System

Abstract

An object of the system according to the embodiment is to automatically extract a task from a task management tool, efficiently prepare materials, and create a draft of an email.SOLUTION: A system according to an embodiment includes an extraction unit, an analysis unit, a search unit, a creation unit, and a notification unit. The extraction unit extracts a task from a calendar or a spreadsheet, or a label of a mail. The analysis unit analyzes the content of the task extracted by the extraction unit. The search unit searches for the material based on the task analyzed by the analysis unit. The creation part creates a draft of the mail based on the material retrieved by the retrieval part. The notification unit notifies that the draft of the mail created by the creation unit is completed.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, the process of extracting tasks from a task management tool, preparing materials based on those tasks, and drafting emails was done manually, which was inefficient.

[0005] The system according to the embodiment aims to automatically extract tasks from a task management tool, efficiently prepare materials, and create email drafts. [Means for solving the problem]

[0006] The system according to the embodiment includes an extraction unit, an analysis unit, a search unit, a creation unit, and a notification unit. The extraction unit extracts tasks from a calendar, a spreadsheet, or an email label. The analysis unit analyzes the content of the tasks extracted by the extraction unit. The search unit searches for materials based on the tasks analyzed by the analysis unit. The creation unit creates a draft of the email based on the materials searched by the search unit. The notification unit notifies the user that the draft of the email created by the creation unit has been completed. [Effects of the Invention]

[0007] The system according to the embodiment can automatically extract tasks from a task management tool, efficiently prepare materials, and create email drafts. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A task management system according to an embodiment of the present invention extracts tasks from calendars, spreadsheets, and email labels and automatically prepares them using a generation AI. Based on tasks entered by a user, the generation AI searches for materials, drafts emails, and notifies the user when preparation is complete. For example, if a user enters "Task: Send materials for Service C to Person B of Company A" into the calendar, the generation AI automatically prepares materials related to Service C and creates a draft email to send. Once preparation is complete, a notification is sent to the user. This allows the task management system to significantly reduce the time users spend preparing tasks and efficiently advance their work. For example, if a user enters "Task: Send materials for Service C to Person B of Company A" into the calendar, the generation AI automatically prepares materials related to Service C and creates a draft email to send. Once preparation is complete, a notification is sent to the user. This allows users to significantly reduce the time they spend preparing tasks and efficiently advance their work.

[0029] A task management system according to an embodiment includes an extraction unit, an analysis unit, a search unit, a creation unit, and a notification unit. The extraction unit extracts tasks from a calendar, a spreadsheet, or an email label. For example, the extraction unit extracts entries containing keywords such as "task:" or "ToDo:" from the calendar, spreadsheet, or email label. The extraction unit can also analyze a user's past task history and select an optimal extraction method. The analysis unit analyzes the content of the tasks extracted by the extraction unit. For example, the analysis unit analyzes the content of the tasks using natural language processing technology and identifies keywords related to the tasks. The analysis unit can also estimate a user's emotions and adjust the expression method of the analysis based on the estimated user emotions. The search unit searches for materials based on the tasks analyzed by the analysis unit. For example, the search unit searches for materials based on the identified keywords. The search unit can also estimate a user's emotions and adjust search criteria based on the estimated user emotions. The creation unit creates a draft email based on the materials searched by the search unit. The creation unit, for example, creates a draft email to be sent based on the searched materials. The creation unit can also estimate the user's emotions and adjust the expression method of the email draft based on the estimated user emotions. The notification unit notifies the user that the draft email created by the creation unit has been completed. The notification unit, for example, notifies the user that the draft email has been completed. The notification unit can also estimate the user's emotions and adjust the expression method of the notification based on the estimated user emotions. This enables the task management system according to the embodiment to efficiently extract, analyze, search, create, and notify the user's tasks.

[0030] The extraction unit can extract entries containing keywords such as "task:" or "ToDo:" from a calendar, spreadsheet, or email label. For example, the extraction unit extracts entries containing keywords such as "task:" or "ToDo:" from a calendar, spreadsheet, or email label. The extraction unit can use regular expressions to efficiently extract entries containing specific keywords. The extraction unit can also analyze a user's past task history and select the optimal extraction method. For example, it can prioritize extraction of keywords that the user has frequently used in the past. This allows entries containing specific keywords to be efficiently extracted.

[0031] The analysis unit can analyze the extracted task content and identify keywords related to the task. The analysis unit can analyze the extracted task content using, for example, natural language processing technology and identify keywords related to the task. The analysis unit can extract frequently occurring words and phrases to identify keywords related to the task. The analysis unit can also estimate the user's emotions and adjust the expression method of the analysis based on the estimated user emotions. For example, if the user is nervous, a simple and highly visible expression method can be provided. This improves the accuracy of the analysis by identifying keywords related to the task.

[0032] The search unit may search for materials based on the identified keywords. For example, the search unit may search for materials based on the identified keywords. The search unit may use a search engine or a database query to search for materials. The search unit may also estimate a user's emotions and adjust search criteria based on the estimated user's emotions. For example, if the user is relaxed, the search unit may provide a broader search result. This allows for efficient searching of materials based on the identified keywords.

[0033] The creation unit can create a draft email to be sent based on the searched materials. The creation unit creates a draft email to be sent based on the searched materials, for example. The creation unit can use a template to create the email draft. The creation unit can also estimate the user's emotions and adjust the way the email draft is expressed based on the estimated user's emotions. For example, if the user is relaxed, the creation unit creates a draft email using polite and detailed expressions. This allows the email draft to be created efficiently based on the searched materials.

[0034] The notification unit can notify the user that the email draft is complete. For example, the notification unit notifies the user that the email draft is complete. The notification unit can use a pop-up notification, an email notification, a push notification, or the like as a notification method. The notification unit can also estimate the user's emotions and adjust the notification expression method based on the estimated user emotions. For example, if the user is nervous, the notification is made in a calm expression method. In this way, by notifying the user that the email draft is complete, the user can respond quickly.

