System

Abstract

An object of a system according to an embodiment is to efficiently record a procedure of computer work and automatically generate an easy-to-understand manual.SOLUTION: A system includes a recording unit, a generation unit, an update unit, and an editing unit. The recording unit records the user's operation in the form of a video, a screen shot, or a text description. The generation unit generates a step-by-step manual based on the operation recorded by the recording unit. The updating unit automatically updates the manual generated by the generating unit at an arbitrary timing. The editing unit edits and customizes the manual generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have had the problem that it is difficult to efficiently record computer work procedures and automatically generate easy-to-understand manuals.

[0005] The system according to the embodiment aims to efficiently record computer work procedures and automatically generate easy-to-understand manuals. [Means for solving the problem]

[0006] The system according to the embodiment includes a recording unit, a generating unit, an updating unit, and an editing unit. The recording unit records user operations in the form of videos, screenshots, and text descriptions. The generating unit generates a step-by-step manual based on the operations recorded by the recording unit. The updating unit automatically updates the manual generated by the generating unit at any timing. The editing unit edits and customizes the manual generated by the generating unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently record computer work procedures and automatically generate easy-to-understand manuals. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An AI-driven tool according to an embodiment of the present invention is a system that records computer tasks performed by teams within a company or organization in real time and automatically generates step-by-step manuals and guides based on the recordings. This allows the AI-driven tool to capture user operations in the form of videos, screenshots, and text descriptions, creating easy-to-understand instruction manuals. Furthermore, the manuals support rich content and Markdown, and can be automatically updated at any time. They can also be edited and customized as needed.

[0029] An AI-driven tool according to an embodiment includes a recording unit, a generating unit, an updating unit, and an editing unit. The recording unit records user operations in the form of video, screenshots, and text descriptions. For example, the recording unit captures the steps a user takes to change software settings in video format and simultaneously takes screenshots. The recording unit also records operations such as the text entered by the user and the buttons clicked. The generating unit generates a step-by-step manual based on the operations recorded by the recording unit. For example, the generation AI analyzes the recorded video and screenshots and adds text descriptions of each operation. The generation AI can also incorporate an algorithm that analyzes operation procedures and suggests optimal procedures. The updating unit automatically updates the manual generated by the generating unit at any time. For example, the generation AI detects new operations performed by the user and updates the existing manual based on the detected operations. The updating unit can also compare new operation procedures with the existing manual and automatically highlight changes when it detects them. The editing unit edits and customizes the manual generated by the generating unit. For example, a user can add additional explanations or delete unnecessary parts from the manual automatically generated by the generation AI. The editing department can also learn the user's editing history and make optimal suggestions for the next editing. As a result, the AI-driven tool according to the embodiment records the user's operations in real time, automatically generates a step-by-step manual, and enables automatic updating and editing at any time.

[0030] The recording unit can infer the intention of the user's operation and automatically generate supplementary explanations based on that intention. For example, when a user changes software settings, the recording unit's generation AI infers the intention of the operation and automatically generates supplementary explanations such as, "Changing this setting will improve performance." Furthermore, when a user inputs data, the recording unit's generation AI infers the intention of the operation and automatically generates supplementary explanations such as, "This data will be used for monthly reports." Furthermore, when a user saves a file, the recording unit's generation AI infers the intention of the operation and automatically generates supplementary explanations such as, "This file will be used for project progress management." In this way, by inferring the intention of the user's operation and automatically generating supplementary explanations, manuals become easier to understand.

[0031] The recording unit can evaluate the efficiency of operations based on the recorded operation data and make improvement suggestions. For example, when a user performs an operation to open a file through multiple steps, the recording unit allows the generating AI to evaluate the efficiency of the operation and make improvement suggestions such as, "Using shortcut keys will speed up the operation." When a user manually inputs data, the recording unit allows the generating AI to evaluate the efficiency of the operation and make improvement suggestions such as, "Using the auto-entry function will reduce the input time." When a user switches between multiple windows, the recording unit allows the generating AI to evaluate the efficiency of the operation and make improvement suggestions such as, "Using the window rearrangement function will make the operation smoother." By evaluating the efficiency of operations and making improvement suggestions, the user's work efficiency is improved.

[0032] The recording unit can simultaneously record operations on different devices and generate cross-platform manuals. For example, the recording unit generates a cross-platform manual by recording operations performed by a user on a smartphone and simultaneously recording operations on a PC. For example, setting changes on a smartphone and data entry on a PC can be combined into a single manual. The recording unit also generates a cross-platform manual by recording operations performed by a user on a tablet and simultaneously recording operations on a desktop. For example, app operations on a tablet and file management on a desktop can be combined into a single manual. The recording unit also generates a cross-platform manual by recording operations performed by a user on a laptop and simultaneously recording operations on a smartwatch. For example, sending an email on a laptop and setting notifications on a smartwatch can be combined into a single manual. This allows operations on different devices to be simultaneously recorded and a cross-platform manual can be generated, improving user convenience.

