system

Abstract

The system according to this embodiment aims to support the smooth start and progress of work for new members. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, and an execution unit. The collection unit collects the contents and update history of past company documents. The analysis unit analyzes the documents collected by the collection unit and visualizes tasks. The proposal unit proposes the next tasks based on the tasks visualized by the analysis unit. The execution unit executes the parts of the tasks proposed by the proposal unit that can be automated.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a lack of an efficient means to support the smooth start and progress of work for new members, and there is a problem that it takes time and labor. <00000​​​​​​​​​The system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and an execution unit. The collection unit collects the contents and update history of past company documents. The analysis unit analyzes the documents collected by the collection unit to visualize tasks. The proposal unit proposes the next tasks based on the tasks visualized by the analysis unit. The execution unit executes the parts of the tasks proposed by the proposal unit that can be automated. [Effects of the Invention]

[0007] The system according to this embodiment can support the smooth start and progress of work for new members. [Brief explanation of the drawing]

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

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

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

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

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

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0028] (Example of form 1) An onboarding agent according to an embodiment of the present invention is a system that supports the smooth start and progress of work for new members. This onboarding agent visualizes tasks aligned with a timeline from the start to the end of a series of tasks, based on the content and update history of past internal documents (e.g., Confluence, Box, etc.). Next, the agent proposes the next task according to the progress and supports the creation of documents and communication with stakeholders. Furthermore, the agent performs tasks that do not require human intervention. This mechanism allows new members to smoothly start and progress with their work. For example, the onboarding agent collects the content and update history of past internal documents. In this case, the internal documents include business flows and task details. For example, documents are collected from platforms such as Confluence and Box. This allows for an understanding of past business flows and task details. Next, based on the collected documents, the agent visualizes tasks aligned with a timeline from the start to the end of a series of tasks. For example, each step from the start to the end of the work is displayed on the timeline, and the tasks corresponding to each step are clearly indicated. This makes it easier for new members to understand the flow of work. Furthermore, the agent proposes the next task according to the progress. For example, the agent suggests the next task based on the progress of the work. This allows new members to clearly understand what they need to do. The agent also supports document creation and communication with stakeholders. For example, they provide templates for necessary documents and support communication with stakeholders. Finally, the agent performs tasks that do not require human intervention. For example, the agent automates routine tasks such as creating standardized documents and entering data. This allows new members to focus on important tasks. This system allows new members to start and progress smoothly in their work. It also reduces the time and effort that existing members spend supporting new members.For example, if a new member is unsure of the process or if the documentation is outdated and doesn't reflect the latest workflow, an onboarding agent can suggest appropriate tasks and provide support, ensuring smooth progress. This allows onboarding agents to help new members get started and progress smoothly with their work.

[0029] The onboarding agent according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and an execution unit. The collection unit collects the contents and update history of past internal company documents. Past internal company documents include, but are not limited to, reports, meeting minutes, and technical documents. The collection unit collects documents from, for example, internal databases and file storage systems. The collection unit can also collect documents that include update history. For example, the collection unit collects update history that includes information such as the date and time of change, the content of the change, and the person who made the change. The analysis unit analyzes the documents collected by the collection unit to visualize tasks. Task visualization is performed in the form of, for example, a Gantt chart, a flowchart, or a task list, but is not limited to these examples. The analysis unit visualizes the start date, end date, and progress of tasks using, for example, a Gantt chart. The analysis unit can also visually display the flow of tasks using a flowchart. The analysis unit can also display detailed information of each task using a task list. The proposal unit proposes the next task based on the tasks visualized by the analysis unit. The proposal unit proposes the next task based on, for example, task priority, dependencies, and progress. The proposal unit can also propose the next task to be performed based on task priority, dependencies, and progress. The execution unit executes the parts of the tasks proposed by the proposal unit that can be automated. The execution unit can automatically create standardized documents and input data, for example. The execution unit can create standardized documents using report templates, for example. The execution unit can also automatically input data using data entry forms. The execution unit can also automatically execute routine tasks. As a result, the onboarding agent according to the embodiment enables new members to smoothly start and progress with their work.

[0030] The data collection department collects the content and update history of past internal company documents. Past internal company documents include, but are not limited to, reports, meeting minutes, and technical documents. The data collection department collects documents from, for example, internal databases and file storage systems. Specifically, internal databases store reports, meeting minutes, and technical documents created by each department, and these documents are updated regularly. The data collection department automatically scans these documents to obtain the latest information. The data collection department can also collect documents that include update history. For example, the data collection department collects update history information including the date and time of change, the content of the change, and the person who made the change. This allows the data collection department to track the change history of documents and understand what changes have been made. Furthermore, the data collection department also collects documents from internal file storage systems. File storage systems store files shared by each department, and these files are backed up regularly. The data collection department scans these files to obtain the latest information. This allows the data collection department to comprehensively collect all internal company documents and stay up-to-date.

[0031] The analysis unit analyzes the data collected by the data collection unit to visualize tasks. Task visualization is performed in the form of, for example, a Gantt chart, a flowchart, or a task list, but is not limited to these examples. For example, the analysis unit uses a Gantt chart to visualize the start date, end date, and progress of tasks. A Gantt chart is a tool for visually displaying task schedules, displaying the start and end dates of each task in bar graph format. This allows for a quick overview of the progress of each task. The analysis unit can also visually display the flow of tasks using a flowchart. A flowchart is a tool for visually displaying the flow of tasks, indicating the order and dependencies of each task with arrows. This allows for a quick overview of the flow of each task. The analysis unit can also display detailed information of each task using a task list. A task list is a tool for displaying detailed information of each task in a table format, displaying the name, start date, end date, and progress of each task in a list. This allows for a quick overview of the detailed information of each task. Furthermore, the analysis unit can use AI to analyze the collected data and automatically extract task priorities and dependencies. This allows the analysis unit to quickly and accurately analyze the collected data and visualize the task.