[0035] When extracting from a calendar, spreadsheet, or email label, the extraction unit can analyze the user's past task history and select an appropriate extraction method. For example, the extraction unit can prioritize extracting keywords that the user has frequently used in the past. The extraction unit can also prioritize extracting tasks related to a specific time period from the user's past task history. Furthermore, the extraction unit can analyze the user's past task history and select the most efficient extraction method. This makes it possible to select the optimal extraction method by analyzing the user's past task history.

[0036] The extraction unit can perform filtering based on the user's current project or area of ​​interest during extraction. For example, the extraction unit preferentially extracts tasks related to the user's current project. The extraction unit can also filter and extract related tasks based on the user's area of ​​interest. Furthermore, the extraction unit can extract appropriate tasks according to the progress of the user's project. In this way, highly relevant tasks can be extracted by filtering based on the user's current project or area of ​​interest.

[0037] The extraction unit can select the optimal extraction means depending on the user's input method during extraction. For example, if the user uses voice input, the extraction unit extracts tasks using voice recognition technology. If the user uses text input, the extraction unit can also extract tasks using text analysis technology. Furthermore, if the user uses image input, the extraction unit can also extract tasks using image recognition technology. This allows tasks to be extracted efficiently by selecting the optimal extraction means depending on the user's input method.

[0038] During extraction, the extraction unit can prioritize extracting highly relevant tasks by taking into account the user's geographical location information. For example, the extraction unit prioritizes extracting tasks that the user will perform in a location close to their current location. The extraction unit can also filter and extract relevant tasks based on the user's geographical location information. Furthermore, if the user is in a specific location, the extraction unit can also prioritize extracting tasks related to that location. In this way, by taking into account the user's geographical location information, highly relevant tasks can be preferentially extracted.

[0039] During extraction, the extraction unit can analyze the user's social media activities and extract related tasks. For example, the extraction unit preferentially extracts tasks mentioned by the user on social media. The extraction unit can also analyze the user's social media activities and extract related tasks. Furthermore, the extraction unit can extract related tasks by referring to the activities of the user's friends on social media. In this way, related tasks can be efficiently extracted by analyzing the user's social media activities.

[0040] The extraction unit can customize the extraction method by reflecting the user's past feedback during extraction. The extraction unit adjusts the extraction method based on, for example, feedback provided by the user in the past. The extraction unit can also select the optimal extraction method based on the user's past feedback. Furthermore, the extraction unit can also customize the extraction method by reflecting the user's feedback. In this way, the extraction method can be optimized by reflecting the user's past feedback.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the task. For example, the analysis unit performs a detailed analysis on a task with a high level of importance. The analysis unit can also perform a simplified analysis on a task with a low level of importance. Furthermore, the analysis unit can select an appropriate level of detail of the analysis depending on the importance of the task. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the task.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the task category. For example, the analysis unit applies a business-oriented analysis algorithm to a business task. The analysis unit can also apply a personal analysis algorithm to a personal task. Furthermore, the analysis unit can select the optimal analysis algorithm depending on the task category. This improves the accuracy of the analysis by applying the optimal analysis algorithm depending on the task category.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit improves the accuracy of the analysis, for example, based on the user's past analysis results. The analysis unit can also select the optimal analysis method from the user's past analysis results. Furthermore, the analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the submission time of the tasks. For example, the analysis unit prioritizes analysis of tasks with upcoming submission deadlines. The analysis unit can also postpone analysis of tasks with more distant submission deadlines. Furthermore, the analysis unit can also determine the priority of analysis based on the submission time of the tasks. This enables efficient analysis by determining the priority of analysis based on the submission time of the tasks.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the tasks. For example, the analysis unit prioritizes analysis of highly relevant tasks. The analysis unit can also postpone analysis of less relevant tasks. Furthermore, the analysis unit can also adjust the order of analysis based on the relevance of the tasks. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the tasks.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit performs an analysis that makes heavy use of technical terms. Also, if the user does not have technical expertise, the analysis unit can perform an analysis that avoids technical terms. Furthermore, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. This makes it possible to provide more appropriate analysis results by adjusting the use of technical terms in the analysis according to the user's level of expertise.

[0047] The search unit can improve the accuracy of the search by taking into account the interrelationships between tasks during a search. For example, the search unit analyzes the interrelationships between tasks and provides relevant search results. The search unit can also improve the accuracy of the search results based on the interrelationships between tasks. Furthermore, the search unit can provide optimal search results by taking into account the interrelationships between tasks. In this way, the accuracy of the search is improved by taking into account the interrelationships between tasks.

[0048] The search unit can perform a search while taking into account the attribute information of the task submitter. The search unit can provide optimal search results, for example, depending on the expertise level of the task submitter. The search unit can also provide relevant search results by referring to the past search history of the task submitter. Furthermore, the search unit can customize the search results based on the attribute information of the task submitter. This allows for more appropriate search results to be provided by taking into account the attribute information of the task submitter.

[0049] During a search, the search unit can weight the search based on the frequency of task submission. For example, the search unit prioritizes searching for tasks that are submitted more frequently. The search unit can also search for tasks that are submitted less frequently later. Furthermore, the search unit can weight search results based on the frequency of task submission. Thus, weighting the search based on the frequency of task submission enables efficient searches.

[0050] The search unit can perform a search while taking into account the geographic distribution of tasks. For example, the search unit preferentially searches for tasks related to locations close to the user's current location. The search unit can also provide optimal search results based on the geographic distribution of tasks. Furthermore, if the user is in a specific area, the search unit can also preferentially search for tasks related to that area. This allows for more appropriate search results to be provided by taking into account the geographic distribution of tasks.

[0051] The search unit can improve the accuracy of the search by referring to literature related to the task during the search. For example, the search unit refers to literature related to the task and provides optimal search results. The search unit can also improve the accuracy of the search results based on the literature related to the task. Furthermore, the search unit can provide optimal search results taking into account the literature related to the task. In this way, the accuracy of the search is improved by referring to literature related to the task.

[0052] The search unit can perform a search while taking into account the market value of the task. For example, the search unit may search for tasks with high market value first. The search unit can also search for tasks with low market value later. Furthermore, the search unit can customize search results based on the market value of the task. This allows for more appropriate search results to be provided by taking the market value of the task into consideration.

