system

Abstract

The system according to this embodiment aims to centrally manage tasks from various tools and achieve efficient schedule management. [Solution] The system according to the embodiment comprises an extraction unit, a generation unit, and a provision unit. The extraction unit extracts tasks from each tool. The generation unit converts the tasks extracted by the extraction unit into a schedule table. The provision unit provides the schedule table generated by the generation unit.

Description

Technical Field

[0006] , , ,

[0005] , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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 centrally manage tasks from various tools and efficient schedule management cannot be achieved.

[0005] The system according to the embodiment aims to centrally manage tasks from various tools and realize efficient schedule management.

Means for Solving the Problems

[0006] The system according to the embodiment includes an extraction unit, a generation unit, and a provision unit. The extraction unit extracts tasks from each tool. The generation unit converts the tasks extracted by the extraction unit into a schedule table. The provision unit provides the schedule table generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can centrally manage tasks from various tools and achieve efficient schedule management. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 Thin Schedule System according to an embodiment of the present invention is an AI tool that aggregates and centrally manages tasks from multiple communication tools. This Thin Schedule System uses AI to extract messages such as "task request," "response request," and "deadline" from various tools such as communication tools, email, and Asana. Next, it converts the extracted tasks into a simple and visually easy-to-understand schedule and provides it to the user. This schedule can be viewed on the web or in an app. For example, the AI ​​extracts tasks from each tool. In this process, the AI ​​identifies request-type messages such as "Please do this," "Get this done by tomorrow!", and "Check this if you have time!", as well as messages containing deadlines such as "Task deadline: Month / Day" and "Response deadline: Month / Day." Next, an image generation AI converts the extracted tasks into a schedule. The schedule is provided in a simple and visually easy-to-understand format. For example, the task content, requester, priority, and response deadline are displayed so that they can be seen at a glance. It is also possible to check the source of the information by clicking on the task. This mechanism allows for centralized management of tasks from multiple tools and prevents tasks from being missed. The target users are salaried employees of large companies who are proficient in using multiple tools, and people who are overwhelmed by information and unable to concentrate on their core work. This allows for efficient task management even in environments where task management tends to become complicated. In this way, by centrally managing tasks from various tools and providing them in a visually easy-to-understand schedule, it prevents tasks from being overlooked and enables efficient task management. Thus, the Shin Schedule System can centrally manage tasks from multiple tools and provide a visually easy-to-understand schedule.

[0029] The Shin Schedule System according to this embodiment comprises an extraction unit, a generation unit, and a provision unit. The extraction unit extracts tasks from each tool. The extraction unit extracts task-related messages from the tools, for example, using AI. The extraction unit identifies request messages such as "Please do this," "Get this done by tomorrow!", and "Check this out if you have time!", as well as messages containing deadlines such as "Task deadline: Month / Day" and "Response deadline: Month / Day." The generation unit converts the tasks extracted by the extraction unit into a schedule table. The generation unit converts the extracted tasks into a simple and visually easy-to-understand schedule table, for example, using generation AI. The generation unit displays, for example, the task content, requester, priority, and response deadline so that they can be seen at a glance. The provision unit provides the schedule table generated by the generation unit. The provision unit provides the generated schedule table via the web or an app, for example. The provision unit also allows users to check the source of the information by clicking on the schedule table. As a result, the thin scheduling system according to this embodiment can centrally manage tasks from multiple tools and provide a visually easy-to-understand schedule.

[0030] The extraction unit extracts tasks from each tool. For example, the extraction unit uses AI to extract task-related messages from the tools. Specifically, the AI ​​uses natural language processing technology to analyze messages within these tools and identify keywords and phrases related to the tasks. For example, it identifies request-type messages such as "Please do this," "Get this done by tomorrow!", and "Check this out if you have time!", as well as messages containing deadlines such as "Task deadline: Month / Day" and "Response deadline: Month / Day". The AI ​​judges the context of the message, the urgency and importance of the request, and extracts it as a task. Furthermore, the AI ​​analyzes metadata such as the sender, recipient, and date and time of sending of the message, and adds it as attribute information for the task. This allows the extraction unit to efficiently extract tasks from each tool and accurately understand the content and attribute information of the tasks. The extracted tasks are stored in a central database and made accessible to the generation and provision units. The extraction unit also periodically scans each tool, and if new tasks arise, it extracts them immediately to maintain up-to-date information for the entire system. This allows the extraction unit to centrally manage tasks from multiple tools, improving the overall efficiency and accuracy of the system.

[0031] The generation unit converts the tasks extracted by the extraction unit into a schedule. The generation unit, for example, uses a generation AI to convert the extracted tasks into a simple and visually easy-to-understand schedule. Specifically, the generation AI places tasks into appropriate time slots based on information such as task content, requester, priority, and deadline, generating a visually organized schedule. The generation AI considers the priority and urgency of tasks, placing important tasks in prominent positions. It also displays the task requester and related project information, allowing the user to understand the background and relationships of tasks at a glance. Furthermore, the generation AI can learn the user's past schedules and work patterns to suggest an optimal schedule. For example, it analyzes when the user typically has the highest concentration and which tasks they spend the most time on, and adjusts the schedule accordingly. This allows the generation unit to provide a schedule that maximizes the user's work efficiency. The generated schedule is designed to be visually easy to understand and intuitive for the user. This enables the generation unit to effectively manage the extracted tasks and improve the user's work efficiency.