[0033] The recording unit allows team members to share operations in real time, thereby improving the efficiency of collaborative work. For example, the recording unit shares operations performed by team members in real time, allowing other members to instantly check those operations. For example, task updates in a project management tool are shared in real time. The recording unit also shares operations performed by team members in real time, allowing other members to refer to those operations and proceed with their work. For example, editing operations in a design tool are shared in real time. The recording unit also shares operations performed by team members in real time, allowing other members to instantly provide feedback on those operations. For example, code reviews are shared in real time, and feedback is provided instantly. This allows team members to share operations in real time, thereby improving the efficiency of collaborative work.

[0034] The generation unit can introduce an algorithm that analyzes operation procedures and proposes the optimal procedure. For example, the generation unit introduces an algorithm in which the generation AI analyzes operation procedures and proposes the optimal procedure. For example, it compares multiple operation procedures and selects the most efficient procedure. The generation unit also introduces an algorithm in which the generation AI analyzes operation procedures and proposes the optimal procedure. For example, it proposes the optimal procedure based on the user's past operation history. The generation unit also introduces an algorithm in which the generation AI analyzes operation procedures and proposes the optimal procedure. For example, it proposes the optimal procedure based on operation data of other users. In this way, by analyzing operation procedures and proposing the optimal procedure, the user's work efficiency is improved.

[0035] The generation unit can automatically add background information and related knowledge for operations. For example, the generation AI analyzes operation procedures and automatically adds background information and related knowledge. For example, it adds an explanation such as, "Changing this setting will improve security." The generation unit also analyzes operation procedures and automatically adds background information and related knowledge. For example, it adds an explanation such as, "This operation is necessary to back up data." The generation unit also analyzes operation procedures and automatically adds background information and related knowledge. For example, it adds an explanation such as, "This procedure is important for optimizing system performance." In this way, the background information and related knowledge for operations are automatically added, deepening the user's understanding.

[0036] The generation unit can automatically generate manuals in different languages, enabling global use. In the generation unit, for example, a generation AI analyzes operation procedures and automatically generates manuals in different languages. For example, a manual is generated that supports multiple languages, such as English, Japanese, and French. In addition, the generation unit analyzes operation procedures and automatically generates manuals in different languages. For example, a manual in an appropriate language is automatically provided depending on the user's language setting. In addition, the generation unit analyzes operation procedures and automatically generates manuals in different languages. For example, manuals in multiple languages ​​are generated simultaneously for use by a global team. In this way, automatic generation of manuals in different languages ​​enables global use.

[0037] The generation unit can customize according to the user's skill level. For example, the generation AI analyzes the operation procedures and customizes them according to the user's skill level. For example, detailed explanations are provided for beginners and concise explanations for advanced users. The generation unit also analyzes the operation procedures and customizes them according to the user's skill level. For example, it makes extensive use of illustrations for beginners and provides text-based explanations for advanced users. The generation unit also analyzes the operation procedures and customizes them according to the user's skill level. For example, it provides detailed step-by-step instructions for beginners and only the main points for advanced users. In this way, customization according to the user's skill level deepens the user's understanding of the manual.

[0038] The generation unit can automatically determine appropriate insertion points for rich content and place it in optimal locations. In the generation unit, for example, the generation AI analyzes operation procedures and automatically determines appropriate insertion points for rich content. For example, a related video is inserted after an explanation of the operation procedures. In addition, the generation AI analyzes operation procedures and automatically determines appropriate insertion points for rich content. For example, a screenshot is inserted in the middle of a setting change procedure. In addition, the generation AI analyzes operation procedures and automatically determines appropriate insertion points for rich content. For example, a related link is inserted to explain background information for the operation. In this way, the appropriate insertion points for rich content are automatically determined and placed in optimal locations, thereby deepening understanding of the manual.

[0039] The generation unit can automatically add links and annotations to Markdown-formatted manuals. For example, the generation AI in the generation unit analyzes Markdown-formatted manuals and automatically adds relevant links. For example, adding links to official documentation related to explanations of operating procedures. The generation unit also analyzes Markdown-formatted manuals and automatically adds annotations. For example, adding notes and tips about specific operating procedures as annotations. The generation AI in the generation unit also analyzes Markdown-formatted manuals and automatically adds links to related resources. For example, adding links to tutorial videos related to operating procedures. In this way, automatically adding links and annotations to Markdown-formatted manuals deepens understanding of the manuals.

[0040] The generation unit can generate an interactive manual using rich content, allowing a user to simulate operations. For example, the generation unit causes a generation AI to generate an interactive manual using rich content, allowing a user to simulate operation procedures. For example, the generation unit creates a manual including clickable buttons and links. The generation unit also causes the generation AI to generate an interactive manual using rich content, allowing a user to actually try out operation procedures. For example, the generation unit provides an operation simulation in a virtual environment. The generation unit also causes the generation AI to generate an interactive manual using rich content, allowing a user to visually understand operation procedures. For example, the generation unit creates a manual including animations and videos. In this way, by generating an interactive manual using rich content, a user can simulate operations and deepen their understanding.

