system

Abstract

The system according to this embodiment aims to prevent overlooking tasks by centrally managing information from multiple tools. [Solution] The system according to the embodiment comprises an extraction unit, a listing unit, a management unit, and an update unit. The extraction unit automatically extracts information from each tool. The listing unit lists the information extracted by the extraction unit as action items. The management unit centrally manages the action items listed by the listing unit. The update unit updates the progress status of the action items managed by the management unit in real time.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to centrally manage information from multiple tools, and there is a risk of overlooking business operations.

[0005] The system according to the embodiment aims to centrally manage information from multiple tools and prevent overlooking business operations.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an extraction unit, a listing unit, a management unit, and an update unit. The extraction unit automatically extracts information from each tool. The listing unit lists the information extracted by the extraction unit as action items. The management unit centrally manages the action items listed by the listing unit. The update unit updates the progress status of the action items managed by the management unit in real time. [Effects of the Invention]

[0007] The system according to this embodiment can centrally manage information from multiple tools and prevent tasks from being overlooked. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving 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 receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The business communication support system according to an embodiment of the present invention is a system for preventing oversights in business communications. This system automatically extracts information from various tools and lists it as action items, thereby preventing omissions and omissions in business operations and achieving efficient task management. For example, the business communication support system extracts important tasks and instructions from the body of emails and chat messages. In this process, it uses a generation AI to analyze the information and extract action items. Next, it lists the extracted information as action items. Each action item is assigned information such as the person in charge, the deadline, and the priority. This makes task management easier. Furthermore, it provides a dashboard that allows for centralized management of the listed action items. It can be filtered and displayed by person in charge or by project. This makes it easier to grasp the progress of the entire team. It also has a function to automatically send reminders for tasks that are approaching their deadline or are incomplete. Notifications are sent via email or chat to prevent oversights. Finally, it has a function to update the progress of action items in real time. This makes it easier to grasp the progress of the entire team. In this way, it prevents omissions and omissions in business operations and supports efficient task management. This allows the business communication support system to prevent business communications from being overlooked and to enable efficient task management.

[0029] The business communication support system according to this embodiment comprises an extraction unit, a listing unit, a management unit, and an update unit. The extraction unit automatically extracts information from each tool. For example, the extraction unit extracts important tasks and instructions from the body of an email or a chat message. The extraction unit analyzes the information using a generation AI and extracts action items. For example, the generation AI uses natural language processing technology to analyze the content of emails and chat messages and extract important tasks and instructions. The listing unit lists the information extracted by the extraction unit as action items. The listing unit adds information such as the person in charge, deadline, and priority to each action item. For example, the listing unit uses a generation AI to analyze the extracted information and automatically adds information such as the person in charge, deadline, and priority. The management unit centrally manages the action items listed by the listing unit. The management unit filters and displays the information by person in charge or by project. For example, the management unit uses a generation AI to classify action items by person in charge or by project and displays them on a dashboard. The update unit updates the progress of action items managed by the management unit in real time. The update unit automatically sends reminders for tasks that are nearing their deadline or are incomplete. For example, the update unit uses a generation AI to monitor the progress of action items and automatically sends reminders for tasks that are nearing their deadline or are incomplete. As a result, the business communication support system according to this embodiment can prevent business communications from being overlooked and achieve efficient task management.

[0030] The extraction unit automatically extracts information from various tools. Specifically, it extracts important tasks and instructions from email bodies and chat messages. The extraction unit uses generative AI to analyze the information and extract action items. The generative AI utilizes natural language processing technology to analyze the content of emails and chat messages in detail. For example, it detects keywords such as "urgent," "please confirm," and "deadline" contained in the email body and identifies important tasks and instructions based on them. Similarly, in chat messages, it understands the context of the conversation and extracts important action items. The generative AI can perform contextual analysis and sentiment analysis to evaluate the importance and urgency of messages. Furthermore, the extraction unit can collect information from multiple tools simultaneously and analyze it integrally. For example, it collects information from email, chat, and project management tools, and integrates and analyzes the information obtained from each tool. This allows the extraction unit to prevent overlooking business communications and quickly identify important tasks and instructions.

[0031] The listing unit creates a list of action items based on the information extracted by the extraction unit. Specifically, it assigns information such as the person in charge, deadline, and priority to each action item. The listing unit uses a generation AI to analyze the extracted information and automatically assign information such as the person in charge, deadline, and priority. The generation AI analyzes the content of the extracted tasks and instructions and identifies the appropriate person in charge. For example, it assigns the optimal person in charge based on past task history and the person in charge's skill set. It also sets an appropriate deadline considering the content of the task and the progress of the project. Furthermore, it evaluates the importance and urgency of the task and assigns a priority. Based on this information, the listing unit lists the action items and displays them in a visually easy-to-understand format. For example, it displays action items in the format of a Gantt chart or Kanban board, allowing the person in charge, deadline, and priority to be seen at a glance. In this way, the listing unit supports efficient task management and enables smooth progress of work.

[0032] The management department centrally manages the action items listed by the listing department. Specifically, it filters and displays them by assignee and project. The management department uses a generation AI to classify action items by assignee and project and displays them on a dashboard. The generation AI analyzes the attribute information of each action item and classifies them based on assignee and project. For example, by classifying tasks by assignee and displaying them on each assignee's dashboard, each assignee can grasp their own tasks at a glance. Tasks can also be classified by project, allowing for visual confirmation of project progress. The management department updates this information in real time, ensuring that the latest status is always known. Furthermore, the management department can monitor the progress of tasks and issue alerts if delays or problems occur. This allows the management department to efficiently manage task progress and support project success.

[0033] The update unit updates the progress of action items managed by the management unit in real time. Specifically, it automatically sends reminders for tasks that are nearing their deadline or are incomplete. The update unit uses a generation AI to monitor the progress of action items and automatically sends reminders for tasks that are nearing their deadline or are incomplete. The generation AI analyzes the progress of each task and identifies tasks that are nearing their deadline or are incomplete. For example, it monitors the progress of a task and sends a reminder to the person in charge when the deadline is approaching. It also sends periodic reminders for incomplete tasks to draw the attention of the person in charge. Furthermore, the update unit updates the progress of tasks in real time, ensuring that the latest status is always known. For example, when a task is completed, the progress is immediately updated and reflected in the dashboard. This allows the update unit to efficiently manage task progress and prevent delays in work. In addition, the update unit can customize the frequency and content of reminders, allowing for flexible responses to the needs of the person in charge. This allows the update unit to efficiently manage the progress of tasks and prevent delays in operations.

[0034] The extraction unit can extract important tasks and instructions from email bodies and chat messages. For example, the extraction unit can detect keywords such as "urgent" or "important" from the email body and extract the corresponding tasks. The extraction unit can also analyze the context of chat messages and prioritize the extraction of instructions from superiors. Furthermore, the extraction unit can detect and extract tasks with clearly stated deadlines from the email body. This prevents overlooking important tasks and instructions by automatically extracting them. Some or all of the above processing in the extraction unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the extraction unit can input the email body or chat messages into a generation AI and have the generation AI perform the extraction of important tasks and instructions.

