system

Abstract

The system according to this embodiment aims to efficiently manage and appropriately distribute a wide range of tasks. [Solution] The system according to the embodiment comprises an acquisition unit, a reconstruction unit, a notification unit, and a distribution unit. The acquisition unit retrieves tasks from a task database. The reconstruction unit reconstructs the tasks retrieved by the acquisition unit. The notification unit provides daily notifications of the tasks reconstructed by the reconstruction unit. The distribution unit distributes the tasks reconstructed by the reconstruction unit to the organization and to agents.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to manage a wide variety of tasks and efficient task distribution is not performed.

[0005] The system according to the embodiment aims to efficiently manage a wide variety of tasks and appropriately distribute them.

Means for Solving the Problems

[0006] The system according to the embodiment comprises an acquisition unit, a reconstruction unit, a notification unit, and a distribution unit. The acquisition unit retrieves tasks from a task database. The reconstruction unit reconstructs the tasks retrieved by the acquisition unit. The notification unit provides daily notifications of the tasks reconstructed by the reconstruction unit. The distribution unit distributes the tasks reconstructed by the reconstruction unit to the organization and to agents. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently manage a wide range of tasks and deliver them appropriately. [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 controls communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 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 task management system according to an embodiment of the present invention is a system for facilitating the understanding of a wide range of tasks from the perspective of consumer sales. This task management system creates a task database that centrally manages various tasks such as training participation and inventory management distributed from each back-office department. This task database includes a variety of tasks distributed at the individual, organizational, and store levels, such as training participation, various inventory management, e-learning, HR-related interviews, and sales promotion interviews. For example, tasks such as demo unit inventory, GINIE inventory, and content learning are registered in the database. Next, an AI agent is launched that can retrieve tasks according to the user's request. This AI agent reconstructs tasks based on the deadline and unit specified by the user and provides daily notifications. For example, if a user requests, "Tell me tomorrow's tasks," the AI ​​agent retrieves the relevant tasks from the task database and generates a daily notification. Furthermore, the AI ​​agent generates a format (table format) that can be used to distribute tasks within the organization or to agencies. For example, the progress and completion status of tasks can be distributed in a table format to each department within the organization or to agencies. This makes it easier to grasp tasks and enables efficient work execution. The task management system makes it easier to understand the diverse tasks from a consumer sales perspective, leading to increased work efficiency. For example, by centrally managing tasks distributed from various back-office departments, it prevents tasks from being overlooked or duplicated, allowing for more efficient work. Furthermore, daily notifications and task distribution by AI agents allow users to track task progress in real time. This is expected to improve work efficiency and productivity.

[0029] The task management system according to this embodiment comprises an acquisition unit, a reconfiguration unit, a notification unit, and a distribution unit. The acquisition unit retrieves tasks from a task database. The acquisition unit can retrieve tasks such as training attendance, various inventory tasks, e-learning, HR-related interviews, and sales promotion interviews from the task database. The reconfiguration unit reconfigures the tasks retrieved by the acquisition unit. The reconfiguration unit can reconfigure tasks based on a date or unit specified by the user, for example. The notification unit provides daily notifications of tasks reconfigured by the reconfiguration unit. The notification unit can provide daily notifications of reconfigured tasks, for example. The distribution unit distributes tasks reconfigured by the reconfiguration unit to the organization or to agents, for example. The distribution unit can distribute reconfigured tasks to the organization or to agents, for example. As a result, the task management system can efficiently acquire, reconfigure, notify, and distribute tasks.

[0030] The retrieval unit retrieves tasks from the task database. For example, it can retrieve tasks such as training sessions, various inventory tasks, e-learning, HR-related interviews, and sales promotion interviews from the task database. Specifically, the retrieval unit accesses the task database and filters and extracts tasks according to the user's permissions and roles. For example, administrators can view and retrieve all tasks, while general users can only retrieve tasks assigned to them. The retrieval unit also retrieves metadata such as task priority, deadline, and progress, allowing users to understand the overall task picture. Furthermore, the retrieval unit allows users to set the frequency and timing of task retrieval and has a function to automatically retrieve new tasks by periodically scanning the task database. This ensures users always have access to the latest task information, preventing missed or delayed tasks. The retrieval unit also integrates with other systems via APIs, retrieving tasks from external task management tools and project management systems. This enables centralized task management across different systems, improving operational efficiency.

[0031] The reconfiguration unit reconfigures tasks retrieved by the retrieval unit. For example, the reconfiguration unit can reconfigure tasks based on user-specified deadlines or units. Specifically, the reconfiguration unit rearranges tasks according to the user's schedule and priorities, supporting efficient task management. For instance, if a user wants to manage tasks on a weekly basis, the reconfiguration unit generates a weekly task list and sorts each task in the optimal order based on its due date and importance. The reconfiguration unit also optimizes the task execution order by considering task dependencies and postponing tasks whose prerequisites are not met. Furthermore, the reconfiguration unit can analyze the user's past task execution history and performance data to suggest the optimal task allocation for each individual user. For example, it can improve overall work efficiency by prioritizing the assignment of tasks to users who perform well on specific tasks. The reconfiguration unit can also monitor task progress in real time and rearrange or reschedule tasks as needed. This allows users to always perform optimal task management, preventing delays and duplication of work.

[0032] The notification unit provides daily notifications for tasks restructured by the restructuring unit. Specifically, the notification unit sends task reminders at a set time each day based on user settings. Multiple notification methods are available, including email, SMS, and push notifications, allowing users to choose their preferred method. Furthermore, the notification unit prioritizes notifications based on task importance and deadlines, using more powerful notification methods for critical or urgent tasks. For example, tasks with deadlines approaching can be alerted via push notifications or voice alerts. The notification unit also monitors task progress and completion status in real time and repeatedly sends reminders for incomplete tasks. This allows users to stay informed about task progress and complete tasks without forgetting. Additionally, the notification unit shares the task status of the entire team, facilitating information sharing and collaboration among team members. This streamlines team task management and ensures smooth project progress.