[0032] The service provider provides the schedule table generated by the generation service provider. For example, the service provider provides the generated schedule table via the web or an app. Specifically, the service provider provides a user-friendly interface, allowing users to view and edit the schedule table in real time. Users can access the schedule table through a web browser or dedicated app to view task details and update task progress. The service provider enables clickable actions for each task in the schedule table, allowing users to see the source of the task's information. For example, it displays links to the task requester's message and related documents, enabling users to quickly access the information they need. Furthermore, the service provider collects user feedback and continuously improves the usability and functionality of the schedule table. For example, it implements a feedback function that allows users to provide opinions on the layout and display method of the schedule table, and customizes it according to user needs. In addition, the service provider stores the schedule table data in the cloud, making it accessible from multiple devices. This allows users to view and edit the schedule table from any device, such as a desktop PC, smartphone, or tablet. This enables the service provider to provide users with flexible and convenient schedule management, supporting the efficient execution of tasks.

[0033] The display unit can show task details, requesters, priority, and deadlines. For example, it can display task details and required resources. It can also display requesters such as project managers and clients. The display unit can show priority levels based on classification criteria such as high, medium, and low. It can also display deadlines such as project deadlines and task start dates. This allows for a visually clear and easy-to-understand display of task details.

[0034] The verification section allows you to check the source of information by clicking on the task. The verification section displays information sources such as emails, chat messages, and documents. For example, the verification section makes it easy to check the source of information for a task. This makes it easy to check the source of information for a task.

[0035] The extraction unit can extract task-related messages from tools. The extraction unit clarifies the specific types and scope of tools, such as project management tools and communication tools. This allows for the efficient extraction of task-related messages from multiple tools.

[0036] The generation unit can convert the extracted tasks into a simple and visually easy-to-understand schedule. The generation unit clarifies specific criteria and methods, such as the use of color coding and icons. This allows the tasks to be transformed into a visually clear schedule.

[0037] The service provider can provide the generated schedule via the web or an app. The service provider will specify the exact delivery method and platform, such as a web browser or mobile app. This will enable the generated schedule to be provided via the web or an app.

[0038] The extraction unit can analyze the user's past task history and select the optimal extraction method when extracting tasks from each tool. For example, the extraction unit can prioritize extracting tasks from tools that the user has frequently used in the past. For example, the extraction unit can prioritize extracting high-priority tasks based on the user's past task history. For example, the extraction unit can analyze the user's past task history and extract tasks based on specific patterns. In this way, the optimal extraction method can be selected by analyzing the user's past task history.

[0039] The extraction unit can filter tasks based on the user's current projects and areas of interest during task extraction. For example, the extraction unit can prioritize tasks related to projects the user is currently working on. It can also extract relevant tasks based on the user's areas of interest. Furthermore, it can filter tasks based on the project priorities set by the user. This allows tasks to be filtered based on the user's current projects and areas of interest.

[0040] The extraction unit can prioritize the extraction of highly relevant tasks by considering the user's geographical location information during task extraction. For example, if the user is in a specific location, the extraction unit will prioritize tasks related to that location. For example, the extraction unit will prioritize tasks that can be performed in locations close to the user's current location. For example, if the user is on the move, the extraction unit will prioritize tasks related to their destination. This allows the extraction of highly relevant tasks to be prioritized by considering the user's geographical location information.

[0041] The extraction unit can analyze a user's social media activity and extract relevant tasks during task extraction. For example, the extraction unit can extract tasks mentioned by the user on social media. For example, the extraction unit can extract tasks based on relevant events or projects from the user's social media activity. For example, the extraction unit can analyze the content of a user's social media posts and extract relevant tasks. In this way, relevant tasks can be extracted by analyzing the user's social media activity.

[0042] The generation unit can adjust the level of detail in the schedule based on the importance of the tasks when generating the schedule. For example, it can display high-importance tasks in detail and simplify low-importance tasks. The generation unit can also adjust the layout of the schedule based on the priority of the tasks. For example, it can place high-importance tasks in prominent positions and low-importance tasks in less prominent positions. This allows the level of detail in the schedule to be adjusted based on the importance of the tasks.

[0043] The generation unit can apply different generation algorithms depending on the task category when generating a schedule. For example, the generation unit can apply a project management generation algorithm to project tasks. For example, it can apply a simple generation algorithm to routine tasks. For example, it can apply a generation algorithm that allows for quick response to urgent tasks. This allows different generation algorithms to be applied depending on the task category.

[0044] The generation unit can determine the priority of tasks in the schedule based on their submission dates when generating the schedule. For example, the generation unit can prioritize tasks with approaching deadlines. For example, the generation unit can display tasks with later deadlines at a later date. For example, the generation unit can adjust the layout of the schedule based on the submission deadlines. This allows the system to determine the priority of tasks in the schedule based on their submission dates.

[0045] The generation unit can adjust the order of tasks in the schedule table based on their relevance when generating the schedule table. For example, the generation unit can group together highly related tasks. For example, the generation unit can distribute and display less related tasks. For example, the generation unit adjusts the order of tasks in the schedule table based on their relevance. This allows the order of tasks in the schedule table to be adjusted based on their relevance.

[0046] The service provider can select the optimal delivery method when providing a schedule by referring to the user's past usage history. For example, the service provider can prioritize delivery methods that the user has frequently used in the past. For example, the service provider can propose the optimal delivery method based on the user's past usage history. For example, the service provider can analyze the user's past usage history and select a delivery method based on specific patterns. In this way, the optimal delivery method can be selected by referring to the user's past usage history.

[0047] The service provider can select the optimal delivery method when providing a schedule, taking into account the user's device information. For example, if the user is using a smartphone, the service provider will select a delivery method optimized for smartphones. For example, if the user is using a tablet, the service provider will select a delivery method optimized for tablets. For example, if the user is using a desktop, the service provider will select a delivery method optimized for desktops. In this way, the service provider can select the optimal delivery method by taking into account the user's device information.

