User actions become input, and the configured information is sent to the server via the terminal. The output is an updated task list.

[0687] Step 6:

[0688] The server uses its storage device to generate a reminder based on the updated information and notifies the device. The server then uses this information to send a push notification to the device indicating the next task to be performed. This ensures the user doesn't forget to complete the task.

[0689] 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.

[0690] This invention provides a system that, in addition to the function of efficiently managing tasks by collecting information from multiple communication tools, is equipped with an emotion engine that recognizes the user's emotions and uses them to assist in task management. This system is composed of a server, terminals, and users, and operates as follows.

[0691] First, the server collects information from sources such as email, chat, and phone calls. The server retrieves and stores data through the APIs and protocols of each tool. The server also uses natural language processing technology to analyze the data and extract work instructions related to the task. Based on this extracted information, the server automatically generates tasks and sends them to the terminal.

[0692] The terminal presents the user with a list of tasks based on the analysis information received from the server and the tasks generated. The terminal is equipped with an emotion engine that analyzes the user's messages and input to recognize their emotional state. For example, it reads phrases such as "tired" or "in a hurry" included in the user's input, as well as negative / positive emotions from the message. Based on the analysis results of this emotion engine, it is possible to dynamically adjust task priorities and reminder settings.

[0693] Users can review generated tasks through their device and manually set priorities. However, the system also takes the user's emotional state into consideration and automatically rearranges tasks and adjusts their importance, thus reducing the user's burden.

[0694] The server can also receive progress information and provide feedback based on emotional states. For example, if a user is feeling stressed, it may reduce the frequency of reminders or send encouraging messages indicating that tasks are progressing smoothly. Specifically, if a user expresses an emotion such as "busy," the emotion engine will recognize this and emphasize reminders to draw attention to tasks with approaching deadlines.

[0695] In this way, the present invention enables flexible and personalized task management that takes user emotions into consideration, providing a less stressful and more efficient work environment.

[0696] The following describes the processing flow.

[0697] Step 1:

[0698] The server collects user data at scheduled times through APIs or protocols of various communication tools. This data includes emails, chat messages, call logs, and more.

[0699] Step 2:

[0700] The server analyzes the collected data using natural language processing algorithms to identify information related to work instructions and tasks. In particular, it extracts sentences containing keywords such as "deadline," "request," and "confirmation," and breaks down the content of the instructions.

[0701] Step 3:

[0702] The terminal uses AI generation based on analysis results sent from the server to automatically generate tasks. At that time, metadata such as due dates and initial priorities are set for the generated tasks.

[0703] Step 4:

[0704] The device uses its built-in emotion engine to recognize the user's emotions. It analyzes the user's interaction history and message content to identify emotions such as "feeling stressed" or "being in a positive state."

[0705] Step 5:

[0706] Based on the analysis results of the emotion engine, the device dynamically adjusts task priorities. For example, if a user indicates high stress levels, it reduces the burden by prioritizing and displaying tasks that require immediate attention.

[0707] Step 6:

[0708] Users can review the task list displayed on their device and manually adjust priorities and deadlines as needed. Feedback on the system's suggestions will be used to improve future adjustments.

[0709] Step 7:

[0710] The server monitors the progress of tasks updated by users and updates the database in a timely manner. The progress is reflected in real time, and the user is given feedback on the current status.

[0711] Step 8:

[0712] Based on the emotional state recognized by the emotion engine, the server sends encouraging messages and relaxation suggestions to the user to support the maintenance of a comfortable work environment.

[0713] Through this series of processes, the present invention provides users with personalized task management and emotionally sensitive work support.

[0714] (Example 2)

[0715] 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".

[0716] Current task management systems do not dynamically adjust priorities while taking into account the user's emotional state, and therefore do not adequately contribute to work efficiency or stress reduction. For this reason, there is a need to provide a personalized management method that reflects the user's emotions, from information gathering to task item generation and notifications.

[0717] 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.

[0718] In this invention, the server includes a device capable of collecting information from multiple communication technologies, an analysis means for extracting relevant work commands from the collected information, and a means for evaluating the user's emotions using emotion analysis technology and dynamically adjusting the priority of work items based on that evaluation. This enables flexible and effective work management based on the user's emotions.