[0033] The distribution department distributes tasks restructured by the restructuring department to the organization and its agents. Specifically, the distribution department assigns tasks to the appropriate personnel or departments based on their content and importance. For example, tasks related to a specific project are distributed to the project team, with each member receiving tasks according to their role. The distribution department also distributes tasks related to sales promotion and marketing to agents, supporting them in efficiently carrying out their duties. The distribution department monitors task distribution status and recipient responses in real time, and can redistribute or follow up on tasks as needed. For example, if a task remains unread or is not completed by the deadline, it sends another notification to encourage completion. The distribution department also records task distribution history for later reference, which is useful for task progress management and evaluation. This allows the distribution department to efficiently distribute and manage tasks, improving overall organizational efficiency. Furthermore, the distribution unit generates reports on task distribution, allowing administrators to quickly grasp the progress and completion status of tasks. This enables administrators to properly monitor task progress and take necessary actions promptly.

[0034] The acquisition unit can retrieve tasks such as training participation, various inventory counts, e-learning, HR-related interviews, and sales promotion interviews from the task database. For example, the acquisition unit can retrieve the content of training sessions, the types of inventory counts, and the subjects of e-learning courses from the task database. This allows for centralized management and efficient retrieval of diverse tasks. Some or all of the above-described processes in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the tasks retrieved from the task database into a generating AI and analyze the content of the tasks.

[0035] The reconfiguration unit can reconfigure tasks based on the due date and unit specified by the user. For example, the reconfiguration unit can reconfigure tasks based on the due date format, the type of unit (hour, day, week, etc.), etc. This makes it possible to reconfigure tasks according to the user's requests. Some or all of the above-described processes in the reconfiguration unit may be performed using AI, for example, or without AI. For example, the reconfiguration unit can input the due date and unit specified by the user into a generating AI and have the generating AI perform the task reconfiguration.

[0036] The notification unit can send daily notifications of the restructured tasks. The notification unit can send daily notifications of the restructured tasks based, for example, on the time of notification and the notification method (email, app notification, etc.). This allows for real-time monitoring of task progress. Some or all of the above-described processes in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the restructured tasks into a generation AI and have the generation AI execute the daily notifications.

[0037] The distribution unit can distribute the restructured tasks within the organization and to its agents. For example, the distribution unit can distribute the restructured tasks within the organization and to its agents based on selection criteria for recipients and distribution methods (email, chat tools, etc.). This allows for efficient distribution of task progress within the organization and to its agents. Some or all of the above-described processes in the distribution unit may be performed using AI, or not. For example, the distribution unit can input the restructured tasks into a generation AI and have the generation AI execute the distribution content.

[0038] The acquisition unit can analyze the user's past task history and select the optimal acquisition method. For example, the acquisition unit can prioritize acquiring tasks that the user has frequently performed in the past. The acquisition unit can also suggest an efficient acquisition method based on the user's past task history. Furthermore, the acquisition unit can analyze the user's past task history and acquire tasks at the optimal timing. This allows for efficient task acquisition based on the user's past task history. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's past task history into a generating AI and have the generating AI execute the optimal acquisition method.

[0039] The acquisition unit can filter tasks based on the user's current work situation and areas of interest when acquiring tasks. For example, the acquisition unit can prioritize acquiring tasks related to the work the user is currently working on. It can also acquire highly relevant tasks based on the user's areas of interest. Furthermore, the acquisition unit can prioritize acquiring tasks that are less burdensome, taking into account the user's work situation. This allows for the efficient acquisition of tasks that match the user's work situation and areas of interest. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input data on the user's work situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0040] The acquisition unit can prioritize acquiring tasks that are highly relevant to the user, taking into account the user's geographical location information when acquiring tasks. For example, if the user is in a specific region, the acquisition unit can prioritize acquiring tasks related to that region. It can also prioritize acquiring tasks that can be performed in locations close to the user's current location. Furthermore, based on the user's travel plans, the acquisition unit can prioritize acquiring tasks that can be performed at their destination. This allows the acquisition of highly relevant tasks based on the user's geographical location information. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's geographical location information into a generating AI and have the generating AI execute highly relevant tasks.

[0041] The acquisition unit can analyze the user's social media activity when acquiring tasks and acquire relevant tasks. For example, the acquisition unit can prioritize acquiring tasks that the user has mentioned on social media. The acquisition unit can also acquire tasks of interest from the user's social media activity. Furthermore, the acquisition unit can acquire tasks at the optimal timing based on the user's activity time on social media. This allows the acquisition of relevant tasks based on the user's social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's social media activity data into a generating AI and have the generating AI execute the relevant tasks.

[0042] The reconstruction unit can adjust the level of detail in the reconstruction based on the importance of the tasks. For example, the reconstruction unit can reconstruct high-importance tasks in detail and low-importance tasks in a simplified manner. The reconstruction unit can also determine the priority of the reconstruction according to the importance of the tasks. Furthermore, the reconstruction unit can perform reconstructions that include detailed procedures for high-importance tasks. This enables detailed reconstruction according to the importance of the tasks. Some or all of the above processes in the reconstruction unit may be performed using AI, for example, or without AI. For example, the reconstruction unit can input task importance data into a generating AI and have the generating AI execute the reconstruction with the appropriate level of detail.

[0043] The reconstruction unit can apply different reconstruction algorithms depending on the task category during reconstruction. For example, the reconstruction unit can apply a reconstruction algorithm that enhances learning effectiveness to training tasks. It can also apply a reconstruction algorithm that includes efficient work procedures to inventory tasks. Furthermore, it can apply an interactive reconstruction algorithm to e-learning tasks. This enables appropriate reconstruction according to the task category. Some or all of the above-described processes in the reconstruction unit may be performed using AI, for example, or without AI. For example, the reconstruction unit can input task category data into a generating AI and have the generating AI execute a reconstruction algorithm.

[0044] The restructuring unit can determine the priority of restructuring based on the task submission timing during the restructuring process. For example, the restructuring unit can prioritize restructuring tasks with approaching submission deadlines. It can also postpone restructuring tasks with distant submission deadlines. Furthermore, the restructuring unit can dynamically adjust the restructuring priority according to the submission deadlines. This enables restructuring with a priority based on the task submission timing. Some or all of the above processing in the restructuring unit may be performed using AI, for example, or without AI. For example, the restructuring unit can input task submission timing data into a generating AI and have the generating AI execute the restructuring priority.

[0045] The reconstruction unit can adjust the reconstruction order based on the relevance of tasks during reconstruction. For example, the reconstruction unit can reconstruct highly related tasks together. It can also reconstruct less relevant tasks individually. Furthermore, the reconstruction unit can dynamically adjust the reconstruction order according to the relevance of tasks. This makes it possible to reconstruct tasks in an order that corresponds to their relevance. Some or all of the above processing in the reconstruction unit may be performed using AI, for example, or without AI. For example, the reconstruction unit can input task relevance data into a generating AI and have the generating AI execute the reconstruction order.