[0048] The service provider can select the optimal delivery method when providing a schedule, taking into account the user's geographical location. For example, if the user is in a specific location, the service provider will prioritize providing tasks related to that location. For example, if the service provider is providing tasks that can be performed in locations close to the user's current location, the service provider will prioritize providing tasks related to the user's destination if the user is on the move. In this way, the service provider can select the optimal delivery method by taking into account the user's geographical location.

[0049] The service provider can analyze users' social media activity and adjust the delivery method when providing schedules. For example, the service provider can prioritize tasks mentioned by users on social media. For example, the service provider can provide tasks based on relevant events and projects from users' social media activity. For example, the service provider can analyze the content of users' social media posts and provide relevant tasks. In this way, the service provider can adjust the optimal delivery method by analyzing users' social media activity.

[0050] The display unit can adjust the level of detail based on the importance of the tasks when displaying them. For example, the display unit can display high-importance tasks in detail and lower-importance tasks in a simplified manner. The display unit can also adjust the display layout based on the priority of the tasks. For example, the display unit can place high-importance tasks in a prominent position and lower-importance tasks in a less prominent position. This allows the level of detail of the display to be adjusted based on the importance of the tasks.

[0051] The display unit can apply different display methods depending on the task category. For example, it can apply a project management display method to project tasks, a simple display method to daily tasks, and a display method that allows for quick response to urgent tasks. This allows for the application of different display methods depending on the task category.

[0052] The display unit can determine the display priority based on the task submission date when displaying tasks. For example, the display unit may prioritize displaying tasks with approaching deadlines. For example, the display unit may postpone displaying tasks with later deadlines. For example, the display unit may adjust the display layout based on the submission deadline. This allows the display priority to be determined based on the task submission date.

[0053] The display unit can adjust the display order based on the relevance of tasks during display. For example, the display unit can group highly relevant tasks together. For example, the display unit can display less relevant tasks in a more dispersed manner. The display unit adjusts the display order based on the relevance of tasks. This allows the display order to be adjusted based on the relevance of tasks.

[0054] The verification unit can adjust the level of detail in the verification process based on the importance of the task. For example, the verification unit can verify high-importance tasks in detail and simplify the verification of low-importance tasks. The verification unit can also adjust the layout of the verification process based on the priority of the tasks. For example, the verification unit can place high-importance tasks in a prominent position and low-importance tasks in a less prominent position. This allows the level of detail in the verification process to be adjusted based on the importance of the task.

[0055] The verification unit can apply different verification methods depending on the task category during verification. For example, the verification unit can apply a project management verification method to project tasks. For example, the verification unit can apply a simple verification method to routine tasks. For example, the verification unit can apply a verification method that allows for quick response to urgent tasks. This allows for the application of different verification methods depending on the task category.

[0056] The verification unit can determine the priority of tasks based on their submission dates during the verification process. For example, the verification unit will prioritize tasks with approaching deadlines. For example, the verification unit will postpone the verification of tasks with later deadlines. For example, the verification unit will adjust the layout of the verification process based on the submission deadlines. This allows the verification unit to determine the priority of tasks based on their submission dates.

[0057] The verification unit can adjust the order of verification based on the relevance of the tasks. For example, the verification unit might group together highly relevant tasks for verification. For example, the verification unit might distribute and verify less relevant tasks. The verification unit adjusts the order of verification based on the relevance of the tasks. This allows the order of verification to be adjusted based on the relevance of the tasks.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The Shin Schedule System can analyze a user's past task history and select the optimal task extraction method. For example, it can prioritize tasks extracted from tools the user has frequently used in the past. It can also prioritize tasks of high importance based on the user's past task history. Furthermore, it can extract tasks based on specific patterns. In this way, it can leverage the user's past task history to provide the optimal task extraction method.

[0060] The Shin Schedule System can prioritize and extract highly relevant tasks by considering the user's geographical location. For example, if a user is in a specific location, it can prioritize tasks related to that location. It can also prioritize tasks that can be performed in locations close to the user's current location. Furthermore, if a user is on the move, it can extract tasks related to their destination. This allows for the efficient extraction of highly relevant tasks by considering the user's geographical location.

[0061] The Shin Schedule System can analyze a user's social media activity and extract relevant tasks. For example, it can extract tasks that a user has mentioned on social media. It can also extract tasks based on relevant events and projects from a user's social media activity. Furthermore, it can analyze the content of a user's social media posts and extract relevant tasks. This allows for the efficient extraction of relevant tasks by leveraging a user's social media activity.

[0062] The Shin Schedule System allows you to adjust the level of detail in your schedule based on the importance of tasks. For example, you can display high-priority tasks in detail and lower-priority tasks in a simplified format. You can also adjust the layout of your schedule based on task priority. Furthermore, you can place high-priority tasks in prominent positions and lower-priority tasks in less prominent positions. This allows you to efficiently adjust the level of detail in your schedule based on the importance of tasks.

[0063] The Shin Schedule System can apply different generation algorithms depending on the task category. For example, project tasks can be assigned a generation algorithm optimized for project management. Routine tasks can be assigned a simpler generation algorithm. Furthermore, urgent tasks can be assigned a generation algorithm designed for rapid response. This allows for the application of the most suitable generation algorithm for each task category.