[0041] The generation unit can add a function to automatically convert a Markdown-formatted manual into other document formats (PDF, HTML, etc.). For example, the generation unit adds a function in which the generation AI analyzes a Markdown-formatted manual and automatically converts it into PDF format. For example, the manual is output as a printable PDF file. The generation unit also adds a function in which the generation AI analyzes a Markdown-formatted manual and automatically converts it into HTML format. For example, the manual is output as an HTML file that can be published as a webpage. The generation unit also adds a function in which the generation AI analyzes a Markdown-formatted manual and automatically converts it into other document formats (for example, DOCX or ODT). This automatically converts a Markdown-formatted manual into other document formats, expanding the range of uses of the manual.

[0042] When a new operating procedure is detected, the update unit can compare it with the existing manual and automatically highlight the changes. For example, the generation AI detects a new operating procedure and compares it with the existing manual to automatically highlight the changes. For example, if a new setting item is added, that part is highlighted. The update unit also detects a new operating procedure and compares it with the existing manual to automatically highlight the changes. For example, if the order of the procedures is changed, that part is highlighted. The update unit also detects a new operating procedure and compares it with the existing manual to automatically highlight the changes. For example, if a new function is added, that part is highlighted. In this way, by detecting a new operating procedure and comparing it with the existing manual to automatically highlight the changes, the user can easily understand the changes.

[0043] The update unit can automatically generate different versions of a manual and allow the user to select one as needed. For example, the update unit causes the generation AI to automatically generate different versions of a manual and allow the user to select one as needed. For example, an old version and a new version of a manual are provided simultaneously. The update unit also causes the generation AI to automatically generate different versions of a manual and allow the user to select one as needed. For example, manuals for beginners and manuals for advanced users are provided simultaneously. The update unit also causes the generation AI to automatically generate different versions of a manual and allow the user to select one as needed. For example, manuals in different languages ​​are provided simultaneously. This improves user convenience by automatically generating different versions of a manual and allowing the user to select one as needed.

[0044] The update unit adds a function to notify users of updates to the manual, allowing the latest information to be shared quickly. For example, the update unit adds a function to notify users of updates to the manual by the generation AI, allowing the latest information to be shared quickly. For example, update information is sent by email or push notification. The update unit also adds a function to notify users of updates to the manual by the generation AI, allowing the latest information to be shared quickly. For example, update information is displayed as a pop-up within the manual. The update unit also adds a function to notify users of updates to the manual by the generation AI, allowing the latest information to be shared quickly. For example, update information is displayed on a dashboard. This allows the latest information to be shared quickly by notifying users of updates to the manual.

[0045] The editing department can learn the user's editing history and make optimal suggestions for the next time they edit. For example, the generation AI in the editing department learns the user's editing history and makes optimal suggestions for the next time they edit. For example, it may suggest phrases or templates that have been frequently used in the past. The editing department also learns the user's editing history and makes optimal suggestions for the next time they edit. For example, it may suggest similar corrections based on corrections made in the past. The editing department also learns the user's editing history and makes optimal suggestions for the next time they edit. For example, it may suggest annotations or links that have been added in the past. In this way, the efficiency of the editing process is improved by learning the user's editing history and making optimal suggestions for the next time they edit.

[0046] The editorial department can automatically perform quality checks on customized manuals to detect errors and inconsistencies. For example, the editorial department automatically performs quality checks on manuals customized by the generation AI to detect errors and inconsistencies. For example, it detects broken links and typos. The editorial department also automatically performs quality checks on manuals customized by the generation AI to detect errors and inconsistencies. For example, it checks whether the order of steps is correct. The editorial department also automatically performs quality checks on manuals customized by the generation AI to detect errors and inconsistencies. For example, it checks whether the terminology used is consistent. In this way, by automatically performing quality checks on customized manuals, errors and inconsistencies are detected and the quality of the manuals is improved.

[0047] The editorial department can add a function to automatically generate manuals customized for different user groups. For example, the editorial department allows the generation AI to automatically generate manuals customized for different user groups. For example, manuals for beginners and advanced users are generated simultaneously. The editorial department also allows the generation AI to automatically generate manuals customized for different user groups. For example, manuals for the sales team and the technical team are generated simultaneously. The editorial department also allows the generation AI to automatically generate manuals customized for different user groups. For example, manuals for domestic users and manuals for overseas users are generated simultaneously. This improves user convenience by automatically generating manuals customized for different user groups.