[0035] The listing unit can assign information such as assignee, deadline, and priority to each action item. For example, the listing unit can automatically assign an assignee to the extracted tasks. The listing unit can also automatically set the deadline for tasks. Furthermore, the listing unit can automatically evaluate and assign a priority to tasks. This makes task management easier by assigning the necessary information to action items. Some or all of the above processing in the listing unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the listing unit can input the extracted tasks into a generation AI and have the generation AI assign assignees, deadlines, and priorities.

[0036] The management department can filter and display information by person in charge or by project. For example, the management department can classify action items by person in charge and display them on the dashboard. It can also classify and display action items by project. Furthermore, the management department can set filtering conditions and display only action items that match specific conditions. This makes it easier to understand the progress status by filtering by person in charge or project. Some or all of the above processes in the management department may be performed using a generation AI, or they may not be performed using a generation AI. For example, the management department can input action items into a generation AI and have the generation AI perform the classification by person in charge or by project.

[0037] The management department can provide a dashboard for centrally managing a list of action items. For example, the management department can build and display a dashboard for centrally managing action items. The management department can also update the progress of action items on the dashboard in real time. Furthermore, the management department can display detailed information about action items on the dashboard. This makes it easier to grasp the progress of the entire team by centrally managing action items. Some or all of the above processes in the management department may be performed using or without a generative AI. For example, the management department can input action items into a generative AI and have the generative AI build and display the dashboard.

[0038] The update unit can automatically send reminders for tasks that are approaching deadlines or are incomplete. For example, the update unit can automatically send reminders via email for tasks that are approaching deadlines. It can also automatically send reminders via chat for incomplete tasks. Furthermore, the update unit can set the timing for sending reminders and send them at specific times. This prevents tasks from being overlooked by automatically sending reminders. Some or all of the above processes in the update unit may be performed using a generation AI, or not. For example, the update unit can input the progress of action items into the generation AI and have the generation AI execute the sending of reminders.

[0039] The extraction unit can analyze the context of emails and chat messages and automatically highlight high-priority tasks. For example, the extraction unit can detect keywords such as "urgent" or "important" from the body of an email and highlight the corresponding tasks. The extraction unit can also analyze the context of chat messages and prioritize highlighting instructions from supervisors. Furthermore, the extraction unit can detect and highlight tasks with clearly stated deadlines from the body of emails. This helps prevent tasks from being overlooked by highlighting high-priority tasks. Some or all of the above processing in the extraction unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the extraction unit can input emails and chat messages into a generation AI and have the generation AI perform the highlighting of high-priority tasks.

[0040] The extraction unit can improve extraction accuracy by referring to past extraction history during the extraction process. For example, the extraction unit can prioritize the extraction of similar tasks based on the history of tasks that have been extracted in the past. The extraction unit can also detect tasks that are frequently overlooked from past extraction history and prioritize their extraction. Furthermore, the extraction unit can analyze past extraction history, and the generating AI can learn patterns to improve extraction accuracy. As a result, extraction accuracy is improved by referring to past extraction history. Some or all of the above processing in the extraction unit may be performed using the generating AI, or it may be performed without the generating AI. For example, the extraction unit can input past extraction history into the generating AI and have the generating AI perform the task of improving extraction accuracy.

[0041] The extraction unit can prioritize extracting highly relevant information based on the user's work schedule during the extraction process. For example, the extraction unit can refer to the user's calendar information and prioritize extracting tasks related to appointments. It can also prioritize extracting tasks related to ongoing projects from the user's work schedule. Furthermore, the extraction unit can prioritize extracting tasks with approaching deadlines based on the user's schedule. This enables efficient task management by extracting highly relevant information based on the user's work schedule. Some or all of the above-described processes in the extraction unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the extraction unit can input the user's work schedule into a generation AI and have the generation AI perform the extraction of highly relevant information.

[0042] The extraction unit can analyze the user's social media activity during the extraction process and extract relevant tasks. For example, the extraction unit can extract business-related information from the user's social media posts. It can also extract relevant tasks from posts by the user's social media followers and friends. Furthermore, the extraction unit can analyze the user's social media activity history and extract relevant tasks. This allows for the efficient extraction of relevant tasks by analyzing the user's social media activity. Some or all of the above processing in the extraction unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the extraction unit can input the user's social media activity into a generation AI and have the generation AI perform the extraction of relevant tasks.

[0043] The listing unit can automatically adjust the order of tasks in a list based on their importance during the listing process. For example, the listing unit can display high-importance tasks at the top of the list. It can also display tasks with approaching deadlines at the top of the list. Furthermore, the listing unit can adjust the order of tasks based on the priority of the person in charge. This allows for priority management of important tasks by adjusting the order of tasks based on their importance. Some or all of the above processing in the listing unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the listing unit can input the importance of tasks into the generation AI and have the generation AI perform the adjustment of the list order.

[0044] The listing unit can apply different listing algorithms depending on the task category during the listing process. For example, the listing unit can apply a different listing algorithm to each project. Furthermore, the listing unit can adjust the listing algorithm according to the type of task (e.g., meeting, report writing). In addition, the listing unit can apply a listing algorithm based on the task priority. This enables efficient task management by applying a listing algorithm according to the task category. Some or all of the above-described processes in the listing unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the listing unit can input the task category into a generative AI and have the generative AI execute the application of the listing algorithm.

[0045] The listing unit can determine the priority of tasks based on their submission dates when creating a list. For example, the listing unit can display tasks with approaching deadlines at the top of the list. It can also display tasks with later deadlines at the bottom of the list. Furthermore, the listing unit can highlight tasks that have already passed their deadlines. This makes it easier to meet deadlines by determining the priority of the list based on the task submission dates. Some or all of the above processing in the listing unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the listing unit can input the task submission dates into the generation AI and have the generation AI determine the priority of the list.

[0046] The listing unit can adjust the order of tasks in a list based on their relevance during the listing process. For example, the listing unit can group related tasks together and list them. It can also display highly relevant tasks at the top of the list, and less relevant tasks at the bottom. This allows for efficient task management by adjusting the order of tasks based on their relevance. Some or all of the above processing in the listing unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the listing unit can input the relevance of tasks into a generation AI and have the generation AI perform the adjustment of the list order.

[0047] The management unit can optimize its management algorithm by referring to past management history during management. For example, the management unit can use a generative AI to learn the optimal management algorithm based on past management history. The management unit can also detect frequently overlooked tasks from past management history and adjust the management algorithm accordingly. Furthermore, the management unit can analyze past management history to improve the accuracy of the management algorithm. Thus, the accuracy of the management algorithm is improved by referring to past management history. Some or all of the above processes in the management unit may be performed using the generative AI, or they may not be performed using the generative AI. For example, the management unit can input past management history into the generative AI and have the generative AI perform the optimization of the management algorithm.