[0064] The Shin Schedule System can prioritize tasks based on their submission deadlines. For example, it can display tasks with approaching deadlines first, and tasks with later deadlines later. Furthermore, it can adjust the schedule layout based on submission deadlines. This allows for efficient prioritization of tasks based on their submission timing.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The extraction unit extracts tasks from each tool. For example, it uses AI to extract task-related messages from the tools. The extraction unit identifies request messages such as "Please do this," "Get this done by tomorrow!", and "Check this out if you have time!", as well as messages that include deadlines such as "Task deadline: Month / Day" and "Response deadline: Month / Day". Step 2: The generation unit converts the tasks extracted by the extraction unit into a schedule. For example, using generation AI, the extracted tasks are converted into a simple and visually easy-to-understand schedule. The generation unit displays the task details, requester, priority, and deadline in a way that can be seen at a glance. Step 3: The provider unit provides the schedule table generated by the generator unit. For example, the generated schedule table is provided via the web or an app. The provider unit also allows users to verify the source of the information by clicking on the schedule table.

[0067] (Example of form 2) The Thin Schedule System according to an embodiment of the present invention is an AI tool that aggregates and centrally manages tasks from multiple communication tools. This Thin Schedule System uses AI to extract messages such as "task request," "response request," and "deadline" from various tools such as communication tools, email, and Asana. Next, it converts the extracted tasks into a simple and visually easy-to-understand schedule and provides it to the user. This schedule can be viewed on the web or in an app. For example, the AI ​​extracts tasks from each tool. In this process, the AI ​​identifies request-type messages such as "Please do this," "Get this done by tomorrow!", and "Check this if you have time!", as well as messages containing deadlines such as "Task deadline: Month Day" and "Response deadline: Month Day." For example, it extracts task-related messages from the tools. Next, an image generation AI converts the extracted tasks into a schedule. The schedule is provided in a simple and visually easy-to-understand format. For example, the task content, requester, priority, and response deadline are displayed so that they can be seen at a glance. It is also possible to check the source of the information by clicking on the task. This mechanism allows for centralized management of tasks from multiple tools and prevents tasks from being missed. The target users are salaried employees of large companies who are proficient in using multiple tools, and people who are overwhelmed by information and unable to concentrate on their core work. This allows for efficient task management even in environments where task management tends to become complicated. In this way, by centrally managing tasks from various tools and providing them in a visually easy-to-understand schedule, it prevents tasks from being overlooked and enables efficient task management. Thus, the Shin Schedule System can centrally manage tasks from multiple tools and provide a visually easy-to-understand schedule.

[0068] The Shin Schedule System according to this embodiment comprises an extraction unit, a generation unit, and a provision unit. The extraction unit extracts tasks from each tool. The extraction unit extracts task-related messages from the tools, for example, using AI. The extraction unit identifies request messages such as "Please do this," "Get this done by tomorrow!", and "Check this out if you have time!", as well as messages containing deadlines such as "Task deadline: Month / Day" and "Response deadline: Month / Day." The generation unit converts the tasks extracted by the extraction unit into a schedule table. The generation unit converts the extracted tasks into a simple and visually easy-to-understand schedule table, for example, using generation AI. The generation unit displays, for example, the task content, requester, priority, and response deadline so that they can be seen at a glance. The provision unit provides the schedule table generated by the generation unit. The provision unit provides the generated schedule table via the web or an app, for example. The provision unit also allows users to check the source of the information by clicking on the schedule table. As a result, the thin scheduling system according to this embodiment can centrally manage tasks from multiple tools and provide a visually easy-to-understand schedule.

[0069] The extraction unit extracts tasks from each tool. For example, the extraction unit uses AI to extract task-related messages from the tools. Specifically, the AI ​​uses natural language processing technology to analyze messages within these tools and identify keywords and phrases related to the tasks. For example, it identifies request-type messages such as "Please do this," "Get this done by tomorrow!", and "Check this out if you have time!", as well as messages containing deadlines such as "Task deadline: Month / Day" and "Response deadline: Month / Day". The AI ​​judges the context of the message, the urgency and importance of the request, and extracts it as a task. Furthermore, the AI ​​analyzes metadata such as the sender, recipient, and date and time of sending of the message, and adds it as attribute information for the task. This allows the extraction unit to efficiently extract tasks from each tool and accurately understand the content and attribute information of the tasks. The extracted tasks are stored in a central database and made accessible to the generation and provision units. The extraction unit also periodically scans each tool, and if new tasks arise, it extracts them immediately to maintain up-to-date information for the entire system. This allows the extraction unit to centrally manage tasks from multiple tools, improving the overall efficiency and accuracy of the system.

[0070] The generation unit converts the tasks extracted by the extraction unit into a schedule. The generation unit, for example, uses a generation AI to convert the extracted tasks into a simple and visually easy-to-understand schedule. Specifically, the generation AI places tasks into appropriate time slots based on information such as task content, requester, priority, and deadline, generating a visually organized schedule. The generation AI considers the priority and urgency of tasks, placing important tasks in prominent positions. It also displays the task requester and related project information, allowing the user to understand the background and relationships of tasks at a glance. Furthermore, the generation AI can learn the user's past schedules and work patterns to suggest an optimal schedule. For example, it analyzes when the user typically has the highest concentration and which tasks they spend the most time on, and adjusts the schedule accordingly. This allows the generation unit to provide a schedule that maximizes the user's work efficiency. The generated schedule is designed to be visually easy to understand and intuitive for the user. This enables the generation unit to effectively manage the extracted tasks and improve the user's work efficiency.