[0719] "Communication technology" refers to all technologies used to send and receive information.

[0720] "Device" refers to a component of hardware or software used to perform a specific function or process.

[0721] "Analysis means" refers to devices and technologies used to analyze information and extract relevant data.

[0722] "Natural language processing technology" refers to the technology used to process human language using computers, enabling the analysis and understanding of text.

[0723] A "generation device" refers to a device that generates specific results or items based on input information.

[0724] A "control device" refers to a device used to operate and manage the entire system.

[0725] A "warning device" refers to a device that notifies the user when certain conditions are met.

[0726] "Emotional analysis technology" refers to technology that analyzes and recognizes a user's emotional state from text and audio data.

[0727] "Feedback" refers to the reactions and evaluations received by users or devices based on the system's output.

[0728] This invention is a system centered on servers, terminals, and users, thereby enabling task management that takes user emotions into consideration. The system components include an information gathering device, analysis means, generation device, control device, warning device, and emotion analysis technology.

[0729] The server acquires information from multiple communication technologies using information gathering devices. This information includes electronic messages, voice recordings, and digital communication logs. After acquisition, the server uses analysis tools to analyze the information using natural language processing technology and extract business instructions and related data. In this process, a specific generative AI model is utilized to interpret complex contexts and identify appropriate data.

[0730] The terminal manages work items automatically generated by a generator based on the analyzed information, using a control device. The terminal displays a task list to the user, which is dynamically changed according to the user's emotional state. Sentiment analysis technology evaluates the user's input text and voice, and uses this as a basis for adjusting the priority of work items. Specifically, it analyzes keywords such as "busy" and "tired" to automatically adjust reminder settings and notification frequencies.

[0731] Users can review these tasks and manually prioritize them, but the workload is reduced through automatic adjustments based on emotions. A feedback function sends user emotional responses to the server in real time, optimizing the overall system operation.

[0732] As a concrete example, here is an example of a prompt message:

[0733] "If a user is worried about a task due tomorrow, the sentiment engine will detect this and set a reminder to prioritize notifications."

[0734] In this way, the present invention enables individually customized task management while taking into account the user's feelings, providing an efficient and less stressful work environment.

[0735] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0736] Step 1:

[0737] The server collects information through multiple communication technologies. Inputs include email, chat messages, and voice call logs. The server uses APIs for each communication technology to retrieve data and store it in a database. The output is raw data awaiting analysis.

[0738] Step 2:

[0739] The server processes the collected raw data using analytical tools. Input data includes text messages and audio data. Using natural language processing techniques, a generative AI model syntactically analyzes the messages and extracts work instructions and emotional states. The output provides task candidates along with the analysis results. For example, if an emotional expression such as "busy" is extracted, time-related tasks will be highlighted.

[0740] Step 3:

[0741] The server generates work items based on the analysis results. Using the analysis results as input, the generation device automatically determines the task content, deadline, and priority, creating a task list. The output is a detailed task list. This list is sent to the terminal and prepared for user access.

[0742] Step 4:

[0743] The terminal displays the task list received from the server. It receives the task list as input and presents it in a user-friendly interface. The terminal utilizes sentiment analysis technology to analyze the user's input and responses, dynamically adjusting the priority of the task list. The output presents a task list optimized for the user.

[0744] Step 5:

[0745] Users view their task list on their terminal and manually adjust priorities as needed. When users input information (e.g., "finished" or "will do later") into their terminal, the task list is updated accordingly. User feedback is sent to the server to help optimize system operation. In this way, the final work progress output is obtained.

[0746] (Application Example 2)

[0747] 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".

[0748] In modern factory environments, worker fatigue and stress often affect productivity and safety. Therefore, accurately understanding workers' emotional states and adjusting work instructions accordingly is crucial. However, current systems lack the flexibility to adjust work instructions based on individual workers' emotions.

[0749] 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.

[0750] In this invention, the server includes means for collecting information from multiple data transmission devices, analysis means for extracting relevant activity instructions from the collected information, and means for sensing the emotional state of the worker and flexibly adjusting the work content. This makes it possible to sense the emotional state of the worker in real time and adjust the work accordingly.

