System

Abstract

An object of the system according to the embodiment is to support a new employee to efficiently perform a task in remote work.SOLUTION: A system according to an embodiment includes a task management unit, a reminder unit, a lecturer unit, a presentation unit, an inquiry unit, an identification unit, an adjustment unit, and a proposal unit. The task management unit performs task management. The reminder unit transmits a reminder based on the progress status of the task managed by the task management unit. The lecturer part lectures a system operation method. The presentation part presents the operation procedure lectured by the lecture part. The inquiry unit makes an inquiry to the senior. The specifying unit makes an inquiry to the senior specified by the inquiring unit. The adjustment unit adjusts the meeting. The proposal unit proposes the date and time proposed by the adjustment unit.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] Existing technology does not provide sufficient support for new employees to carry out their work efficiently while working remotely, and there is room for improvement.

[0005] The system according to the embodiment aims to support new employees so that they can efficiently carry out their work through remote work. [Means for solving the problem]

[0006] The system according to the embodiment includes a task management unit, a reminder unit, a lecture unit, a presentation unit, an inquiry unit, an identification unit, an adjustment unit, and a proposal unit. The task management unit manages tasks. The reminder unit sends reminders based on the progress of tasks managed by the task management unit. The lecture unit gives lectures on how to operate the system. The presentation unit presents the operating procedures lectured by the lecture unit. The inquiry unit makes inquiries to senior employees. The identification unit makes inquiries to senior employees identified by the inquiry unit. The adjustment unit arranges meetings. The proposal unit proposes dates and times proposed by the adjustment unit. [Effects of the Invention]

[0007] The system according to the embodiment can support new employees to efficiently carry out their work through remote work. [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 support system according to an embodiment of the present invention is a system that helps new employees efficiently perform their work while working remotely. The support system manages tasks, sends reminders, provides instructions on how to operate the system, confers inquiries with senior employees, and arranges meetings. For example, the support system monitors the progress of each task and sends reminders as necessary. Next, the support system presents appropriate operating procedures when a new employee has difficulty operating the system. Furthermore, when a new employee inputs a question, the support system identifies an appropriate senior employee and automatically confers inquiries. Finally, when a new employee requests a meeting, the support system checks the schedules of participants and suggests the optimal date and time. This allows the support system to help new employees efficiently perform their work while working remotely. For example, by providing various functions such as task management, case management, instruction on how to operate the system, confers inquiries with senior employees, and arranges meetings, new employees can smoothly perform their work even while working remotely.

[0029] The support system according to the embodiment includes a task management unit, a reminder unit, a lecture unit, a presentation unit, an inquiry unit, an identification unit, an adjustment unit, and a proposal unit. The task management unit performs task management. For example, the task management unit monitors the progress of each task. The task management unit can also manage tasks based on a task classification method and progress evaluation criteria. The reminder unit sends reminders based on the progress of tasks managed by the task management unit. For example, the reminder unit sends reminders when a task deadline is approaching. The reminder unit can also send reminders based on the transmission timing and notification method. The lecture unit gives lectures on how to operate the system. For example, the lecture unit presents appropriate operating procedures when a new employee has trouble operating the system. The lecture unit can also present operating procedures using text, video, or interactive tutorials. The presentation unit presents the operating procedures lectured by the lecture unit. The presentation unit provides, for example, video or text instructions for performing a specific operation. The presentation unit can also present the operation instructions using a step-by-step guide or visual aid. The inquiry unit makes an inquiry to a senior employee. For example, when a new employee inputs a question, the inquiry unit identifies an appropriate senior employee. The inquiry unit can also make an inquiry based on the inquiry format and selection criteria for the contact person. The identification unit makes an inquiry to the senior employee identified by the inquiry unit. For example, when a question is about a specific task, the identification unit identifies a senior employee who is knowledgeable about that task. The identification unit can also identify a senior employee based on a specific task, such as project management or technical support. The coordination unit arranges a meeting. For example, when a new employee requests a meeting, the coordination unit checks the schedules of the participants. The coordination unit can also arrange the meeting based on the calendar synchronization method and schedule priority. The proposal unit proposes a date and time proposed by the coordination unit. For example, the proposal unit proposes the most convenient date and time taking into account everyone's schedules.In addition, the suggestion unit can suggest a date and time based on the format of the proposal and the evaluation criteria of the proposal content. As a result, the support system according to the embodiment can support new employees to efficiently perform their work while working remotely.

[0030] The task management unit can monitor the progress of each task. For example, the task management unit can monitor the progress of each task using a real-time data collection method. The task management unit can also monitor the degree of task completion and deadline compliance based on progress evaluation criteria. Furthermore, the task management unit can provide a dashboard for visualizing the progress, allowing the progress of tasks to be understood in real time.

[0031] The reminder unit can send a reminder when a task deadline is approaching. For example, the reminder unit sends a reminder when a task deadline is approaching. The reminder unit can also send a reminder based on the sending timing and notification method. For example, the reminder unit can set how many days before the deadline the reminder should be sent. The reminder unit can also select the notification method, such as email or push notification. This can prevent task delays.

[0032] The lecture department can provide operating procedures when new employees have trouble operating the system. For example, the lecture department provides appropriate operating procedures when new employees have trouble operating the system. The lecture department can also provide operating procedures using text, video, or interactive tutorials. For example, the lecture department can provide a solution when an error message is displayed. The lecture department can also provide a detailed explanation of the procedure when a specific operation cannot be completed. This allows new employees to use the system smoothly.

[0033] The presentation unit can provide procedures for performing a specific operation using video or text. For example, the presentation unit can provide procedures for performing a specific operation using video or text. The presentation unit can also present the operation procedures using a step-by-step guide or visual aid. For example, the presentation unit can explain operation procedures such as how to change system settings or how to input data using video. The presentation unit can also use visual aids to make the operation procedures visually easier to understand. This makes it possible to make the operation procedures visually easier to understand.

[0034] The inquiry unit can identify an appropriate senior when a new employee inputs a question. For example, when a new employee inputs a question, the inquiry unit can identify an appropriate senior. The inquiry unit can also make inquiries based on the form of the inquiry and selection criteria for the person to contact. For example, the inquiry unit can identify an appropriate senior based on the senior's expertise and past response history. The inquiry unit can also select the most appropriate senior depending on the content of the question. This allows for a quick response.

[0035] When there is a question about a specific task, the identification unit can identify a senior who is knowledgeable about that task. For example, when there is a question about a specific task, the identification unit can identify a senior who is knowledgeable about that task. The identification unit can also identify a senior based on a specific task such as project management or technical support. For example, when there is a question about project management, the identification unit can identify a senior who is knowledgeable about that project. Also, when there is a question about technical support, the identification unit can identify a senior who is knowledgeable about that technology. This allows the appropriate senior to be contacted.

[0036] The coordination unit can check the schedules of participants when a new employee requests a meeting. For example, when a new employee requests a meeting, the coordination unit checks the schedules of participants. The coordination unit can also coordinate meetings based on calendar synchronization methods and schedule priorities. For example, the coordination unit can check everyone's schedules using calendar synchronization methods. The coordination unit can also suggest the optimal date and time based on schedule priorities. This allows meetings to be coordinated smoothly.

[0037] The suggestion unit can propose the most convenient date and time taking into consideration everyone's schedules. For example, the suggestion unit can propose the most convenient date and time taking into consideration everyone's schedules. The suggestion unit can also propose a date and time based on the format of the proposal and evaluation criteria for the proposal content. For example, the suggestion unit can propose the optimal date and time taking into consideration everyone's free time. The suggestion unit can also propose a date and time based on the priority of tasks with high importance. This allows the meeting to be held at the optimal date and time.

[0038] During task management, the task management unit can analyze the user's past task completion history and select a task management method. For example, the task management unit can analyze patterns of tasks that the user has completed quickly in the past and prioritize similar tasks. The task management unit can also assign tasks in a way that avoids tasks that the user has struggled with in the past. Furthermore, the task management unit can propose an optimal task schedule based on the user's past task completion times. This enables optimal task management based on the user's past history. Some or all of the above-mentioned processing in the task management unit may be performed, for example, using AI, or may be performed without using AI.

[0039] During task management, the task management unit can filter tasks based on the user's current project or areas of interest. For example, the task management unit may preferentially display tasks related to the project the user is currently working on. The task management unit can also suggest related tasks based on the user's areas of interest. Furthermore, the task management unit can filter and display tasks related to areas in which the user has previously shown interest. This enables task management based on the user's interests. Some or all of the above-described processing in the task management unit may be performed, for example, using AI or without using AI.

[0040] The task management unit can select a management method depending on the user's input method when managing tasks. For example, if the user uses voice input, the task management unit can manage tasks using voice recognition technology. Also, if the user uses text input, the task management unit can manage tasks using text analysis technology. Furthermore, if the user uses image input, the task management unit can manage tasks using image recognition technology. This enables task management according to the user's input method. Some or all of the above-mentioned processing in the task management unit may be performed using AI, for example, or may be performed without using AI.

[0041] When managing tasks, the task management unit can prioritize managing highly relevant tasks by taking into account the user's geographical location information. For example, when the user is in the office, the task management unit can prioritize displaying tasks that should be done at the office. Furthermore, when the user is at home, the task management unit can also prioritize displaying tasks that should be done at home. Furthermore, when the user is on a business trip, the task management unit can also prioritize displaying tasks that should be done at the business trip destination. This makes it possible to manage tasks based on the user's geographical location information. Some or all of the above-mentioned processing in the task management unit may be performed, for example, using AI, or may be performed without using AI.

[0042] The task management unit can analyze the user's social media activities during task management and manage related tasks. For example, the task management unit can prioritize displaying tasks related to projects mentioned by the user on social media. The task management unit can also suggest tasks related to the user's areas of interest based on the user's social media activities. Furthermore, the task management unit can also suggest related tasks based on the activities of the user's friends on social media. This enables task management based on the user's social media activities. Some or all of the above-mentioned processing in the task management unit may be performed using, for example, AI, or may be performed without using AI.

[0043] The task management unit can customize the task management method by reflecting the user's past feedback during task management. For example, the task management unit customizes the task management interface based on feedback provided by the user in the past. The task management unit can also suggest a preferred task management method based on the user's past feedback. Furthermore, the task management unit can adjust task priorities by reflecting the user's feedback. This enables customized task management based on the user's feedback. Some or all of the above-described processing in the task management unit may be performed using, for example, AI, or may be performed without using AI.