[0048] The management department can monitor task progress in real time during management and issue alerts if an anomaly is detected. For example, the management department can monitor task progress in real time and issue an alert if a delay occurs. The management department can also issue an alert if there are incomplete tasks. Furthermore, the management department can issue an alert if an anomaly is detected. This allows for early detection and response to anomalies by monitoring task progress in real time. Some or all of the above processes in the management department may be performed using a generation AI, or they may not be performed using a generation AI. For example, the management department can input task progress into a generation AI and have the generation AI perform anomaly detection and issue alerts.

[0049] The management department can adjust its management methods based on the geographical distribution of tasks during the management process. For example, the management department can propose the optimal management method based on the geographical distribution of tasks. The management department can also group and manage tasks that are geographically close together. Furthermore, the management department can prioritize the management of tasks that are geographically far apart. By adjusting the management method based on the geographical distribution of tasks, efficient task management becomes possible. Some or all of the above processes in the management department may be performed using a generative AI, or they may not be performed using a generative AI. For example, the management department can input the geographical distribution of tasks into a generative AI and have the generative AI perform the adjustment of the management method.

[0050] The management department can improve the accuracy of its management by referring to relevant literature for tasks during the management process. For example, the management department can refer to relevant literature for tasks to improve the accuracy of management. The management department can also predict the progress of tasks from the relevant literature and adjust the management method accordingly. Furthermore, the management department can re-evaluate the priority of tasks based on the relevant literature. In this way, the accuracy of management is improved by referring to relevant literature for tasks. Some or all of the above processes in the management department may be performed using a generative AI, or they may be performed without a generative AI. For example, the management department can input relevant literature for tasks into a generative AI and have the generative AI perform the improvement of management accuracy.

[0051] The update unit can optimize the update algorithm by referring to past update history during the update process. For example, the update unit's generating AI learns the optimal update algorithm based on past update history. The update unit can also detect frequently overlooked tasks from past update history and adjust the update algorithm accordingly. Furthermore, the update unit can analyze past update history to improve the accuracy of the update algorithm. Thus, the accuracy of the update algorithm is improved by referring to past update history. Some or all of the above processes in the update unit may be performed using the generating AI or not. For example, the update unit can input past update history into the generating AI and have the generating AI perform the optimization of the update algorithm.

[0052] The update unit can monitor the progress of tasks in real time during updates and issue alerts if an anomaly is detected. For example, the update unit can monitor the progress of tasks in real time and issue an alert if a delay occurs. The update unit can also issue an alert if there are incomplete tasks. Furthermore, the update unit can issue an alert if an anomaly is detected. This allows for early detection and response to anomalies by monitoring the progress of tasks in real time. Some or all of the above processing in the update unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the update unit can input the progress of tasks into a generation AI and have the generation AI perform anomaly detection and issue alerts.

[0053] The update unit can adjust the update method based on the geographical distribution of tasks during the update process. For example, the update unit can propose the optimal update method based on the geographical distribution of tasks. The update unit can also group geographically close tasks together and update them. Furthermore, the update unit can prioritize updating geographically distant tasks. This allows for efficient task management by adjusting the update method based on the geographical distribution of tasks. Some or all of the above processing in the update unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the update unit can input the geographical distribution of tasks into a generative AI and have the generative AI perform the adjustment of the update method.

[0054] The update unit can improve the accuracy of updates by referring to relevant literature for the task during the update process. For example, the update unit can refer to relevant literature for the task to improve the accuracy of updates. The update unit can also predict the progress of the task from the relevant literature and adjust the update method accordingly. Furthermore, the update unit can re-evaluate the priority of tasks based on the relevant literature. This improves the accuracy of updates by referring to relevant literature for the task. Some or all of the above processing in the update unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the update unit can input relevant literature for the task into a generative AI and have the generative AI perform the update accuracy improvement.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The extraction unit can prioritize extracting highly relevant information based on the user's work schedule. For example, the extraction unit can refer to the user's calendar information and prioritize extracting tasks related to appointments. The extraction unit can also prioritize extracting tasks related to ongoing projects from the user's work schedule. Furthermore, the extraction unit can prioritize extracting tasks with approaching deadlines based on the user's schedule. This enables efficient task management by extracting highly relevant information based on the user's work schedule. Some or all of the above processing in the extraction unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the extraction unit can input the user's work schedule into a generation AI and have the generation AI perform the extraction of highly relevant information.

[0057] The listing unit can automatically adjust the order of tasks in a list based on their importance during the listing process. For example, the listing unit can display high-importance tasks at the top of the list. It can also display tasks with approaching deadlines at the top of the list. Furthermore, the listing unit can adjust the order of tasks based on the priority of the person in charge. This allows for priority management of important tasks by adjusting the order of tasks based on their importance. Some or all of the above processing in the listing unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the listing unit can input the importance of tasks into the generation AI and have the generation AI perform the adjustment of the list order.

[0058] The management unit can optimize its management algorithm by referring to past management history during management. For example, the management unit can use a generative AI to learn the optimal management algorithm based on past management history. The management unit can also detect frequently overlooked tasks from past management history and adjust the management algorithm accordingly. Furthermore, the management unit can analyze past management history to improve the accuracy of the management algorithm. Thus, the accuracy of the management algorithm is improved by referring to past management history. Some or all of the above processes in the management unit may be performed using the generative AI, or they may not be performed using the generative AI. For example, the management unit can input past management history into the generative AI and have the generative AI perform the optimization of the management algorithm.

[0059] The update unit can monitor the progress of tasks in real time during updates and issue alerts if an anomaly is detected. For example, the update unit can monitor the progress of tasks in real time and issue an alert if a delay occurs. The update unit can also issue an alert if there are incomplete tasks. Furthermore, the update unit can issue an alert if an anomaly is detected. This allows for early detection and response to anomalies by monitoring the progress of tasks in real time. Some or all of the above processing in the update unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the update unit can input the progress of tasks into a generation AI and have the generation AI perform anomaly detection and issue alerts.

[0060] The extraction unit can improve extraction accuracy by referring to past extraction history during the extraction process. For example, the extraction unit can prioritize the extraction of similar tasks based on the history of tasks that have been extracted in the past. The extraction unit can also detect tasks that are frequently overlooked from past extraction history and prioritize their extraction. Furthermore, the extraction unit can analyze past extraction history, and the generating AI can learn patterns to improve extraction accuracy. As a result, extraction accuracy is improved by referring to past extraction history. Some or all of the above processing in the extraction unit may be performed using the generating AI, or it may be performed without the generating AI. For example, the extraction unit can input past extraction history into the generating AI and have the generating AI perform the task of improving extraction accuracy.