[0751] A "data transmission device" is a means of collecting information and transmitting that information between other devices or systems.

[0752] "Activity instructions" refer to specific instructions necessary to perform a particular task or operation.

[0753] "Analysis means" are technical methods for interpreting collected information and deriving useful information or instructions from it.

[0754] A "generation method" is a means of automatically creating new tasks or operations based on analyzed information.

[0755] A "management tool" is a means that has the function of tracking the progress of generated work or tasks and making corrections or adjustments as needed.

[0756] A "notification method" is a means of informing workers of necessary information based on the deadlines and priorities of tasks and work.

[0757] "Emotional state" refers to the psychological state and emotions of workers, and these can be factors that affect work efficiency and safety.

[0758] "Flexible work adjustment" refers to appropriately changing the content and schedule of work in accordance with the emotional state and circumstances of the workers.

[0759] To realize this invention, it is necessary to build a system in which servers, terminals, and users work together.

[0760] First, the server collects information from multiple data transmission devices, such as email servers and chat platforms via the internet. Then, it uses information processing technology to analyze the collected data and extract activity instructions. Natural language processing technology is used for this analysis, with examples including the Python library Spacy and the BERT model. Tasks are automatically generated from the information obtained through the analysis and sent to the terminal.

[0761] The device is equipped with an emotion engine that senses the user's emotional state. This engine identifies the user's emotions through speech and text analysis and dynamically adjusts the task content as needed. This process requires real-time analysis, and a real-time processing framework such as Apache Kafka is used.

[0762] For example, if a user says "I'm tired," the device recognizes this emotion and notifies the server. This automatically adjusts the task content and schedule, reducing the user's burden.

[0763] Examples of prompts for a generative AI model include the following:

[0764] "We need a program designed to analyze user emotions from voice input and optimize work instructions accordingly. The program should adjust workload and feedback based on specific emotional keywords."

[0765] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0766] Step 1:

[0767] The server collects information from multiple data transmission devices. Specifically, it receives messages and instructions sent by users via email servers and chat platform APIs. The input is text data. This text data is extracted and stored in a database.

[0768] Step 2:

[0769] The server performs natural language processing on the collected text data. Here, Spacy and BERT models are used to analyze and extract useful activity instructions from the data. The input is the text data saved in step 1. The output is the extracted activity instructions. Data processing involves calculating word embeddings and extracting relevant phrases.

[0770] Step 3:

[0771] The server automatically generates tasks based on the extracted activity instructions. The input here is the activity instructions obtained in step 2. The generated work tasks are structured based on priority and deadline. This information is ready to be sent to the terminal. The output is a series of generated work tasks. Data calculations include prioritizing and optimizing each related task.

[0772] Step 4:

[0773] The terminal receives work tasks sent from the server. It then uses an emotion engine to understand the user's emotions in real time. Input is the user's voice or entered text, which is analyzed to identify their emotional state. Output is task priorities and schedules that reflect the user's emotions. Real-time analysis is performed, and the emotional state is extracted through necessary data processing.

[0774] Step 5:

[0775] Users can review the work tasks displayed on the terminal and manually modify them as needed. The input is the task list displayed on the terminal, and the output is the task settings manually adjusted by the user. By flexibly changing the priority and timing of each task at the user's discretion, a more efficient work environment is provided.

[0776] Step 6:

[0777] The server generates and sends feedback to the user based on the user's emotional state and work progress. The input consists of real-time updated emotional state data and work progress data. The output is a feedback message sent to the user. This process utilizes a generative AI model to generate prompts for appropriate encouragement and warnings as needed.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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."

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] The following is further disclosed regarding the embodiments described above.

[0800] (Claim 1)

[0801] A means to collect information from multiple communication tools,

[0802] An analysis means for extracting relevant work instructions from the collected information,

[0803] A generation means for automatically generating tasks based on the extracted information,

[0804] A management means for managing the generated tasks and tracking their progress,

[0805] A system including a notification mechanism for generating reminders for the aforementioned managed tasks based on deadlines and priorities.