[0032] The suggestion unit proposes the next task based on the tasks visualized by the analysis unit. The suggestion unit proposes the next task based, for example, on task priority, dependencies, and progress. Specifically, the suggestion unit proposes the next task to be performed based on task priority. Task priority is determined based on the importance and urgency of the task. The suggestion unit evaluates the task priority and proposes the most important task as the next task to be performed. The suggestion unit can also propose the next task to be performed based on task dependencies. Task dependencies indicate whether one task depends on another. The suggestion unit evaluates the task dependencies and proposes the dependent tasks in order. Furthermore, the suggestion unit can also propose the next task to be performed based on task progress. Task progress indicates the degree of completion or progress of a task. The suggestion unit evaluates the task progress and proposes the ongoing task as the next task to be performed. This allows the suggestion unit to quickly and accurately propose the next task to be performed based on task priority, dependencies, and progress. Furthermore, the suggestion unit can automate task proposals using AI. The AI ​​learns from the history and patterns of past tasks and suggests the most suitable task. This allows the suggestion unit to propose the next task efficiently and effectively.

[0033] The execution unit will carry out the parts of the tasks proposed by the proposal unit that can be automated. For example, the execution unit will automatically create standardized documents and input data. Specifically, the execution unit will create standardized documents using report templates. Report templates are documents with a standardized format, allowing for easy report creation simply by entering the necessary information. The execution unit will automatically input the necessary information into the report template and create standardized documents. The execution unit can also automatically input data using data entry forms. Data entry forms are forms with a standardized format, allowing for easy data entry simply by entering the necessary information. The execution unit will automatically input the necessary information into the data entry forms and input the data. Furthermore, the execution unit can also automatically execute routine tasks. Routine tasks are tasks that are performed periodically and are time-consuming to perform manually, but can be performed efficiently through automation. The execution unit will automatically execute routine tasks and complete them efficiently. This allows the execution unit to quickly and accurately carry out the parts of the tasks proposed by the proposal unit that can be automated. Furthermore, the execution unit can automate task execution using AI. AI learns from the history and patterns of past tasks and executes them in the most optimal way. This allows the execution unit to perform tasks efficiently and effectively.

[0034] The suggestion department can propose the next task according to the progress. For example, the suggestion department can propose the next task based on the completion rate and adherence to deadlines of the current task. For example, the suggestion department can propose the next task when the completion rate of the current task exceeds 50%. The suggestion department can also propose the next task based on adherence to deadlines. For example, the suggestion department can prioritize proposing tasks with approaching deadlines. This improves work efficiency by proposing appropriate tasks according to progress. Some or all of the above processes in the suggestion department may be performed using AI, for example, or not using AI. For example, the suggestion department can input progress data into a generating AI and have the generating AI propose the next task.

[0035] The execution unit can automatically create standardized documents and input data. For example, the execution unit can create standardized documents using report templates. For example, the execution unit can automatically input data using data entry forms. The execution unit can also automatically perform routine tasks. For example, the execution unit can improve work efficiency by automating the creation of standardized documents. The execution unit can also improve work efficiency by automating data input. This improves work efficiency by automating routine tasks. Some or all of the above processes in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can have a generation AI create standardized documents.

[0036] The analysis unit can display each step from the start to the end of a task on a timeline and clearly indicate the tasks corresponding to each step. The analysis unit can, for example, use a Gantt chart to display each step from the start to the end of a task on a timeline. The analysis unit makes it easier to understand the flow of work by, for example, clearly indicating the tasks corresponding to each step. For example, the analysis unit makes it easier for new members to understand the flow of work by displaying each step from the start to the end of a task on a timeline and clearly indicating the tasks corresponding to each step. This makes it easier to understand the flow of work. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input data for displaying each step from the start to the end of a task on a timeline into a generating AI and have the generating AI perform the generation of the timeline.

[0037] The proposal department can provide templates for necessary documents. For example, the proposal department can provide report templates and presentation templates. The proposal department can improve the efficiency of document creation by providing document templates. For example, the proposal department can streamline report creation by providing report templates. The proposal department can also streamline the creation of presentation materials by providing presentation templates. This improves the efficiency of document creation. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can have a generation AI generate document templates.

[0038] The proposal department can support communication with stakeholders. The proposal department can support communication with stakeholders through methods such as email, chat, and meetings. The proposal department facilitates communication by supporting communication with stakeholders. For example, the proposal department can support communication with stakeholders using email. The proposal department can also support communication with stakeholders using chat. The proposal department can also support communication with stakeholders using meetings. This facilitates communication with stakeholders. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can have a generation AI support communication with stakeholders.

[0039] The collection unit can determine collection priorities based on the importance of past internal company documents. For example, the collection unit can prioritize the collection of highly important documents and provide them to users. For example, the collection unit can postpone the collection of less important documents and collect them as needed. For example, when collecting highly important documents, the collection unit can also collect related documents at the same time. This improves operational efficiency by prioritizing the collection of important documents. Some or all of the above processes in the collection unit may be performed using AI, for example, or not using AI. For example, the collection unit can input data for evaluating the importance of documents into a generating AI and have the generating AI determine the collection priority.

[0040] The data collection unit can filter data based on the user's current projects and areas of interest. For example, the data collection unit can prioritize collecting data related to the user's current projects. For example, the data collection unit can filter and provide relevant data based on the user's areas of interest. For example, the data collection unit can collect necessary data in a timely manner according to the progress of the user's projects. This improves work efficiency by prioritizing the collection of data related to the user's projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data about the user's projects and areas of interest into a generating AI and have the generating AI perform the filtering of relevant data.

[0041] The data collection unit can prioritize collecting highly relevant data based on the user's geographical location. For example, if the user is in a specific region, the data collection unit will prioritize collecting data related to that region. For example, if the user is on a business trip, the data collection unit will prioritize collecting data related to the destination of the business trip. For example, if the user is working remotely, the data collection unit will prioritize collecting data related to their home or remote work location. This improves work efficiency by collecting highly relevant data based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location into a generating AI and have the generating AI collect highly relevant data.