[0046] The notification unit can adjust the level of detail of notifications based on the importance of the task. For example, it can provide detailed notifications for high-importance tasks and simplified notifications for low-importance tasks. The notification unit can also determine the priority of notifications according to the importance of the task. Furthermore, for high-importance tasks, the notification unit can provide notifications that include detailed instructions. This enables detailed notifications according to the importance of the task. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input task importance data into a generating AI and have the generating AI determine the level of detail of the notifications.

[0047] The notification unit can apply different notification algorithms depending on the task category when sending notifications. For example, the notification unit can apply a notification algorithm that enhances learning effectiveness to training tasks. It can also apply a notification algorithm that includes efficient work procedures to inventory tasks. Furthermore, it can apply an interactive notification algorithm to e-learning tasks. This enables appropriate notifications according to the task category. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input task category data into a generating AI and have the generating AI execute a notification algorithm.

[0048] The notification unit can determine the priority of notifications based on the task submission timing when sending notifications. For example, the notification unit can prioritize notifications for tasks with approaching deadlines. It can also postpone notifications for tasks with distant deadlines. Furthermore, the notification unit can dynamically adjust the notification priority according to the submission deadline. This enables notifications to be sent with priority according to the task submission timing. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input task submission timing data into a generating AI and have the generating AI execute the notification priority.

[0049] The notification unit can adjust the order of notifications based on the relevance of tasks when sending notifications. For example, the notification unit can group together highly relevant tasks for notification. It can also individually notify users of less relevant tasks. Furthermore, the notification unit can dynamically adjust the order of notifications according to the relevance of tasks. This makes it possible to send notifications in an order that corresponds to the relevance of tasks. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input task relevance data into a generating AI and have the generating AI execute the notification order.

[0050] The distribution unit can adjust the level of detail in the distribution based on the importance of the task at the time of distribution. For example, the distribution unit can distribute high-importance tasks in detail and low-importance tasks in a simplified manner. The distribution unit can also determine the priority of distribution according to the importance of the task. Furthermore, the distribution unit can distribute high-importance tasks with detailed instructions. This enables detailed distribution according to the importance of the task. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input task importance data into a generating AI and have the generating AI determine the level of detail in the distribution.

[0051] The distribution unit can apply different distribution algorithms depending on the task category during distribution. For example, the distribution unit can apply a distribution algorithm that enhances learning effectiveness to training tasks. It can also apply a distribution algorithm that includes efficient work procedures to inventory tasks. Furthermore, it can apply an interactive distribution algorithm to e-learning tasks. This enables appropriate distribution according to the task category. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input task category data into a generating AI and have the generating AI execute the distribution algorithm.

[0052] The distribution unit can determine the distribution priority based on the task submission date at the time of distribution. For example, the distribution unit can prioritize the distribution of tasks with approaching deadlines. It can also postpone the distribution of tasks with later deadlines. Furthermore, the distribution unit can dynamically adjust the distribution priority according to the submission deadlines. This enables distribution with priority according to the task submission date. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input task submission date data into a generating AI and have the generating AI determine the distribution priority.

[0053] The distribution unit can adjust the distribution order based on the relevance of tasks during distribution. For example, the distribution unit can distribute highly relevant tasks together. It can also distribute less relevant tasks individually. Furthermore, the distribution unit can dynamically adjust the distribution order according to the relevance of tasks. This enables distribution in an order that corresponds to the relevance of tasks. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input task relevance data into a generating AI and have the generating AI execute the distribution order.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The acquisition unit can analyze the user's past task history and select the optimal acquisition method. For example, it can prioritize acquiring tasks that the user has frequently performed in the past. The acquisition unit can also suggest efficient acquisition methods based on the user's past task history. Furthermore, the acquisition unit can analyze the user's past task history and acquire tasks at the optimal timing. This allows for efficient task acquisition based on the user's past task history. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input the user's past task history into a generating AI and have the generating AI execute the optimal acquisition method.

[0056] The acquisition unit can filter tasks based on the user's current work situation and areas of interest when acquiring tasks. For example, it can prioritize acquiring tasks related to the work the user is currently working on. Furthermore, the acquisition unit can acquire highly relevant tasks based on the user's areas of interest. In addition, the acquisition unit can prioritize acquiring tasks that are less burdensome, taking into account the user's work situation. This allows for efficient acquisition of tasks tailored to the user's work situation and areas of interest. Some or all of the above processing in the acquisition unit may be performed using AI, or without AI. For example, the acquisition unit can input data on the user's work situation and areas of interest into a generating AI, and have the generating AI perform the filtering.

[0057] The acquisition unit can prioritize acquiring tasks that are highly relevant to the user, taking into account the user's geographical location information. For example, if the user is in a specific region, it can prioritize acquiring tasks related to that region. The acquisition unit can also prioritize acquiring tasks that can be performed in locations close to the user's current location. Furthermore, based on the user's travel plans, the acquisition unit can prioritize acquiring tasks that can be performed at their destination. This allows the acquisition of highly relevant tasks based on the user's geographical location information. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input the user's geographical location information into a generation AI and have the generation AI execute highly relevant tasks.

[0058] The acquisition unit can analyze the user's social media activity when acquiring tasks and acquire relevant tasks. For example, it can prioritize acquiring tasks that the user has mentioned on social media. The acquisition unit can also acquire tasks of interest from the user's social media activity. Furthermore, the acquisition unit can acquire tasks at the optimal timing based on the user's activity time on social media. This allows the acquisition of relevant tasks based on the user's social media activity. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input the user's social media activity data into a generating AI and have the generating AI execute relevant tasks.