[0048] The editorial department can add a function that allows edited content to be shared with other users and enables collaborative editing. For example, the editorial department adds a function that allows the generation AI to share edited content with other users and enables collaborative editing. For example, it allows multiple users to edit a manual simultaneously in real time. The editorial department also adds a function that allows the generation AI to share edited content with other users and enables collaborative editing. For example, it allows sharing of editing history and confirmation of who edited which part. The editorial department also adds a function that allows the generation AI to share edited content with other users and enables collaborative editing. For example, it adds a comment function that allows feedback on edited content to be given in real time. This allows edited content to be shared with other users and enables collaborative editing, improving the efficiency of editing work.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The recording unit records user operations in the form of video, screenshots, and text descriptions. For example, the recording unit captures the steps a user takes to change software settings in video format and simultaneously takes screenshots. It also records operations such as the text the user enters and the buttons they click. The generation unit generates a step-by-step manual based on the operations recorded by the recording unit. For example, the generation AI analyzes the recorded video and screenshots and adds text descriptions of each operation. The generation AI can also incorporate algorithms that analyze operation procedures and suggest optimal procedures. The update unit automatically updates the manual generated by the generation unit at any time. For example, the generation AI detects new operations performed by the user and updates the existing manual based on those operations. The update unit can also compare new operation procedures with the existing manual and automatically highlight changes when it detects them. The editing unit edits and customizes the manual generated by the generation unit. For example, users can add additional explanations or delete unnecessary parts of the manual automatically generated by the generation AI. The editing unit can also learn the user's editing history and make optimal suggestions for the next editing session. As a result, the AI-driven tool according to the embodiment can record user operations in real time, automatically generate step-by-step manuals, and automatically update and edit them at any time.

[0051] The recording unit can infer the intention of the user's operation and automatically generate supplementary explanations based on that intention. For example, when a user changes software settings, the recording unit's generation AI infers the intention of the operation and automatically generates supplementary explanations such as, "Changing this setting will improve performance." Furthermore, when a user inputs data, the recording unit's generation AI infers the intention of the operation and automatically generates supplementary explanations such as, "This data will be used for monthly reports." Furthermore, when a user saves a file, the recording unit's generation AI infers the intention of the operation and automatically generates supplementary explanations such as, "This file will be used for project progress management." In this way, by inferring the intention of the user's operation and automatically generating supplementary explanations, manuals become easier to understand.

[0052] The recording unit can evaluate the efficiency of operations based on the recorded operation data and make improvement suggestions. For example, when a user performs an operation to open a file through multiple steps, the recording unit allows the generating AI to evaluate the efficiency of the operation and make improvement suggestions such as, "Using shortcut keys will speed up the operation." When a user manually inputs data, the recording unit allows the generating AI to evaluate the efficiency of the operation and make improvement suggestions such as, "Using the auto-entry function will reduce the input time." When a user switches between multiple windows, the recording unit allows the generating AI to evaluate the efficiency of the operation and make improvement suggestions such as, "Using the window rearrangement function will make the operation smoother." By evaluating the efficiency of operations and making improvement suggestions, the user's work efficiency is improved.

[0053] The recording unit can simultaneously record operations on different devices and generate cross-platform manuals. For example, the recording unit generates a cross-platform manual by recording operations performed by a user on a smartphone and simultaneously recording operations on a PC. For example, setting changes on a smartphone and data entry on a PC can be combined into a single manual. The recording unit also generates a cross-platform manual by recording operations performed by a user on a tablet and simultaneously recording operations on a desktop. For example, app operations on a tablet and file management on a desktop can be combined into a single manual. The recording unit also generates a cross-platform manual by recording operations performed by a user on a laptop and simultaneously recording operations on a smartwatch. For example, sending an email on a laptop and setting notifications on a smartwatch can be combined into a single manual. This allows operations on different devices to be simultaneously recorded and a cross-platform manual can be generated, improving user convenience.

[0054] The recording unit allows team members to share operations in real time, thereby improving the efficiency of collaborative work. For example, the recording unit shares operations performed by team members in real time, allowing other members to instantly check those operations. For example, task updates in a project management tool are shared in real time. The recording unit also shares operations performed by team members in real time, allowing other members to refer to those operations and proceed with their work. For example, editing operations in a design tool are shared in real time. The recording unit also shares operations performed by team members in real time, allowing other members to instantly provide feedback on those operations. For example, code reviews are shared in real time, and feedback is provided instantly. This allows team members to share operations in real time, thereby improving the efficiency of collaborative work.

[0055] The generation unit can introduce an algorithm that analyzes operation procedures and proposes the optimal procedure. For example, the generation unit introduces an algorithm in which the generation AI analyzes operation procedures and proposes the optimal procedure. For example, it compares multiple operation procedures and selects the most efficient procedure. The generation unit also introduces an algorithm in which the generation AI analyzes operation procedures and proposes the optimal procedure. For example, it proposes the optimal procedure based on the user's past operation history. The generation unit also introduces an algorithm in which the generation AI analyzes operation procedures and proposes the optimal procedure. For example, it proposes the optimal procedure based on operation data of other users. In this way, by analyzing operation procedures and proposing the optimal procedure, the user's work efficiency is improved.