[0042] The data collection unit can analyze a user's social media activity and collect relevant data when collecting data. For example, the data collection unit can collect data related to topics the user has shown interest in on social media. For example, the data collection unit can collect data related to accounts the user follows on social media. For example, the data collection unit can collect relevant data based on information the user has shared on social media. This improves operational efficiency by collecting relevant data based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect relevant data.

[0043] The analysis unit can adjust the level of detail of the visualization based on the importance of the tasks during analysis. For example, the analysis unit visualizes high-importance tasks in detail and low-importance tasks in a simplified visualization. For example, the analysis unit can highlight high-importance tasks by color-coding them. For example, the analysis unit can place high-importance tasks in a prominent position on the timeline. This improves work efficiency by adjusting the level of detail of the visualization according to the importance of the tasks. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input task importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the visualization.

[0044] The analysis unit can apply different analysis algorithms depending on the task category during analysis. For example, the analysis unit visualizes project management tasks using Gantt charts. For example, it visualizes communication tasks by analyzing chat history. For example, it visualizes document creation tasks by analyzing document progress. This improves work efficiency by applying the appropriate analysis algorithm according to the task category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input task category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0045] The analysis unit can determine the visualization priority based on the task submission timing during analysis. For example, the analysis unit may prioritize the visualization of tasks with approaching deadlines. For example, the analysis unit may postpone the visualization of tasks with distant deadlines. For example, the analysis unit may color-code and highlight tasks with approaching deadlines. This improves work efficiency by determining the visualization priority based on the task submission timing. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input task submission timing data into a generating AI and have the generating AI perform the determination of visualization priorities.

[0046] The analysis unit can adjust the visualization order based on the relevance of tasks during analysis. For example, the analysis unit can group and visualize highly relevant tasks. For example, the analysis unit can display highly relevant tasks consecutively. For example, the analysis unit can highlight highly relevant tasks by color-coding them. This improves work efficiency by adjusting the visualization order based on the relevance of tasks. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input task relevance data into a generating AI and have the generating AI perform the adjustment of the visualization order.

[0047] The proposal unit can adjust the level of detail in a proposal based on the importance of the task. For example, the proposal unit can provide detailed proposals for high-importance tasks, and simplified proposals for low-importance tasks. For example, it can highlight high-importance tasks using color coding. This improves work efficiency by adjusting the level of detail in proposals according to the importance of the task. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input task importance data into a generating AI and have the generating AI adjust the level of detail in the proposals.

[0048] The proposal unit can apply different proposal algorithms depending on the task category when making a proposal. For example, the proposal unit might use a Gantt chart for project management tasks, analyze chat history for communication tasks, or analyze document progress for document creation tasks. This improves work efficiency by applying the appropriate proposal algorithm according to the task category. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input task category data into a generating AI and have the generating AI apply the proposal algorithm.

[0049] The proposal department can determine the priority of proposals based on the task submission deadlines. For example, the proposal department can prioritize tasks with approaching deadlines. For example, it can postpone proposing tasks with distant deadlines. For example, the proposal department can highlight tasks with approaching deadlines using color coding. This improves work efficiency by prioritizing proposals based on task submission deadlines. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input task submission data into a generating AI and have the generating AI determine the priority of proposals.

[0050] The proposal unit can adjust the order of proposals based on the relevance of tasks. For example, the proposal unit can group highly relevant tasks together and propose them. For example, the proposal unit can propose highly relevant tasks consecutively. For example, the proposal unit can highlight highly relevant tasks by color-coding them. This improves work efficiency by adjusting the order of proposals based on the relevance of tasks. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input task relevance data into a generating AI and have the generating AI perform the adjustment of the proposal order.

[0051] The execution unit can improve the accuracy of execution by considering the interrelationships of tasks during execution. For example, the execution unit can improve efficiency by executing related tasks simultaneously. For example, the execution unit can execute tasks in the appropriate order by considering their dependencies. For example, the execution unit can analyze the interrelationships of tasks and select the optimal execution method. This improves the efficiency of operations by executing tasks while considering their interrelationships. Some or all of the above processes in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input task interrelationship data into a generating AI and have the generating AI perform the task accuracy improvement.

[0052] The execution unit can perform tasks while considering the attribute information of the task submitter. For example, the execution unit can select an appropriate execution method according to the submitter's position and responsibilities. For example, the execution unit can select the optimal execution method considering the submitter's past performance. For example, the execution unit can select an appropriate execution method according to the submitter's skill level. This improves work efficiency by considering the attribute information of the task submitter during execution. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the submitter's attribute information into a generating AI and have the generating AI select the execution method.

[0053] The execution unit can perform tasks while considering their geographical distribution. For example, if a user is in a specific region, the execution unit will prioritize tasks related to that region. For example, if a user is on a business trip, the execution unit will prioritize tasks related to the business trip destination. For example, if a user is working remotely, the execution unit will prioritize tasks related to their home or remote work location. By considering the geographical distribution of tasks, the efficiency of work is improved. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the user's geographical distribution data into a generating AI and have the generating AI determine the execution priority.

[0054] The execution unit can improve the accuracy of its execution by referring to relevant literature for the task during execution. For example, the execution unit can refer to relevant literature for the task and select the optimal execution method. For example, the execution unit can obtain information necessary for task execution from the literature and reflect it in the execution. For example, the execution unit can refer to past cases related to task execution from the literature and improve the accuracy of the execution. As a result, the efficiency of work is improved by performing the task by referring to relevant literature. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input relevant literature data into a generating AI and have the generating AI perform the task accuracy improvement.

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

[0056] The analysis unit can adjust the level of detail in the visualization based on the importance of the tasks. For example, high-importance tasks can be visualized in detail, while low-importance tasks can be visualized in a simplified manner. The analysis unit can also highlight high-importance tasks by color-coding them. Furthermore, high-importance tasks can be placed in a prominent position on the timeline. This enables visualization according to the importance of tasks, improving work efficiency.

[0057] The proposal department can apply different proposal algorithms depending on the task category. For example, it can use a Gantt chart to propose solutions for project management tasks. For communication tasks, it can analyze chat history to make suggestions. It can also analyze the progress of documents to make suggestions for document creation tasks. This enables appropriate suggestions tailored to the task category, improving work efficiency.