[0053] When creating a draft of an email, the creation unit can adjust the level of detail of the draft based on the importance of the task. For example, the creation unit creates a detailed draft of an email for a task with a high level of importance. The creation unit can also create a simplified draft of an email for a task with a low level of importance. Furthermore, the creation unit can also create a draft of an email with an appropriate level of detail depending on the importance of the task. This allows for efficient drafting of emails by adjusting the level of detail of the draft based on the importance of the task.

[0054] The creation unit can apply different templates depending on the task category when creating a draft of an email. For example, the creation unit applies a business template to a business task. The creation unit can also apply a personal template to a personal task. Furthermore, the creation unit can select the optimal template depending on the task category. This allows for efficient drafting of emails by applying the optimal template depending on the task category.

[0055] When creating a draft of an email, the creation unit can improve the accuracy of the draft by referring to the user's past draft results. The creation unit improves the accuracy of the draft, for example, based on the user's past draft results. The creation unit can also select an optimal drafting method from the user's past draft results. Furthermore, the creation unit can also improve the accuracy of the draft by referring to the user's past draft results. In this way, the accuracy of the draft is improved by referring to the user's past draft results.

[0056] When creating email drafts, the creation unit can determine the priority of the drafts based on the submission time of the tasks. For example, the creation unit prioritizes creating email drafts for tasks with upcoming submission deadlines. The creation unit can also postpone creating email drafts for tasks with more distant submission deadlines. Furthermore, the creation unit can also determine the priority of email drafts based on the submission time of the tasks. This allows for efficient email drafting by determining the priority of drafts based on the submission time of the tasks.

[0057] The creation unit can adjust the order of the email drafts based on the relevance of the tasks when creating email drafts. For example, the creation unit prioritizes creating email drafts for highly relevant tasks. The creation unit can also postpone creating email drafts for less relevant tasks. Furthermore, the creation unit can adjust the order of the email drafts based on the relevance of the tasks. This allows for efficient email drafting by adjusting the order of the drafts based on the relevance of the tasks.

[0058] When creating an email draft, the creation unit can adjust the use of technical terms in the draft according to the user's level of expertise. For example, if the user has technical expertise, the creation unit creates an email draft that uses a lot of technical terms. Also, if the user does not have technical expertise, the creation unit can create an email draft that avoids technical terms. Furthermore, the creation unit can adjust the use of technical terms in the email draft according to the user's level of expertise. In this way, by adjusting the use of technical terms in the draft according to the user's level of expertise, a more appropriate email draft can be provided.

[0059] The notification unit can adjust the level of detail of the notification based on the importance of the task when issuing a notification. For example, the notification unit issues a detailed notification for a task with a high level of importance. The notification unit can also issue a simplified notification for a task with a low level of importance. Furthermore, the notification unit can also issue a notification with an appropriate level of detail depending on the importance of the task. This allows for efficient notification by adjusting the level of detail of the notification based on the importance of the task.

[0060] The notification unit can apply different notification methods depending on the task category when sending a notification. For example, the notification unit can apply a business-oriented notification method to a business task. The notification unit can also apply a personal notification method to a personal task. Furthermore, the notification unit can select the optimal notification method depending on the task category. This allows for efficient notification by applying the optimal notification method depending on the task category.

[0061] The notification unit can improve the accuracy of notification by referring to the user's past notification results when making a notification. The notification unit improves the accuracy of notification, for example, based on the user's past notification results. The notification unit can also select the optimal notification method from the user's past notification results. Furthermore, the notification unit can also improve the accuracy of notification by referring to the user's past notification results. In this way, the accuracy of notification is improved by referring to the user's past notification results.

[0062] The notification unit can determine the priority of notifications based on the submission time of the tasks when sending notifications. For example, the notification unit prioritizes notifications for tasks with upcoming submission deadlines. The notification unit can also postpone notifications for tasks with more distant submission deadlines. Furthermore, the notification unit can also determine the priority of notifications based on the submission time of the tasks. This enables efficient notifications by determining the priority of notifications based on the submission time of the tasks.

[0063] The notification unit can adjust the order of notifications based on the relevance of the tasks when notifying. For example, the notification unit can prioritize notifications of highly relevant tasks. The notification unit can also postpone notifications of less relevant tasks. Furthermore, the notification unit can also adjust the order of notifications based on the relevance of the tasks. This allows for efficient notifications by adjusting the order of notifications based on the relevance of the tasks.

[0064] The notification unit can adjust the use of technical terms in the notification according to the user's level of expertise when making a notification. For example, if the user has technical expertise, the notification unit can provide a notification that uses a lot of technical terms. Also, if the user does not have technical expertise, the notification unit can provide a notification that avoids technical terms. Furthermore, the notification unit can adjust the use of technical terms in the notification according to the user's level of expertise. This allows for more appropriate notifications to be provided by adjusting the use of technical terms in the notification according to the user's level of expertise.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The task management system may further include a priority setting unit. The priority setting unit can automatically set task priorities based on the importance and urgency of the tasks entered by the user. For example, tasks tagged as "urgent" by the user are given priority over other tasks. The priority setting unit can also analyze the user's past task completion history and prioritize frequently delayed tasks. Furthermore, the priority setting unit can suggest the optimal timing for task execution, taking into account the available time on the user's calendar. This allows the user to efficiently manage tasks and complete important tasks without missing them.

[0067] A task management system can also include a collaboration section. This section provides functionality that allows multiple users to collaboratively manage tasks. For example, tasks can be shared within a project team, and each member's progress can be checked in real time. The collaboration section also has a function to automatically assign task assignees, selecting the most appropriate assignee based on each member's skill set and past performance. Furthermore, the collaboration section can automatically send reminders according to the progress of tasks, improving the efficiency of the entire team. This allows projects to proceed smoothly and increases the productivity of the entire team.

[0068] The processing flow of the first embodiment will be briefly explained below.