[0056] The generation unit can automatically add background information and related knowledge for operations. For example, the generation AI analyzes operation procedures and automatically adds background information and related knowledge. For example, it adds an explanation such as, "Changing this setting will improve security." The generation unit also analyzes operation procedures and automatically adds background information and related knowledge. For example, it adds an explanation such as, "This operation is necessary to back up data." The generation unit also analyzes operation procedures and automatically adds background information and related knowledge. For example, it adds an explanation such as, "This procedure is important for optimizing system performance." In this way, the background information and related knowledge for operations are automatically added, deepening the user's understanding.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The recorder records user actions in the form of video, screenshots, and text descriptions. For example, the recorder captures video of the steps a user takes to change software settings and simultaneously takes screenshots. It also records user actions such as the text typed and the buttons clicked. Step 2: The generator generates a step-by-step manual based on the operations recorded by the recorder. For example, the generator analyzes the recorded video and screenshots and adds textual explanations of each operation. The generator can also incorporate an algorithm that analyzes the operation procedures and suggests optimal procedures. Step 3: The update unit automatically updates the manual generated by the generation unit at any time. For example, the generation AI detects new operations performed by the user and updates the existing manual based on those operations. The update unit can also compare new operating procedures detected with the existing manual and automatically highlight changes. Step 4: The editorial department edits and customizes the manual generated by the generation department. For example, the user can add additional explanations to the manual automatically generated by the generation AI or delete unnecessary parts. The editorial department can also learn the user's editing history and make optimal suggestions for the next time they edit.

[0059] (Example 2) An AI-driven tool according to an embodiment of the present invention is a system that records computer tasks performed by teams within a company or organization in real time and automatically generates step-by-step manuals and guides based on the recordings. This allows the AI-driven tool to capture user operations in the form of videos, screenshots, and text descriptions, creating easy-to-understand instruction manuals. Furthermore, the manuals support rich content and Markdown, and can be automatically updated at any time. They can also be edited and customized as needed.

[0060] An AI-driven tool according to an embodiment includes a recording unit, a generating unit, an updating unit, and an editing unit. The recording unit records user operations in the form of video, screenshots, and text descriptions. For example, the recording unit captures the steps a user takes to change software settings in video format and simultaneously takes screenshots. The recording unit also records operations such as the text entered by the user and the buttons clicked. The generating unit generates a step-by-step manual based on the operations recorded by the recording unit. For example, the generation AI analyzes the recorded video and screenshots and adds text descriptions of each operation. The generation AI can also incorporate an algorithm that analyzes operation procedures and suggests optimal procedures. The updating unit automatically updates the manual generated by the generating unit at any time. For example, the generation AI detects new operations performed by the user and updates the existing manual based on the detected operations. The updating unit can also compare new operation procedures with the existing manual and automatically highlight changes when it detects them. The editing unit edits and customizes the manual generated by the generating unit. For example, a user can add additional explanations or delete unnecessary parts from the manual automatically generated by the generation AI. The editing department can also learn the user's editing history and make optimal suggestions for the next editing. As a result, the AI-driven tool according to the embodiment records the user's operations in real time, automatically generates a step-by-step manual, and enables automatic updating and editing at any time.

[0061] The recording unit can infer the intention of the user's operation and automatically generate supplementary explanations based on that intention. For example, when a user changes software settings, the recording unit's generation AI infers the intention of the operation and automatically generates supplementary explanations such as, "Changing this setting will improve performance." Furthermore, when a user inputs data, the recording unit's generation AI infers the intention of the operation and automatically generates supplementary explanations such as, "This data will be used for monthly reports." Furthermore, when a user saves a file, the recording unit's generation AI infers the intention of the operation and automatically generates supplementary explanations such as, "This file will be used for project progress management." In this way, by inferring the intention of the user's operation and automatically generating supplementary explanations, manuals become easier to understand.

[0062] The recording unit can evaluate the efficiency of operations based on the recorded operation data and make improvement suggestions. For example, when a user performs an operation to open a file through multiple steps, the recording unit allows the generating AI to evaluate the efficiency of the operation and make improvement suggestions such as, "Using shortcut keys will speed up the operation." When a user manually inputs data, the recording unit allows the generating AI to evaluate the efficiency of the operation and make improvement suggestions such as, "Using the auto-entry function will reduce the input time." When a user switches between multiple windows, the recording unit allows the generating AI to evaluate the efficiency of the operation and make improvement suggestions such as, "Using the window rearrangement function will make the operation smoother." By evaluating the efficiency of operations and making improvement suggestions, the user's work efficiency is improved.