[0071] The service provider provides the schedule table generated by the generation service provider. For example, the service provider provides the generated schedule table via the web or an app. Specifically, the service provider provides a user-friendly interface, allowing users to view and edit the schedule table in real time. Users can access the schedule table through a web browser or dedicated app to view task details and update task progress. The service provider enables clickable actions for each task in the schedule table, allowing users to see the source of the task's information. For example, it displays links to the task requester's message and related documents, enabling users to quickly access the information they need. Furthermore, the service provider collects user feedback and continuously improves the usability and functionality of the schedule table. For example, it implements a feedback function that allows users to provide opinions on the layout and display method of the schedule table, and customizes it according to user needs. In addition, the service provider stores the schedule table data in the cloud, making it accessible from multiple devices. This allows users to view and edit the schedule table from any device, such as a desktop PC, smartphone, or tablet. This enables the service provider to provide users with flexible and convenient schedule management, supporting the efficient execution of tasks.

[0072] The display unit can show task details, requesters, priority, and deadlines. For example, it can display task details and required resources. It can also display requesters such as project managers and clients. The display unit can show priority levels based on classification criteria such as high, medium, and low. It can also display deadlines such as project deadlines and task start dates. This allows for a visually clear and easy-to-understand display of task details.

[0073] The verification section allows you to check the source of information by clicking on the task. The verification section displays information sources such as emails, chat messages, and documents. For example, the verification section makes it easy to check the source of information for a task. This makes it easy to check the source of information for a task.

[0074] The extraction unit can extract task-related messages from tools. The extraction unit clarifies the specific types and scope of tools, such as project management tools and communication tools. This allows for the efficient extraction of task-related messages from multiple tools.

[0075] The generation unit can convert the extracted tasks into a simple and visually easy-to-understand schedule. The generation unit clarifies specific criteria and methods, such as the use of color coding and icons. This allows the tasks to be transformed into a visually clear schedule.

[0076] The service provider can provide the generated schedule via the web or an app. The service provider will specify the exact delivery method and platform, such as a web browser or mobile app. This will enable the generated schedule to be provided via the web or an app.

[0077] The extraction unit can estimate the user's emotions and adjust the timing of task extraction based on the estimated emotions. For example, if the user is stressed, the AI ​​will delay task extraction and perform it when the user is relaxed. For example, if the user is focused, the AI ​​will immediately extract a task and notify the user. For example, if the user is tired, the AI ​​will refrain from extracting a task and perform it after the user has rested. This allows the timing of task extraction to be adjusted 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.

[0078] The extraction unit can analyze the user's past task history and select the optimal extraction method when extracting tasks from each tool. For example, the extraction unit can prioritize extracting tasks from tools that the user has frequently used in the past. For example, the extraction unit can prioritize extracting high-priority tasks based on the user's past task history. For example, the extraction unit can analyze the user's past task history and extract tasks based on specific patterns. In this way, the optimal extraction method can be selected by analyzing the user's past task history.

[0079] The extraction unit can filter tasks based on the user's current projects and areas of interest during task extraction. For example, the extraction unit can prioritize tasks related to projects the user is currently working on. It can also extract relevant tasks based on the user's areas of interest. Furthermore, it can filter tasks based on the project priorities set by the user. This allows tasks to be filtered based on the user's current projects and areas of interest.

[0080] The extraction unit can estimate the user's emotions and determine the priority of tasks to extract based on the estimated emotions. For example, if the user is stressed, the extraction unit will postpone less important tasks. For example, if the user is relaxed, the extraction unit will prioritize extracting more important tasks. For example, if the user is in a hurry, the extraction unit will prioritize extracting tasks with approaching deadlines. This allows for the prioritization of tasks 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.

[0081] The extraction unit can prioritize the extraction of highly relevant tasks by considering the user's geographical location information during task extraction. For example, if the user is in a specific location, the extraction unit will prioritize tasks related to that location. For example, the extraction unit will prioritize tasks that can be performed in locations close to the user's current location. For example, if the user is on the move, the extraction unit will prioritize tasks related to their destination. This allows the extraction of highly relevant tasks to be prioritized by considering the user's geographical location information.

[0082] The extraction unit can analyze a user's social media activity and extract relevant tasks during task extraction. For example, the extraction unit can extract tasks mentioned by the user on social media. For example, the extraction unit can extract tasks based on relevant events or projects from the user's social media activity. For example, the extraction unit can analyze the content of a user's social media posts and extract relevant tasks. In this way, relevant tasks can be extracted by analyzing the user's social media activity.

[0083] The generation unit can estimate the user's emotions and adjust the presentation of the schedule based on the estimated emotions. For example, if the user is stressed, the generation unit will generate a simple and visually easy-to-understand schedule. For example, if the user is relaxed, the generation unit will generate a schedule with detailed information. For example, if the user is in a hurry, the generation unit will generate a schedule that highlights important tasks. This allows the presentation of the schedule to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The generation unit can adjust the level of detail in the schedule based on the importance of the tasks when generating the schedule. For example, it can display high-importance tasks in detail and simplify low-importance tasks. The generation unit can also adjust the layout of the schedule based on the priority of the tasks. For example, it can place high-importance tasks in prominent positions and low-importance tasks in less prominent positions. This allows the level of detail in the schedule to be adjusted based on the importance of the tasks.

[0085] The generation unit can apply different generation algorithms depending on the task category when generating a schedule. For example, the generation unit can apply a project management generation algorithm to project tasks. For example, it can apply a simple generation algorithm to routine tasks. For example, it can apply a generation algorithm that allows for quick response to urgent tasks. This allows different generation algorithms to be applied depending on the task category.