[0069] Step 1: The extraction unit extracts tasks from calendars, spreadsheets, and email labels. For example, it extracts entries containing keywords such as "Task:" or "ToDo:" from calendars, spreadsheets, and email labels. The extraction unit can also analyze the user's past task history and select the optimal extraction method. Step 2: The analysis unit analyzes the content of the tasks extracted by the extraction unit. For example, the analysis unit may use natural language processing technology to analyze the content of the tasks and identify keywords related to the tasks. The analysis unit may also estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. Step 3: The search unit searches for materials based on the task analyzed by the analysis unit. For example, the search unit searches for materials based on the identified keywords. The search unit can also estimate the user's emotions and adjust search criteria based on the estimated user emotions. Step 4: The creation unit creates a draft of an email based on the materials searched by the search unit. For example, the creation unit creates a draft of an email to be sent based on the searched materials. The creation unit can also estimate the user's emotions and adjust the way the email draft is written based on the estimated user's emotions. Step 5: The notification unit notifies the user that the draft of the email created by the creation unit has been completed. For example, the notification unit notifies the user that the draft of the email has been completed. The notification unit can also estimate the user's emotions and adjust the way the notification is expressed based on the estimated user's emotions.

[0070] (Example 2) A task management system according to an embodiment of the present invention extracts tasks from calendars, spreadsheets, and email labels and automatically prepares them using a generation AI. Based on tasks entered by a user, the generation AI searches for materials, drafts emails, and notifies the user when preparation is complete. For example, if a user enters "Task: Send materials for Service C to Person B of Company A" into the calendar, the generation AI automatically prepares materials related to Service C and creates a draft email to send. Once preparation is complete, a notification is sent to the user. This allows the task management system to significantly reduce the time users spend preparing tasks and efficiently advance their work. For example, if a user enters "Task: Send materials for Service C to Person B of Company A" into the calendar, the generation AI automatically prepares materials related to Service C and creates a draft email to send. Once preparation is complete, a notification is sent to the user. This allows users to significantly reduce the time they spend preparing tasks and efficiently advance their work.

[0071] A task management system according to an embodiment includes an extraction unit, an analysis unit, a search unit, a creation unit, and a notification unit. The extraction unit extracts tasks from a calendar, a spreadsheet, or an email label. For example, the extraction unit extracts entries containing keywords such as "task:" or "ToDo:" from the calendar, spreadsheet, or email label. The extraction unit can also analyze a user's past task history and select an optimal extraction method. The analysis unit analyzes the content of the tasks extracted by the extraction unit. For example, the analysis unit analyzes the content of the tasks using natural language processing technology and identifies keywords related to the tasks. The analysis unit can also estimate a user's emotions and adjust the expression method of the analysis based on the estimated user emotions. The search unit searches for materials based on the tasks analyzed by the analysis unit. For example, the search unit searches for materials based on the identified keywords. The search unit can also estimate a user's emotions and adjust search criteria based on the estimated user emotions. The creation unit creates a draft email based on the materials searched by the search unit. The creation unit, for example, creates a draft email to be sent based on the searched materials. The creation unit can also estimate the user's emotions and adjust the expression method of the email draft based on the estimated user emotions. The notification unit notifies the user that the draft email created by the creation unit has been completed. The notification unit, for example, notifies the user that the draft email has been completed. The notification unit can also estimate the user's emotions and adjust the expression method of the notification based on the estimated user emotions. This enables the task management system according to the embodiment to efficiently extract, analyze, search, create, and notify the user's tasks.

[0072] The extraction unit can extract entries containing keywords such as "task:" or "ToDo:" from a calendar, spreadsheet, or email label. For example, the extraction unit extracts entries containing keywords such as "task:" or "ToDo:" from a calendar, spreadsheet, or email label. The extraction unit can use regular expressions to efficiently extract entries containing specific keywords. The extraction unit can also analyze a user's past task history and select the optimal extraction method. For example, it can prioritize extraction of keywords that the user has frequently used in the past. This allows entries containing specific keywords to be efficiently extracted.

[0073] The analysis unit can analyze the extracted task content and identify keywords related to the task. The analysis unit can analyze the extracted task content using, for example, natural language processing technology and identify keywords related to the task. The analysis unit can extract frequently occurring words and phrases to identify keywords related to the task. The analysis unit can also estimate the user's emotions and adjust the expression method of the analysis based on the estimated user emotions. For example, if the user is nervous, a simple and highly visible expression method can be provided. This improves the accuracy of the analysis by identifying keywords related to the task.

[0074] The search unit may search for materials based on the identified keywords. For example, the search unit may search for materials based on the identified keywords. The search unit may use a search engine or a database query to search for materials. The search unit may also estimate a user's emotions and adjust search criteria based on the estimated user's emotions. For example, if the user is relaxed, the search unit may provide a broader search result. This allows for efficient searching of materials based on the identified keywords.

[0075] The creation unit can create a draft email to be sent based on the searched materials. The creation unit creates a draft email to be sent based on the searched materials, for example. The creation unit can use a template to create the email draft. The creation unit can also estimate the user's emotions and adjust the way the email draft is expressed based on the estimated user's emotions. For example, if the user is relaxed, the creation unit creates a draft email using polite and detailed expressions. This allows the email draft to be created efficiently based on the searched materials.

[0076] The notification unit can notify the user that the email draft is complete. For example, the notification unit notifies the user that the email draft is complete. The notification unit can use a pop-up notification, an email notification, a push notification, or the like as a notification method. The notification unit can also estimate the user's emotions and adjust the notification expression method based on the estimated user emotions. For example, if the user is nervous, the notification is made in a calm expression method. In this way, by notifying the user that the email draft is complete, the user can respond quickly.

[0077] The extraction unit can estimate the user's emotions and adjust the timing of task extraction based on the estimated user emotions. For example, if the user is feeling stressed, the extraction unit delays the extraction timing and extracts tasks when the user is relaxed. Furthermore, if the user is relaxed, the extraction unit can immediately extract tasks and quickly start processing them. Furthermore, if the user is in a hurry, the extraction unit can immediately extract tasks and prioritize processing. This allows tasks to be extracted at more appropriate times by adjusting the task extraction timing 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 can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0078] When extracting from a calendar, spreadsheet, or email label, the extraction unit can analyze the user's past task history and select an appropriate extraction method. For example, the extraction unit can prioritize extracting keywords that the user has frequently used in the past. The extraction unit can also prioritize extracting tasks related to a specific time period from the user's past task history. Furthermore, the extraction unit can analyze the user's past task history and select the most efficient extraction method. This makes it possible to select the optimal extraction method by analyzing the user's past task history.