[0063] The recording unit can use the emotion estimation function to record the user's emotional state and provide operation advice according to changes in emotion. For example, if the user feels stressed while operating the device, the emotion estimation function detects this state and provides advice such as, "We recommend that you take a break to relax." If the user feels confused while operating the device, the emotion estimation function detects this state and provides advice such as, "Please refer to the help documentation, which will help you solve the problem." If the user loses concentration while operating the device, the emotion estimation function detects this state and provides advice such as, "You will be more efficient if you break the task into smaller pieces." By recording the user's emotional state and providing advice according to changes in emotion, the system reduces the user's stress and improves work efficiency.

[0064] The recording unit can simultaneously record operations on different devices and generate cross-platform manuals. For example, the recording unit generates a cross-platform manual by recording operations performed by a user on a smartphone and simultaneously recording operations on a PC. For example, setting changes on a smartphone and data entry on a PC can be combined into a single manual. The recording unit also generates a cross-platform manual by recording operations performed by a user on a tablet and simultaneously recording operations on a desktop. For example, app operations on a tablet and file management on a desktop can be combined into a single manual. The recording unit also generates a cross-platform manual by recording operations performed by a user on a laptop and simultaneously recording operations on a smartwatch. For example, sending an email on a laptop and setting notifications on a smartwatch can be combined into a single manual. This allows operations on different devices to be simultaneously recorded and a cross-platform manual can be generated, improving user convenience.

[0065] The recording unit allows team members to share operations in real time, thereby improving the efficiency of collaborative work. For example, the recording unit shares operations performed by team members in real time, allowing other members to instantly check those operations. For example, task updates in a project management tool are shared in real time. The recording unit also shares operations performed by team members in real time, allowing other members to refer to those operations and proceed with their work. For example, editing operations in a design tool are shared in real time. The recording unit also shares operations performed by team members in real time, allowing other members to instantly provide feedback on those operations. For example, code reviews are shared in real time, and feedback is provided instantly. This allows team members to share operations in real time, thereby improving the efficiency of collaborative work.

[0066] The recording unit can use the emotion estimation function to identify stress points felt by the user during operation and suggest solutions to those stress points. For example, the recording unit can use the emotion estimation function to identify stress points felt by the user during operation and suggest solutions such as, "We recommend using a shortcut key to simplify this operation." The recording unit can also use the emotion estimation function to identify stress points felt by the user during operation and suggest solutions such as, "We recommend using a macro to automate this procedure." The recording unit can also use the emotion estimation function to identify stress points felt by the user during operation and suggest solutions such as, "We recommend using a template to streamline this operation." In this way, by identifying stress points felt by the user during operation and suggesting solutions to those stress points, the user's stress is reduced and work efficiency is improved.

[0067] The generation unit can introduce an algorithm that analyzes operation procedures and proposes the optimal procedure. For example, the generation unit introduces an algorithm in which the generation AI analyzes operation procedures and proposes the optimal procedure. For example, it compares multiple operation procedures and selects the most efficient procedure. The generation unit also introduces an algorithm in which the generation AI analyzes operation procedures and proposes the optimal procedure. For example, it proposes the optimal procedure based on the user's past operation history. The generation unit also introduces an algorithm in which the generation AI analyzes operation procedures and proposes the optimal procedure. For example, it proposes the optimal procedure based on operation data of other users. In this way, by analyzing operation procedures and proposing the optimal procedure, the user's work efficiency is improved.

[0068] The generation unit can automatically add background information and related knowledge for operations. For example, the generation AI analyzes operation procedures and automatically adds background information and related knowledge. For example, it adds an explanation such as, "Changing this setting will improve security." The generation unit also analyzes operation procedures and automatically adds background information and related knowledge. For example, it adds an explanation such as, "This operation is necessary to back up data." The generation unit also analyzes operation procedures and automatically adds background information and related knowledge. For example, it adds an explanation such as, "This procedure is important for optimizing system performance." In this way, the background information and related knowledge for operations are automatically added, deepening the user's understanding.

[0069] The generation unit can automatically generate manuals in different languages, enabling global use. In the generation unit, for example, a generation AI analyzes operation procedures and automatically generates manuals in different languages. For example, a manual is generated that supports multiple languages, such as English, Japanese, and French. In addition, the generation unit analyzes operation procedures and automatically generates manuals in different languages. For example, a manual in an appropriate language is automatically provided depending on the user's language setting. In addition, the generation unit analyzes operation procedures and automatically generates manuals in different languages. For example, manuals in multiple languages ​​are generated simultaneously for use by a global team. In this way, automatic generation of manuals in different languages ​​enables global use.

[0070] The generation unit can customize according to the user's skill level. For example, the generation AI analyzes the operation procedures and customizes them according to the user's skill level. For example, detailed explanations are provided for beginners and concise explanations for advanced users. The generation unit also analyzes the operation procedures and customizes them according to the user's skill level. For example, it makes extensive use of illustrations for beginners and provides text-based explanations for advanced users. The generation unit also analyzes the operation procedures and customizes them according to the user's skill level. For example, it provides detailed step-by-step instructions for beginners and only the main points for advanced users. In this way, customization according to the user's skill level deepens the user's understanding of the manual.