[0061] The listing unit can apply different listing algorithms depending on the task category during the listing process. For example, the listing unit can apply a different listing algorithm to each project. Furthermore, the listing unit can adjust the listing algorithm according to the type of task (e.g., meeting, report writing). In addition, the listing unit can apply a listing algorithm based on the task priority. This enables efficient task management by applying a listing algorithm according to the task category. Some or all of the above-described processes in the listing unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the listing unit can input the task category into a generative AI and have the generative AI execute the application of the listing algorithm.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The extraction unit automatically extracts information from each tool. For example, it extracts important tasks and instructions from the body of emails or chat messages. The extraction unit uses generative AI to analyze the information and extract action items. The generative AI uses natural language processing technology to analyze the content of emails and chat messages and extract important tasks and instructions. Step 2: The listing unit creates a list of action items based on the information extracted by the extraction unit. The listing unit adds information such as the person in charge, deadline, and priority to each action item. Using a generation AI, the extracted information is analyzed and information such as the person in charge, deadline, and priority is automatically added. Step 3: The management department centrally manages the action items listed by the listing department. The management department filters and displays them by person in charge or by project. Using generation AI, action items are categorized by person in charge or by project and displayed on the dashboard. Step 4: The update unit updates the progress of action items managed by the management unit in real time. The update unit automatically sends reminders for tasks that are approaching deadlines or are incomplete. Using generation AI, it monitors the progress of action items and automatically sends reminders for tasks that are approaching deadlines or are incomplete.

[0064] (Example of form 2) The business communication support system according to an embodiment of the present invention is a system for preventing oversights in business communications. This system automatically extracts information from various tools and lists it as action items, thereby preventing omissions and omissions in business operations and achieving efficient task management. For example, the business communication support system extracts important tasks and instructions from the body of emails and chat messages. In this process, it uses a generation AI to analyze the information and extract action items. Next, it lists the extracted information as action items. Each action item is assigned information such as the person in charge, the deadline, and the priority. This makes task management easier. Furthermore, it provides a dashboard that allows for centralized management of the listed action items. It can be filtered and displayed by person in charge or by project. This makes it easier to grasp the progress of the entire team. It also has a function to automatically send reminders for tasks that are approaching their deadline or are incomplete. Notifications are sent via email or chat to prevent oversights. Finally, it has a function to update the progress of action items in real time. This makes it easier to grasp the progress of the entire team. In this way, it prevents omissions and omissions in business operations and supports efficient task management. This allows the business communication support system to prevent business communications from being overlooked and to enable efficient task management.

[0065] The business communication support system according to this embodiment comprises an extraction unit, a listing unit, a management unit, and an update unit. The extraction unit automatically extracts information from each tool. For example, the extraction unit extracts important tasks and instructions from the body of an email or a chat message. The extraction unit analyzes the information using a generation AI and extracts action items. For example, the generation AI uses natural language processing technology to analyze the content of emails and chat messages and extract important tasks and instructions. The listing unit lists the information extracted by the extraction unit as action items. The listing unit adds information such as the person in charge, deadline, and priority to each action item. For example, the listing unit uses a generation AI to analyze the extracted information and automatically adds information such as the person in charge, deadline, and priority. The management unit centrally manages the action items listed by the listing unit. The management unit filters and displays the information by person in charge or by project. For example, the management unit uses a generation AI to classify action items by person in charge or by project and displays them on a dashboard. The update unit updates the progress of action items managed by the management unit in real time. The update unit automatically sends reminders for tasks that are nearing their deadline or are incomplete. For example, the update unit uses a generation AI to monitor the progress of action items and automatically sends reminders for tasks that are nearing their deadline or are incomplete. As a result, the business communication support system according to this embodiment can prevent business communications from being overlooked and achieve efficient task management.

[0066] The extraction unit automatically extracts information from various tools. Specifically, it extracts important tasks and instructions from email bodies and chat messages. The extraction unit uses generative AI to analyze the information and extract action items. The generative AI utilizes natural language processing technology to analyze the content of emails and chat messages in detail. For example, it detects keywords such as "urgent," "please confirm," and "deadline" contained in the email body and identifies important tasks and instructions based on them. Similarly, in chat messages, it understands the context of the conversation and extracts important action items. The generative AI can perform contextual analysis and sentiment analysis to evaluate the importance and urgency of messages. Furthermore, the extraction unit can collect information from multiple tools simultaneously and analyze it integrally. For example, it collects information from email, chat, and project management tools, and integrates and analyzes the information obtained from each tool. This allows the extraction unit to prevent overlooking business communications and quickly identify important tasks and instructions.

[0067] The listing unit creates a list of action items based on the information extracted by the extraction unit. Specifically, it assigns information such as the person in charge, deadline, and priority to each action item. The listing unit uses a generation AI to analyze the extracted information and automatically assign information such as the person in charge, deadline, and priority. The generation AI analyzes the content of the extracted tasks and instructions and identifies the appropriate person in charge. For example, it assigns the optimal person in charge based on past task history and the person in charge's skill set. It also sets an appropriate deadline considering the content of the task and the progress of the project. Furthermore, it evaluates the importance and urgency of the task and assigns a priority. Based on this information, the listing unit lists the action items and displays them in a visually easy-to-understand format. For example, it displays action items in the format of a Gantt chart or Kanban board, allowing the person in charge, deadline, and priority to be seen at a glance. In this way, the listing unit supports efficient task management and enables smooth progress of work.

[0068] The management department centrally manages the action items listed by the listing department. Specifically, it filters and displays them by assignee and project. The management department uses a generation AI to classify action items by assignee and project and displays them on a dashboard. The generation AI analyzes the attribute information of each action item and classifies them based on assignee and project. For example, by classifying tasks by assignee and displaying them on each assignee's dashboard, each assignee can grasp their own tasks at a glance. Tasks can also be classified by project, allowing for visual confirmation of project progress. The management department updates this information in real time, ensuring that the latest status is always known. Furthermore, the management department can monitor the progress of tasks and issue alerts if delays or problems occur. This allows the management department to efficiently manage task progress and support project success.

[0069] The update unit updates the progress of action items managed by the management unit in real time. Specifically, it automatically sends reminders for tasks that are nearing their deadline or are incomplete. The update unit uses a generation AI to monitor the progress of action items and automatically sends reminders for tasks that are nearing their deadline or are incomplete. The generation AI analyzes the progress of each task and identifies tasks that are nearing their deadline or are incomplete. For example, it monitors the progress of a task and sends a reminder to the person in charge when the deadline is approaching. It also sends periodic reminders for incomplete tasks to draw the attention of the person in charge. Furthermore, the update unit updates the progress of tasks in real time, ensuring that the latest status is always known. For example, when a task is completed, the progress is immediately updated and reflected in the dashboard. This allows the update unit to efficiently manage task progress and prevent delays in work. In addition, the update unit can customize the frequency and content of reminders, allowing for flexible responses to the needs of the person in charge. This allows the update unit to efficiently manage the progress of tasks and prevent delays in operations.