[0058] The execution unit can improve the accuracy of execution by considering the interrelationships between tasks. For example, it can improve efficiency by executing related tasks simultaneously. The execution unit can also execute tasks in the appropriate order, for example, by considering the dependencies between tasks. Furthermore, it can analyze the interrelationships between tasks and select the optimal execution method. This enables execution that takes into account the interrelationships of tasks, thereby improving work efficiency.

[0059] The data collection unit can prioritize collecting highly relevant data based on the user's geographical location. For example, if a user is in a specific region, it will prioritize collecting data related to that region. If a user is on a business trip, the data collection unit can prioritize collecting data related to their destination. Furthermore, if a user is working remotely, it can prioritize collecting data related to their home or remote work location. This enables data collection based on the user's geographical location, improving work efficiency.

[0060] The proposal department can prioritize proposals based on the task submission deadline. For example, tasks with approaching deadlines can be proposed first. Tasks with later deadlines can be postponed. Tasks with approaching deadlines can also be highlighted using color coding. This allows for proposals based on task submission timing, improving work efficiency.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The collection department collects the content and update history of past internal company documents. Past internal company documents include reports, meeting minutes, technical documents, etc. The collection department collects documents from internal databases and file storage systems, and also collects update history information, including the date and time of change, the content of the change, and the person who made the change. Step 2: The analysis unit analyzes the data collected by the data collection unit to visualize the tasks. Task visualization is performed in the form of Gantt charts, flowcharts, task lists, etc. The analysis unit uses Gantt charts to visualize the start date, end date, and progress of tasks, flowcharts to visually display the flow of tasks, and task lists to display detailed information for each task. Step 3: The proposal team proposes the next task based on the tasks visualized by the analysis team. The proposal team proposes the next task based on the task's priority, dependencies, progress, etc. Step 4: The execution unit executes the parts of the tasks proposed by the proposal unit that can be automated. The execution unit automatically creates standardized documents and enters data, creates standardized documents using report templates, and automatically enters data using data entry forms. It also automatically executes routine tasks.

[0063] (Example of form 2) An onboarding agent according to an embodiment of the present invention is a system that supports the smooth start and progress of work for new members. This onboarding agent visualizes tasks aligned with a timeline from the start to the end of a series of tasks, based on the content and update history of past internal documents (e.g., Confluence, Box, etc.). Next, the agent proposes the next task according to the progress and supports the creation of documents and communication with stakeholders. Furthermore, the agent performs tasks that do not require human intervention. This mechanism allows new members to smoothly start and progress with their work. For example, the onboarding agent collects the content and update history of past internal documents. In this case, the internal documents include business flows and task details. For example, documents are collected from platforms such as Confluence and Box. This allows for an understanding of past business flows and task details. Next, based on the collected documents, the agent visualizes tasks aligned with a timeline from the start to the end of a series of tasks. For example, each step from the start to the end of the work is displayed on the timeline, and the tasks corresponding to each step are clearly indicated. This makes it easier for new members to understand the flow of work. Furthermore, the agent proposes the next task according to the progress. For example, the agent suggests the next task based on the progress of the work. This allows new members to clearly understand what they need to do. The agent also supports document creation and communication with stakeholders. For example, they provide templates for necessary documents and support communication with stakeholders. Finally, the agent performs tasks that do not require human intervention. For example, the agent automates routine tasks such as creating standardized documents and entering data. This allows new members to focus on important tasks. This system allows new members to start and progress smoothly in their work. It also reduces the time and effort that existing members spend supporting new members.For example, if a new member is unsure of the process or if the documentation is outdated and doesn't reflect the latest workflow, an onboarding agent can suggest appropriate tasks and provide support, ensuring smooth progress. This allows onboarding agents to help new members get started and progress smoothly with their work.

[0064] The onboarding agent according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and an execution unit. The collection unit collects the contents and update history of past internal company documents. Past internal company documents include, but are not limited to, reports, meeting minutes, and technical documents. The collection unit collects documents from, for example, internal databases and file storage systems. The collection unit can also collect documents that include update history. For example, the collection unit collects update history that includes information such as the date and time of change, the content of the change, and the person who made the change. The analysis unit analyzes the documents collected by the collection unit to visualize tasks. Task visualization is performed in the form of, for example, a Gantt chart, a flowchart, or a task list, but is not limited to these examples. The analysis unit visualizes the start date, end date, and progress of tasks using, for example, a Gantt chart. The analysis unit can also visually display the flow of tasks using a flowchart. The analysis unit can also display detailed information of each task using a task list. The proposal unit proposes the next task based on the tasks visualized by the analysis unit. The proposal unit proposes the next task based on, for example, task priority, dependencies, and progress. The proposal unit can also propose the next task to be performed based on task priority, dependencies, and progress. The execution unit executes the parts of the tasks proposed by the proposal unit that can be automated. The execution unit can automatically create standardized documents and input data, for example. The execution unit can create standardized documents using report templates, for example. The execution unit can also automatically input data using data entry forms. The execution unit can also automatically execute routine tasks. As a result, the onboarding agent according to the embodiment enables new members to smoothly start and progress with their work.

[0065] The data collection department collects the content and update history of past internal company documents. Past internal company documents include, but are not limited to, reports, meeting minutes, and technical documents. The data collection department collects documents from, for example, internal databases and file storage systems. Specifically, internal databases store reports, meeting minutes, and technical documents created by each department, and these documents are updated regularly. The data collection department automatically scans these documents to obtain the latest information. The data collection department can also collect documents that include update history. For example, the data collection department collects update history information including the date and time of change, the content of the change, and the person who made the change. This allows the data collection department to track the change history of documents and understand what changes have been made. Furthermore, the data collection department also collects documents from internal file storage systems. File storage systems store files shared by each department, and these files are backed up regularly. The data collection department scans these files to obtain the latest information. This allows the data collection department to comprehensively collect all internal company documents and stay up-to-date.