[0071] The generation unit can use the emotion estimation function to select expressions that are easiest for the user to understand and reflect them in the manual. For example, the generation unit uses the emotion estimation function to select expressions that are easiest for the user to understand and reflect them in the manual. For example, if the user is confused, a more specific example is added. The generation unit also uses the emotion estimation function to select expressions that are easiest for the user to understand and reflect them in the manual. For example, if the user is feeling stressed, a concise and clear expression is used. The generation unit also uses the emotion estimation function to select expressions that are easiest for the user to understand and reflect them in the manual. For example, if the user is concentrating, a detailed technical explanation is added. In this way, by selecting expressions that are easiest for the user to understand and reflecting them in the manual, the manual can be more easily understood.

[0072] The generation unit can automatically determine appropriate insertion points for rich content and place it in optimal locations. In the generation unit, for example, the generation AI analyzes operation procedures and automatically determines appropriate insertion points for rich content. For example, a related video is inserted after an explanation of the operation procedures. In addition, the generation AI analyzes operation procedures and automatically determines appropriate insertion points for rich content. For example, a screenshot is inserted in the middle of a setting change procedure. In addition, the generation AI analyzes operation procedures and automatically determines appropriate insertion points for rich content. For example, a related link is inserted to explain background information for the operation. In this way, the appropriate insertion points for rich content are automatically determined and placed in optimal locations, thereby deepening understanding of the manual.

[0073] The generation unit can automatically add links and annotations to Markdown-formatted manuals. For example, the generation AI in the generation unit analyzes Markdown-formatted manuals and automatically adds relevant links. For example, adding links to official documentation related to explanations of operating procedures. The generation unit also analyzes Markdown-formatted manuals and automatically adds annotations. For example, adding notes and tips about specific operating procedures as annotations. The generation AI in the generation unit also analyzes Markdown-formatted manuals and automatically adds links to related resources. For example, adding links to tutorial videos related to operating procedures. In this way, automatically adding links and annotations to Markdown-formatted manuals deepens understanding of the manuals.

[0074] The generation unit can use the emotion estimation function to analyze emotional responses when a user views rich content and improve the quality of the content. For example, the generation unit uses the emotion estimation function to analyze emotional responses when a user views rich content and preferentially display content with a large number of positive responses. The generation unit also uses the emotion estimation function to analyze emotional responses when a user views rich content and improve content with a large number of negative responses. The generation unit also uses the emotion estimation function to analyze emotional responses when a user views rich content and identify the most effective content format. In this way, by analyzing emotional responses when a user views rich content and improving the quality of the content, user satisfaction is increased.

[0075] The generation unit can generate an interactive manual using rich content, allowing a user to simulate operations. For example, the generation unit causes a generation AI to generate an interactive manual using rich content, allowing a user to simulate operation procedures. For example, the generation unit creates a manual including clickable buttons and links. The generation unit also causes the generation AI to generate an interactive manual using rich content, allowing a user to actually try out operation procedures. For example, the generation unit provides an operation simulation in a virtual environment. The generation unit also causes the generation AI to generate an interactive manual using rich content, allowing a user to visually understand operation procedures. For example, the generation unit creates a manual including animations and videos. In this way, by generating an interactive manual using rich content, a user can simulate operations and deepen their understanding.

[0076] The generation unit can add a function to automatically convert a Markdown-formatted manual into other document formats (PDF, HTML, etc.). For example, the generation unit adds a function in which the generation AI analyzes a Markdown-formatted manual and automatically converts it into PDF format. For example, the manual is output as a printable PDF file. The generation unit also adds a function in which the generation AI analyzes a Markdown-formatted manual and automatically converts it into HTML format. For example, the manual is output as an HTML file that can be published as a webpage. The generation unit also adds a function in which the generation AI analyzes a Markdown-formatted manual and automatically converts it into other document formats (for example, DOCX or ODT). This automatically converts a Markdown-formatted manual into other document formats, expanding the range of uses of the manual.

[0077] The generation unit can use the emotion estimation function to identify rich content in which the user is most interested and highlight that content. The generation unit, for example, uses the emotion estimation function to identify rich content in which the user is most interested and highlight that content. For example, videos with many positive emotional responses are placed in prominent positions. The generation unit also uses the emotion estimation function to identify rich content in which the user is most interested and highlight that content. For example, links that the user is most interested in are highlighted. The generation unit also uses the emotion estimation function to identify rich content in which the user is most interested and highlight that content. For example, images that the user responds to are displayed larger. In this way, by identifying rich content in which the user is most interested and highlighting that content, user satisfaction is improved.