[0806] (Claim 2)

[0807] The system according to claim 1, wherein the analysis means is a means for analyzing information using natural language processing.

[0808] (Claim 3)

[0809] The system according to claim 1, wherein the management means is a means for updating the progress of a task in real time.

[0810] "Example 1"

[0811] (Claim 1)

[0812] A device capable of collecting data from numerous digital communication media,

[0813] An analysis device that identifies relevant work instructions from the collected data,

[0814] A generation device that automatically generates work instructions based on the identified information, and a device that adds deadline and importance as metadata,

[0815] A management device for managing the generated work instructions and tracking their progress,

[0816] A system including a device that generates and notifies reminders based on deadlines and importance for the aforementioned managed work instructions.

[0817] (Claim 2)

[0818] The system according to claim 1, wherein the analysis device is a device that analyzes data using natural language processing technology.

[0819] (Claim 3)

[0820] The system according to claim 1, wherein the management device is a device that immediately updates the progress of work instructions.

[0821] "Application Example 1"

[0822] (Claim 1)

[0823] A means of collecting information from multiple means of information exchange,

[0824] An analysis means for extracting relevant work instructions from the collected information,

[0825] A generation means for automatically generating tasks based on the extracted information,

[0826] A management means for managing the generated work and tracking its progress,

[0827] A notification means for generating reminders for the aforementioned managed tasks using a storage device based on deadlines and priorities,

[0828] A system that includes control means for efficiently managing tasks that occur within the home and for physically executing those tasks using a device with autonomous driving capabilities.

[0829] (Claim 2)

[0830] The system according to claim 1, wherein the analysis means is a means for analyzing information using natural language processing technology.

[0831] (Claim 3)

[0832] The system according to claim 1, wherein the management means is a means for updating the progress of work in real time and giving instructions to a device having an autonomous driving function.

[0833] "Example 2 of combining an emotion engine"

[0834] (Claim 1)

[0835] A device capable of collecting information from multiple communication technologies,

[0836] An analysis means for extracting relevant work instructions from the collected information,

[0837] A means of analyzing information using natural language processing technology,

[0838] A generation device that automatically generates work items based on the analyzed information,

[0839] A control device for managing the generated work items and tracking their progress,

[0840] A warning device that generates notifications based on deadlines and priorities for the aforementioned managed work items,

[0841] A means for evaluating the user's emotions using emotion analysis technology and dynamically adjusting the priority of work items based on that evaluation,

[0842] A means to adjust notification settings for work items based on user feedback.

[0843] A system that includes this.

[0844] (Claim 2)

[0845] The system according to claim 1, wherein the control device is a means for adjusting the priority of work items using emotion analysis technology.

[0846] (Claim 3)

[0847] The system according to claim 1, wherein the warning device is a means of providing an emotion-based message to the user.

[0848] "Application example 2 when combining with an emotional engine"

[0849] (Claim 1)

[0850] A means capable of collecting information from multiple data transmission devices,

[0851] An analysis means for extracting relevant activity instructions from the collected information,

[0852] A generation means for automatically generating tasks based on the extracted information,

[0853] A management means for managing the generated work and tracking its progress,

[0854] A means for generating notifications based on deadlines and priorities for the aforementioned managed tasks,

[0855] A system that includes means to sense the emotional state of workers and flexibly adjust the work content accordingly.

[0856] (Claim 2)

[0857] The system according to claim 1, wherein the analysis means is a means for analyzing information using information processing technology and evaluating an emotional state.

[0858] (Claim 3)

[0859] The system according to claim 1, wherein the management means is a means for updating the progress of the work and the emotional state of the worker in real time. [Explanation of Symbols]

[0860] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means to collect information from multiple communication tools, An analysis means for extracting relevant work instructions from the collected information, A generation means for automatically generating tasks based on the extracted information, A management means for managing the generated tasks and tracking their progress, A system including a notification mechanism for generating reminders for the aforementioned managed tasks based on deadlines and priorities.

2. The system according to claim 1, wherein the analysis means is a means for analyzing information using natural language processing.

3. The system according to claim 1, wherein the management means is a means for updating the progress of a task in real time.

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