[0066] The analysis unit analyzes the data collected by the data collection unit to visualize tasks. Task visualization is performed in the form of, for example, a Gantt chart, a flowchart, or a task list, but is not limited to these examples. For example, the analysis unit uses a Gantt chart to visualize the start date, end date, and progress of tasks. A Gantt chart is a tool for visually displaying task schedules, displaying the start and end dates of each task in bar graph format. This allows for a quick overview of the progress of each task. The analysis unit can also visually display the flow of tasks using a flowchart. A flowchart is a tool for visually displaying the flow of tasks, indicating the order and dependencies of each task with arrows. This allows for a quick overview of the flow of each task. The analysis unit can also display detailed information of each task using a task list. A task list is a tool for displaying detailed information of each task in a table format, displaying the name, start date, end date, and progress of each task in a list. This allows for a quick overview of the detailed information of each task. Furthermore, the analysis unit can use AI to analyze the collected data and automatically extract task priorities and dependencies. This allows the analysis unit to quickly and accurately analyze the collected data and visualize the task.

[0067] The suggestion unit proposes the next task based on the tasks visualized by the analysis unit. The suggestion unit proposes the next task based, for example, on task priority, dependencies, and progress. Specifically, the suggestion unit proposes the next task to be performed based on task priority. Task priority is determined based on the importance and urgency of the task. The suggestion unit evaluates the task priority and proposes the most important task as the next task to be performed. The suggestion unit can also propose the next task to be performed based on task dependencies. Task dependencies indicate whether one task depends on another. The suggestion unit evaluates the task dependencies and proposes the dependent tasks in order. Furthermore, the suggestion unit can also propose the next task to be performed based on task progress. Task progress indicates the degree of completion or progress of a task. The suggestion unit evaluates the task progress and proposes the ongoing task as the next task to be performed. This allows the suggestion unit to quickly and accurately propose the next task to be performed based on task priority, dependencies, and progress. Furthermore, the suggestion unit can automate task proposals using AI. The AI ​​learns from the history and patterns of past tasks and suggests the most suitable task. This allows the suggestion unit to propose the next task efficiently and effectively.

[0068] The execution unit will carry out the parts of the tasks proposed by the proposal unit that can be automated. For example, the execution unit will automatically create standardized documents and input data. Specifically, the execution unit will create standardized documents using report templates. Report templates are documents with a standardized format, allowing for easy report creation simply by entering the necessary information. The execution unit will automatically input the necessary information into the report template and create standardized documents. The execution unit can also automatically input data using data entry forms. Data entry forms are forms with a standardized format, allowing for easy data entry simply by entering the necessary information. The execution unit will automatically input the necessary information into the data entry forms and input the data. Furthermore, the execution unit can also automatically execute routine tasks. Routine tasks are tasks that are performed periodically and are time-consuming to perform manually, but can be performed efficiently through automation. The execution unit will automatically execute routine tasks and complete them efficiently. This allows the execution unit to quickly and accurately carry out the parts of the tasks proposed by the proposal unit that can be automated. Furthermore, the execution unit can automate task execution using AI. AI learns from the history and patterns of past tasks and executes them in the most optimal way. This allows the execution unit to perform tasks efficiently and effectively.

[0069] The suggestion department can propose the next task according to the progress. For example, the suggestion department can propose the next task based on the completion rate and adherence to deadlines of the current task. For example, the suggestion department can propose the next task when the completion rate of the current task exceeds 50%. The suggestion department can also propose the next task based on adherence to deadlines. For example, the suggestion department can prioritize proposing tasks with approaching deadlines. This improves work efficiency by proposing appropriate tasks according to progress. Some or all of the above processes in the suggestion department may be performed using AI, for example, or not using AI. For example, the suggestion department can input progress data into a generating AI and have the generating AI propose the next task.

[0070] The execution unit can automatically create standardized documents and input data. For example, the execution unit can create standardized documents using report templates. For example, the execution unit can automatically input data using data entry forms. The execution unit can also automatically perform routine tasks. For example, the execution unit can improve work efficiency by automating the creation of standardized documents. The execution unit can also improve work efficiency by automating data input. This improves work efficiency by automating routine tasks. Some or all of the above processes in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can have a generation AI create standardized documents.

[0071] The analysis unit can display each step from the start to the end of a task on a timeline and clearly indicate the tasks corresponding to each step. The analysis unit can, for example, use a Gantt chart to display each step from the start to the end of a task on a timeline. The analysis unit makes it easier to understand the flow of work by, for example, clearly indicating the tasks corresponding to each step. For example, the analysis unit makes it easier for new members to understand the flow of work by displaying each step from the start to the end of a task on a timeline and clearly indicating the tasks corresponding to each step. This makes it easier to understand the flow of work. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input data for displaying each step from the start to the end of a task on a timeline into a generating AI and have the generating AI perform the generation of the timeline.

[0072] The proposal department can provide templates for necessary documents. For example, the proposal department can provide report templates and presentation templates. The proposal department can improve the efficiency of document creation by providing document templates. For example, the proposal department can streamline report creation by providing report templates. The proposal department can also streamline the creation of presentation materials by providing presentation templates. This improves the efficiency of document creation. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can have a generation AI generate document templates.

[0073] The proposal department can support communication with stakeholders. The proposal department can support communication with stakeholders through methods such as email, chat, and meetings. The proposal department facilitates communication by supporting communication with stakeholders. For example, the proposal department can support communication with stakeholders using email. The proposal department can also support communication with stakeholders using chat. The proposal department can also support communication with stakeholders using meetings. This facilitates communication with stakeholders. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can have a generation AI support communication with stakeholders.

[0074] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit will delay data collection and wait until the user is relaxed. If the user is relaxed, the data collection unit will collect data quickly and provide it while the user is focused. If the user is in a hurry, the data collection unit will prioritize collecting important data and provide it quickly. This reduces the user's burden by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0075] The collection unit can determine collection priorities based on the importance of past internal company documents. For example, the collection unit can prioritize the collection of highly important documents and provide them to users. For example, the collection unit can postpone the collection of less important documents and collect them as needed. For example, when collecting highly important documents, the collection unit can also collect related documents at the same time. This improves operational efficiency by prioritizing the collection of important documents. Some or all of the above processes in the collection unit may be performed using AI, for example, or not using AI. For example, the collection unit can input data for evaluating the importance of documents into a generating AI and have the generating AI determine the collection priority.