[0044] When sending a reminder, the reminder unit can adjust the level of detail of the reminder based on the importance of the task. For example, the reminder unit sends a detailed reminder for a highly important task. The reminder unit can also send a brief reminder for a less important task. Furthermore, the reminder unit can also send a detailed reminder for a task with an approaching deadline. This makes it possible to send reminders according to the importance of the task. Some or all of the above-described processing in the reminder unit may be performed using, for example, AI, or may be performed without using AI.

[0045] The reminder unit can apply different reminder algorithms depending on the task category when sending a reminder. For example, in the case of a project task, the reminder unit sends a reminder according to the progress of the project. In addition, in the case of a daily work task, the reminder unit can also send a reminder according to the progress of the daily work. Furthermore, in the case of an urgent task, the reminder unit can also send a reminder immediately. This makes it possible to send reminders according to the task category. Some or all of the above-mentioned processing in the reminder unit may be performed using, for example, AI, or may be performed without using AI.

[0046] When sending a reminder, the reminder unit can improve the accuracy of the reminder by referring to the user's past reminder history. The reminder unit, for example, analyzes reminders that the user has ignored in the past and reduces reminders with low importance. The reminder unit can also analyze reminders that the user has responded to in the past and increase the number of similar reminders. Furthermore, the reminder unit can also suggest the optimal timing for sending a reminder based on the user's past reminder history. This makes it possible to send reminders with high accuracy based on the past reminder history. Some or all of the above-mentioned processing in the reminder unit may be performed, for example, using AI or without using AI.

[0047] When sending a reminder, the reminder unit can determine the priority of the reminder based on the submission time of the task. For example, the reminder unit prioritizes sending reminders for tasks with upcoming deadlines. The reminder unit can also postpone sending reminders for tasks with distant submission times. Furthermore, the reminder unit can automatically adjust the priority of the reminder if the submission time is changed. This makes it possible to determine the priority of reminders according to the submission time of the task. Some or all of the above-mentioned processing in the reminder unit may be performed, for example, using AI, or may be performed without using AI.

[0048] The reminder unit can adjust the order of reminders based on the relevance of tasks when sending reminders. For example, the reminder unit prioritizes sending reminders for highly relevant tasks. The reminder unit can also postpone sending reminders for less relevant tasks. Furthermore, the reminder unit can automatically adjust the order of reminders when the relevance of tasks changes. This makes it possible to adjust the order of reminders according to the relevance of tasks. Some or all of the above-described processing in the reminder unit may be performed using AI, for example, or may be performed without using AI.

[0049] When sending a reminder, the reminder unit can adjust the use of technical terms in the reminder depending on the user's level of expertise. For example, if the user has technical expertise, the reminder unit can send a reminder using technical terms. Also, if the user does not have technical expertise, the reminder unit can send a reminder in simple language. Furthermore, the reminder unit can automatically adjust the content of the reminder if the user's level of expertise changes. This makes it possible to adjust the content of the reminder depending on the user's level of expertise. Some or all of the above-described processing in the reminder unit may be performed using AI, for example, or may be performed without using AI.

[0050] During the lecture, the lecture unit can adjust the level of detail of the lecture based on the importance of the operation procedure. For example, the lecture unit provides a detailed lecture for an operation procedure with high importance. The lecture unit can also provide a brief lecture for an operation procedure with low importance. Furthermore, the lecture unit can also provide an immediate lecture for an operation procedure with high urgency. This makes it possible to adjust the level of detail of the lecture depending on the importance of the operation procedure. Some or all of the above-mentioned processing in the lecture unit may be performed using AI, for example, or may be performed without using AI.

[0051] During the lecture, the lecture unit can apply different lecture algorithms depending on the category of the operation procedure. For example, in the case of an operation procedure related to system setting, the lecture unit can provide a lecture specialized in system setting. In addition, in the case of an operation procedure related to data entry, the lecture unit can also provide a lecture specialized in data entry. Furthermore, in the case of an operation procedure related to troubleshooting, the lecture unit can also provide a lecture specialized in troubleshooting. This makes it possible to provide lectures according to the category of the operation procedure. Some or all of the above-mentioned processing in the lecture unit may be performed using, for example, AI, or may be performed without using AI.

[0052] The lecture unit can improve the accuracy of the lecture by referring to the user's past lecture history when giving a lecture. For example, the lecture unit can analyze the content of lectures the user has received in the past and provide similar content. The lecture unit can also suggest the optimal lecture format based on lecture formats that the user found easy to understand in the past. Furthermore, the lecture unit can also suggest the optimal timing of the lecture based on the user's past lecture history. This makes it possible to provide a highly accurate lecture based on the user's past lecture history. Some or all of the above-mentioned processing in the lecture unit may be performed, for example, using AI, or may be performed without using AI.

[0053] During a lecture, the lecture section can determine the priority of the lecture based on the submission time of the operation procedures. For example, the lecture section gives priority to lectures on operation procedures with an approaching deadline. The lecture section can also postpone lectures on operation procedures with a more distant submission time. Furthermore, the lecture section can automatically adjust the priority of the lecture when the submission time is changed. This makes it possible to determine the priority of the lecture according to the submission time of the operation procedures. Some or all of the above-mentioned processing in the lecture section may be performed, for example, using AI, or may be performed without using AI.

[0054] During a lecture, the lecture section can adjust the order of the lecture based on the relevance of the operation procedures. For example, the lecture section gives priority to lecturing on highly relevant operation procedures. The lecture section can also postpone lecturing on less relevant operation procedures. Furthermore, the lecture section can automatically adjust the order of the lecture when the relevance of the operation procedures changes. This makes it possible to adjust the order of the lecture according to the relevance of the operation procedures. Some or all of the above-described processing in the lecture section may be performed using, for example, AI, or may be performed without using AI.

[0055] During a lecture, the lecture unit can adjust the use of technical terms in the lecture according to the user's level of expertise. For example, if the user has technical knowledge, the lecture unit can provide a lecture using technical terms. Also, if the user does not have technical knowledge, the lecture unit can provide a lecture in simple language. Furthermore, the lecture unit can automatically adjust the content of the lecture if the user's level of expertise changes. This makes it possible to adjust the content of the lecture according to the user's level of expertise. Some or all of the above-described processing in the lecture unit may be performed, for example, using AI or without using AI.

[0056] The presentation unit can adjust the level of detail of the presentation based on the importance of the operation procedure when presenting it. For example, the presentation unit presents detailed procedures for operation procedures with high importance. The presentation unit can also present concise procedures for operation procedures with low importance. Furthermore, the presentation unit can also immediately present procedures for operation procedures with high urgency. This makes it possible to adjust the level of detail of the presentation depending on the importance of the operation procedure. Some or all of the above-mentioned processing in the presentation unit may be performed using AI, for example, or may be performed without using AI.

[0057] The presentation unit can apply different presentation algorithms depending on the category of the operation procedure when presenting the operation procedure. For example, in the case of an operation procedure related to system setting, the presentation unit presents a procedure specialized for system setting. In addition, in the case of an operation procedure related to data entry, the presentation unit can also present a procedure specialized for data entry. Furthermore, in the case of an operation procedure related to troubleshooting, the presentation unit can also present a procedure specialized for troubleshooting. This makes it possible to present procedures according to the category of the operation procedure. Some or all of the above-mentioned processing in the presentation unit may be performed using AI, for example, or may be performed without using AI.

[0058] The presentation unit can improve the accuracy of the presentation by referring to the user's past presentation history when presenting the procedure. The presentation unit, for example, analyzes the content of presentations the user has received in the past and provides similar content. The presentation unit can also suggest an optimal format based on presentation formats that the user has found easy to understand in the past. Furthermore, the presentation unit can also suggest the optimal timing of presentation based on the user's past presentation history. This makes it possible to present procedures with high accuracy based on the past presentation history. Some or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI.

[0059] The presentation unit can determine the priority of presentation based on the submission time of the operation procedures when presenting them. For example, the presentation unit preferentially presents operation procedures with an approaching deadline. The presentation unit can also postpone the presentation of operation procedures with a distant submission time. Furthermore, the presentation unit can automatically adjust the priority of presentation when the submission time is changed. This makes it possible to present procedures according to the submission time of the operation procedures. Some or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI.

[0060] The presentation unit can adjust the order of presentation based on the relevance of the operation procedures when presenting them. For example, the presentation unit prioritizes the presentation of highly relevant operation procedures. The presentation unit can also postpone the presentation of less relevant operation procedures. Furthermore, the presentation unit can automatically adjust the order of presentation when the relevance of the operation procedures changes. This makes it possible to present the operation procedures according to the relevance of the operation procedures. Some or all of the above-mentioned processing in the presentation unit may be performed using AI, for example, or may be performed without using AI.

[0061] The presentation unit can adjust the use of technical terms in the presentation depending on the user's level of expertise. For example, if the user has technical expertise, the presentation unit provides the presentation using technical terms. Also, if the user does not have technical expertise, the presentation unit can provide the presentation in simple language. Furthermore, the presentation unit can automatically adjust the content of the presentation when the user's level of expertise changes. This makes it possible to present procedures according to the user's level of expertise. Some or all of the above-mentioned processing in the presentation unit may be performed using AI, for example, or may be performed without using AI.

[0062] The inquiry unit can adjust the level of detail of the inquiry based on the importance of the question when making an inquiry. For example, the inquiry unit provides a detailed inquiry for a question of high importance. The inquiry unit can also provide a concise inquiry for a question of low importance. Furthermore, the inquiry unit can also provide an inquiry immediately for a question of high urgency. This makes it possible to adjust the level of detail of the inquiry depending on the importance of the question. Some or all of the above-mentioned processing in the inquiry unit may be performed using AI, for example, or may be performed without using AI.

[0063] When making an inquiry, the inquiry unit can apply different inquiry algorithms depending on the category of the question. For example, in the case of a technical question, the inquiry unit applies a technical inquiry algorithm. In addition, in the case of a question about a business process, the inquiry unit can also apply an inquiry algorithm specialized for the business process. Furthermore, in the case of a question about human resources, the inquiry unit can also apply an inquiry algorithm specialized for human resources. This makes it possible to make inquiries according to the category of the question. Some or all of the above-mentioned processing in the inquiry unit may be performed, for example, using AI or may be performed without using AI.