[0070] The extraction unit can extract important tasks and instructions from email bodies and chat messages. For example, the extraction unit can detect keywords such as "urgent" or "important" from the email body and extract the corresponding tasks. The extraction unit can also analyze the context of chat messages and prioritize the extraction of instructions from superiors. Furthermore, the extraction unit can detect and extract tasks with clearly stated deadlines from the email body. This prevents overlooking important tasks and instructions by automatically extracting them. Some or all of the above processing in the extraction unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the extraction unit can input the email body or chat messages into a generation AI and have the generation AI perform the extraction of important tasks and instructions.

[0071] The listing unit can assign information such as assignee, deadline, and priority to each action item. For example, the listing unit can automatically assign an assignee to the extracted tasks. The listing unit can also automatically set the deadline for tasks. Furthermore, the listing unit can automatically evaluate and assign a priority to tasks. This makes task management easier by assigning the necessary information to action items. Some or all of the above processing in the listing unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the listing unit can input the extracted tasks into a generation AI and have the generation AI assign assignees, deadlines, and priorities.

[0072] The management department can filter and display information by person in charge or by project. For example, the management department can classify action items by person in charge and display them on the dashboard. It can also classify and display action items by project. Furthermore, the management department can set filtering conditions and display only action items that match specific conditions. This makes it easier to understand the progress status by filtering by person in charge or project. Some or all of the above processes in the management department may be performed using a generation AI, or they may not be performed using a generation AI. For example, the management department can input action items into a generation AI and have the generation AI perform the classification by person in charge or by project.

[0073] The management department can provide a dashboard for centrally managing a list of action items. For example, the management department can build and display a dashboard for centrally managing action items. The management department can also update the progress of action items on the dashboard in real time. Furthermore, the management department can display detailed information about action items on the dashboard. This makes it easier to grasp the progress of the entire team by centrally managing action items. Some or all of the above processes in the management department may be performed using or without a generative AI. For example, the management department can input action items into a generative AI and have the generative AI build and display the dashboard.

[0074] The update unit can automatically send reminders for tasks that are approaching deadlines or are incomplete. For example, the update unit can automatically send reminders via email for tasks that are approaching deadlines. It can also automatically send reminders via chat for incomplete tasks. Furthermore, the update unit can set the timing for sending reminders and send them at specific times. This prevents tasks from being overlooked by automatically sending reminders. Some or all of the above processes in the update unit may be performed using a generation AI, or not. For example, the update unit can input the progress of action items into the generation AI and have the generation AI execute the sending of reminders.

[0075] The extraction unit can estimate the user's emotions and determine the priority of information to extract based on the estimated emotions. For example, if the user is stressed, the extraction unit will prioritize extracting high-priority tasks. If the user is relaxed, the extraction unit can also extract all tasks equally. Furthermore, if the user is in a hurry, the extraction unit can prioritize extracting tasks with approaching deadlines. This allows for the extraction of more appropriate information by prioritizing information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the extraction unit may be performed using or without a generative AI. For example, the extraction unit can input user emotion data into a generative AI and have the generative AI determine the priority of information.

[0076] The extraction unit can analyze the context of emails and chat messages and automatically highlight high-priority tasks. For example, the extraction unit can detect keywords such as "urgent" or "important" from the body of an email and highlight the corresponding tasks. The extraction unit can also analyze the context of chat messages and prioritize highlighting instructions from supervisors. Furthermore, the extraction unit can detect and highlight tasks with clearly stated deadlines from the body of emails. This helps prevent tasks from being overlooked by highlighting high-priority tasks. Some or all of the above processing in the extraction unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the extraction unit can input emails and chat messages into a generation AI and have the generation AI perform the highlighting of high-priority tasks.

[0077] The extraction unit can improve extraction accuracy by referring to past extraction history during the extraction process. For example, the extraction unit can prioritize the extraction of similar tasks based on the history of tasks that have been extracted in the past. The extraction unit can also detect tasks that are frequently overlooked from past extraction history and prioritize their extraction. Furthermore, the extraction unit can analyze past extraction history, and the generating AI can learn patterns to improve extraction accuracy. As a result, extraction accuracy is improved by referring to past extraction history. Some or all of the above processing in the extraction unit may be performed using the generating AI, or it may be performed without the generating AI. For example, the extraction unit can input past extraction history into the generating AI and have the generating AI perform the task of improving extraction accuracy.

[0078] The extraction unit can estimate the user's emotions and adjust the filtering criteria for the information extracted based on the estimated emotions. For example, if the user is stressed, the extraction unit may filter out tasks of low importance. If the user is relaxed, the extraction unit may also display all tasks without filtering. Furthermore, if the user is in a hurry, the extraction unit may filter out only tasks with approaching deadlines. This allows for the extraction of more relevant information by adjusting the filtering criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the extraction unit may be performed using or without a generative AI. For example, the extraction unit can input user emotion data into a generative AI and have the generative AI adjust the filtering criteria.

[0079] The extraction unit can prioritize extracting highly relevant information based on the user's work schedule during the extraction process. For example, the extraction unit can refer to the user's calendar information and prioritize extracting tasks related to appointments. It can also prioritize extracting tasks related to ongoing projects from the user's work schedule. Furthermore, the extraction unit can prioritize extracting tasks with approaching deadlines based on the user's schedule. This enables efficient task management by extracting highly relevant information based on the user's work schedule. Some or all of the above-described processes in the extraction unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the extraction unit can input the user's work schedule into a generation AI and have the generation AI perform the extraction of highly relevant information.

[0080] The extraction unit can analyze the user's social media activity during the extraction process and extract relevant tasks. For example, the extraction unit can extract business-related information from the user's social media posts. It can also extract relevant tasks from posts by the user's social media followers and friends. Furthermore, the extraction unit can analyze the user's social media activity history and extract relevant tasks. This allows for the efficient extraction of relevant tasks by analyzing the user's social media activity. Some or all of the above processing in the extraction unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the extraction unit can input the user's social media activity into a generation AI and have the generation AI perform the extraction of relevant tasks.

[0081] The listing unit can estimate the user's emotions and adjust how action items are displayed based on the estimated emotions. For example, if the user is stressed, the listing unit can provide a simple list display. If the user is relaxed, the listing unit can also provide a list display with more detailed information. Furthermore, if the user is in a hurry, the listing unit can provide a list that highlights important tasks. This allows for a more appropriate list display by adjusting the display method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the listing unit may be performed using or without a generative AI. For example, the listing unit can input user emotion data into a generative AI and have the generative AI adjust the display method.

[0082] The listing unit can automatically adjust the order of tasks in a list based on their importance during the listing process. For example, the listing unit can display high-importance tasks at the top of the list. It can also display tasks with approaching deadlines at the top of the list. Furthermore, the listing unit can adjust the order of tasks based on the priority of the person in charge. This allows for priority management of important tasks by adjusting the order of tasks based on their importance. Some or all of the above processing in the listing unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the listing unit can input the importance of tasks into the generation AI and have the generation AI perform the adjustment of the list order.