[0076] The data collection unit can filter data based on the user's current projects and areas of interest. For example, the data collection unit can prioritize collecting data related to the user's current projects. For example, the data collection unit can filter and provide relevant data based on the user's areas of interest. For example, the data collection unit can collect necessary data in a timely manner according to the progress of the user's projects. This improves work efficiency by prioritizing the collection of data related to the user's projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data about the user's projects and areas of interest into a generating AI and have the generating AI perform the filtering of relevant data.

[0077] The data collection unit can estimate the user's emotions and determine the priority of the materials to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting high-importance materials and postpone collecting low-importance materials. For example, if the user is relaxed, the data collection unit will collect all materials equally. For example, if the user is in a hurry, the data collection unit will prioritize collecting the most important materials. This reduces the user's burden by prioritizing materials according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of materials.

[0078] The data collection unit can prioritize collecting highly relevant data based on the user's geographical location. For example, if the user is in a specific region, the data collection unit will prioritize collecting data related to that region. For example, if the user is on a business trip, the data collection unit will prioritize collecting data related to the destination of the business trip. For example, if the user is working remotely, the data collection unit will prioritize collecting data related to their home or remote work location. This improves work efficiency by collecting highly relevant data based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location into a generating AI and have the generating AI collect highly relevant data.

[0079] The data collection unit can analyze a user's social media activity and collect relevant data when collecting data. For example, the data collection unit can collect data related to topics the user has shown interest in on social media. For example, the data collection unit can collect data related to accounts the user follows on social media. For example, the data collection unit can collect relevant data based on information the user has shared on social media. This improves operational efficiency by collecting relevant data based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect relevant data.

[0080] The analysis unit can estimate the user's emotions and change the task visualization method based on the estimated user emotions. For example, if the user is stressed, the analysis unit provides a simple and highly visible task visualization method. For example, if the user is relaxed, the analysis unit provides a task visualization method that includes detailed information. For example, if the user is in a hurry, the analysis unit provides a task visualization method that gets straight to the point. This reduces the user's burden by adjusting the task visualization method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform the change in the task visualization method.

[0081] The analysis unit can adjust the level of detail of the visualization based on the importance of the tasks during analysis. For example, the analysis unit visualizes high-importance tasks in detail and low-importance tasks in a simplified visualization. For example, the analysis unit can highlight high-importance tasks by color-coding them. For example, the analysis unit can place high-importance tasks in a prominent position on the timeline. This improves work efficiency by adjusting the level of detail of the visualization according to the importance of the tasks. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input task importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the visualization.

[0082] The analysis unit can apply different analysis algorithms depending on the task category during analysis. For example, the analysis unit visualizes project management tasks using Gantt charts. For example, it visualizes communication tasks by analyzing chat history. For example, it visualizes document creation tasks by analyzing document progress. This improves work efficiency by applying the appropriate analysis algorithm according to the task category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input task category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0083] The analysis unit can estimate the user's emotions and change the display order of tasks based on the estimated emotions. For example, if the user is stressed, the analysis unit will display high-priority tasks first. If the user is relaxed, the analysis unit will display all tasks equally. If the user is in a hurry, the analysis unit will prioritize displaying the most important tasks. This reduces the user's burden by adjusting the task display order according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI change the display order of tasks.

[0084] The analysis unit can determine the visualization priority based on the task submission timing during analysis. For example, the analysis unit may prioritize the visualization of tasks with approaching deadlines. For example, the analysis unit may postpone the visualization of tasks with distant deadlines. For example, the analysis unit may color-code and highlight tasks with approaching deadlines. This improves work efficiency by determining the visualization priority based on the task submission timing. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input task submission timing data into a generating AI and have the generating AI perform the determination of visualization priorities.

[0085] The analysis unit can adjust the visualization order based on the relevance of tasks during analysis. For example, the analysis unit can group and visualize highly relevant tasks. For example, the analysis unit can display highly relevant tasks consecutively. For example, the analysis unit can highlight highly relevant tasks by color-coding them. This improves work efficiency by adjusting the visualization order based on the relevance of tasks. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input task relevance data into a generating AI and have the generating AI perform the adjustment of the visualization order.

[0086] The suggestion unit can estimate the user's emotions and modify the way suggestions are presented based on those emotions. For example, if the user is stressed, the suggestion unit provides a simple and easily visible suggestion. If the user is relaxed, the suggestion unit provides a suggestion that includes detailed information. If the user is in a hurry, the suggestion unit provides a concise suggestion. This reduces the user's burden by adjusting the way suggestions are presented according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into the generative AI and have the generative AI modify the way suggestions are presented.

[0087] The proposal unit can adjust the level of detail in a proposal based on the importance of the task. For example, the proposal unit can provide detailed proposals for high-importance tasks, and simplified proposals for low-importance tasks. For example, it can highlight high-importance tasks using color coding. This improves work efficiency by adjusting the level of detail in proposals according to the importance of the task. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input task importance data into a generating AI and have the generating AI adjust the level of detail in the proposals.

[0088] The proposal unit can apply different proposal algorithms depending on the task category when making a proposal. For example, the proposal unit might use a Gantt chart for project management tasks, analyze chat history for communication tasks, or analyze document progress for document creation tasks. This improves work efficiency by applying the appropriate proposal algorithm according to the task category. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input task category data into a generating AI and have the generating AI apply the proposal algorithm.

[0089] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is stressed, the suggestion unit will make short, concise suggestions. If the user is relaxed, the suggestion unit will make detailed suggestions. If the user is in a hurry, the suggestion unit will make quick and concise suggestions. This reduces the user's burden by adjusting the length of suggestions according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into the generative AI and have the generative AI change the length of the suggestions.