[0064] When making an inquiry, the inquiry unit can improve the accuracy of the inquiry by referring to the user's past inquiry history. The inquiry unit, for example, analyzes the content of inquiries made by the user in the past and provides similar content. The inquiry unit can also suggest the optimal format based on inquiry formats that the user found easy to understand in the past. Furthermore, the inquiry unit can also suggest the optimal timing for making an inquiry based on the user's past inquiry history. This enables highly accurate inquiries based on the past inquiry history. Some or all of the above-mentioned processing in the inquiry unit may be performed, for example, using AI, or may be performed without using AI.

[0065] The inquiry unit can determine the priority of an inquiry based on the time of submission of the question at the time of inquiry. For example, the inquiry unit prioritizes inquiries for questions with an approaching deadline. The inquiry unit can also postpone inquiries for questions with a more distant submission date. Furthermore, the inquiry unit can automatically adjust the priority of an inquiry when the submission date is changed. This makes it possible to determine the priority of an inquiry according to the time of submission of the question. Some or all of the above-described processing in the inquiry unit may be performed, for example, using AI, or may be performed without using AI.

[0066] The inquiry unit can adjust the order of inquiries based on the relevance of the questions when making inquiries. For example, the inquiry unit prioritizes inquiries for highly relevant questions. The inquiry unit can also postpone inquiries for less relevant questions. Furthermore, the inquiry unit can automatically adjust the order of inquiries when the relevance of questions changes. This makes it possible to adjust the order of inquiries according to the relevance of questions. Some or all of the above-described processing in the inquiry unit may be performed, for example, using AI, or may be performed without using AI.

[0067] The query unit can adjust the use of technical terms in the query depending on the user's level of expertise when making a query. For example, if the user has technical expertise, the query unit can provide a query using technical terms. Also, if the user does not have technical expertise, the query unit can provide a query in simple language. Furthermore, the query unit can automatically adjust the content of the query when the user's level of expertise changes. This makes it possible to make a query depending on the user's level of expertise. Some or all of the above-mentioned processing in the query unit may be performed using AI, for example, or may be performed without using AI.

[0068] The identification unit can adjust the level of detail of the identification based on the importance of the question during identification. For example, in the case of a question of high importance, the identification unit performs detailed identification. In addition, in the case of a question of low importance, the identification unit can also perform concise identification. Furthermore, in the case of a question of high urgency, the identification unit can also perform immediate identification. This makes it possible to identify a senior employee according to the importance of the question. Some or all of the above-mentioned processing in the identification unit may be performed, for example, using AI or may be performed without using AI.

[0069] During identification, the identification unit can apply different identification algorithms depending on the category of the question. For example, in the case of a technical question, the identification unit applies a technical identification algorithm. Furthermore, in the case of a question about a business process, the identification unit can also apply an identification algorithm specialized for the business process. Furthermore, in the case of a question about human resources, the identification unit can also apply an identification algorithm specialized for human resources. This makes it possible to identify a senior colleague according to the category of the question. Some or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI.

[0070] During identification, the identification unit can improve the accuracy of identification by referring to the user's past identification history. The identification unit, for example, analyzes the identification content that the user has performed in the past and provides similar content. The identification unit can also suggest the optimal format based on identification formats that the user found easy to understand in the past. Furthermore, the identification unit can also suggest the optimal timing for identification based on the user's past identification history. This enables highly accurate identification of seniors based on the past identification history. Some or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI.

[0071] At the time of identification, the identification unit can determine the priority of identification based on the time of submission of the question. For example, the identification unit prioritizes identifying questions with an approaching deadline. The identification unit can also postpone identification of questions with a more distant submission date. Furthermore, the identification unit can automatically adjust the priority of identification when the submission date is changed. This makes it possible to identify a senior questioner according to the time of submission of the question. Some or all of the above-described processing in the identification unit may be performed, for example, using AI, or may be performed without using AI.

[0072] The identification unit can adjust the identification order based on the relevance of the questions during identification. For example, the identification unit prioritizes identifying highly relevant questions. The identification unit can also postpone identification of less relevant questions. Furthermore, the identification unit can automatically adjust the identification order when the relevance of the questions changes. This makes it possible to identify seniors according to the relevance of the questions. Some or all of the above-described processing in the identification unit may be performed, for example, using AI, or may be performed without using AI.

[0073] The identification unit can adjust the use of specific technical terms during identification according to the user's level of expertise. For example, if the user has specialized knowledge, the identification unit provides identification using technical terms. Also, if the user does not have specialized knowledge, the identification unit can provide identification in simple language. Furthermore, the identification unit can automatically adjust the content of the identification if the user's level of expertise changes. This makes it possible to identify seniors according to the user's level of expertise. Some or all of the above-mentioned processing in the identification unit may be performed, for example, using AI or without using AI.

[0074] The adjustment unit can adjust the level of detail of the adjustment based on the importance of the meeting during adjustment. For example, in the case of a highly important meeting, the adjustment unit performs detailed adjustment. In addition, in the case of a less important meeting, the adjustment unit can also perform simple adjustment. Furthermore, in the case of a highly urgent meeting, the adjustment unit can also perform immediate adjustment. This makes it possible to make adjustments according to the importance of the meeting. Some or all of the above-mentioned processing in the adjustment unit may be performed using AI, for example, or may be performed without using AI.

[0075] During coordination, the coordination unit can apply different coordination algorithms depending on the category of the meeting. For example, in the case of a technical meeting, the coordination unit applies a technical coordination algorithm. Furthermore, in the case of a meeting regarding a business process, the coordination unit can also apply a coordination algorithm specialized for the business process. Furthermore, in the case of a meeting regarding personnel, the coordination unit can also apply a coordination algorithm specialized for personnel. This makes it possible to coordinate according to the category of the meeting. Some or all of the above-mentioned processing in the coordination unit may be performed, for example, using AI, or may be performed without using AI.

[0076] During adjustment, the adjustment unit can improve the accuracy of the adjustment by referring to the user's past adjustment history. The adjustment unit, for example, analyzes the content of adjustments made by the user in the past and provides similar content. The adjustment unit can also suggest an optimal format based on adjustment formats that the user found easy to understand in the past. Furthermore, the adjustment unit can also suggest the optimal timing for adjustment based on the user's past adjustment history. This enables highly accurate adjustment based on the past adjustment history. Some or all of the above-described processing in the adjustment unit may be performed, for example, using AI, or may be performed without using AI.

[0077] During adjustment, the adjustment unit can determine the priority of adjustment based on the submission date of the meeting. For example, the adjustment unit prioritizes adjustment of meetings with an approaching deadline. The adjustment unit can also postpone adjustment of meetings with a distant submission date. Furthermore, the adjustment unit can automatically adjust the priority of adjustment when the submission date is changed. This makes it possible to make adjustments according to the submission date of the meeting. Some or all of the above-mentioned processing in the adjustment unit may be performed using AI, for example, or may be performed without using AI.

[0078] The adjustment unit can adjust the order of adjustments based on the relevance of meetings during adjustment. For example, the adjustment unit prioritizes adjustments for highly relevant meetings. The adjustment unit can also postpone adjustments for less relevant meetings. Furthermore, the adjustment unit can automatically adjust the order of adjustments when the relevance of meetings changes. This makes it possible to adjust according to the relevance of meetings. Some or all of the above-described processing in the adjustment unit may be performed using AI, for example, or may be performed without using AI.

[0079] During the adjustment, the adjustment unit can adjust the use of technical terms in the adjustment according to the user's level of expertise. For example, if the user has technical expertise, the adjustment unit provides the adjustment using technical terms. Furthermore, if the user does not have technical expertise, the adjustment unit can also provide the adjustment in simple language. Furthermore, the adjustment unit can automatically adjust the content of the adjustment if the user's level of expertise changes. This enables adjustment according to the user's level of expertise. Some or all of the above-described processing in the adjustment unit may be performed using AI, for example, or may be performed without using AI.

[0080] When making a proposal, the proposal unit can adjust the level of detail of the proposal based on the importance of the meeting. For example, in the case of a highly important meeting, the proposal unit makes a detailed proposal. In addition, in the case of a less important meeting, the proposal unit can also make a concise proposal. Furthermore, in the case of a highly urgent meeting, the proposal unit can also make an immediate proposal. This makes it possible to make a proposal according to the importance of the meeting. Some or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI or may be performed without using AI.

[0081] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the meeting. For example, in the case of a technical meeting, the proposal unit applies a technical proposal algorithm. In addition, in the case of a meeting regarding a business process, the proposal unit can also apply a proposal algorithm specialized for the business process. Furthermore, in the case of a meeting regarding human resources, the proposal unit can also apply a proposal algorithm specialized for human resources. This makes it possible to make proposals according to the category of the meeting. Some or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI, or may be performed without using AI.

[0082] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal history. The suggestion unit, for example, analyzes the content of proposals made by the user in the past and provides similar content. The suggestion unit can also suggest the optimal format based on proposal formats that the user found easy to understand in the past. Furthermore, the suggestion unit can also suggest the optimal timing for making the proposal based on the user's past proposal history. This enables highly accurate suggestions based on the past proposal history. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI.

[0083] When making a proposal, the proposal unit can determine the priority of the proposal based on the submission date of the meeting. For example, the proposal unit prioritizes proposing meetings with an approaching deadline. The proposal unit can also postpone proposals for meetings with a distant submission date. Furthermore, the proposal unit can automatically adjust the priority of the proposal when the submission date is changed. This makes it possible to make proposals according to the submission date of the meeting. Some or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI, or may be performed without using AI.

[0084] The proposal unit can adjust the order of proposals based on the relevance of meetings when making proposals. For example, the proposal unit prioritizes proposing highly relevant meetings. The proposal unit can also postpone proposing less relevant meetings. Furthermore, the proposal unit can automatically adjust the order of proposals when the relevance of meetings changes. This makes it possible to make proposals based on the relevance of meetings. Some or all of the above-described processing in the proposal unit may be performed using AI, for example, or may be performed without using AI.