[0059] The reconstruction unit can adjust the level of detail in the reconstruction based on the importance of the tasks. For example, high-importance tasks can be reconstructed in detail, while low-importance tasks can be reconstructed in a simplified manner. The reconstruction unit can also determine the priority of the reconstruction according to the importance of the tasks. Furthermore, the reconstruction unit can perform reconstructions that include detailed procedures for high-importance tasks. This enables detailed reconstruction according to the importance of the tasks. Some or all of the above processes in the reconstruction unit may be performed using AI or not. For example, the reconstruction unit can input task importance data into a generating AI and have the generating AI execute the level of detail in the reconstruction.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The retrieval unit retrieves tasks from the task database. For example, tasks such as training participation, various inventory counts, e-learning, HR-related interviews, and sales promotion interviews can be retrieved from the task database. Step 2: The reconfiguration unit reconfigures the tasks retrieved by the acquisition unit. For example, tasks can be reconfigured based on a deadline or unit specified by the user. Step 3: The notification unit provides daily notifications for tasks reconfigured by the reconfiguration unit. For example, it can provide daily notifications for reconfigured tasks. Step 4: The distribution unit distributes the reconfigured tasks within the organization and to agencies. For example, the reconfigured tasks can be distributed within the organization and to agencies.

[0062] (Example of form 2) The task management system according to an embodiment of the present invention is a system for facilitating the understanding of a wide range of tasks from the perspective of consumer sales. This task management system creates a task database that centrally manages various tasks such as training participation and inventory management distributed from each back-office department. This task database includes a variety of tasks distributed at the individual, organizational, and store levels, such as training participation, various inventory management, e-learning, HR-related interviews, and sales promotion interviews. For example, tasks such as demo unit inventory, GINIE inventory, and content learning are registered in the database. Next, an AI agent is launched that can retrieve tasks according to the user's request. This AI agent reconstructs tasks based on the deadline and unit specified by the user and provides daily notifications. For example, if a user requests, "Tell me tomorrow's tasks," the AI ​​agent retrieves the relevant tasks from the task database and generates a daily notification. Furthermore, the AI ​​agent generates a format (table format) that can be used to distribute tasks within the organization or to agencies. For example, the progress and completion status of tasks can be distributed in a table format to each department within the organization or to agencies. This makes it easier to grasp tasks and enables efficient work execution. The task management system makes it easier to understand the diverse tasks from a consumer sales perspective, leading to increased work efficiency. For example, by centrally managing tasks distributed from various back-office departments, it prevents tasks from being overlooked or duplicated, allowing for more efficient work. Furthermore, daily notifications and task distribution by AI agents allow users to track task progress in real time. This is expected to improve work efficiency and productivity.

[0063] The task management system according to this embodiment comprises an acquisition unit, a reconfiguration unit, a notification unit, and a distribution unit. The acquisition unit retrieves tasks from a task database. The acquisition unit can retrieve tasks such as training attendance, various inventory tasks, e-learning, HR-related interviews, and sales promotion interviews from the task database. The reconfiguration unit reconfigures the tasks retrieved by the acquisition unit. The reconfiguration unit can reconfigure tasks based on a date or unit specified by the user, for example. The notification unit provides daily notifications of tasks reconfigured by the reconfiguration unit. The notification unit can provide daily notifications of reconfigured tasks, for example. The distribution unit distributes tasks reconfigured by the reconfiguration unit to the organization or to agents, for example. The distribution unit can distribute reconfigured tasks to the organization or to agents, for example. As a result, the task management system can efficiently acquire, reconfigure, notify, and distribute tasks.

[0064] The retrieval unit retrieves tasks from the task database. For example, it can retrieve tasks such as training sessions, various inventory tasks, e-learning, HR-related interviews, and sales promotion interviews from the task database. Specifically, the retrieval unit accesses the task database and filters and extracts tasks according to the user's permissions and roles. For example, administrators can view and retrieve all tasks, while general users can only retrieve tasks assigned to them. The retrieval unit also retrieves metadata such as task priority, deadline, and progress, allowing users to understand the overall task picture. Furthermore, the retrieval unit allows users to set the frequency and timing of task retrieval and has a function to automatically retrieve new tasks by periodically scanning the task database. This ensures users always have access to the latest task information, preventing missed or delayed tasks. The retrieval unit also integrates with other systems via APIs, retrieving tasks from external task management tools and project management systems. This enables centralized task management across different systems, improving operational efficiency.

[0065] The reconfiguration unit reconfigures tasks retrieved by the retrieval unit. For example, the reconfiguration unit can reconfigure tasks based on user-specified deadlines or units. Specifically, the reconfiguration unit rearranges tasks according to the user's schedule and priorities, supporting efficient task management. For instance, if a user wants to manage tasks on a weekly basis, the reconfiguration unit generates a weekly task list and sorts each task in the optimal order based on its due date and importance. The reconfiguration unit also optimizes the task execution order by considering task dependencies and postponing tasks whose prerequisites are not met. Furthermore, the reconfiguration unit can analyze the user's past task execution history and performance data to suggest the optimal task allocation for each individual user. For example, it can improve overall work efficiency by prioritizing the assignment of tasks to users who perform well on specific tasks. The reconfiguration unit can also monitor task progress in real time and rearrange or reschedule tasks as needed. This allows users to always perform optimal task management, preventing delays and duplication of work.

[0066] The notification unit provides daily notifications for tasks restructured by the restructuring unit. Specifically, the notification unit sends task reminders at a set time each day based on user settings. Multiple notification methods are available, including email, SMS, and push notifications, allowing users to choose their preferred method. Furthermore, the notification unit prioritizes notifications based on task importance and deadlines, using more powerful notification methods for critical or urgent tasks. For example, tasks with deadlines approaching can be alerted via push notifications or voice alerts. The notification unit also monitors task progress and completion status in real time and repeatedly sends reminders for incomplete tasks. This allows users to stay informed about task progress and complete tasks without forgetting. Additionally, the notification unit shares the task status of the entire team, facilitating information sharing and collaboration among team members. This streamlines team task management and ensures smooth project progress.

[0067] The distribution department distributes tasks restructured by the restructuring department to the organization and its agents. Specifically, the distribution department assigns tasks to the appropriate personnel or departments based on their content and importance. For example, tasks related to a specific project are distributed to the project team, with each member receiving tasks according to their role. The distribution department also distributes tasks related to sales promotion and marketing to agents, supporting them in efficiently carrying out their duties. The distribution department monitors task distribution status and recipient responses in real time, and can redistribute or follow up on tasks as needed. For example, if a task remains unread or is not completed by the deadline, it sends another notification to encourage completion. The distribution department also records task distribution history for later reference, which is useful for task progress management and evaluation. This allows the distribution department to efficiently distribute and manage tasks, improving overall organizational efficiency. Furthermore, the distribution unit generates reports on task distribution, allowing administrators to quickly grasp the progress and completion status of tasks. This enables administrators to properly monitor task progress and take necessary actions promptly.