[0079] The extraction unit can perform filtering based on the user's current project or area of ​​interest during extraction. For example, the extraction unit preferentially extracts tasks related to the user's current project. The extraction unit can also filter and extract related tasks based on the user's area of ​​interest. Furthermore, the extraction unit can extract appropriate tasks according to the progress of the user's project. In this way, highly relevant tasks can be extracted by filtering based on the user's current project or area of ​​interest.

[0080] The extraction unit can select the optimal extraction means depending on the user's input method during extraction. For example, if the user uses voice input, the extraction unit extracts tasks using voice recognition technology. If the user uses text input, the extraction unit can also extract tasks using text analysis technology. Furthermore, if the user uses image input, the extraction unit can also extract tasks using image recognition technology. This allows tasks to be extracted efficiently by selecting the optimal extraction means depending on the user's input method.

[0081] The extraction unit can estimate the user's emotions and determine the priority of tasks to be extracted based on the estimated user emotions. For example, if the user is feeling stressed, the extraction unit can prioritize extracting tasks with low importance. Furthermore, if the user is relaxed, the extraction unit can also prioritize extracting tasks with high importance. Furthermore, if the user is in a hurry, the extraction unit can prioritize extracting tasks with high urgency. In this way, by determining the priority of tasks according to the user's emotions, more appropriate tasks can be preferentially extracted. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0082] During extraction, the extraction unit can prioritize extracting highly relevant tasks by taking into account the user's geographical location information. For example, the extraction unit prioritizes extracting tasks that the user will perform in a location close to their current location. The extraction unit can also filter and extract relevant tasks based on the user's geographical location information. Furthermore, if the user is in a specific location, the extraction unit can also prioritize extracting tasks related to that location. In this way, by taking into account the user's geographical location information, highly relevant tasks can be preferentially extracted.

[0083] During extraction, the extraction unit can analyze the user's social media activities and extract related tasks. For example, the extraction unit preferentially extracts tasks mentioned by the user on social media. The extraction unit can also analyze the user's social media activities and extract related tasks. Furthermore, the extraction unit can extract related tasks by referring to the activities of the user's friends on social media. In this way, related tasks can be efficiently extracted by analyzing the user's social media activities.

[0084] The extraction unit can customize the extraction method by reflecting the user's past feedback during extraction. The extraction unit adjusts the extraction method based on, for example, feedback provided by the user in the past. The extraction unit can also select the optimal extraction method based on the user's past feedback. Furthermore, the extraction unit can also customize the extraction method by reflecting the user's feedback. In this way, the extraction method can be optimized by reflecting the user's past feedback.

[0085] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible presentation. If the user is relaxed, the analysis unit can also provide a presentation that includes detailed information. If the user is in a hurry, the analysis unit can also provide a presentation that focuses on the main points. This allows the analysis presentation to be adjusted according to the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0086] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the task. For example, the analysis unit performs a detailed analysis on a task with a high level of importance. The analysis unit can also perform a simplified analysis on a task with a low level of importance. Furthermore, the analysis unit can select an appropriate level of detail of the analysis depending on the importance of the task. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the task.

[0087] During analysis, the analysis unit can apply different analysis algorithms depending on the task category. For example, the analysis unit applies a business-oriented analysis algorithm to a business task. The analysis unit can also apply a personal analysis algorithm to a personal task. Furthermore, the analysis unit can select the optimal analysis algorithm depending on the task category. This improves the accuracy of the analysis by applying the optimal analysis algorithm depending on the task category.

[0088] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit improves the accuracy of the analysis, for example, based on the user's past analysis results. The analysis unit can also select the optimal analysis method from the user's past analysis results. Furthermore, the analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results.

[0089] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can perform a short, to-the-point analysis. The analysis unit can also perform a detailed analysis if the user is relaxed. Furthermore, if the user is excited, the analysis unit can perform a visually stimulating analysis. This allows for adjusting the length of the analysis according to the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0090] During analysis, the analysis unit can determine the priority of analysis based on the submission time of the tasks. For example, the analysis unit prioritizes analysis of tasks with upcoming submission deadlines. The analysis unit can also postpone analysis of tasks with more distant submission deadlines. Furthermore, the analysis unit can also determine the priority of analysis based on the submission time of the tasks. This enables efficient analysis by determining the priority of analysis based on the submission time of the tasks.

[0091] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the tasks. For example, the analysis unit prioritizes analysis of highly relevant tasks. The analysis unit can also postpone analysis of less relevant tasks. Furthermore, the analysis unit can also adjust the order of analysis based on the relevance of the tasks. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the tasks.

[0092] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit performs an analysis that makes heavy use of technical terms. Also, if the user does not have technical expertise, the analysis unit can perform an analysis that avoids technical terms. Furthermore, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. This makes it possible to provide more appropriate analysis results by adjusting the use of technical terms in the analysis according to the user's level of expertise.

[0093] The search unit can estimate the user's emotions and adjust search criteria based on the estimated user emotions. For example, if the user is relaxed, the search unit can provide a wide range of search results. Furthermore, if the user is in a hurry, the search unit can prioritize the most relevant search results. Furthermore, if the user is excited, the search unit can provide visually stimulating search results. This allows for more appropriate search results to be provided by adjusting the search criteria 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 can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0094] The search unit can improve the accuracy of the search by taking into account the interrelationships between tasks during a search. For example, the search unit analyzes the interrelationships between tasks and provides relevant search results. The search unit can also improve the accuracy of the search results based on the interrelationships between tasks. Furthermore, the search unit can provide optimal search results by taking into account the interrelationships between tasks. In this way, the accuracy of the search is improved by taking into account the interrelationships between tasks.

[0095] The search unit can perform a search while taking into account the attribute information of the task submitter. The search unit can provide optimal search results, for example, depending on the expertise level of the task submitter. The search unit can also provide relevant search results by referring to the past search history of the task submitter. Furthermore, the search unit can customize the search results based on the attribute information of the task submitter. This allows for more appropriate search results to be provided by taking into account the attribute information of the task submitter.