[0083] The listing unit can apply different listing algorithms depending on the task category during the listing process. For example, the listing unit can apply a different listing algorithm to each project. Furthermore, the listing unit can adjust the listing algorithm according to the type of task (e.g., meeting, report writing). In addition, the listing unit can apply a listing algorithm based on the task priority. This enables efficient task management by applying a listing algorithm according to the task category. Some or all of the above-described processes in the listing unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the listing unit can input the task category into a generative AI and have the generative AI execute the application of the listing algorithm.

[0084] The listing unit can estimate the user's emotions and adjust the level of detail of the action items listed based on the estimated emotions. For example, if the user is stressed, the listing unit will list concise action items. If the user is relaxed, the listing unit can also list action items with detailed explanations. Furthermore, if the user is in a hurry, the listing unit can list action items containing only the essentials. This allows for more appropriate listing by adjusting the level of detail based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the listing unit may be performed using or without a generative AI. For example, the listing unit can input user emotion data into a generative AI and have the generative AI perform the level of detail adjustment.

[0085] The listing unit can determine the priority of tasks based on their submission dates when creating a list. For example, the listing unit can display tasks with approaching deadlines at the top of the list. It can also display tasks with later deadlines at the bottom of the list. Furthermore, the listing unit can highlight tasks that have already passed their deadlines. This makes it easier to meet deadlines by determining the priority of the list based on the task submission dates. Some or all of the above processing in the listing unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the listing unit can input the task submission dates into the generation AI and have the generation AI determine the priority of the list.

[0086] The listing unit can adjust the order of tasks in a list based on their relevance during the listing process. For example, the listing unit can group related tasks together and list them. It can also display highly relevant tasks at the top of the list, and less relevant tasks at the bottom. This allows for efficient task management by adjusting the order of tasks based on their relevance. Some or all of the above processing in the listing unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the listing unit can input the relevance of tasks into a generation AI and have the generation AI perform the adjustment of the list order.

[0087] The management unit can estimate the user's emotions and adjust the display method of the action items it manages based on the estimated user emotions. For example, if the user is stressed, the management unit can provide a simple display method. If the user is relaxed, the management unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the management unit can provide a display method that highlights important tasks. This allows for more appropriate management by adjusting the display method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using or without generative AI. For example, the management unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the display method.

[0088] The management unit can optimize its management algorithm by referring to past management history during management. For example, the management unit can use a generative AI to learn the optimal management algorithm based on past management history. The management unit can also detect frequently overlooked tasks from past management history and adjust the management algorithm accordingly. Furthermore, the management unit can analyze past management history to improve the accuracy of the management algorithm. Thus, the accuracy of the management algorithm is improved by referring to past management history. Some or all of the above processes in the management unit may be performed using the generative AI, or they may not be performed using the generative AI. For example, the management unit can input past management history into the generative AI and have the generative AI perform the optimization of the management algorithm.

[0089] The management department can monitor task progress in real time during management and issue alerts if an anomaly is detected. For example, the management department can monitor task progress in real time and issue an alert if a delay occurs. The management department can also issue an alert if there are incomplete tasks. Furthermore, the management department can issue an alert if an anomaly is detected. This allows for early detection and response to anomalies by monitoring task progress in real time. Some or all of the above processes in the management department may be performed using a generation AI, or they may not be performed using a generation AI. For example, the management department can input task progress into a generation AI and have the generation AI perform anomaly detection and issue alerts.

[0090] The management unit can estimate the user's emotions and determine the priority of action items to manage based on the estimated emotions. For example, if the user is stressed, the management unit will prioritize high-priority tasks. If the user is relaxed, the management unit can also manage all tasks equally. Furthermore, if the user is in a hurry, the management unit can prioritize tasks with approaching deadlines. This allows for more appropriate task management by determining priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using or without generative AI. For example, the management unit can input user emotion data into a generative AI and have the generative AI perform the priority determination.

[0091] The management department can adjust its management methods based on the geographical distribution of tasks during the management process. For example, the management department can propose the optimal management method based on the geographical distribution of tasks. The management department can also group and manage tasks that are geographically close together. Furthermore, the management department can prioritize the management of tasks that are geographically far apart. By adjusting the management method based on the geographical distribution of tasks, efficient task management becomes possible. Some or all of the above processes in the management department may be performed using a generative AI, or they may not be performed using a generative AI. For example, the management department can input the geographical distribution of tasks into a generative AI and have the generative AI perform the adjustment of the management method.

[0092] The management department can improve the accuracy of its management by referring to relevant literature for tasks during the management process. For example, the management department can refer to relevant literature for tasks to improve the accuracy of management. The management department can also predict the progress of tasks from the relevant literature and adjust the management method accordingly. Furthermore, the management department can re-evaluate the priority of tasks based on the relevant literature. In this way, the accuracy of management is improved by referring to relevant literature for tasks. Some or all of the above processes in the management department may be performed using a generative AI, or they may be performed without a generative AI. For example, the management department can input relevant literature for tasks into a generative AI and have the generative AI perform the improvement of management accuracy.

[0093] The update unit can estimate the user's emotions and adjust how the update action items are displayed based on the estimated emotions. For example, if the user is stressed, the update unit can provide a simple display. If the user is relaxed, it can also provide a display that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display that highlights important tasks. This allows for more appropriate updates by adjusting the display based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the update unit may be performed using or without a generative AI. For example, the update unit can input user emotion data into a generative AI and have the generative AI adjust the display.

[0094] The update unit can optimize the update algorithm by referring to past update history during the update process. For example, the update unit's generating AI learns the optimal update algorithm based on past update history. The update unit can also detect frequently overlooked tasks from past update history and adjust the update algorithm accordingly. Furthermore, the update unit can analyze past update history to improve the accuracy of the update algorithm. Thus, the accuracy of the update algorithm is improved by referring to past update history. Some or all of the above processes in the update unit may be performed using the generating AI or not. For example, the update unit can input past update history into the generating AI and have the generating AI perform the optimization of the update algorithm.

[0095] The update unit can monitor the progress of tasks in real time during updates and issue alerts if an anomaly is detected. For example, the update unit can monitor the progress of tasks in real time and issue an alert if a delay occurs. The update unit can also issue an alert if there are incomplete tasks. Furthermore, the update unit can issue an alert if an anomaly is detected. This allows for early detection and response to anomalies by monitoring the progress of tasks in real time. Some or all of the above processing in the update unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the update unit can input the progress of tasks into a generation AI and have the generation AI perform anomaly detection and issue alerts.

[0096] The update unit can estimate the user's emotions and determine the priority of action items to update based on the estimated emotions. For example, if the user is stressed, the update unit will prioritize updating high-priority tasks. If the user is relaxed, the update unit can update all tasks evenly. Furthermore, if the user is in a hurry, the update unit can prioritize updating tasks with approaching deadlines. This allows for more appropriate task management by prioritizing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the update unit may be performed using or without a generative AI. For example, the update unit can input user emotion data into a generative AI and have the generative AI perform the priority determination.