[0085] When making a proposal, the suggestion unit can adjust the use of technical terms in the proposal depending on the user's level of expertise. For example, if the user has technical expertise, the suggestion unit provides a proposal using technical terms. Also, if the user does not have technical expertise, the suggestion unit can provide a proposal in simple language. Furthermore, the suggestion unit can automatically adjust the content of the proposal when the user's level of expertise changes. This makes it possible to make a proposal according to the user's level of expertise. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The support system can further include a feedback collection unit. The feedback collection unit prompts new employees to enter feedback after each task or meeting. For example, the feedback collection unit can automatically display a feedback form after a task is completed to collect opinions about the difficulty of the task and the time required. The feedback collection unit can also request feedback from all participants after a meeting is completed to collect information about the effectiveness of the meeting and areas for improvement. Furthermore, the feedback collection unit can periodically request feedback from new employees about the overall work environment and usability of the support system. This allows the support system to improve the system and optimize the support method for new employees based on the collected feedback.

[0088] The support system can further include a learning support section. The learning support section supports new employees in acquiring the knowledge and skills necessary for their jobs. For example, the learning support section can provide information on online courses and webinars related to the job. It can also propose learning plans based on the new employee's skill level and monitor their progress. The learning support section can also periodically conduct quizzes and tests to evaluate their level of learning. This allows new employees to efficiently acquire the knowledge and skills necessary for their jobs.

[0089] The support system can also be equipped with a project management section. The project management section centrally manages the progress of projects in which new employees participate, supporting efficient project management. For example, the project management section displays a list of tasks for each project and updates the progress in real time. It can also provide a function to remind employees of important project milestones and deadlines. The project management section can also provide tools to promote communication between project members and facilitate information sharing. This makes it easier for new employees to grasp the overall picture of the project and work efficiently.

[0090] The support system can also be equipped with a data analysis unit. This unit collects and analyzes new employees' work data and provides insights that help improve work efficiency. For example, the data analysis unit can analyze task completion times and frequency to suggest efficient task management methods. It can also analyze meeting participation and comment content to suggest areas for improvement in communication. Furthermore, the data analysis unit can regularly evaluate new employees' work performance and provide feedback. This allows new employees to implement specific improvement measures based on the data and improve their work efficiency.

[0091] The support system can also be equipped with a resource management department, which efficiently manages and provides the resources required by new employees for their work. For example, the resource management department can accept requests for necessary software and hardware and provide them promptly. It can also centrally manage documents and manuals related to work, making them immediately accessible when needed. The resource management department can also grasp the usage status of in-house experts and resources and allocate resources optimally. This allows new employees to smoothly use the resources they need and carry out their work efficiently.

[0092] The processing flow of the first embodiment will be briefly explained below.

[0093] Step 1: The task management unit performs task management. For example, the task management unit monitors the progress of each task and manages the tasks based on the task classification method and progress evaluation criteria. Step 2: The reminder unit sends reminders based on the progress of the tasks managed by the task management unit. For example, the reminder unit can send reminders when a task deadline is approaching, and can also send reminders based on the timing and notification method. Step 3: The lecture department provides lectures on how to operate the system. For example, if a new employee has trouble using the system, the lecture department can provide appropriate operating procedures. They can also provide operating procedures using text, videos, or interactive tutorials. Step 4: The presentation unit presents the operation procedure lectured by the lecture unit. For example, the presentation unit may provide a video or text of the procedure for performing a specific operation, and may also present the operation procedure using a step-by-step guide or visual aid. Step 5: The inquiry unit makes an inquiry to a senior employee. For example, when a new employee inputs a question, the inquiry unit can identify an appropriate senior employee and make an inquiry based on the inquiry format and selection criteria for the person to contact. Step 6: The identification unit inquires of the senior colleague identified by the inquiry unit. For example, when there is a question about a specific task, the identification unit can identify a senior colleague who is knowledgeable about that task, and can also identify a senior colleague based on a specific task such as project management or technical support. Step 7: The coordination department arranges the meeting. For example, if a new employee requests a meeting, the coordination department can check the schedules of the participants and arrange the meeting based on the calendar synchronization method and schedule priority. Step 8: The proposal unit proposes the date and time proposed by the adjustment unit. For example, the proposal unit may propose the most convenient date and time taking into account everyone's schedules, and may also propose the date and time based on the format of the proposal and evaluation criteria of the proposal content.

[0094] (Example 2) A support system according to an embodiment of the present invention is a system that helps new employees efficiently perform their work while working remotely. The support system manages tasks, sends reminders, provides instructions on how to operate the system, confers inquiries with senior employees, and arranges meetings. For example, the support system monitors the progress of each task and sends reminders as necessary. Next, the support system presents appropriate operating procedures when a new employee has difficulty operating the system. Furthermore, when a new employee inputs a question, the support system identifies an appropriate senior employee and automatically confers inquiries. Finally, when a new employee requests a meeting, the support system checks the schedules of participants and suggests the optimal date and time. This allows the support system to help new employees efficiently perform their work while working remotely. For example, by providing various functions such as task management, case management, instruction on how to operate the system, confers inquiries with senior employees, and arranges meetings, new employees can smoothly perform their work even while working remotely.

[0095] The support system according to the embodiment includes a task management unit, a reminder unit, a lecture unit, a presentation unit, an inquiry unit, an identification unit, an adjustment unit, and a proposal unit. The task management unit performs task management. For example, the task management unit monitors the progress of each task. The task management unit can also manage tasks based on a task classification method and progress evaluation criteria. The reminder unit sends reminders based on the progress of tasks managed by the task management unit. For example, the reminder unit sends reminders when a task deadline is approaching. The reminder unit can also send reminders based on the transmission timing and notification method. The lecture unit gives lectures on how to operate the system. For example, the lecture unit presents appropriate operating procedures when a new employee has trouble operating the system. The lecture unit can also present operating procedures using text, video, or interactive tutorials. The presentation unit presents the operating procedures lectured by the lecture unit. The presentation unit provides, for example, video or text instructions for performing a specific operation. The presentation unit can also present the operation instructions using a step-by-step guide or visual aid. The inquiry unit makes an inquiry to a senior employee. For example, when a new employee inputs a question, the inquiry unit identifies an appropriate senior employee. The inquiry unit can also make an inquiry based on the inquiry format and selection criteria for the contact person. The identification unit makes an inquiry to the senior employee identified by the inquiry unit. For example, when a question is about a specific task, the identification unit identifies a senior employee who is knowledgeable about that task. The identification unit can also identify a senior employee based on a specific task, such as project management or technical support. The coordination unit arranges a meeting. For example, when a new employee requests a meeting, the coordination unit checks the schedules of the participants. The coordination unit can also arrange the meeting based on the calendar synchronization method and schedule priority. The proposal unit proposes a date and time proposed by the coordination unit. For example, the proposal unit proposes the most convenient date and time taking into account everyone's schedules.In addition, the suggestion unit can suggest a date and time based on the format of the proposal and the evaluation criteria of the proposal content. As a result, the support system according to the embodiment can support new employees to efficiently perform their work while working remotely.

[0096] The task management unit can monitor the progress of each task. For example, the task management unit can monitor the progress of each task using a real-time data collection method. The task management unit can also monitor the degree of task completion and deadline compliance based on progress evaluation criteria. Furthermore, the task management unit can provide a dashboard for visualizing the progress, allowing the progress of tasks to be understood in real time.

[0097] The reminder unit can send a reminder when a task deadline is approaching. For example, the reminder unit sends a reminder when a task deadline is approaching. The reminder unit can also send a reminder based on the sending timing and notification method. For example, the reminder unit can set how many days before the deadline the reminder should be sent. The reminder unit can also select the notification method, such as email or push notification. This can prevent task delays.

[0098] The lecture department can provide operating procedures when new employees have trouble operating the system. For example, the lecture department provides appropriate operating procedures when new employees have trouble operating the system. The lecture department can also provide operating procedures using text, video, or interactive tutorials. For example, the lecture department can provide a solution when an error message is displayed. The lecture department can also provide a detailed explanation of the procedure when a specific operation cannot be completed. This allows new employees to use the system smoothly.

[0099] The presentation unit can provide procedures for performing a specific operation using video or text. For example, the presentation unit can provide procedures for performing a specific operation using video or text. The presentation unit can also present the operation procedures using a step-by-step guide or visual aid. For example, the presentation unit can explain operation procedures such as how to change system settings or how to input data using video. The presentation unit can also use visual aids to make the operation procedures visually easier to understand. This makes it possible to make the operation procedures visually easier to understand.

[0100] The inquiry unit can identify an appropriate senior when a new employee inputs a question. For example, when a new employee inputs a question, the inquiry unit can identify an appropriate senior. The inquiry unit can also make inquiries based on the form of the inquiry and selection criteria for the person to contact. For example, the inquiry unit can identify an appropriate senior based on the senior's expertise and past response history. The inquiry unit can also select the most appropriate senior depending on the content of the question. This allows for a quick response.

[0101] When there is a question about a specific task, the identification unit can identify a senior who is knowledgeable about that task. For example, when there is a question about a specific task, the identification unit can identify a senior who is knowledgeable about that task. The identification unit can also identify a senior based on a specific task such as project management or technical support. For example, when there is a question about project management, the identification unit can identify a senior who is knowledgeable about that project. Also, when there is a question about technical support, the identification unit can identify a senior who is knowledgeable about that technology. This allows the appropriate senior to be contacted.

[0102] The coordination unit can check the schedules of participants when a new employee requests a meeting. For example, when a new employee requests a meeting, the coordination unit checks the schedules of participants. The coordination unit can also coordinate meetings based on calendar synchronization methods and schedule priorities. For example, the coordination unit can check everyone's schedules using calendar synchronization methods. The coordination unit can also suggest the optimal date and time based on schedule priorities. This allows meetings to be coordinated smoothly.

[0103] The suggestion unit can propose the most convenient date and time taking into consideration everyone's schedules. For example, the suggestion unit can propose the most convenient date and time taking into consideration everyone's schedules. The suggestion unit can also propose a date and time based on the format of the proposal and evaluation criteria for the proposal content. For example, the suggestion unit can propose the optimal date and time taking into consideration everyone's free time. The suggestion unit can also propose a date and time based on the priority of tasks with high importance. This allows the meeting to be held at the optimal date and time.