[0096] During a search, the search unit can weight the search based on the frequency of task submission. For example, the search unit prioritizes searching for tasks that are submitted more frequently. The search unit can also search for tasks that are submitted less frequently later. Furthermore, the search unit can weight search results based on the frequency of task submission. Thus, weighting the search based on the frequency of task submission enables efficient searches.

[0097] The search unit can estimate the user's emotions and adjust the order in which search results are displayed based on the estimated user emotions. For example, if the user is relaxed, the search unit can prioritize displaying detailed search results. Furthermore, if the user is in a hurry, the search unit can prioritize displaying the most relevant search results. Furthermore, if the user is excited, the search unit can prioritize displaying visually stimulating search results. This allows for more appropriate search results to be provided by adjusting the order in which search results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0098] The search unit can perform a search while taking into account the geographic distribution of tasks. For example, the search unit preferentially searches for tasks related to locations close to the user's current location. The search unit can also provide optimal search results based on the geographic distribution of tasks. Furthermore, if the user is in a specific area, the search unit can also preferentially search for tasks related to that area. This allows for more appropriate search results to be provided by taking into account the geographic distribution of tasks.

[0099] The search unit can improve the accuracy of the search by referring to literature related to the task during the search. For example, the search unit refers to literature related to the task and provides optimal search results. The search unit can also improve the accuracy of the search results based on the literature related to the task. Furthermore, the search unit can provide optimal search results taking into account the literature related to the task. In this way, the accuracy of the search is improved by referring to literature related to the task.

[0100] The search unit can perform a search while taking into account the market value of the task. For example, the search unit may search for tasks with high market value first. The search unit can also search for tasks with low market value later. Furthermore, the search unit can customize search results based on the market value of the task. This allows for more appropriate search results to be provided by taking the market value of the task into consideration.

[0101] The creation unit can estimate the user's emotions and adjust the expression style of the email draft based on the estimated user emotions. For example, if the user is relaxed, the creation unit can create an email draft using polite and detailed expression. If the user is in a hurry, the creation unit can also create an email draft that is concise and to the point. Furthermore, if the user is excited, the creation unit can also create an email draft using visually stimulating expression. This allows for adjusting the expression style of the email draft according to the user's emotions, thereby providing a more appropriate email draft. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0102] When creating a draft of an email, the creation unit can adjust the level of detail of the draft based on the importance of the task. For example, the creation unit creates a detailed draft of an email for a task with a high level of importance. The creation unit can also create a simplified draft of an email for a task with a low level of importance. Furthermore, the creation unit can also create a draft of an email with an appropriate level of detail depending on the importance of the task. This allows for efficient drafting of emails by adjusting the level of detail of the draft based on the importance of the task.

[0103] The creation unit can apply different templates depending on the task category when creating a draft of an email. For example, the creation unit applies a business template to a business task. The creation unit can also apply a personal template to a personal task. Furthermore, the creation unit can select the optimal template depending on the task category. This allows for efficient drafting of emails by applying the optimal template depending on the task category.

[0104] When creating a draft of an email, the creation unit can improve the accuracy of the draft by referring to the user's past draft results. The creation unit improves the accuracy of the draft, for example, based on the user's past draft results. The creation unit can also select an optimal drafting method from the user's past draft results. Furthermore, the creation unit can also improve the accuracy of the draft by referring to the user's past draft results. In this way, the accuracy of the draft is improved by referring to the user's past draft results.

[0105] The creation unit can estimate the user's emotions and adjust the length of the email draft based on the estimated user emotions. For example, if the user is in a hurry, the creation unit can create a short, to-the-point email draft. Furthermore, if the user is relaxed, the creation unit can create a longer email draft with detailed explanations. Furthermore, if the user is excited, the creation unit can create an email draft using visually stimulating expressions. This allows the length of the email draft to be adjusted according to the user's emotions, thereby providing a more appropriate email draft. The emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0106] When creating email drafts, the creation unit can determine the priority of the drafts based on the submission time of the tasks. For example, the creation unit prioritizes creating email drafts for tasks with upcoming submission deadlines. The creation unit can also postpone creating email drafts for tasks with more distant submission deadlines. Furthermore, the creation unit can also determine the priority of email drafts based on the submission time of the tasks. This allows for efficient email drafting by determining the priority of drafts based on the submission time of the tasks.

[0107] The creation unit can adjust the order of the email drafts based on the relevance of the tasks when creating email drafts. For example, the creation unit prioritizes creating email drafts for highly relevant tasks. The creation unit can also postpone creating email drafts for less relevant tasks. Furthermore, the creation unit can adjust the order of the email drafts based on the relevance of the tasks. This allows for efficient email drafting by adjusting the order of the drafts based on the relevance of the tasks.

[0108] When creating an email draft, the creation unit can adjust the use of technical terms in the draft according to the user's level of expertise. For example, if the user has technical expertise, the creation unit creates an email draft that uses a lot of technical terms. Also, if the user does not have technical expertise, the creation unit can create an email draft that avoids technical terms. Furthermore, the creation unit can adjust the use of technical terms in the email draft according to the user's level of expertise. In this way, by adjusting the use of technical terms in the draft according to the user's level of expertise, a more appropriate email draft can be provided.

[0109] The notification unit can estimate the user's emotions and adjust the notification expression method based on the estimated user's emotions. For example, if the user is nervous, the notification unit can provide the notification in a calm expression method. If the user is relaxed, the notification unit can also provide the notification in a cheerful expression method. Furthermore, if the user is in a hurry, the notification unit can also provide the notification in a quick and concise expression method. This allows for more appropriate notifications to be provided by adjusting the notification expression method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0110] The notification unit can adjust the level of detail of the notification based on the importance of the task when issuing a notification. For example, the notification unit issues a detailed notification for a task with a high level of importance. The notification unit can also issue a simplified notification for a task with a low level of importance. Furthermore, the notification unit can also issue a notification with an appropriate level of detail depending on the importance of the task. This allows for efficient notification by adjusting the level of detail of the notification based on the importance of the task.

[0111] The notification unit can apply different notification methods depending on the task category when sending a notification. For example, the notification unit can apply a business-oriented notification method to a business task. The notification unit can also apply a personal notification method to a personal task. Furthermore, the notification unit can select the optimal notification method depending on the task category. This allows for efficient notification by applying the optimal notification method depending on the task category.