[0086] The generation unit can estimate the user's emotions and adjust the length of the schedule based on the estimated emotions. For example, if the user is stressed, the generation unit will generate a short, concise schedule. If the user is relaxed, for example, the generation unit will generate a longer schedule with detailed explanations. If the user is in a hurry, for example, the generation unit will generate a short schedule that highlights important tasks. This allows the length of the schedule to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The generation unit can determine the priority of tasks in the schedule based on their submission dates when generating the schedule. For example, the generation unit can prioritize tasks with approaching deadlines. For example, the generation unit can display tasks with later deadlines at a later date. For example, the generation unit can adjust the layout of the schedule based on the submission deadlines. This allows the system to determine the priority of tasks in the schedule based on their submission dates.

[0088] The generation unit can adjust the order of tasks in the schedule table based on their relevance when generating the schedule table. For example, the generation unit can group together highly related tasks. For example, the generation unit can distribute and display less related tasks. For example, the generation unit adjusts the order of tasks in the schedule table based on their relevance. This allows the order of tasks in the schedule table to be adjusted based on their relevance.

[0089] The service provider can estimate the user's emotions and adjust how the schedule is presented based on the estimated emotions. For example, if the user is stressed, the service provider will present the schedule with a simple interface. For example, if the user is relaxed, the service provider will present the schedule with an interface that includes detailed information. For example, if the user is in a hurry, the service provider will present the schedule with an interface that highlights important tasks. This allows the service provider to adjust how the schedule is presented 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.

[0090] The service provider can select the optimal delivery method when providing a schedule by referring to the user's past usage history. For example, the service provider can prioritize delivery methods that the user has frequently used in the past. For example, the service provider can propose the optimal delivery method based on the user's past usage history. For example, the service provider can analyze the user's past usage history and select a delivery method based on specific patterns. In this way, the optimal delivery method can be selected by referring to the user's past usage history.

[0091] The service provider can select the optimal delivery method when providing a schedule, taking into account the user's device information. For example, if the user is using a smartphone, the service provider will select a delivery method optimized for smartphones. For example, if the user is using a tablet, the service provider will select a delivery method optimized for tablets. For example, if the user is using a desktop, the service provider will select a delivery method optimized for desktops. In this way, the service provider can select the optimal delivery method by taking into account the user's device information.

[0092] The service provider can estimate the user's emotions and adjust the frequency of schedule delivery based on the estimated emotions. For example, if the user is stressed, the service provider will reduce the frequency of delivery and provide only important tasks. For example, if the user is relaxed, the service provider will increase the frequency of delivery and provide more detailed information. For example, if the user is in a hurry, the service provider will quickly deliver important tasks. This allows the service provider to adjust the frequency of schedule delivery according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The service provider can select the optimal delivery method when providing a schedule, taking into account the user's geographical location. For example, if the user is in a specific location, the service provider will prioritize providing tasks related to that location. For example, if the service provider is providing tasks that can be performed in locations close to the user's current location, the service provider will prioritize providing tasks related to the user's destination if the user is on the move. In this way, the service provider can select the optimal delivery method by taking into account the user's geographical location.

[0094] The service provider can analyze users' social media activity and adjust the delivery method when providing schedules. For example, the service provider can prioritize tasks mentioned by users on social media. For example, the service provider can provide tasks based on relevant events and projects from users' social media activity. For example, the service provider can analyze the content of users' social media posts and provide relevant tasks. In this way, the service provider can adjust the optimal delivery method by analyzing users' social media activity.

[0095] The display unit can estimate the user's emotions and adjust the displayed content based on the estimated emotions. For example, if the user is stressed, the display unit provides simple and visually easy-to-understand content. For example, if the user is relaxed, the display unit provides content that includes detailed information. For example, if the user is in a hurry, the display unit provides content that highlights important tasks. This allows the display content to be adjusted 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.

[0096] The display unit can adjust the level of detail based on the importance of the tasks when displaying them. For example, the display unit can display high-importance tasks in detail and lower-importance tasks in a simplified manner. The display unit can also adjust the display layout based on the priority of the tasks. For example, the display unit can place high-importance tasks in a prominent position and lower-importance tasks in a less prominent position. This allows the level of detail of the display to be adjusted based on the importance of the tasks.

[0097] The display unit can apply different display methods depending on the task category. For example, it can apply a project management display method to project tasks, a simple display method to daily tasks, and a display method that allows for quick response to urgent tasks. This allows for the application of different display methods depending on the task category.

[0098] The display unit can estimate the user's emotions and adjust the display order based on the estimated emotions. For example, if the user is stressed, the display unit will postpone less important tasks. For example, if the user is relaxed, the display unit will prioritize displaying more important tasks. For example, if the user is in a hurry, the display unit will prioritize displaying tasks with approaching deadlines. This allows the display order to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The display unit can determine the display priority based on the task submission date when displaying tasks. For example, the display unit may prioritize displaying tasks with approaching deadlines. For example, the display unit may postpone displaying tasks with later deadlines. For example, the display unit may adjust the display layout based on the submission deadline. This allows the display priority to be determined based on the task submission date.

[0100] The display unit can adjust the display order based on the relevance of tasks during display. For example, the display unit can group highly relevant tasks together. For example, the display unit can display less relevant tasks in a more dispersed manner. The display unit adjusts the display order based on the relevance of tasks. This allows the display order to be adjusted based on the relevance of tasks.

[0101] The confirmation unit can estimate the user's emotions and adjust the confirmation method based on the estimated emotions. For example, if the user is stressed, the confirmation unit provides a simple and visually easy-to-understand confirmation method. For example, if the user is relaxed, the confirmation unit provides a confirmation method that includes detailed information. For example, if the user is in a hurry, the confirmation unit provides a confirmation method that highlights important tasks. This allows the confirmation method to be adjusted 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.