[0068] The acquisition unit can retrieve tasks such as training participation, various inventory counts, e-learning, HR-related interviews, and sales promotion interviews from the task database. For example, the acquisition unit can retrieve the content of training sessions, the types of inventory counts, and the subjects of e-learning courses from the task database. This allows for centralized management and efficient retrieval of diverse tasks. Some or all of the above-described processes in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the tasks retrieved from the task database into a generating AI and analyze the content of the tasks.

[0069] The reconfiguration unit can reconfigure tasks based on the due date and unit specified by the user. For example, the reconfiguration unit can reconfigure tasks based on the due date format, the type of unit (hour, day, week, etc.), etc. This makes it possible to reconfigure tasks according to the user's requests. Some or all of the above-described processes in the reconfiguration unit may be performed using AI, for example, or without AI. For example, the reconfiguration unit can input the due date and unit specified by the user into a generating AI and have the generating AI perform the task reconfiguration.

[0070] The notification unit can send daily notifications of the restructured tasks. The notification unit can send daily notifications of the restructured tasks based, for example, on the time of notification and the notification method (email, app notification, etc.). This allows for real-time monitoring of task progress. Some or all of the above-described processes in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the restructured tasks into a generation AI and have the generation AI execute the daily notifications.

[0071] The distribution unit can distribute the restructured tasks within the organization and to its agents. For example, the distribution unit can distribute the restructured tasks within the organization and to its agents based on selection criteria for recipients and distribution methods (email, chat tools, etc.). This allows for efficient distribution of task progress within the organization and to its agents. Some or all of the above-described processes in the distribution unit may be performed using AI, or not. For example, the distribution unit can input the restructured tasks into a generation AI and have the generation AI execute the distribution content.

[0072] The acquisition unit can estimate the user's emotions and adjust the timing of task acquisition based on the estimated emotions. For example, if the user is stressed, the acquisition unit can delay task acquisition and acquire it when the user is relaxed. If the user is focused, the acquisition unit can acquire the task immediately to allow for efficient task processing. If the user is tired, the acquisition unit can postpone task acquisition to the next day, prioritizing rest. This allows tasks to be acquired at an appropriate time according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 acquisition unit may be performed using AI, or not using AI. For example, the acquisition unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0073] The acquisition unit can analyze the user's past task history and select the optimal acquisition method. For example, the acquisition unit can prioritize acquiring tasks that the user has frequently performed in the past. The acquisition unit can also suggest an efficient acquisition method based on the user's past task history. Furthermore, the acquisition unit can analyze the user's past task history and acquire tasks at the optimal timing. This allows for efficient task acquisition based on the user's past task history. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's past task history into a generating AI and have the generating AI execute the optimal acquisition method.

[0074] The acquisition unit can filter tasks based on the user's current work situation and areas of interest when acquiring tasks. For example, the acquisition unit can prioritize acquiring tasks related to the work the user is currently working on. It can also acquire highly relevant tasks based on the user's areas of interest. Furthermore, the acquisition unit can prioritize acquiring tasks that are less burdensome, taking into account the user's work situation. This allows for the efficient acquisition of tasks that match the user's work situation and areas of interest. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input data on the user's work situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0075] The acquisition unit can estimate the user's emotions and determine the priority of tasks to acquire based on the estimated emotions. For example, if the user is stressed, the acquisition unit can prioritize acquiring low-priority tasks. If the user is relaxed, the acquisition unit can prioritize acquiring high-priority tasks. If the user is in a hurry, the acquisition unit can prioritize acquiring tasks that can be processed quickly. This allows tasks to be acquired with priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 acquisition unit may be performed using AI, for example, or not using AI. For example, the acquisition unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0076] The acquisition unit can prioritize acquiring tasks that are highly relevant to the user, taking into account the user's geographical location information when acquiring tasks. For example, if the user is in a specific region, the acquisition unit can prioritize acquiring tasks related to that region. It can also prioritize acquiring tasks that can be performed in locations close to the user's current location. Furthermore, based on the user's travel plans, the acquisition unit can prioritize acquiring tasks that can be performed at their destination. This allows the acquisition of highly relevant tasks based on the user's geographical location information. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's geographical location information into a generating AI and have the generating AI execute highly relevant tasks.

[0077] The acquisition unit can analyze the user's social media activity when acquiring tasks and acquire relevant tasks. For example, the acquisition unit can prioritize acquiring tasks that the user has mentioned on social media. The acquisition unit can also acquire tasks of interest from the user's social media activity. Furthermore, the acquisition unit can acquire tasks at the optimal timing based on the user's activity time on social media. This allows the acquisition of relevant tasks based on the user's social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's social media activity data into a generating AI and have the generating AI execute the relevant tasks.

[0078] The reconstruction unit can estimate the user's emotions and adjust the task reconstruction method based on the estimated user emotions. For example, if the user is stressed, the reconstruction unit can simplify and reconstruct the task. If the user is relaxed, the reconstruction unit can reconstruct a detailed task. If the user is in a hurry, the reconstruction unit can reconstruct a task that can be completed quickly. This allows tasks to be reconstructed in an appropriate way according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 reconstruction unit may be performed using AI, for example, or not using AI. For example, the reconstruction unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0079] The reconstruction unit can adjust the level of detail in the reconstruction based on the importance of the tasks. For example, the reconstruction unit can reconstruct high-importance tasks in detail and low-importance tasks in a simplified manner. The reconstruction unit can also determine the priority of the reconstruction according to the importance of the tasks. Furthermore, the reconstruction unit can perform reconstructions that include detailed procedures for high-importance tasks. This enables detailed reconstruction according to the importance of the tasks. Some or all of the above processes in the reconstruction unit may be performed using AI, for example, or without AI. For example, the reconstruction unit can input task importance data into a generating AI and have the generating AI execute the reconstruction with the appropriate level of detail.

[0080] The reconstruction unit can apply different reconstruction algorithms depending on the task category during reconstruction. For example, the reconstruction unit can apply a reconstruction algorithm that enhances learning effectiveness to training tasks. It can also apply a reconstruction algorithm that includes efficient work procedures to inventory tasks. Furthermore, it can apply an interactive reconstruction algorithm to e-learning tasks. This enables appropriate reconstruction according to the task category. Some or all of the above-described processes in the reconstruction unit may be performed using AI, for example, or without AI. For example, the reconstruction unit can input task category data into a generating AI and have the generating AI execute a reconstruction algorithm.