[0104] The task management unit can estimate the user's emotions and adjust task priorities based on the estimated user emotions. For example, when the user is feeling stressed, the task management unit postpones less important tasks and prioritizes more important tasks. The task management unit can also prioritize more difficult tasks when the user is relaxed. Furthermore, when the user is in a hurry, the task management unit can prioritize tasks with upcoming deadlines. This enables task management according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0105] During task management, the task management unit can analyze the user's past task completion history and select a task management method. For example, the task management unit can analyze patterns of tasks that the user has completed quickly in the past and prioritize similar tasks. The task management unit can also assign tasks in a way that avoids tasks that the user has struggled with in the past. Furthermore, the task management unit can propose an optimal task schedule based on the user's past task completion times. This enables optimal task management based on the user's past history. Some or all of the above-mentioned processing in the task management unit may be performed, for example, using AI, or may be performed without using AI.

[0106] During task management, the task management unit can filter tasks based on the user's current project or areas of interest. For example, the task management unit may preferentially display tasks related to the project the user is currently working on. The task management unit can also suggest related tasks based on the user's areas of interest. Furthermore, the task management unit can filter and display tasks related to areas in which the user has previously shown interest. This enables task management based on the user's interests. Some or all of the above-described processing in the task management unit may be performed, for example, using AI or without using AI.

[0107] The task management unit can select a management method depending on the user's input method when managing tasks. For example, if the user uses voice input, the task management unit can manage tasks using voice recognition technology. Also, if the user uses text input, the task management unit can manage tasks using text analysis technology. Furthermore, if the user uses image input, the task management unit can manage tasks using image recognition technology. This enables task management according to the user's input method. Some or all of the above-mentioned processing in the task management unit may be performed using AI, for example, or may be performed without using AI.

[0108] The task management unit can estimate the user's emotions and adjust the order in which task progress is displayed based on the estimated user emotions. For example, if the user is feeling stressed, the task management unit can prioritize displaying tasks that are progressing smoothly, thereby increasing motivation. Furthermore, if the user is relaxed, the task management unit can prioritize displaying tasks that are lagging behind, thereby enabling work to proceed efficiently. Furthermore, if the user is in a hurry, the task management unit can prioritize displaying tasks with upcoming deadlines. This makes it possible to display task progress 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.

[0109] When managing tasks, the task management unit can prioritize managing highly relevant tasks by taking into account the user's geographical location information. For example, when the user is in the office, the task management unit can prioritize displaying tasks that should be done at the office. Furthermore, when the user is at home, the task management unit can also prioritize displaying tasks that should be done at home. Furthermore, when the user is on a business trip, the task management unit can also prioritize displaying tasks that should be done at the business trip destination. This makes it possible to manage tasks based on the user's geographical location information. Some or all of the above-mentioned processing in the task management unit may be performed, for example, using AI, or may be performed without using AI.

[0110] The task management unit can analyze the user's social media activities during task management and manage related tasks. For example, the task management unit can prioritize displaying tasks related to projects mentioned by the user on social media. The task management unit can also suggest tasks related to the user's areas of interest based on the user's social media activities. Furthermore, the task management unit can also suggest related tasks based on the activities of the user's friends on social media. This enables task management based on the user's social media activities. Some or all of the above-mentioned processing in the task management unit may be performed using, for example, AI, or may be performed without using AI.

[0111] The task management unit can customize the task management method by reflecting the user's past feedback during task management. For example, the task management unit customizes the task management interface based on feedback provided by the user in the past. The task management unit can also suggest a preferred task management method based on the user's past feedback. Furthermore, the task management unit can adjust task priorities by reflecting the user's feedback. This enables customized task management based on the user's feedback. Some or all of the above-described processing in the task management unit may be performed using, for example, AI, or may be performed without using AI.

[0112] The reminder unit can estimate the user's emotions and adjust the timing of sending reminders based on the estimated user emotions. For example, if the user is feeling stressed, the reminder unit can reduce the frequency of reminder sending and notify only important tasks. Also, if the user is relaxed, the reminder unit can increase the frequency of reminder sending and notify the user of small tasks as well. Furthermore, if the user is in a hurry, the reminder unit can prioritize notifying the user of tasks with upcoming deadlines. This makes it possible to send reminders according to the user's emotions. 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.

[0113] When sending a reminder, the reminder unit can adjust the level of detail of the reminder based on the importance of the task. For example, the reminder unit sends a detailed reminder for a highly important task. The reminder unit can also send a brief reminder for a less important task. Furthermore, the reminder unit can also send a detailed reminder for a task with an approaching deadline. This makes it possible to send reminders according to the importance of the task. Some or all of the above-described processing in the reminder unit may be performed using, for example, AI, or may be performed without using AI.

[0114] The reminder unit can apply different reminder algorithms depending on the task category when sending a reminder. For example, in the case of a project task, the reminder unit sends a reminder according to the progress of the project. In addition, in the case of a daily work task, the reminder unit can also send a reminder according to the progress of the daily work. Furthermore, in the case of an urgent task, the reminder unit can also send a reminder immediately. This makes it possible to send reminders according to the task category. Some or all of the above-mentioned processing in the reminder unit may be performed using, for example, AI, or may be performed without using AI.

[0115] When sending a reminder, the reminder unit can improve the accuracy of the reminder by referring to the user's past reminder history. The reminder unit, for example, analyzes reminders that the user has ignored in the past and reduces reminders with low importance. The reminder unit can also analyze reminders that the user has responded to in the past and increase the number of similar reminders. Furthermore, the reminder unit can also suggest the optimal timing for sending a reminder based on the user's past reminder history. This makes it possible to send reminders with high accuracy based on the past reminder history. Some or all of the above-mentioned processing in the reminder unit may be performed, for example, using AI or without using AI.

[0116] The reminder unit can estimate the user's emotions and adjust the content of the reminder based on the estimated user emotions. For example, if the user is feeling stressed, the reminder unit can simplify the content of the reminder and notify only important information. Furthermore, if the user is relaxed, the reminder unit can provide detailed information and notify even minor details. Furthermore, if the user is in a hurry, the reminder unit can provide only the main points and notify quickly. This makes it possible to adjust the content of the reminder according to the user's emotions. 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.

[0117] When sending a reminder, the reminder unit can determine the priority of the reminder based on the submission time of the task. For example, the reminder unit prioritizes sending reminders for tasks with upcoming deadlines. The reminder unit can also postpone sending reminders for tasks with distant submission times. Furthermore, the reminder unit can automatically adjust the priority of the reminder if the submission time is changed. This makes it possible to determine the priority of reminders according to the submission time of the task. Some or all of the above-mentioned processing in the reminder unit may be performed, for example, using AI, or may be performed without using AI.

[0118] The reminder unit can adjust the order of reminders based on the relevance of tasks when sending reminders. For example, the reminder unit prioritizes sending reminders for highly relevant tasks. The reminder unit can also postpone sending reminders for less relevant tasks. Furthermore, the reminder unit can automatically adjust the order of reminders when the relevance of tasks changes. This makes it possible to adjust the order of reminders according to the relevance of tasks. Some or all of the above-described processing in the reminder unit may be performed using AI, for example, or may be performed without using AI.

[0119] When sending a reminder, the reminder unit can adjust the use of technical terms in the reminder depending on the user's level of expertise. For example, if the user has technical expertise, the reminder unit can send a reminder using technical terms. Also, if the user does not have technical expertise, the reminder unit can send a reminder in simple language. Furthermore, the reminder unit can automatically adjust the content of the reminder if the user's level of expertise changes. This makes it possible to adjust the content of the reminder depending on the user's level of expertise. Some or all of the above-described processing in the reminder unit may be performed using AI, for example, or may be performed without using AI.

[0120] The lecture unit can estimate the user's emotions and adjust the presentation method of the lecture based on the estimated user's emotions. For example, if the user is nervous, the lecture unit can provide a simple, highly visible lecture. If the user is relaxed, the lecture unit can also provide a lecture with detailed information. Furthermore, if the user is in a hurry, the lecture unit can also provide a lecture that focuses on the main points. This makes it possible to present the lecture in a way that suits 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 these examples.

[0121] During the lecture, the lecture unit can adjust the level of detail of the lecture based on the importance of the operation procedure. For example, the lecture unit provides a detailed lecture for an operation procedure with high importance. The lecture unit can also provide a brief lecture for an operation procedure with low importance. Furthermore, the lecture unit can also provide an immediate lecture for an operation procedure with high urgency. This makes it possible to adjust the level of detail of the lecture depending on the importance of the operation procedure. Some or all of the above-mentioned processing in the lecture unit may be performed using AI, for example, or may be performed without using AI.

[0122] During the lecture, the lecture unit can apply different lecture algorithms depending on the category of the operation procedure. For example, in the case of an operation procedure related to system setting, the lecture unit can provide a lecture specialized in system setting. In addition, in the case of an operation procedure related to data entry, the lecture unit can also provide a lecture specialized in data entry. Furthermore, in the case of an operation procedure related to troubleshooting, the lecture unit can also provide a lecture specialized in troubleshooting. This makes it possible to provide lectures according to the category of the operation procedure. Some or all of the above-mentioned processing in the lecture unit may be performed using, for example, AI, or may be performed without using AI.

[0123] The lecture unit can improve the accuracy of the lecture by referring to the user's past lecture history when giving a lecture. For example, the lecture unit can analyze the content of lectures the user has received in the past and provide similar content. The lecture unit can also suggest the optimal lecture format based on lecture formats that the user found easy to understand in the past. Furthermore, the lecture unit can also suggest the optimal timing of the lecture based on the user's past lecture history. This makes it possible to provide a highly accurate lecture based on the user's past lecture history. Some or all of the above-mentioned processing in the lecture unit may be performed, for example, using AI, or may be performed without using AI.

[0124] The lecture unit can estimate the user's emotions and adjust the length of the lecture based on the estimated user emotions. For example, if the user is nervous, the lecture unit can provide a short, to-the-point lecture. If the user is relaxed, the lecture unit can also provide a longer lecture with detailed explanations. Furthermore, if the user is in a hurry, the lecture unit can also provide a short lecture that can be quickly understood. This makes it possible to adjust the length of the lecture according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0125] During a lecture, the lecture section can determine the priority of the lecture based on the submission time of the operation procedures. For example, the lecture section gives priority to lectures on operation procedures with an approaching deadline. The lecture section can also postpone lectures on operation procedures with a more distant submission time. Furthermore, the lecture section can automatically adjust the priority of the lecture when the submission time is changed. This makes it possible to determine the priority of the lecture according to the submission time of the operation procedures. Some or all of the above-mentioned processing in the lecture section may be performed, for example, using AI, or may be performed without using AI.