[0097] The update unit can adjust the update method based on the geographical distribution of tasks during the update process. For example, the update unit can propose the optimal update method based on the geographical distribution of tasks. The update unit can also group geographically close tasks together and update them. Furthermore, the update unit can prioritize updating geographically distant tasks. This allows for efficient task management by adjusting the update method based on the geographical distribution of tasks. Some or all of the above processing in the update unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the update unit can input the geographical distribution of tasks into a generative AI and have the generative AI perform the adjustment of the update method.

[0098] The update unit can improve the accuracy of updates by referring to relevant literature for the task during the update process. For example, the update unit can refer to relevant literature for the task to improve the accuracy of updates. The update unit can also predict the progress of the task from the relevant literature and adjust the update method accordingly. Furthermore, the update unit can re-evaluate the priority of tasks based on the relevant literature. This improves the accuracy of updates by referring to relevant literature for the task. Some or all of the above processing in the update unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the update unit can input relevant literature for the task into a generative AI and have the generative AI perform the update accuracy improvement.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The extraction unit can estimate the user's emotions and determine the priority of information to extract based on the estimated emotions. For example, if the user is stressed, the extraction unit will prioritize extracting high-priority tasks. If the user is relaxed, the extraction unit can also extract all tasks equally. Furthermore, if the user is in a hurry, the extraction unit can prioritize extracting tasks with approaching deadlines. This allows for the extraction of more appropriate information by prioritizing information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the extraction unit may be performed using or without a generative AI. For example, the extraction unit can input user emotion data into a generative AI and have the generative AI determine the priority of information.

[0101] The listing unit can estimate the user's emotions and adjust how action items are displayed based on the estimated emotions. For example, if the user is stressed, the listing unit can provide a simple list display. If the user is relaxed, the listing unit can also provide a list display with more detailed information. Furthermore, if the user is in a hurry, the listing unit can provide a list that highlights important tasks. This allows for a more appropriate list display by adjusting the display method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the listing unit may be performed using or without a generative AI. For example, the listing unit can input user emotion data into a generative AI and have the generative AI adjust the display method.

[0102] The management unit can estimate the user's emotions and adjust the display method of the action items it manages based on the estimated user emotions. For example, if the user is stressed, the management unit can provide a simple display method. If the user is relaxed, the management unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the management unit can provide a display method that highlights important tasks. This allows for more appropriate management by adjusting the display method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using or without generative AI. For example, the management unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the display method.

[0103] The update unit can estimate the user's emotions and adjust how the update action items are displayed based on the estimated emotions. For example, if the user is stressed, the update unit can provide a simple display. If the user is relaxed, it can also provide a display that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display that highlights important tasks. This allows for more appropriate updates by adjusting the display based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the update unit may be performed using or without a generative AI. For example, the update unit can input user emotion data into a generative AI and have the generative AI adjust the display.

[0104] The extraction unit can prioritize extracting highly relevant information based on the user's work schedule. For example, the extraction unit can refer to the user's calendar information and prioritize extracting tasks related to appointments. The extraction unit can also prioritize extracting tasks related to ongoing projects from the user's work schedule. Furthermore, the extraction unit can prioritize extracting tasks with approaching deadlines based on the user's schedule. This enables efficient task management by extracting highly relevant information based on the user's work schedule. Some or all of the above processing in the extraction unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the extraction unit can input the user's work schedule into a generation AI and have the generation AI perform the extraction of highly relevant information.

[0105] The listing unit can automatically adjust the order of tasks in a list based on their importance during the listing process. For example, the listing unit can display high-importance tasks at the top of the list. It can also display tasks with approaching deadlines at the top of the list. Furthermore, the listing unit can adjust the order of tasks based on the priority of the person in charge. This allows for priority management of important tasks by adjusting the order of tasks based on their importance. Some or all of the above processing in the listing unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the listing unit can input the importance of tasks into the generation AI and have the generation AI perform the adjustment of the list order.

[0106] The management unit can optimize its management algorithm by referring to past management history during management. For example, the management unit can use a generative AI to learn the optimal management algorithm based on past management history. The management unit can also detect frequently overlooked tasks from past management history and adjust the management algorithm accordingly. Furthermore, the management unit can analyze past management history to improve the accuracy of the management algorithm. Thus, the accuracy of the management algorithm is improved by referring to past management history. Some or all of the above processes in the management unit may be performed using the generative AI, or they may not be performed using the generative AI. For example, the management unit can input past management history into the generative AI and have the generative AI perform the optimization of the management algorithm.

[0107] The update unit can monitor the progress of tasks in real time during updates and issue alerts if an anomaly is detected. For example, the update unit can monitor the progress of tasks in real time and issue an alert if a delay occurs. The update unit can also issue an alert if there are incomplete tasks. Furthermore, the update unit can issue an alert if an anomaly is detected. This allows for early detection and response to anomalies by monitoring the progress of tasks in real time. Some or all of the above processing in the update unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the update unit can input the progress of tasks into a generation AI and have the generation AI perform anomaly detection and issue alerts.

[0108] The extraction unit can improve extraction accuracy by referring to past extraction history during the extraction process. For example, the extraction unit can prioritize the extraction of similar tasks based on the history of tasks that have been extracted in the past. The extraction unit can also detect tasks that are frequently overlooked from past extraction history and prioritize their extraction. Furthermore, the extraction unit can analyze past extraction history, and the generating AI can learn patterns to improve extraction accuracy. As a result, extraction accuracy is improved by referring to past extraction history. Some or all of the above processing in the extraction unit may be performed using the generating AI, or it may be performed without the generating AI. For example, the extraction unit can input past extraction history into the generating AI and have the generating AI perform the task of improving extraction accuracy.

[0109] The listing unit can apply different listing algorithms depending on the task category during the listing process. For example, the listing unit can apply a different listing algorithm to each project. Furthermore, the listing unit can adjust the listing algorithm according to the type of task (e.g., meeting, report writing). In addition, the listing unit can apply a listing algorithm based on the task priority. This enables efficient task management by applying a listing algorithm according to the task category. Some or all of the above-described processes in the listing unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the listing unit can input the task category into a generative AI and have the generative AI execute the application of the listing algorithm.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The extraction unit automatically extracts information from each tool. For example, it extracts important tasks and instructions from the body of emails or chat messages. The extraction unit uses generative AI to analyze the information and extract action items. The generative AI uses natural language processing technology to analyze the content of emails and chat messages and extract important tasks and instructions. Step 2: The listing unit creates a list of action items based on the information extracted by the extraction unit. The listing unit adds information such as the person in charge, deadline, and priority to each action item. Using a generation AI, the extracted information is analyzed and information such as the person in charge, deadline, and priority is automatically added. Step 3: The management department centrally manages the action items listed by the listing department. The management department filters and displays them by person in charge or by project. Using generation AI, action items are categorized by person in charge or by project and displayed on the dashboard. Step 4: The update unit updates the progress of action items managed by the management unit in real time. The update unit automatically sends reminders for tasks that are approaching deadlines or are incomplete. Using generation AI, it monitors the progress of action items and automatically sends reminders for tasks that are approaching deadlines or are incomplete.