[0081] The reconstruction unit can estimate the user's emotions and adjust the order of tasks to be reconstructed based on the estimated emotions. For example, if the user is stressed, the reconstruction unit can reconstruct easy tasks first. If the user is relaxed, the reconstruction unit can reconstruct important tasks first. If the user is in a hurry, the reconstruction unit can reconstruct tasks that can be completed quickly first. This allows tasks to be reconstructed in an order that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 reconstruction unit may be performed using AI, for example, or not using AI. For example, the reconstruction unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0082] The restructuring unit can determine the priority of restructuring based on the task submission timing during the restructuring process. For example, the restructuring unit can prioritize restructuring tasks with approaching submission deadlines. It can also postpone restructuring tasks with distant submission deadlines. Furthermore, the restructuring unit can dynamically adjust the restructuring priority according to the submission deadlines. This enables restructuring with a priority based on the task submission timing. Some or all of the above processing in the restructuring unit may be performed using AI, for example, or without AI. For example, the restructuring unit can input task submission timing data into a generating AI and have the generating AI execute the restructuring priority.

[0083] The reconstruction unit can adjust the reconstruction order based on the relevance of tasks during reconstruction. For example, the reconstruction unit can reconstruct highly related tasks together. It can also reconstruct less relevant tasks individually. Furthermore, the reconstruction unit can dynamically adjust the reconstruction order according to the relevance of tasks. This makes it possible to reconstruct tasks in an order that corresponds to their relevance. Some or all of the above processing in the reconstruction unit may be performed using AI, for example, or without AI. For example, the reconstruction unit can input task relevance data into a generating AI and have the generating AI execute the reconstruction order.

[0084] The notification unit can estimate the user's emotions and adjust the way notifications are presented based on the estimated emotions. For example, if the user is stressed, the notification unit can provide a simple and highly visible notification. If the user is relaxed, the notification unit can provide a notification containing detailed information. If the user is in a hurry, the notification unit can provide a concise notification that gets straight to the point. This enables notifications to be presented in an appropriate manner according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 notification unit may be performed using AI, or not using AI. For example, the notification unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0085] The notification unit can adjust the level of detail of notifications based on the importance of the task. For example, it can provide detailed notifications for high-importance tasks and simplified notifications for low-importance tasks. The notification unit can also determine the priority of notifications according to the importance of the task. Furthermore, for high-importance tasks, the notification unit can provide notifications that include detailed instructions. This enables detailed notifications according to the importance of the task. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input task importance data into a generating AI and have the generating AI determine the level of detail of the notifications.

[0086] The notification unit can apply different notification algorithms depending on the task category when sending notifications. For example, the notification unit can apply a notification algorithm that enhances learning effectiveness to training tasks. It can also apply a notification algorithm that includes efficient work procedures to inventory tasks. Furthermore, it can apply an interactive notification algorithm to e-learning tasks. This enables appropriate notifications according to the task category. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input task category data into a generating AI and have the generating AI execute a notification algorithm.

[0087] The notification unit can estimate the user's emotions and adjust the timing of notifications based on the estimated emotions. For example, if the user is stressed, the notification unit can delay the notification until the user is relaxed. If the user is focused, the notification unit can send an immediate notification to allow them to efficiently process tasks. If the user is tired, the notification unit can postpone the notification until the next day, prioritizing rest. This enables notifications to be sent at the appropriate time according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 notification unit may be performed using AI or not. For example, the notification unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0088] The notification unit can determine the priority of notifications based on the task submission timing when sending notifications. For example, the notification unit can prioritize notifications for tasks with approaching deadlines. It can also postpone notifications for tasks with distant deadlines. Furthermore, the notification unit can dynamically adjust the notification priority according to the submission deadline. This enables notifications to be sent with priority according to the task submission timing. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input task submission timing data into a generating AI and have the generating AI execute the notification priority.

[0089] The notification unit can adjust the order of notifications based on the relevance of tasks when sending notifications. For example, the notification unit can group together highly relevant tasks for notification. It can also individually notify users of less relevant tasks. Furthermore, the notification unit can dynamically adjust the order of notifications according to the relevance of tasks. This makes it possible to send notifications in an order that corresponds to the relevance of tasks. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input task relevance data into a generating AI and have the generating AI execute the notification order.

[0090] The delivery unit can estimate the user's emotions and adjust the delivery method based on the estimated emotions. For example, if the user is stressed, the delivery unit can provide a simple and highly visible delivery method. If the user is relaxed, the delivery unit can provide a delivery method that includes detailed information. If the user is in a hurry, the delivery unit can provide a concise delivery method that gets straight to the point. This enables delivery in an appropriate way according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0091] The distribution unit can adjust the level of detail in the distribution based on the importance of the task at the time of distribution. For example, the distribution unit can distribute high-importance tasks in detail and low-importance tasks in a simplified manner. The distribution unit can also determine the priority of distribution according to the importance of the task. Furthermore, the distribution unit can distribute high-importance tasks with detailed instructions. This enables detailed distribution according to the importance of the task. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input task importance data into a generating AI and have the generating AI determine the level of detail in the distribution.

[0092] The distribution unit can apply different distribution algorithms depending on the task category during distribution. For example, the distribution unit can apply a distribution algorithm that enhances learning effectiveness to training tasks. It can also apply a distribution algorithm that includes efficient work procedures to inventory tasks. Furthermore, it can apply an interactive distribution algorithm to e-learning tasks. This enables appropriate distribution according to the task category. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input task category data into a generating AI and have the generating AI execute the distribution algorithm.

[0093] The delivery unit can estimate the user's emotions and adjust the timing of delivery based on the estimated emotions. For example, if the user is stressed, the delivery unit can delay delivery and deliver it when the user is relaxed. If the user is focused, the delivery unit can deliver immediately to allow them to process tasks efficiently. If the user is tired, the delivery unit can postpone delivery to the next day to prioritize rest. This enables delivery at an appropriate time according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 delivery unit may be performed using AI or not using AI. For example, the delivery unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0094] The distribution unit can determine the distribution priority based on the task submission date at the time of distribution. For example, the distribution unit can prioritize the distribution of tasks with approaching deadlines. It can also postpone the distribution of tasks with later deadlines. Furthermore, the distribution unit can dynamically adjust the distribution priority according to the submission deadlines. This enables distribution with priority according to the task submission date. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input task submission date data into a generating AI and have the generating AI determine the distribution priority.