[0126] During a lecture, the lecture section can adjust the order of the lecture based on the relevance of the operation procedures. For example, the lecture section gives priority to lecturing on highly relevant operation procedures. The lecture section can also postpone lecturing on less relevant operation procedures. Furthermore, the lecture section can automatically adjust the order of the lecture when the relevance of the operation procedures changes. This makes it possible to adjust the order of the lecture according to the relevance of the operation procedures. Some or all of the above-described processing in the lecture section may be performed using, for example, AI, or may be performed without using AI.

[0127] During a lecture, the lecture unit can adjust the use of technical terms in the lecture according to the user's level of expertise. For example, if the user has technical knowledge, the lecture unit can provide a lecture using technical terms. Also, if the user does not have technical knowledge, the lecture unit can provide a lecture in simple language. Furthermore, the lecture unit can automatically adjust the content of the lecture if the user's level of expertise changes. This makes it possible to adjust the content of the lecture according to the user's level of expertise. Some or all of the above-described processing in the lecture unit may be performed, for example, using AI or without using AI.

[0128] The presentation unit can estimate the user's emotions and adjust the presentation method of the steps to be presented based on the estimated user's emotions. For example, if the user is nervous, the presentation unit can present simple, highly visible steps. If the user is relaxed, the presentation unit can also present steps that include detailed information. Furthermore, if the user is in a hurry, the presentation unit can present steps that focus on the main points. This makes it possible to present steps that correspond to the user's emotions. Emotion estimation is realized 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.

[0129] The presentation unit can adjust the level of detail of the presentation based on the importance of the operation procedure when presenting it. For example, the presentation unit presents detailed procedures for operation procedures with high importance. The presentation unit can also present concise procedures for operation procedures with low importance. Furthermore, the presentation unit can also immediately present procedures for operation procedures with high urgency. This makes it possible to adjust the level of detail of the presentation depending on the importance of the operation procedure. Some or all of the above-mentioned processing in the presentation unit may be performed using AI, for example, or may be performed without using AI.

[0130] The presentation unit can apply different presentation algorithms depending on the category of the operation procedure when presenting the operation procedure. For example, in the case of an operation procedure related to system setting, the presentation unit presents a procedure specialized for system setting. In addition, in the case of an operation procedure related to data entry, the presentation unit can also present a procedure specialized for data entry. Furthermore, in the case of an operation procedure related to troubleshooting, the presentation unit can also present a procedure specialized for troubleshooting. This makes it possible to present procedures according to the category of the operation procedure. Some or all of the above-mentioned processing in the presentation unit may be performed using AI, for example, or may be performed without using AI.

[0131] The presentation unit can improve the accuracy of the presentation by referring to the user's past presentation history when presenting the procedure. The presentation unit, for example, analyzes the content of presentations the user has received in the past and provides similar content. The presentation unit can also suggest an optimal format based on presentation formats that the user has found easy to understand in the past. Furthermore, the presentation unit can also suggest the optimal timing of presentation based on the user's past presentation history. This makes it possible to present procedures with high accuracy based on the past presentation history. Some or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI.

[0132] The presentation unit can estimate the user's emotions and adjust the length of the presentation based on the estimated user emotions. For example, if the user is nervous, the presentation unit can provide a short, to-the-point presentation. If the user is relaxed, the presentation unit can also provide a longer presentation with detailed explanations. Furthermore, if the user is in a hurry, the presentation unit can also provide a short presentation that can be quickly understood. This makes it possible to adjust the length of the procedure according to the user's emotions. Emotion estimation is realized 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.

[0133] The presentation unit can determine the priority of presentation based on the submission time of the operation procedures when presenting them. For example, the presentation unit preferentially presents operation procedures with an approaching deadline. The presentation unit can also postpone the presentation of operation procedures with a distant submission time. Furthermore, the presentation unit can automatically adjust the priority of presentation when the submission time is changed. This makes it possible to present procedures according to the submission time of the operation procedures. Some or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI.

[0134] The presentation unit can adjust the order of presentation based on the relevance of the operation procedures when presenting them. For example, the presentation unit prioritizes the presentation of highly relevant operation procedures. The presentation unit can also postpone the presentation of less relevant operation procedures. Furthermore, the presentation unit can automatically adjust the order of presentation when the relevance of the operation procedures changes. This makes it possible to present the operation procedures according to the relevance of the operation procedures. Some or all of the above-mentioned processing in the presentation unit may be performed using AI, for example, or may be performed without using AI.

[0135] The presentation unit can adjust the use of technical terms in the presentation depending on the user's level of expertise. For example, if the user has technical expertise, the presentation unit provides the presentation using technical terms. Also, if the user does not have technical expertise, the presentation unit can provide the presentation in simple language. Furthermore, the presentation unit can automatically adjust the content of the presentation when the user's level of expertise changes. This makes it possible to present procedures according to the user's level of expertise. Some or all of the above-mentioned processing in the presentation unit may be performed using AI, for example, or may be performed without using AI.

[0136] The query unit can estimate the user's emotions and adjust the way the query is expressed based on the estimated user's emotions. For example, if the user is nervous, the query unit can provide a simple, highly visible query. If the user is relaxed, the query unit can also provide a query that includes detailed information. Furthermore, if the user is in a hurry, the query unit can also provide a query that focuses on the main points. This makes it possible to express the query in a way that suits 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 these examples.

[0137] The inquiry unit can adjust the level of detail of the inquiry based on the importance of the question when making an inquiry. For example, the inquiry unit provides a detailed inquiry for a question of high importance. The inquiry unit can also provide a concise inquiry for a question of low importance. Furthermore, the inquiry unit can also provide an inquiry immediately for a question of high urgency. This makes it possible to adjust the level of detail of the inquiry depending on the importance of the question. Some or all of the above-mentioned processing in the inquiry unit may be performed using AI, for example, or may be performed without using AI.

[0138] When making an inquiry, the inquiry unit can apply different inquiry algorithms depending on the category of the question. For example, in the case of a technical question, the inquiry unit applies a technical inquiry algorithm. In addition, in the case of a question about a business process, the inquiry unit can also apply an inquiry algorithm specialized for the business process. Furthermore, in the case of a question about human resources, the inquiry unit can also apply an inquiry algorithm specialized for human resources. This makes it possible to make inquiries according to the category of the question. Some or all of the above-mentioned processing in the inquiry unit may be performed, for example, using AI or may be performed without using AI.

[0139] When making an inquiry, the inquiry unit can improve the accuracy of the inquiry by referring to the user's past inquiry history. The inquiry unit, for example, analyzes the content of inquiries made by the user in the past and provides similar content. The inquiry unit can also suggest the optimal format based on inquiry formats that the user found easy to understand in the past. Furthermore, the inquiry unit can also suggest the optimal timing for making an inquiry based on the user's past inquiry history. This enables highly accurate inquiries based on the past inquiry history. Some or all of the above-mentioned processing in the inquiry unit may be performed, for example, using AI, or may be performed without using AI.

[0140] The query unit can estimate the user's emotions and adjust the length of the query based on the estimated user's emotions. For example, if the user is nervous, the query unit can provide a short, to-the-point query. If the user is relaxed, the query unit can also provide a longer query with detailed explanations. Furthermore, if the user is in a hurry, the query unit can also provide a short query that can be quickly understood. This makes it possible to adjust the length of the query according to the user's emotions. Emotion estimation is achieved 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 these examples.

[0141] The inquiry unit can determine the priority of an inquiry based on the time of submission of the question at the time of inquiry. For example, the inquiry unit prioritizes inquiries for questions with an approaching deadline. The inquiry unit can also postpone inquiries for questions with a more distant submission date. Furthermore, the inquiry unit can automatically adjust the priority of an inquiry when the submission date is changed. This makes it possible to determine the priority of an inquiry according to the time of submission of the question. Some or all of the above-described processing in the inquiry unit may be performed, for example, using AI, or may be performed without using AI.

[0142] The inquiry unit can adjust the order of inquiries based on the relevance of the questions when making inquiries. For example, the inquiry unit prioritizes inquiries for highly relevant questions. The inquiry unit can also postpone inquiries for less relevant questions. Furthermore, the inquiry unit can automatically adjust the order of inquiries when the relevance of questions changes. This makes it possible to adjust the order of inquiries according to the relevance of questions. Some or all of the above-described processing in the inquiry unit may be performed, for example, using AI, or may be performed without using AI.

[0143] The query unit can adjust the use of technical terms in the query depending on the user's level of expertise when making a query. For example, if the user has technical expertise, the query unit can provide a query using technical terms. Also, if the user does not have technical expertise, the query unit can provide a query in simple language. Furthermore, the query unit can automatically adjust the content of the query when the user's level of expertise changes. This makes it possible to make a query depending on the user's level of expertise. Some or all of the above-mentioned processing in the query unit may be performed using AI, for example, or may be performed without using AI.

[0144] The identification unit can estimate the user's emotions and adjust the selection criteria for the senior colleague to be identified based on the estimated user's emotions. For example, when the user is nervous, the identification unit can prioritize identifying a friendly senior colleague. Furthermore, when the user is relaxed, the identification unit can prioritize identifying a senior colleague with high expertise. Furthermore, when the user is in a hurry, the identification unit can prioritize identifying a senior colleague who can respond quickly. This makes it possible to select a senior colleague according to the user's emotions. 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 these examples.

[0145] The identification unit can adjust the level of detail of the identification based on the importance of the question during identification. For example, in the case of a question of high importance, the identification unit performs detailed identification. In addition, in the case of a question of low importance, the identification unit can also perform concise identification. Furthermore, in the case of a question of high urgency, the identification unit can also perform immediate identification. This makes it possible to identify a senior employee according to the importance of the question. Some or all of the above-mentioned processing in the identification unit may be performed, for example, using AI or may be performed without using AI.

[0146] During identification, the identification unit can apply different identification algorithms depending on the category of the question. For example, in the case of a technical question, the identification unit applies a technical identification algorithm. Furthermore, in the case of a question about a business process, the identification unit can also apply an identification algorithm specialized for the business process. Furthermore, in the case of a question about human resources, the identification unit can also apply an identification algorithm specialized for human resources. This makes it possible to identify a senior colleague according to the category of the question. Some or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI.