[0112] 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.

[0113] Data generation model 58 is a form of 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0114] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0115] Each of the multiple elements described above, including the extraction unit, listing unit, management unit, and update unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the extraction unit is implemented by the computer 36 of the smart device 14 and extracts important tasks and instructions from the body of emails or chat messages. The listing unit is implemented by the identification processing unit 290 of the data processing unit 12 and lists the extracted information as action items. The management unit is implemented by the control unit 46A of the smart device 14 and centrally manages the listed action items. The update unit is implemented by the identification processing unit 290 of the data processing unit 12 and updates the progress of the action items in real time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] 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.

[0118] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0119] 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.

[0120] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0121] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0122] 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.

[0123] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0124] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0125] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0126] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0128] 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.

[0129] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0130] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements described above, including the extraction unit, listing unit, management unit, and update unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the extraction unit is implemented by the computer 36 of the smart glasses 214 and extracts important tasks and instructions from the body of emails or chat messages. The listing unit is implemented by the identification processing unit 290 of the data processing unit 12 and lists the extracted information as action items. The management unit is implemented by the control unit 46A of the smart glasses 214 and centrally manages the listed action items. The update unit is implemented by the identification processing unit 290 of the data processing unit 12 and updates the progress of the action items in real time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

[0134] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0135] 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.

[0136] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, 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.

[0137] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0138] 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.

[0139] 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.

[0140] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0141] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0142] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0144] 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.

[0145] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0146] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements described above, including the extraction unit, listing unit, management unit, and update unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the extraction unit is implemented by the computer 36 of the headset terminal 314 and extracts important tasks and instructions from the body of emails and chat messages. The listing unit is implemented by the identification processing unit 290 of the data processing unit 12 and lists the extracted information as action items. The management unit is implemented by the control unit 46A of the headset terminal 314 and centrally manages the listed action items. The update unit is implemented by the identification processing unit 290 of the data processing unit 12 and updates the progress of the action items in real time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0151] 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.

[0152] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0153] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0154] 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.

[0155] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0156] 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.

[0157] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] 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.

[0162] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements described above, including the extraction unit, listing unit, management unit, and update unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the extraction unit is implemented by the computer 36 of the robot 414 and extracts important tasks and instructions from the body of emails or chat messages. The listing unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and lists the extracted information as action items. The management unit is implemented by, for example, the control unit 46A of the robot 414 and centrally manages the listed action items. The update unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and updates the progress of the action items in real time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0165] 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.

[0166] Figure 9 shows the 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.

[0167] 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.

[0168] 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.

[0169] 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, and motorcycles, 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 based, for example, 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.

[0170] 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."

[0171] 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.

[0172] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0181] 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 other things 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.

[0182] 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.

[0183] (Note 1) An extraction unit that automatically extracts information from each tool, A listing unit that lists the information extracted by the extraction unit as action items, A management unit that centrally manages the action items listed by the aforementioned listing unit, The system includes an update unit that updates the progress status of action items managed by the aforementioned management unit in real time. A system characterized by the following features. (Note 2) The extraction unit is Extract important tasks and instructions from email bodies and chat messages. The system described in Appendix 1, characterized by the features described herein. (Note 3) The listing unit, Each action item will be assigned information such as the person in charge, deadline, and priority. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned management department, Filter and display by person in charge or by project. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned management department, Provides a dashboard for centralized management of listed action items. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned update unit is Automatically send reminders for tasks with approaching deadlines or those that are not yet completed. The system described in Appendix 1, characterized by the features described herein. (Note 7) The extraction unit is It estimates the user's emotions and determines the priority of information to extract based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The extraction unit is It analyzes the context of emails and chat messages and automatically highlights high-priority tasks. The system described in Appendix 1, characterized by the features described herein. (Note 9) The extraction unit is During extraction, past extraction history is referenced to improve extraction accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 10) The extraction unit is We estimate the user's emotions and adjust the filtering criteria for the information extracted based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The extraction unit is During extraction, the system prioritizes extracting highly relevant information based on the user's work schedule. The system described in Appendix 1, characterized by the features described herein. (Note 12) The extraction unit is During the extraction process, the system analyzes the user's social media activity and extracts relevant tasks. The system described in Appendix 1, characterized by the features described herein. (Note 13) The listing unit, We estimate the user's emotions and adjust how action items are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The listing unit, When creating a list, the list order is automatically adjusted based on the importance of the tasks. The system described in Appendix 1, characterized by the features described herein. (Note 15) The listing unit, When creating lists, different listing algorithms are applied depending on the task category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The listing unit, It estimates the user's emotions and adjusts the level of detail of the action items listed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The listing unit, When creating the list, prioritize the tasks based on their due dates. The system described in Appendix 1, characterized by the features described herein. (Note 18) The listing unit, When creating a list, adjust the order of the list based on the relevance of the tasks. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned management department, We estimate the user's emotions and adjust how action items are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned management department, During management, the management algorithm is optimized by referring to past management history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned management department, During management, the system monitors task progress in real time and issues alerts if an anomaly is detected. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned management department, It estimates the user's emotions and determines the priority of action items to manage based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned management department, During management, adjust management methods based on the geographical distribution of tasks. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned management department, During management, refer to relevant literature for the task to improve the accuracy of management. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned update unit is We estimate the user's sentiment and adjust how action items are displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned update unit is During updates, the update algorithm is optimized by referring to past update history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned update unit is During updates, the system monitors task progress in real time and issues alerts if an anomaly is detected. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned update unit is It estimates the user's emotions and determines the priority of action items to update based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned update unit is During updates, the update method will be adjusted based on the geographical distribution of tasks. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned update unit is When updating, refer to relevant literature for the task to improve the accuracy of the update. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An extraction unit that automatically extracts information from each tool, A listing unit that lists the information extracted by the extraction unit as action items, A management unit that centrally manages the action items listed by the aforementioned listing unit, The system includes an update unit that updates the progress status of action items managed by the aforementioned management unit in real time. A system characterized by the following features.

2. The extraction unit is Extract important tasks and instructions from email bodies and chat messages. The system according to feature 1.

3. The listing unit, Each action item will be assigned information such as the person in charge, deadline, and priority. The system according to feature 1.

4. The aforementioned management department, Filter and display by person in charge or by project. The system according to feature 1.

5. The aforementioned management department, Provides a dashboard for centralized management of listed action items. The system according to feature 1.

6. The aforementioned update unit is Automatically send reminders for tasks with approaching deadlines or those that are not yet completed. The system according to feature 1.

7. The extraction unit is It estimates the user's emotions and determines the priority of information to extract based on the estimated user emotions. The system according to feature 1.

8. The extraction unit is It analyzes the context of emails and chat messages and automatically highlights high-priority tasks. The system according to feature 1.

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