[0095] The distribution unit can adjust the distribution order based on the relevance of tasks during distribution. For example, the distribution unit can distribute highly relevant tasks together. It can also distribute less relevant tasks individually. Furthermore, the distribution unit can dynamically adjust the distribution order according to the relevance of tasks. This enables distribution in an order that corresponds to the relevance of tasks. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input task relevance data into a generating AI and have the generating AI execute the distribution order.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The acquisition unit can estimate the user's emotions and adjust the task acquisition method based on the estimated user emotions. For example, if the user is stressed, the acquisition unit can delay task acquisition and acquire it when the user is relaxed. If the user is focused, the acquisition unit can acquire the task immediately to allow for efficient task processing. Furthermore, if the user is tired, the acquisition unit can postpone task acquisition to the next day, prioritizing rest. This allows tasks to be acquired at an appropriate time according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI is, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0098] The reconstruction unit can estimate the user's emotions and adjust the task reconstruction method based on the estimated user emotions. For example, if the user is stressed, the reconstruction unit can simplify and reconstruct the task. If the user is relaxed, it can reconstruct a detailed task. Furthermore, if the user is in a hurry, the reconstruction unit can reconstruct a task that can be completed quickly. This allows tasks to be reconstructed in an appropriate way according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI is, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the reconstruction unit may be performed using AI or not. For example, the reconstruction unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0099] The notification unit can estimate the user's emotions and adjust the way notifications are presented based on those emotions. For example, if the user is stressed, the notification unit can provide a simple and highly visible notification. If the user is relaxed, the notification unit can provide a notification with detailed information. Furthermore, if the user is in a hurry, the notification unit can provide a concise notification that gets straight to the point. This enables notifications to be presented in an appropriate manner according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI may be, but is not limited to, text-generating AI or multimodal-generating AI. Some or all of the above-described processes in the notification unit may be performed using AI or not. For example, the notification unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0100] The delivery unit can estimate the user's emotions and adjust the delivery method based on the estimated emotions. For example, if the user is stressed, the delivery unit can provide a simple and highly visible delivery method. If the user is relaxed, the delivery unit can provide a delivery method that includes detailed information. Furthermore, if the user is in a hurry, the delivery unit can provide a concise delivery method that gets straight to the point. This enables delivery in an appropriate manner according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI is, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0101] The notification unit can estimate the user's emotions and adjust the timing of notifications based on the estimated emotions. For example, if the user is stressed, the notification unit can delay the notification until the user is relaxed. If the user is focused, the notification unit can send an immediate notification to allow them to efficiently process the task. Furthermore, if the user is tired, the notification unit can postpone the notification until the next day, prioritizing rest. This enables notifications to be sent at the appropriate time according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0102] The acquisition unit can analyze the user's past task history and select the optimal acquisition method. For example, it can prioritize acquiring tasks that the user has frequently performed in the past. The acquisition unit can also suggest efficient acquisition methods based on the user's past task history. Furthermore, the acquisition unit can analyze the user's past task history and acquire tasks at the optimal timing. This allows for efficient task acquisition based on the user's past task history. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input the user's past task history into a generating AI and have the generating AI execute the optimal acquisition method.

[0103] The acquisition unit can filter tasks based on the user's current work situation and areas of interest when acquiring tasks. For example, it can prioritize acquiring tasks related to the work the user is currently working on. Furthermore, the acquisition unit can acquire highly relevant tasks based on the user's areas of interest. In addition, the acquisition unit can prioritize acquiring tasks that are less burdensome, taking into account the user's work situation. This allows for efficient acquisition of tasks tailored to the user's work situation and areas of interest. Some or all of the above processing in the acquisition unit may be performed using AI, or without AI. For example, the acquisition unit can input data on the user's work situation and areas of interest into a generating AI, and have the generating AI perform the filtering.

[0104] The acquisition unit can prioritize acquiring tasks that are highly relevant to the user, taking into account the user's geographical location information. For example, if the user is in a specific region, it can prioritize acquiring tasks related to that region. The acquisition unit can also prioritize acquiring tasks that can be performed in locations close to the user's current location. Furthermore, based on the user's travel plans, the acquisition unit can prioritize acquiring tasks that can be performed at their destination. This allows the acquisition of highly relevant tasks based on the user's geographical location information. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input the user's geographical location information into a generation AI and have the generation AI execute highly relevant tasks.

[0105] The acquisition unit can analyze the user's social media activity when acquiring tasks and acquire relevant tasks. For example, it can prioritize acquiring tasks that the user has mentioned on social media. The acquisition unit can also acquire tasks of interest from the user's social media activity. Furthermore, the acquisition unit can acquire tasks at the optimal timing based on the user's activity time on social media. This allows the acquisition of relevant tasks based on the user's social media activity. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input the user's social media activity data into a generating AI and have the generating AI execute relevant tasks.