[0112] The notification unit can improve the accuracy of notification by referring to the user's past notification results when making a notification. The notification unit improves the accuracy of notification, for example, based on the user's past notification results. The notification unit can also select the optimal notification method from the user's past notification results. Furthermore, the notification unit can also improve the accuracy of notification by referring to the user's past notification results. In this way, the accuracy of notification is improved by referring to the user's past notification results.

[0113] The notification unit can estimate the user's emotion and adjust the timing of the notification based on the estimated user's emotion. For example, if the user is relaxed, the notification unit can immediately notify the user. Furthermore, if the user is feeling stressed, the notification unit can delay the timing of the notification. Furthermore, if the user is in a hurry, the notification unit can quickly notify the user. In this way, by adjusting the timing of the notification according to the user's emotion, the notification can be provided at a more appropriate time. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0114] The notification unit can determine the priority of notifications based on the submission time of the tasks when sending notifications. For example, the notification unit prioritizes notifications for tasks with upcoming submission deadlines. The notification unit can also postpone notifications for tasks with more distant submission deadlines. Furthermore, the notification unit can also determine the priority of notifications based on the submission time of the tasks. This enables efficient notifications by determining the priority of notifications based on the submission time of the tasks.

[0115] The notification unit can adjust the order of notifications based on the relevance of the tasks when notifying. For example, the notification unit can prioritize notifications of highly relevant tasks. The notification unit can also postpone notifications of less relevant tasks. Furthermore, the notification unit can also adjust the order of notifications based on the relevance of the tasks. This allows for efficient notifications by adjusting the order of notifications based on the relevance of the tasks.

[0116] The notification unit can adjust the use of technical terms in the notification according to the user's level of expertise when making a notification. For example, if the user has technical expertise, the notification unit can provide a notification that uses a lot of technical terms. Also, if the user does not have technical expertise, the notification unit can provide a notification that avoids technical terms. Furthermore, the notification unit can adjust the use of technical terms in the notification according to the user's level of expertise. This allows for more appropriate notifications to be provided by adjusting the use of technical terms in the notification according to the user's level of expertise. === Hard Collateral 1-1 === Each of the multiple elements including the extraction unit, analysis unit, search unit, creation unit, and notification unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the extraction unit extracts tasks from a calendar, spreadsheet, or email label by the control unit 46A of the smart device 14. The analysis unit analyzes the content of the tasks extracted by the specific processing unit 290 of the data processing device 12. For example, the search unit searches for materials based on the tasks analyzed by the specific processing unit 290 of the data processing device 12. For example, the creation unit creates a draft of an email based on the materials searched by the control unit 46A of the smart device 14. For example, the notification unit notifies the control unit 46A of the smart device 14 that the email draft has been completed. === Hard Collateral 1-2 === Each of the multiple elements including the extraction unit, analysis unit, search unit, creation unit, and notification unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the extraction unit extracts tasks from a calendar, spreadsheet, or email label by the control unit 46A of the smart glasses 214. The analysis unit analyzes the content of the tasks extracted by the specific processing unit 290 of the data processing device 12, for example. The search unit searches for materials based on the tasks analyzed by the specific processing unit 290 of the data processing device 12, for example. The creation unit creates a draft of an email based on the materials searched by the control unit 46A of the smart glasses 214, for example. The notification unit notifies the control unit 46A of the smart glasses 214 that the email draft has been completed, for example. === Hard Collateral 1-3 === Each of the multiple elements including the extraction unit, analysis unit, search unit, creation unit, and notification unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the extraction unit extracts tasks from a calendar, spreadsheet, or email label by the control unit 46A of the headset type terminal 314. The analysis unit analyzes the content of the tasks extracted by the specific processing unit 290 of the data processing device 12, for example. The search unit searches for materials based on the tasks analyzed by the specific processing unit 290 of the data processing device 12, for example. The creation unit creates a draft of an email based on the materials searched by the control unit 46A of the headset type terminal 314, for example. The notification unit notifies, for example, the control unit 46A of the headset type terminal 314 that the email draft has been completed. === Hard Collateral 1-4 === Each of the multiple elements including the extraction unit, analysis unit, search unit, creation unit, and notification unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the extraction unit extracts tasks from a calendar, spreadsheet, or email label by the control unit 46A of the robot 414. The analysis unit analyzes the content of the tasks extracted by the specific processing unit 290 of the data processing device 12, for example. The search unit searches for materials based on the tasks analyzed by the specific processing unit 290 of the data processing device 12, for example. The creation unit creates a draft of an email based on the materials searched by the control unit 46A of the robot 414, for example. The notification unit notifies, for example, the control unit 46A of the robot 414 that the email draft has been completed.

[0117] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0118] The task management system may further include a priority setting unit. The priority setting unit can automatically set task priorities based on the importance and urgency of the tasks entered by the user. For example, tasks tagged as "urgent" by the user are given priority over other tasks. The priority setting unit can also analyze the user's past task completion history and prioritize frequently delayed tasks. Furthermore, the priority setting unit can suggest the optimal timing for task execution, taking into account the available time on the user's calendar. This allows the user to efficiently manage tasks and complete important tasks without missing them.

[0119] The task management system can further include a reminder unit. The reminder unit can periodically notify the user of the progress of a task based on a deadline set by the user. For example, for a task set by the user to be completed in one week, the reminder unit can send reminders three days before, one day before, and on the day of the task. The reminder unit can also analyze the user's past task completion patterns and suggest appropriate reminder timing. Furthermore, the reminder unit can estimate the user's emotions and reduce the frequency of reminders if the user is feeling stressed. This allows the user to efficiently manage the progress of tasks and meet deadlines.

[0120] A task management system can also include a collaboration section. This section provides functionality that allows multiple users to collaboratively manage tasks. For example, tasks can be shared within a project team, and each member's progress can be checked in real time. The collaboration section also has a function to automatically assign task assignees, selecting the most appropriate assignee based on each member's skill set and past performance. Furthermore, the collaboration section can automatically send reminders according to the progress of tasks, improving the efficiency of the entire team. This allows projects to proceed smoothly and increases the productivity of the entire team.