[0102] The verification unit can adjust the level of detail in the verification process based on the importance of the task. For example, the verification unit can verify high-importance tasks in detail and simplify the verification of low-importance tasks. The verification unit can also adjust the layout of the verification process based on the priority of the tasks. For example, the verification unit can place high-importance tasks in a prominent position and low-importance tasks in a less prominent position. This allows the level of detail in the verification process to be adjusted based on the importance of the task.

[0103] The verification unit can apply different verification methods depending on the task category during verification. For example, the verification unit can apply a project management verification method to project tasks. For example, the verification unit can apply a simple verification method to routine tasks. For example, the verification unit can apply a verification method that allows for quick response to urgent tasks. This allows for the application of different verification methods depending on the task category.

[0104] The verification unit can estimate the user's emotions and determine the priority of verification based on the estimated emotions. For example, if the user is stressed, the verification unit will postpone less important tasks. For example, if the user is relaxed, the verification unit will prioritize checking high-importance tasks. For example, if the user is in a hurry, the verification unit will prioritize checking tasks with approaching deadlines. This allows the verification unit to determine the priority of verification 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.

[0105] The verification unit can determine the priority of tasks based on their submission dates during the verification process. For example, the verification unit will prioritize tasks with approaching deadlines. For example, the verification unit will postpone the verification of tasks with later deadlines. For example, the verification unit will adjust the layout of the verification process based on the submission deadlines. This allows the verification unit to determine the priority of tasks based on their submission dates.

[0106] The verification unit can adjust the order of verification based on the relevance of the tasks. For example, the verification unit might group together highly relevant tasks for verification. For example, the verification unit might distribute and verify less relevant tasks. The verification unit adjusts the order of verification based on the relevance of the tasks. This allows the order of verification to be adjusted based on the relevance of the tasks.

[0107] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0108] The Shin Schedule System can estimate a user's emotions and dynamically change task priorities based on those emotions. For example, if a user is stressed, the system will postpone less important tasks, while if they are relaxed, it will prioritize and display more important tasks. It can also highlight tasks with approaching deadlines if the user is in a hurry. This allows for flexible adjustment of task priorities according to the user's emotions.

[0109] The Shin Schedule System can analyze a user's past task history and select the optimal task extraction method. For example, it can prioritize tasks extracted from tools the user has frequently used in the past. It can also prioritize tasks of high importance based on the user's past task history. Furthermore, it can extract tasks based on specific patterns. In this way, it can leverage the user's past task history to provide the optimal task extraction method.

[0110] The Shin Schedule System can prioritize and extract highly relevant tasks by considering the user's geographical location. For example, if a user is in a specific location, it can prioritize tasks related to that location. It can also prioritize tasks that can be performed in locations close to the user's current location. Furthermore, if a user is on the move, it can extract tasks related to their destination. This allows for the efficient extraction of highly relevant tasks by considering the user's geographical location.

[0111] The Shin Schedule System can analyze a user's social media activity and extract relevant tasks. For example, it can extract tasks that a user has mentioned on social media. It can also extract tasks based on relevant events and projects from a user's social media activity. Furthermore, it can analyze the content of a user's social media posts and extract relevant tasks. This allows for the efficient extraction of relevant tasks by leveraging a user's social media activity.

[0112] The Shin Schedule System can estimate the user's emotions and adjust the way the schedule is presented based on those emotions. For example, if the user is stressed, it can generate a simple and visually easy-to-understand schedule. If the user is relaxed, it can generate a schedule with more detailed information. Furthermore, if the user is in a hurry, it can generate a schedule that highlights important tasks. This allows for flexible adjustment of the schedule's presentation according to the user's emotions.

[0113] The Shin Schedule System allows you to adjust the level of detail in your schedule based on the importance of tasks. For example, you can display high-priority tasks in detail and lower-priority tasks in a simplified format. You can also adjust the layout of your schedule based on task priority. Furthermore, you can place high-priority tasks in prominent positions and lower-priority tasks in less prominent positions. This allows you to efficiently adjust the level of detail in your schedule based on the importance of tasks.

[0114] The Shin Schedule System can apply different generation algorithms depending on the task category. For example, project tasks can be assigned a generation algorithm optimized for project management. Routine tasks can be assigned a simpler generation algorithm. Furthermore, urgent tasks can be assigned a generation algorithm designed for rapid response. This allows for the application of the most suitable generation algorithm for each task category.

[0115] The Shin Schedule System can estimate the user's emotions and adjust the length of the schedule based on those emotions. For example, if the user is stressed, it can generate a short, to-the-point schedule. If the user is relaxed, it can generate a longer schedule with detailed explanations. Furthermore, if the user is in a hurry, it can generate a short schedule that highlights important tasks. This allows for flexible adjustment of schedule length according to the user's emotions.

[0116] The Shin Schedule System can prioritize tasks based on their submission deadlines. For example, it can display tasks with approaching deadlines first, and tasks with later deadlines later. Furthermore, it can adjust the schedule layout based on submission deadlines. This allows for efficient prioritization of tasks based on their submission timing.

[0117] The Shin Schedule System can estimate the user's emotions and adjust how the schedule is presented based on those emotions. For example, if the user is stressed, the schedule can be presented with a simple interface. If the user is relaxed, the schedule can be presented with an interface containing detailed information. Furthermore, if the user is in a hurry, the schedule can be presented with an interface that highlights important tasks. This allows for flexible adjustment of how the schedule is presented according to the user's emotions.