[0090] The proposal department can determine the priority of proposals based on the task submission deadlines. For example, the proposal department can prioritize tasks with approaching deadlines. For example, it can postpone proposing tasks with distant deadlines. For example, the proposal department can highlight tasks with approaching deadlines using color coding. This improves work efficiency by prioritizing proposals based on task submission deadlines. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input task submission data into a generating AI and have the generating AI determine the priority of proposals.

[0091] The proposal unit can adjust the order of proposals based on the relevance of tasks. For example, the proposal unit can group highly relevant tasks together and propose them. For example, the proposal unit can propose highly relevant tasks consecutively. For example, the proposal unit can highlight highly relevant tasks by color-coding them. This improves work efficiency by adjusting the order of proposals based on the relevance of tasks. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input task relevance data into a generating AI and have the generating AI perform the adjustment of the proposal order.

[0092] The execution unit can estimate the user's emotions and set the priority of tasks to be performed based on the estimated emotions. For example, if the user is stressed, the execution unit will prioritize high-priority tasks. If the user is relaxed, the execution unit will perform all tasks equally. If the user is in a hurry, the execution unit will prioritize the most important tasks. This reduces the user's burden by determining the priority of tasks to be performed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the execution unit may be performed using AI, for example, or not using AI. For example, the execution unit can input user emotion data into a generative AI and have the generative AI set the priority of tasks to be performed.

[0093] The execution unit can improve the accuracy of execution by considering the interrelationships of tasks during execution. For example, the execution unit can improve efficiency by executing related tasks simultaneously. For example, the execution unit can execute tasks in the appropriate order by considering their dependencies. For example, the execution unit can analyze the interrelationships of tasks and select the optimal execution method. This improves the efficiency of operations by executing tasks while considering their interrelationships. Some or all of the above processes in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input task interrelationship data into a generating AI and have the generating AI perform the task accuracy improvement.

[0094] The execution unit can perform tasks while considering the attribute information of the task submitter. For example, the execution unit can select an appropriate execution method according to the submitter's position and responsibilities. For example, the execution unit can select the optimal execution method considering the submitter's past performance. For example, the execution unit can select an appropriate execution method according to the submitter's skill level. This improves work efficiency by considering the attribute information of the task submitter during execution. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the submitter's attribute information into a generating AI and have the generating AI select the execution method.

[0095] The execution unit can estimate the user's emotions and change how tasks are displayed based on the estimated emotions. For example, if the user is stressed, the execution unit provides a simple and highly visible display method. For example, if the user is relaxed, the execution unit provides a display method that includes detailed information. For example, if the user is in a hurry, the execution unit provides a display method that gets straight to the point. This reduces the user's burden by adjusting how tasks are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input user emotion data into a generative AI and have the generative AI change the display method.

[0096] The execution unit can perform tasks while considering their geographical distribution. For example, if a user is in a specific region, the execution unit will prioritize tasks related to that region. For example, if a user is on a business trip, the execution unit will prioritize tasks related to the business trip destination. For example, if a user is working remotely, the execution unit will prioritize tasks related to their home or remote work location. By considering the geographical distribution of tasks, the efficiency of work is improved. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the user's geographical distribution data into a generating AI and have the generating AI determine the execution priority.

[0097] The execution unit can improve the accuracy of its execution by referring to relevant literature for the task during execution. For example, the execution unit can refer to relevant literature for the task and select the optimal execution method. For example, the execution unit can obtain information necessary for task execution from the literature and reflect it in the execution. For example, the execution unit can refer to past cases related to task execution from the literature and improve the accuracy of the execution. As a result, the efficiency of work is improved by performing the task by referring to relevant literature. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input relevant literature data into a generating AI and have the generating AI perform the task accuracy improvement.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] Onboarding agents can estimate a user's emotions and suggest tasks based on those estimates. For example, if a user is stressed, the suggestion system will prioritize suggesting simple, low-burden tasks. Conversely, if a user is relaxed, the suggestion system can suggest complex and important tasks. Furthermore, if a user is in a hurry, the suggestion system can quickly suggest the most important tasks. This enables task suggestions tailored to the user's emotions, improving work efficiency.

[0100] The analysis unit can estimate the user's emotions and adjust the task visualization method based on those emotions. For example, if the user is stressed, the analysis unit provides a simple and highly visual task visualization method. If the user is relaxed, the analysis unit can provide a task visualization method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a task visualization method that gets straight to the point. This enables task visualization that responds to the user's emotions, improving work efficiency.

[0101] The execution unit can estimate the user's emotions and prioritize tasks based on those emotions. For example, if the user is stressed, the execution unit will prioritize high-priority tasks. If the user is relaxed, the execution unit can distribute all tasks evenly. Furthermore, if the user is in a hurry, the execution unit can prioritize the most important tasks. This enables task execution tailored to the user's emotions, improving work efficiency.

[0102] The proposal function can estimate the user's emotions and modify the way it presents proposals based on those emotions. For example, if the user is stressed, the proposal function can provide a simple and highly visible proposal. If the user is relaxed, the proposal function can provide a proposal that includes detailed information. Furthermore, if the user is in a hurry, the proposal function can provide a proposal that gets straight to the point. This enables proposals tailored to the user's emotions, improving work efficiency.

[0103] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on those estimates. For example, if the user is stressed, the unit will delay data collection and wait until the user is relaxed. If the user is relaxed, the unit can quickly collect the data and provide it while the user is focused. Furthermore, if the user is in a hurry, the unit can prioritize collecting important data and provide it quickly. This enables data collection tailored to the user's emotions, improving work efficiency.

[0104] The analysis unit can adjust the level of detail in the visualization based on the importance of the tasks. For example, high-importance tasks can be visualized in detail, while low-importance tasks can be visualized in a simplified manner. The analysis unit can also highlight high-importance tasks by color-coding them. Furthermore, high-importance tasks can be placed in a prominent position on the timeline. This enables visualization according to the importance of tasks, improving work efficiency.

[0105] The proposal department can apply different proposal algorithms depending on the task category. For example, it can use a Gantt chart to propose solutions for project management tasks. For communication tasks, it can analyze chat history to make suggestions. It can also analyze the progress of documents to make suggestions for document creation tasks. This enables appropriate suggestions tailored to the task category, improving work efficiency.