[0106] The reconstruction unit can adjust the level of detail in the reconstruction based on the importance of the tasks. For example, high-importance tasks can be reconstructed in detail, while low-importance tasks can be reconstructed in a simplified manner. The reconstruction unit can also determine the priority of the reconstruction according to the importance of the tasks. Furthermore, the reconstruction unit can perform reconstructions that include detailed procedures for high-importance tasks. This enables detailed reconstruction according to the importance of the tasks. Some or all of the above processes in the reconstruction unit may be performed using AI or not. For example, the reconstruction unit can input task importance data into a generating AI and have the generating AI execute the level of detail in the reconstruction.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The retrieval unit retrieves tasks from the task database. For example, tasks such as training participation, various inventory counts, e-learning, HR-related interviews, and sales promotion interviews can be retrieved from the task database. Step 2: The reconfiguration unit reconfigures the tasks retrieved by the acquisition unit. For example, tasks can be reconfigured based on a deadline or unit specified by the user. Step 3: The notification unit provides daily notifications for tasks reconfigured by the reconfiguration unit. For example, it can provide daily notifications for reconfigured tasks. Step 4: The distribution unit distributes the reconfigured tasks within the organization and to agencies. For example, the reconfigured tasks can be distributed within the organization and to agencies.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the acquisition unit, reconstruction unit, notification unit, and distribution unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit is implemented by the control unit 46A of the smart device 14 and retrieves tasks from the task database. The reconstruction unit is implemented by the specific processing unit 290 of the data processing device 12 and reconstructs tasks based on the due date and unit specified by the user. The notification unit is implemented by the control unit 46A of the smart device 14 and provides daily notifications of the reconstructed tasks. The distribution unit is implemented by the specific processing unit 290 of the data processing device 12 and can distribute the reconstructed tasks within the organization or to agents. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the acquisition unit, reconstruction unit, notification unit, and distribution unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit is implemented by the control unit 46A of the smart glasses 214 and retrieves tasks from the task database. The reconstruction unit is implemented, for example, by the identification processing unit 290 of the data processing device 12 and reconstructs tasks based on the due date and unit specified by the user. The notification unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides daily notifications of the reconstructed tasks. The distribution unit is implemented, for example, by the identification processing unit 290 of the data processing device 12 and can distribute the reconstructed tasks within the organization or to agents. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the acquisition unit, reconstruction unit, notification unit, and distribution unit, is implemented in at least one of the headset terminal 314 and the data processing device 12. For example, the acquisition unit is implemented by the control unit 46A of the headset terminal 314 and retrieves tasks from the task database. The reconstruction unit is implemented by the identification processing unit 290 of the data processing device 12 and reconstructs tasks based on the due date and unit specified by the user. The notification unit is implemented by the control unit 46A of the headset terminal 314 and provides daily notifications of the reconstructed tasks. The distribution unit is implemented by the identification processing unit 290 of the data processing device 12 and can distribute the reconstructed tasks within the organization or to agents. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the acquisition unit, reconstruction unit, notification unit, and distribution unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit is implemented by the control unit 46A of the robot 414 and retrieves tasks from the task database. The reconstruction unit is implemented by the specific processing unit 290 of the data processing unit 12 and reconstructs tasks based on the due date and unit specified by the user. The notification unit is implemented by the control unit 46A of the robot 414 and provides daily notifications of the reconstructed tasks. The distribution unit is implemented by the specific processing unit 290 of the data processing unit 12 and can distribute the reconstructed tasks within the organization or to agents. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) A task retrieval unit that retrieves tasks from the task database, A reconstruction unit that reconstructs the tasks extracted by the acquisition unit, A notification unit that provides daily notifications of tasks reconfigured by the aforementioned reconfiguration unit, The system includes a distribution unit that distributes the tasks reconfigured by the aforementioned reconfiguration unit to organizations and agencies. A system characterized by the following features. (Note 2) The acquisition unit is, Retrieve tasks such as training participation, various inventory checks, e-learning, HR-related interviews, and sales promotion interviews from the task database. The system described in Appendix 1, characterized by the features described herein. (Note 3) The reconstruction unit is Restructure tasks based on the deadlines and units specified by the user. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned notification unit, Daily notifications for restructured tasks. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned distribution unit, Distribute the restructured tasks within the organization and to agencies. The system described in Appendix 1, characterized by the features described herein. (Note 6) The acquisition unit is, It estimates the user's emotions and adjusts the timing of task acquisition based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, Analyze the user's past task history and select the optimal method for data acquisition. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, When retrieving tasks, filtering is performed based on the user's current work status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, It estimates the user's emotions and determines the priority of tasks to acquire based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, When retrieving tasks, the system prioritizes retrieving highly relevant tasks by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, When retrieving tasks, the system analyzes the user's social media activity and retrieves relevant tasks. The system described in Appendix 1, characterized by the features described herein. (Note 12) The reconstruction unit is It estimates the user's emotions and adjusts how the task is restructured based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The reconstruction unit is During reconfiguration, adjust the level of detail based on the importance of the tasks. The system described in Appendix 1, characterized by the features described herein. (Note 14) The reconstruction unit is During reconstruction, different reconstruction algorithms are applied depending on the task category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The reconstruction unit is The task sequence is adjusted to estimate user emotions and reconstruct based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The reconstruction unit is During restructuring, prioritize tasks based on when they were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The reconstruction unit is During reconfiguration, adjust the reconfiguration order based on the relevance of the tasks. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned notification unit, It estimates the user's emotions and adjusts the way notifications are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned notification unit, When a notification is sent, adjust the level of detail based on the importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned notification unit, When sending notifications, different notification algorithms are applied depending on the task category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned notification unit, It estimates the user's emotions and adjusts the timing of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned notification unit, When sending notifications, we prioritize them based on when the task was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned notification unit, When sending notifications, adjust the order of notifications based on the relevance of the tasks. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned distribution unit, It estimates user sentiment and adjusts delivery methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned distribution unit, When delivering a task, adjust the level of detail based on the task's importance. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned distribution unit, When delivering tasks, different delivery algorithms are applied depending on the task category. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned distribution unit, It estimates the user's emotions and adjusts the delivery timing based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned distribution unit, When distributing tasks, the distribution priority will be determined based on when the tasks were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned distribution unit, During delivery, adjust the delivery order based on the relevance of the tasks. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0181] 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. A task retrieval unit that retrieves tasks from the task database, A reconstruction unit that reconstructs the tasks extracted by the acquisition unit, A notification unit that provides daily notifications of tasks reconfigured by the aforementioned reconfiguration unit, The system includes a distribution unit that distributes the tasks reconfigured by the aforementioned reconfiguration unit to organizations and agencies. A system characterized by the following features.

2. The acquisition unit is, Retrieve tasks such as training participation, various inventory checks, e-learning, HR-related interviews, and sales promotion interviews from the task database. The system according to feature 1.

3. The reconstruction unit is Restructure tasks based on the deadlines and units specified by the user. The system according to feature 1.

4. The aforementioned notification unit, Daily notifications for restructured tasks. The system according to feature 1.

5. The aforementioned distribution unit, Distribute the restructured tasks within the organization and to agencies. The system according to feature 1.

6. The acquisition unit is, It estimates the user's emotions and adjusts the timing of task acquisition based on the estimated user emotions. The system according to feature 1.

7. The acquisition unit is, Analyze the user's past task history and select the optimal method for data acquisition. The system according to feature 1.

8. The acquisition unit is, When retrieving tasks, filtering is performed based on the user's current work status and areas of interest. The system according to feature 1.

9. The acquisition unit is, It estimates the user's emotions and determines the priority of tasks to acquire based on the estimated user emotions. The system according to feature 1.

10. The acquisition unit is, When retrieving tasks, the system prioritizes retrieving highly relevant tasks by considering the user's geographical location. The system according to feature 1.

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