[0118] The following briefly describes the processing flow for example form 2.

[0119] Step 1: The extraction unit extracts tasks from each tool. For example, it uses AI to extract task-related messages from the tools. The extraction unit identifies request messages such as "Please do this," "Get this done by tomorrow!", and "Check this out if you have time!", as well as messages that include deadlines such as "Task deadline: Month / Day" and "Response deadline: Month / Day". Step 2: The generation unit converts the tasks extracted by the extraction unit into a schedule. For example, using generation AI, the extracted tasks are converted into a simple and visually easy-to-understand schedule. The generation unit displays the task details, requester, priority, and deadline in a way that can be seen at a glance. Step 3: The provider unit provides the schedule table generated by the generator unit. For example, the generated schedule table is provided via the web or an app. The provider unit also allows users to verify the source of the information by clicking on the schedule table.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] Each of the multiple elements described above, including the extraction unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the extraction unit is implemented by the processor 46 of the smart device 14 and extracts task-related messages from the tool. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and converts the extracted tasks into a simple and visually easy-to-understand schedule. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the generated schedule via the web or an app. 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.

[0124] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] Each of the multiple elements described above, including the extraction unit, generation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the extraction unit is implemented by the processor 46 of the smart glasses 214 and extracts task-related messages from the tool. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and converts the extracted tasks into a simple and visually easy-to-understand schedule. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the generated schedule via the web or an app. 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.

[0140] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] Each of the multiple elements described above, including the extraction unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the extraction unit is implemented by the processor 46 of the headset terminal 314 and extracts task-related messages from the tool. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and converts the extracted tasks into a simple and visually easy-to-understand schedule table. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the generated schedule table via the web or an app. 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.

[0156] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] Each of the multiple elements, including the extraction unit, generation unit, and provision unit described above, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the extraction unit is implemented by the processor 46 of the robot 414 and extracts task-related messages from the tool. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and converts the extracted tasks into a simple and visually easy-to-understand schedule. The provision unit is implemented by the control unit 46A of the robot 414 and provides the generated schedule via the web or an app. 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] (Note 1) An extraction unit that extracts tasks from each tool, A generation unit that converts the tasks extracted by the extraction unit into a schedule table, The system includes a providing unit that provides the schedule table generated by the generation unit. A system characterized by the following features. (Note 2) It features a display unit that shows the task details, requester, priority, and deadline. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a verification section where you can check the source of information by clicking on the task. The system described in Appendix 1, characterized by the features described herein. (Note 4) The extraction unit is Extract task-related messages from the tool. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Convert the extracted tasks into a simple and visually easy-to-understand schedule. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Provide the generated schedule via web or app. The system described in Appendix 1, characterized by the features described herein. (Note 7) The extraction unit is It estimates the user's emotions and adjusts the timing of task extraction based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The extraction unit is When extracting tasks from each tool, the system analyzes the user's past task history to select the optimal extraction method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The extraction unit is When extracting tasks, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The extraction unit is Estimate the user's emotions and determine the priority of tasks to extract based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The extraction unit is When extracting tasks, the system prioritizes extracting highly relevant tasks by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The extraction unit is During task extraction, the system analyzes the user's social media activity and extracts relevant tasks. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is The system estimates the user's emotions and adjusts the way the schedule is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating a schedule, adjust the level of detail in the schedule based on the importance of the tasks. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating the schedule, different generation algorithms are applied depending on the task category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the length of the schedule based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating a schedule, the priority of tasks is determined based on their submission dates. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating a schedule, adjust the order of tasks based on their relevance. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, The system estimates the user's emotions and adjusts how the schedule is presented based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing the schedule, the optimal delivery method is selected by referring to the user's past usage history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing the schedule, the optimal delivery method will be selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the frequency of schedule updates based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing the schedule, the optimal delivery method will be selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing schedules, we analyze users' social media activity and adjust the delivery method accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned display unit is It estimates the user's emotions and adjusts the displayed content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned display unit is When displaying, 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 27) The aforementioned display unit is When displaying, apply different display methods depending on the task category. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned display unit is It estimates the user's emotions and adjusts the display order based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned display unit is When displaying tasks, the display priority is determined based on when the tasks were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned display unit is When displaying tasks, adjust the display order based on their relevance. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned verification unit is We estimate the user's emotions and adjust the confirmation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned verification unit is During the review process, 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 33) The aforementioned verification unit is During verification, different verification methods are applied depending on the task category. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned verification unit is The system estimates the user's emotions and determines the priority of confirmations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned verification unit is During the review process, we will prioritize the review based on the task submission date. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned verification unit is During the review process, adjust the order of review based on the relevance of the tasks. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0192] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An extraction unit that extracts tasks from each tool, A generation unit that converts the tasks extracted by the extraction unit into a schedule table, The system includes a providing unit that provides the schedule table generated by the generation unit. A system characterized by the following features.

2. It features a display unit that shows the task details, requester, priority, and deadline. The system according to feature 1.

3. It includes a verification section where you can check the source of information by clicking on the task. The system according to feature 1.

4. The generating unit is Convert the extracted tasks into a simple and visually easy-to-understand schedule. The system according to feature 1.

5. The aforementioned supply unit is, Provide the generated schedule via web or app. The system according to feature 1.

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

7. The extraction unit is When extracting tasks from each tool, the system analyzes the user's past task history to select the optimal extraction method. The system according to feature 1.

8. The extraction unit is When extracting tasks, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

9. The extraction unit is Estimate the user's emotions and determine the priority of tasks to extract based on the estimated user emotions. The system according to feature 1.

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