[0147] During identification, the identification unit can improve the accuracy of identification by referring to the user's past identification history. The identification unit, for example, analyzes the identification content that the user has performed in the past and provides similar content. The identification unit can also suggest the optimal format based on identification formats that the user found easy to understand in the past. Furthermore, the identification unit can also suggest the optimal timing for identification based on the user's past identification history. This enables highly accurate identification of seniors based on the past identification history. Some or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI.

[0148] The identification unit can estimate the user's emotions and adjust the length of the identification based on the estimated user's emotions. For example, if the user is nervous, the identification unit can provide a short, to-the-point identification. If the user is relaxed, the identification unit can also provide a longer identification with detailed explanations. Furthermore, if the user is in a hurry, the identification unit can also provide a short identification that can be quickly understood. This makes it possible to identify a senior according to the user's emotions. 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.

[0149] At the time of identification, the identification unit can determine the priority of identification based on the time of submission of the question. For example, the identification unit prioritizes identifying questions with an approaching deadline. The identification unit can also postpone identification of questions with a more distant submission date. Furthermore, the identification unit can automatically adjust the priority of identification when the submission date is changed. This makes it possible to identify a senior questioner according to the time of submission of the question. Some or all of the above-described processing in the identification unit may be performed, for example, using AI, or may be performed without using AI.

[0150] The identification unit can adjust the identification order based on the relevance of the questions during identification. For example, the identification unit prioritizes identifying highly relevant questions. The identification unit can also postpone identification of less relevant questions. Furthermore, the identification unit can automatically adjust the identification order when the relevance of the questions changes. This makes it possible to identify seniors according to the relevance of the questions. Some or all of the above-described processing in the identification unit may be performed, for example, using AI, or may be performed without using AI.

[0151] The identification unit can adjust the use of specific technical terms during identification according to the user's level of expertise. For example, if the user has specialized knowledge, the identification unit provides identification using technical terms. Also, if the user does not have specialized knowledge, the identification unit can provide identification in simple language. Furthermore, the identification unit can automatically adjust the content of the identification if the user's level of expertise changes. This makes it possible to identify seniors according to the user's level of expertise. Some or all of the above-mentioned processing in the identification unit may be performed, for example, using AI or without using AI.

[0152] The adjustment unit can estimate the user's emotion and adjust the adjustment method based on the estimated user's emotion. For example, if the user is nervous, the adjustment unit provides a simple and highly visible adjustment method. Furthermore, if the user is relaxed, the adjustment unit can also provide an adjustment method that includes detailed information. Furthermore, if the user is in a hurry, the adjustment unit can also provide an adjustment method that focuses on the main points. This makes it possible to use an adjustment method that suits the user's emotion. Emotion estimation is realized 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.

[0153] The adjustment unit can adjust the level of detail of the adjustment based on the importance of the meeting during adjustment. For example, in the case of a highly important meeting, the adjustment unit performs detailed adjustment. In addition, in the case of a less important meeting, the adjustment unit can also perform simple adjustment. Furthermore, in the case of a highly urgent meeting, the adjustment unit can also perform immediate adjustment. This makes it possible to make adjustments according to the importance of the meeting. Some or all of the above-mentioned processing in the adjustment unit may be performed using AI, for example, or may be performed without using AI.

[0154] During coordination, the coordination unit can apply different coordination algorithms depending on the category of the meeting. For example, in the case of a technical meeting, the coordination unit applies a technical coordination algorithm. Furthermore, in the case of a meeting regarding a business process, the coordination unit can also apply a coordination algorithm specialized for the business process. Furthermore, in the case of a meeting regarding personnel, the coordination unit can also apply a coordination algorithm specialized for personnel. This makes it possible to coordinate according to the category of the meeting. Some or all of the above-mentioned processing in the coordination unit may be performed, for example, using AI, or may be performed without using AI.

[0155] During adjustment, the adjustment unit can improve the accuracy of the adjustment by referring to the user's past adjustment history. The adjustment unit, for example, analyzes the content of adjustments made by the user in the past and provides similar content. The adjustment unit can also suggest an optimal format based on adjustment formats that the user found easy to understand in the past. Furthermore, the adjustment unit can also suggest the optimal timing for adjustment based on the user's past adjustment history. This enables highly accurate adjustment based on the past adjustment history. Some or all of the above-described processing in the adjustment unit may be performed, for example, using AI, or may be performed without using AI.

[0156] The adjustment unit can estimate the user's emotion and adjust the length of the adjustment based on the estimated user's emotion. For example, if the user is nervous, the adjustment unit can provide a short, to-the-point adjustment. If the user is relaxed, the adjustment unit can also provide a longer adjustment with detailed explanations. Furthermore, if the user is in a hurry, the adjustment unit can also provide a short adjustment that can be quickly understood. This allows the length of the adjustment to be adjusted according to the user's emotion. 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.

[0157] During adjustment, the adjustment unit can determine the priority of adjustment based on the submission date of the meeting. For example, the adjustment unit prioritizes adjustment of meetings with an approaching deadline. The adjustment unit can also postpone adjustment of meetings with a distant submission date. Furthermore, the adjustment unit can automatically adjust the priority of adjustment when the submission date is changed. This makes it possible to make adjustments according to the submission date of the meeting. Some or all of the above-mentioned processing in the adjustment unit may be performed using AI, for example, or may be performed without using AI.

[0158] The adjustment unit can adjust the order of adjustments based on the relevance of meetings during adjustment. For example, the adjustment unit prioritizes adjustments for highly relevant meetings. The adjustment unit can also postpone adjustments for less relevant meetings. Furthermore, the adjustment unit can automatically adjust the order of adjustments when the relevance of meetings changes. This makes it possible to adjust according to the relevance of meetings. Some or all of the above-described processing in the adjustment unit may be performed using AI, for example, or may be performed without using AI.

[0159] During the adjustment, the adjustment unit can adjust the use of technical terms in the adjustment according to the user's level of expertise. For example, if the user has technical expertise, the adjustment unit provides the adjustment using technical terms. Furthermore, if the user does not have technical expertise, the adjustment unit can also provide the adjustment in simple language. Furthermore, the adjustment unit can automatically adjust the content of the adjustment if the user's level of expertise changes. This enables adjustment according to the user's level of expertise. Some or all of the above-described processing in the adjustment unit may be performed using AI, for example, or may be performed without using AI.

[0160] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. For example, if the user is nervous, the suggestion unit can provide simple, highly visible suggestions. If the user is relaxed, the suggestion unit can also provide suggestions that include detailed information. Furthermore, if the user is in a hurry, the suggestion unit can also provide suggestions that focus on the main points. This enables suggestions to be expressed in a way that suits 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 these examples.

[0161] When making a proposal, the proposal unit can adjust the level of detail of the proposal based on the importance of the meeting. For example, in the case of a highly important meeting, the proposal unit makes a detailed proposal. In addition, in the case of a less important meeting, the proposal unit can also make a concise proposal. Furthermore, in the case of a highly urgent meeting, the proposal unit can also make an immediate proposal. This makes it possible to make a proposal according to the importance of the meeting. Some or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI or may be performed without using AI.

[0162] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the meeting. For example, in the case of a technical meeting, the proposal unit applies a technical proposal algorithm. In addition, in the case of a meeting regarding a business process, the proposal unit can also apply a proposal algorithm specialized for the business process. Furthermore, in the case of a meeting regarding human resources, the proposal unit can also apply a proposal algorithm specialized for human resources. This makes it possible to make proposals according to the category of the meeting. Some or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI, or may be performed without using AI.

[0163] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal history. The suggestion unit, for example, analyzes the content of proposals made by the user in the past and provides similar content. The suggestion unit can also suggest the optimal format based on proposal formats that the user found easy to understand in the past. Furthermore, the suggestion unit can also suggest the optimal timing for making the proposal based on the user's past proposal history. This enables highly accurate suggestions based on the past proposal history. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI.

[0164] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is nervous, the suggestion unit can provide short, to-the-point suggestions. If the user is relaxed, the suggestion unit can also provide longer suggestions with detailed explanations. If the user is in a hurry, the suggestion unit can also provide short suggestions that can be quickly understood. This allows the length of the suggestions to be tailored to the user's emotions. 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0165] When making a proposal, the proposal unit can determine the priority of the proposal based on the submission date of the meeting. For example, the proposal unit prioritizes proposing meetings with an approaching deadline. The proposal unit can also postpone proposals for meetings with a distant submission date. Furthermore, the proposal unit can automatically adjust the priority of the proposal when the submission date is changed. This makes it possible to make proposals according to the submission date of the meeting. Some or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI, or may be performed without using AI.

[0166] The proposal unit can adjust the order of proposals based on the relevance of meetings when making proposals. For example, the proposal unit prioritizes proposing highly relevant meetings. The proposal unit can also postpone proposing less relevant meetings. Furthermore, the proposal unit can automatically adjust the order of proposals when the relevance of meetings changes. This makes it possible to make proposals based on the relevance of meetings. Some or all of the above-described processing in the proposal unit may be performed using AI, for example, or may be performed without using AI.