[0106] The execution unit can improve the accuracy of execution by considering the interrelationships between tasks. For example, it can improve efficiency by executing related tasks simultaneously. The execution unit can also execute tasks in the appropriate order, for example, by considering the dependencies between tasks. Furthermore, it can analyze the interrelationships between tasks and select the optimal execution method. This enables execution that takes into account the interrelationships of tasks, thereby improving work efficiency.

[0107] The data collection unit can prioritize collecting highly relevant data based on the user's geographical location. For example, if a user is in a specific region, it will prioritize collecting data related to that region. If a user is on a business trip, the data collection unit can prioritize collecting data related to their destination. Furthermore, if a user is working remotely, it can prioritize collecting data related to their home or remote work location. This enables data collection based on the user's geographical location, improving work efficiency.

[0108] The proposal department can prioritize proposals based on the task submission deadline. For example, tasks with approaching deadlines can be proposed first. Tasks with later deadlines can be postponed. Tasks with approaching deadlines can also be highlighted using color coding. This allows for proposals based on task submission timing, improving work efficiency.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The collection department collects the content and update history of past internal company documents. Past internal company documents include reports, meeting minutes, technical documents, etc. The collection department collects documents from internal databases and file storage systems, and also collects update history information, including the date and time of change, the content of the change, and the person who made the change. Step 2: The analysis unit analyzes the data collected by the data collection unit to visualize the tasks. Task visualization is performed in the form of Gantt charts, flowcharts, task lists, etc. The analysis unit uses Gantt charts to visualize the start date, end date, and progress of tasks, flowcharts to visually display the flow of tasks, and task lists to display detailed information for each task. Step 3: The proposal team proposes the next task based on the tasks visualized by the analysis team. The proposal team proposes the next task based on the task's priority, dependencies, progress, etc. Step 4: The execution unit executes the parts of the tasks proposed by the proposal unit that can be automated. The execution unit automatically creates standardized documents and enters data, creates standardized documents using report templates, and automatically enters data using data entry forms. It also automatically executes routine tasks.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and execution unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects data from the company's database and file storage system. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to visualize tasks. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the next task based on the visualized tasks. The execution unit is implemented by the control unit 46A of the smart device 14 and executes the parts of the proposed tasks that can be automated. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and execution unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects data from the company's database and file storage system. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data to visualize tasks. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes the next task based on the visualized tasks. The execution unit is implemented by the control unit 46A of the smart glasses 214 and executes the parts of the proposed tasks that can be automated. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and execution unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects data from the company's database and file storage system. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to visualize tasks. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the next task based on the visualized tasks. The execution unit is implemented by the control unit 46A of the headset terminal 314 and executes the parts of the proposed tasks that can be automated. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and execution unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects data from the company's database and file storage system. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the collected data to visualize tasks. The proposal unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and proposes the next task based on the visualized tasks. The execution unit is implemented by, for example, the control unit 46A of the robot 414 and executes the parts of the proposed tasks that can be automated. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0164] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0173] 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.

[0174] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] (Note 1) The collection department collects the contents and update history of past internal company documents, An analysis unit analyzes the data collected by the aforementioned collection unit to visualize the task, Based on the tasks visualized by the analysis unit, the proposal unit proposes the next task, The system comprises an execution unit that performs the automatable portion of the tasks proposed by the proposal unit. A system characterized by the following features. (Note 2) The aforementioned proposal section is, Suggest the next task based on the progress. The system described in Appendix 1, characterized by the features described herein. (Note 3) The execution unit is, Automate the creation of standardized documents and data entry. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, Display each step from the start to the end of a task on a timeline, and clearly indicate the task corresponding to each step. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We provide templates for necessary documents. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, Support for contacting relevant parties The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Prioritize the collection of past internal company documents based on their importance. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting materials, filter them based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and determines the priority of the materials to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting materials, the system prioritizes collecting highly relevant materials based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting data, analyze users' social media activity and collect relevant materials. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, Estimate user emotions and change how tasks are visualized based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail of the visualization based on the importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the task category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and changes the display order of tasks based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the visualization priorities are determined based on the task submission date. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, adjust the order of visualizations based on the relevance of the tasks. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, The system estimates the user's emotions and modifies the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, apply a different proposal algorithm depending on the task category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's sentiment and adjusts the length of the suggestion based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When submitting a proposal, prioritize the proposals based on the task submission deadline. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the tasks. The system described in Appendix 1, characterized by the features described herein. (Note 25) The execution unit is, It estimates the user's emotions and sets the priority of tasks to be performed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The execution unit is, During execution, the system improves execution accuracy by considering the interrelationships between tasks. The system described in Appendix 1, characterized by the features described herein. (Note 27) The execution unit is, During execution, the task will be executed while taking into account the attribute information of the task submitter. The system described in Appendix 1, characterized by the features described herein. (Note 28) The execution unit is, Estimate the user's emotions and change how tasks are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The execution unit is, During execution, the geographical distribution of tasks will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 30) The execution unit is, During execution, the system references relevant literature for the task to improve execution accuracy. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection department collects the contents and update history of past internal company documents, An analysis unit analyzes the data collected by the aforementioned collection unit to visualize the task, Based on the tasks visualized by the analysis unit, the proposal unit proposes the next task, The system comprises an execution unit that performs the automatable portion of the tasks proposed by the proposal unit. A system characterized by the following features.

2. The aforementioned proposal section is, Suggest the next task based on the progress. The system according to feature 1.

3. The execution unit is, Automate the creation of standardized documents and data entry. The system according to feature 1.

4. The aforementioned analysis unit, Display each step from the start to the end of a task on a timeline, and clearly indicate the task corresponding to each step. The system according to feature 1.

5. The aforementioned proposal section is, We provide templates for necessary documents. The system according to feature 1.

6. The aforementioned proposal section is, Support for contacting relevant parties The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Prioritize the collection of past internal company documents based on their importance. The system according to feature 1.

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