[0121] The task management system may further include a feedback unit. The feedback unit has a function of providing feedback to the user for the tasks that the user has completed. For example, after completing a task, the feedback unit may ask the user a question such as, "How satisfied were you with this task?" and collect the user's response. The feedback unit may also analyze the user's feedback and reflect it in future task management. Furthermore, the feedback unit may estimate the user's emotions and emphasize positive feedback to improve the user's motivation. This allows the user to understand areas for improvement in task management and complete tasks more efficiently.

[0122] The task management system can further include a learning unit. The learning unit has the function of learning the user's task management patterns and proposing optimal task management methods. For example, if a user frequently delays a task, the learning unit can analyze the cause and propose improvements. The learning unit can also automatically generate an optimal task schedule based on the user's past task completion data. Furthermore, the learning unit can estimate the user's emotions and propose a task division method to reduce stress. This allows the user to improve their task management skills and complete tasks efficiently.

[0123] The task management system may further include a prediction unit. The prediction unit has the function of predicting future task completion based on the user's past task history. For example, the prediction unit may analyze data on tasks previously completed by the user and present a predicted completion date for the next task to be completed. The prediction unit may also take into account the user's current task progress and provide advance warning of tasks that may be delayed. Furthermore, the prediction unit may estimate the user's emotions and adjust the predicted completion date if the user is feeling stressed. This allows the user to more accurately grasp task progress and complete tasks in a planned manner.

[0124] The task management system can further include an integration unit. The integration unit has the function of linking with other business tools and applications. For example, it can link with project management tools and chat applications to share task progress in real time. The integration unit can also link with the user's calendar and email account to automatically extract tasks and provide notifications. Furthermore, the integration unit can estimate the user's emotions and adjust the frequency of notifications if the user is feeling stressed. This allows users to efficiently utilize multiple tools and improve work efficiency.

[0125] The task management system may further include a customization unit. The customization unit has a function that allows the user to customize the settings of the task management system according to their preferences. For example, the user can set a specific task category to be displayed preferentially, or customize the timing and method of notifications. The customization unit can also analyze the user's past setting history and suggest optimal customization options. Furthermore, the customization unit can estimate the user's emotions and provide a simple interface if the user is feeling stressed. This allows the user to optimize the task management system according to their needs and manage tasks efficiently.

[0126] The task management system may further include an archive unit. The archive unit has a function of automatically archiving completed tasks so that they can be referenced later. For example, a user can save completed tasks in the archive unit and search past task history when necessary. The archive unit may also analyze the user's past task history and suggest similar tasks. Furthermore, the archive unit may estimate the user's emotions and emphasize positive feedback to improve the user's motivation. This allows the user to efficiently manage past task history and use it for future task management.

[0127] The task management system may further include a reporting unit. The reporting unit has the function of providing regular reports on the user's task management status. For example, it may visually display the task progress and completion rate on a weekly or monthly basis using graphs and charts. The reporting unit may also analyze the user's past task data and suggest improvements and efficiency measures. Furthermore, the reporting unit may estimate the user's emotions and highlight positive results to increase the user's motivation. This allows the user to understand their own task management status and find improvements to complete tasks more efficiently.

[0128] The processing flow of the second embodiment will be briefly explained below.

[0129] Step 1: The extraction unit extracts tasks from calendars, spreadsheets, and email labels. For example, it extracts entries containing keywords such as "Task:" or "ToDo:" from calendars, spreadsheets, and email labels. The extraction unit can also analyze the user's past task history and select the optimal extraction method. Step 2: The analysis unit analyzes the content of the tasks extracted by the extraction unit. For example, the analysis unit may use natural language processing technology to analyze the content of the tasks and identify keywords related to the tasks. The analysis unit may also estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. Step 3: The search unit searches for materials based on the task analyzed by the analysis unit. For example, the search unit searches for materials based on the identified keywords. The search unit can also estimate the user's emotions and adjust search criteria based on the estimated user emotions. Step 4: The creation unit creates a draft of an email based on the materials searched by the search unit. For example, the creation unit creates a draft of an email to be sent based on the searched materials. The creation unit can also estimate the user's emotions and adjust the way the email draft is written based on the estimated user's emotions. Step 5: The notification unit notifies the user that the draft of the email created by the creation unit has been completed. For example, the notification unit notifies the user that the draft of the email has been completed. The notification unit can also estimate the user's emotions and adjust the way the notification is expressed based on the estimated user's emotions.

[0130] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0131] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0135] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0136] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0137] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, 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. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0138] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0140] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0141] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0142] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0143] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0144] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0146] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0148] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0151] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0152] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0153] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. 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. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0154] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0156] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0157] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0158] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0159] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0160] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0161] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0162] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0163] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0164] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0167] 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.

[0168] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0169] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. 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. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0170] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0171] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0172] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0173] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0174] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0175] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0176] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0177] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0178] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0179] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0180] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0181] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0182] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0183] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0184] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0185] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0186] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0187] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0188] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0189] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0190] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0191] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0192] 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.

[0193] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0194] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0195] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0196] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0197] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0198] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0199] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0200] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0201] [Explanation of symbols]

[0202] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An extractor that extracts tasks from calendars, spreadsheets, and email labels. an analysis unit that analyzes the content of the task extracted by the extraction unit; a search unit that searches for materials based on the tasks analyzed by the analysis unit; a creation unit that creates a draft of an email based on the materials searched by the search unit; a notification unit that notifies that the draft of the email created by the creation unit has been completed. A system characterized by:

2. The extraction unit Extract entries containing specific keywords from a calendar, spreadsheet, or email label 2. The system of claim 1.

3. The analysis unit Analyze the extracted task content and identify task-related keywords 2. The system of claim 1.

4. The search unit Search for materials based on identified keywords 2. The system of claim 1.

5. The creation unit Create a draft email to send based on the searched materials 2. The system of claim 1.

6. The notification unit Notify users when an email draft is complete 2. The system of claim 1.

7. The extraction unit Estimate the user's emotions and adjust the timing of task extraction based on the estimated user emotions.

2. The system of claim 1.

8. The extraction unit When extracting from a calendar, spreadsheet, or email label, analyze the user's past task history to select the appropriate extraction method.

2. The system of claim 1.

Images