[0167] When making a proposal, the suggestion unit can adjust the use of technical terms in the proposal depending on the user's level of expertise. For example, if the user has technical expertise, the suggestion unit provides a proposal using technical terms. Also, if the user does not have technical expertise, the suggestion unit can provide a proposal in simple language. Furthermore, the suggestion unit can automatically adjust the content of the proposal when the user's level of expertise changes. This makes it possible to make a proposal according to the user's level of expertise. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. === Hard Collateral 1-1 === Each of the multiple elements, including the task management unit, reminder unit, lecture unit, presentation unit, inquiry unit, identification unit, adjustment unit, and proposal unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the task management unit is realized by the control unit 46A of the smart device 14 and monitors the progress of each task. The reminder unit is realized by the identification processing unit 290 of the data processing device 12 and sends a reminder when a task deadline approaches. The lecture unit and presentation unit are realized by the control unit 46A of the smart device 14 and present appropriate operating procedures when a new employee is unsure how to operate the system. The inquiry unit and proposal unit are realized by the identification processing unit 290 of the data processing device 12 and, when a new employee inputs a question, identify an appropriate senior colleague and make an inquiry. The adjustment unit and proposal unit are realized by the control unit 46A of the smart device 14 and, when a new employee requests a meeting, check the schedules of the participants and propose the optimal date and time. === Hard Collateral 1-2 === Each of the multiple elements, including the task management unit, reminder unit, lecture unit, presentation unit, inquiry unit, identification unit, adjustment unit, and proposal unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the task management unit is realized by the control unit 46A of the smart glasses 214 and monitors the progress of each task. The reminder unit is realized by the identification processing unit 290 of the data processing device 12 and sends a reminder when a task deadline approaches. The lecture unit and presentation unit are realized by the control unit 46A of the smart glasses 214 and present appropriate operating procedures when a new employee is unsure how to operate the system. The inquiry unit and proposal unit are realized by the identification processing unit 290 of the data processing device 12 and, when a new employee inputs a question, identify an appropriate senior colleague and make an inquiry. The adjustment unit and proposal unit are realized by the control unit 46A of the smart glasses 214 and, when a new employee requests a meeting, check the schedules of the participants and propose the optimal date and time. === Hard Collateral 1-3 === Each of the multiple elements, including the task management unit, reminder unit, lecture unit, presentation unit, inquiry unit, identification unit, adjustment unit, and proposal unit, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the task management unit is realized by the control unit 46A of the headset terminal 314 and monitors the progress of each task. The reminder unit is realized by the identification processing unit 290 of the data processing device 12 and sends a reminder when a task deadline approaches. The lecture unit and presentation unit are realized by the control unit 46A of the headset terminal 314 and present appropriate operating procedures when a new employee is unsure how to operate the system. The inquiry unit and proposal unit are realized by the identification processing unit 290 of the data processing device 12 and, when a new employee inputs a question, identify an appropriate senior colleague and make an inquiry. The adjustment unit and proposal unit are realized by the control unit 46A of the headset terminal 314 and, when a new employee requests a meeting, check the schedules of the participants and propose the optimal date and time. === Hard Collateral 1-4 === Each of the multiple elements, including the task management unit, reminder unit, lecture unit, presentation unit, inquiry unit, identification unit, adjustment unit, and proposal unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the task management unit is realized by the control unit 46A of the robot 414 and monitors the progress of each task. The reminder unit is realized by the identification processing unit 290 of the data processing device 12 and sends a reminder when a task deadline approaches. The lecture unit and presentation unit are realized by the control unit 46A of the robot 414 and present appropriate operating procedures when a new employee is unsure how to operate the system. The inquiry unit and identification unit are realized by the identification processing unit 290 of the data processing device 12 and, when a new employee inputs a question, identify an appropriate senior employee and make an inquiry. The adjustment unit and proposal unit are realized by the control unit 46A of the robot 414 and, when a new employee requests a meeting, check the schedules of the participants and propose the optimal date and time.

[0168] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0169] The support system can further include a feedback collection unit. The feedback collection unit prompts new employees to enter feedback after each task or meeting. For example, the feedback collection unit can automatically display a feedback form after a task is completed to collect opinions about the difficulty of the task and the time required. The feedback collection unit can also request feedback from all participants after a meeting is completed to collect information about the effectiveness of the meeting and areas for improvement. Furthermore, the feedback collection unit can periodically request feedback from new employees about the overall work environment and usability of the support system. This allows the support system to improve the system and optimize the support method for new employees based on the collected feedback.

[0170] The support system can also be equipped with a motivation management unit, which provides functions to maintain and improve the motivation of new employees. For example, the motivation management unit can display messages and badges that give new employees a sense of accomplishment when they complete a task. It can also provide a system that awards reward points each time a certain task is completed, allowing employees to earn benefits by accumulating points. Furthermore, the motivation management unit can send encouraging messages and advice according to the new employee's progress. This allows new employees to perform their work while maintaining high motivation.

[0171] The support system can also include a health management unit. The health management unit monitors the health status of new employees and provides appropriate advice. For example, the health management unit can send reminders to take breaks if employees have been working for a long period of time. It can also send notifications to encourage regular stretching and light exercise. Furthermore, the health management unit can provide advice on the new employees' work environment and posture, and support them in maintaining a healthy remote work environment. This allows new employees to perform their work efficiently while staying healthy.

[0172] The support system can further include a learning support section. The learning support section supports new employees in acquiring the knowledge and skills necessary for their jobs. For example, the learning support section can provide information on online courses and webinars related to the job. It can also propose learning plans based on the new employee's skill level and monitor their progress. The learning support section can also periodically conduct quizzes and tests to evaluate their level of learning. This allows new employees to efficiently acquire the knowledge and skills necessary for their jobs.

[0173] The support system can also be equipped with a communication promotion section, which provides functions to facilitate smooth communication between new employees and senior employees and colleagues. For example, the communication promotion section can propose regular online meetings and team-building events. It can also integrate chat and video call functions to support quick communication. Furthermore, the communication promotion section can introduce a mentor system that new employees can easily consult when they are in trouble and schedule regular meetings with the mentor. This allows new employees to work without feeling isolated.

[0174] The support system can also be equipped with a project management section. The project management section centrally manages the progress of projects in which new employees participate, supporting efficient project management. For example, the project management section displays a list of tasks for each project and updates the progress in real time. It can also provide a function to remind employees of important project milestones and deadlines. The project management section can also provide tools to promote communication between project members and facilitate information sharing. This makes it easier for new employees to grasp the overall picture of the project and work efficiently.

[0175] The support system can also include a career planning department. The career planning department sets career goals for new employees and provides support to help them achieve them. For example, the career planning department can suggest career paths based on the new employees' skills and interests. It can also provide regular career counseling and advice to help them achieve their goals. Furthermore, the career planning department can notify new employees of opportunities for internal transfers and promotions and provide them with information to help them proactively advance their careers. This allows new employees to work while having long-term career goals in mind.

[0176] The support system can also be equipped with a data analysis unit. This unit collects and analyzes new employees' work data and provides insights that help improve work efficiency. For example, the data analysis unit can analyze task completion times and frequency to suggest efficient task management methods. It can also analyze meeting participation and comment content to suggest areas for improvement in communication. Furthermore, the data analysis unit can regularly evaluate new employees' work performance and provide feedback. This allows new employees to implement specific improvement measures based on the data and improve their work efficiency.

[0177] The support system can also be equipped with a resource management department, which efficiently manages and provides the resources required by new employees for their work. For example, the resource management department can accept requests for necessary software and hardware and provide them promptly. It can also centrally manage documents and manuals related to work, making them immediately accessible when needed. The resource management department can also grasp the usage status of in-house experts and resources and allocate resources optimally. This allows new employees to smoothly use the resources they need and carry out their work efficiently.

[0178] The support system can also include a cultural adaptation support department, which helps new employees adapt to the company's culture and values ​​as quickly as possible. For example, the cultural adaptation support department can provide information about the company's history, mission, and vision. It can also provide opportunities to participate in internal events and activities, increasing opportunities for new employees to be exposed to the company's culture. Furthermore, the cultural adaptation support department can provide training using specific examples and scenarios to help new employees understand the company's values ​​and code of conduct. This helps new employees become more aware of themselves as part of the company and adapt quickly.

[0179] The processing flow of the second embodiment will be briefly explained below.

[0180] Step 1: The task management unit performs task management. For example, the task management unit monitors the progress of each task and manages the tasks based on the task classification method and progress evaluation criteria. Step 2: The reminder unit sends reminders based on the progress of the tasks managed by the task management unit. For example, the reminder unit can send reminders when a task deadline is approaching, and can also send reminders based on the timing and notification method. Step 3: The lecture department provides lectures on how to operate the system. For example, if a new employee has trouble using the system, the lecture department can provide appropriate operating procedures. They can also provide operating procedures using text, videos, or interactive tutorials. Step 4: The presentation unit presents the operation procedure lectured by the lecture unit. For example, the presentation unit may provide a video or text of the procedure for performing a specific operation, and may also present the operation procedure using a step-by-step guide or visual aid. Step 5: The inquiry unit makes an inquiry to a senior employee. For example, when a new employee inputs a question, the inquiry unit can identify an appropriate senior employee and make an inquiry based on the inquiry format and selection criteria for the person to contact. Step 6: The identification unit inquires of the senior colleague identified by the inquiry unit. For example, when there is a question about a specific task, the identification unit can identify a senior colleague who is knowledgeable about that task, and can also identify a senior colleague based on a specific task such as project management or technical support. Step 7: The coordination department arranges the meeting. For example, if a new employee requests a meeting, the coordination department can check the schedules of the participants and arrange the meeting based on the calendar synchronization method and schedule priority. Step 8: The proposal unit proposes the date and time proposed by the adjustment unit. For example, the proposal unit may propose the most convenient date and time taking into account everyone's schedules, and may also propose the date and time based on the format of the proposal and evaluation criteria of the proposal content.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0185] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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).

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0201] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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).

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0217] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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).

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] 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).

[0238] 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.

[0239] 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."

[0240] 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.

[0241] 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.

[0242] 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.

[0243] 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.

[0244] 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.

[0245] 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.

[0246] 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.

[0247] 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.

[0248] 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.

[0249] 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.

[0250] 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.

[0251] 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.

[0252] [Explanation of symbols]

[0253] 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. a task management section that manages tasks; a reminder unit that sends reminders based on the progress of the tasks managed by the task management unit; A lecture section that gives lectures on how to operate the system, a presentation unit that presents the operation procedure lectured by the lecture unit; An inquiry department that makes inquiries to seniors; an identification unit that makes an inquiry to the senior employee identified by the inquiry unit; A coordination department that coordinates meetings; a proposal unit that proposes a date and time proposed by the adjustment unit. A system characterized by:

2. The task management unit Monitor the progress of each task 2. The system of claim 1.

3. The reminder unit Send reminders when a task is due soon 2. The system of claim 1.

4. The lecture section Provide instructions to new employees when they are having trouble using the system 2. The system of claim 1.

5. The presentation unit Providing video and text instructions for performing specific operations 2. The system of claim 1.

6. The inquiry unit New employees can input their questions and the system will identify the appropriate senior employee.

2. The system of claim 1.

7. The identification unit If you have a question about a specific job, identify a senior colleague who is knowledgeable about that job.

2. The system of claim 1.

8. The adjustment unit Check the schedules of participants when a new employee requests a meeting 2. The system of claim 1.

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