[0078] When a new operating procedure is detected, the update unit can compare it with the existing manual and automatically highlight the changes. For example, the generation AI detects a new operating procedure and compares it with the existing manual to automatically highlight the changes. For example, if a new setting item is added, that part is highlighted. The update unit also detects a new operating procedure and compares it with the existing manual to automatically highlight the changes. For example, if the order of the procedures is changed, that part is highlighted. The update unit also detects a new operating procedure and compares it with the existing manual to automatically highlight the changes. For example, if a new function is added, that part is highlighted. In this way, by detecting a new operating procedure and comparing it with the existing manual to automatically highlight the changes, the user can easily understand the changes.

[0079] The update unit can use the emotion estimation function to analyze how the user felt about the update content and improve the quality of the update. For example, the update unit uses the emotion estimation function to analyze how the user felt about the update content and prioritizes adopting update content that has received many positive responses. The update unit also uses the emotion estimation function to analyze how the user felt about the update content and improves update content that has received many negative responses. The update unit also uses the emotion estimation function to analyze how the user felt about the update content and identify the most effective update method. In this way, by analyzing how the user felt about the update content and improving the quality of the update, user satisfaction is improved.

[0080] The update unit can automatically generate different versions of a manual and allow the user to select one as needed. For example, the update unit causes the generation AI to automatically generate different versions of a manual and allow the user to select one as needed. For example, an old version and a new version of a manual are provided simultaneously. The update unit also causes the generation AI to automatically generate different versions of a manual and allow the user to select one as needed. For example, manuals for beginners and manuals for advanced users are provided simultaneously. The update unit also causes the generation AI to automatically generate different versions of a manual and allow the user to select one as needed. For example, manuals in different languages ​​are provided simultaneously. This improves user convenience by automatically generating different versions of a manual and allowing the user to select one as needed.

[0081] The update unit adds a function to notify users of updates to the manual, allowing the latest information to be shared quickly. For example, the update unit adds a function to notify users of updates to the manual by the generation AI, allowing the latest information to be shared quickly. For example, update information is sent by email or push notification. The update unit also adds a function to notify users of updates to the manual by the generation AI, allowing the latest information to be shared quickly. For example, update information is displayed as a pop-up within the manual. The update unit also adds a function to notify users of updates to the manual by the generation AI, allowing the latest information to be shared quickly. For example, update information is displayed on a dashboard. This allows the latest information to be shared quickly by notifying users of updates to the manual.

[0082] The update unit can use the emotion estimation function to identify update content that the user is most interested in and prioritize notify the user of that content. The update unit, for example, uses the emotion estimation function to identify update content that the user is most interested in and prioritize notify the user of that content. For example, update content that has a large number of positive emotional responses is prioritized for notification. The update unit also uses the emotion estimation function to identify update content that the user is most interested in and prioritize notify the user of that content. For example, update information for a function that the user is most interested in is prioritized for notification. The update unit also uses the emotion estimation function to identify update content that the user is most interested in and prioritize notify the user of that content. For example, update content that has a good user response is displayed in a prominent position. In this way, the update content that the user is most interested in is identified and priority is given to notification of that content, thereby improving user satisfaction.

[0083] The editing department can learn the user's editing history and make optimal suggestions for the next time they edit. For example, the generation AI in the editing department learns the user's editing history and makes optimal suggestions for the next time they edit. For example, it may suggest phrases or templates that have been frequently used in the past. The editing department also learns the user's editing history and makes optimal suggestions for the next time they edit. For example, it may suggest similar corrections based on corrections made in the past. The editing department also learns the user's editing history and makes optimal suggestions for the next time they edit. For example, it may suggest annotations or links that have been added in the past. In this way, the efficiency of the editing process is improved by learning the user's editing history and making optimal suggestions for the next time they edit.

[0084] The editorial department can automatically perform quality checks on customized manuals to detect errors and inconsistencies. For example, the editorial department automatically performs quality checks on manuals customized by the generation AI to detect errors and inconsistencies. For example, it detects broken links and typos. The editorial department also automatically performs quality checks on manuals customized by the generation AI to detect errors and inconsistencies. For example, it checks whether the order of steps is correct. The editorial department also automatically performs quality checks on manuals customized by the generation AI to detect errors and inconsistencies. For example, it checks whether the terminology used is consistent. In this way, by automatically performing quality checks on customized manuals, errors and inconsistencies are detected and the quality of the manuals is improved.

[0085] The editing department can use the emotion estimation function to analyze the emotions felt by the user during editing work and improve editing efficiency. For example, the editing department can use the emotion estimation function to analyze the emotions felt by the user during editing work and provide advice to relax if the user is feeling stressed. The editing department can also use the emotion estimation function to analyze the emotions felt by the user during editing work and suggest taking a break if the user's concentration is declining. The editing department can also use the emotion estimation function to analyze the emotions felt by the user during editing work and provide advice to maintain that state if the user is feeling a strong positive emotion. In this way, the editing department can analyze the emotions felt by the user during editing work and improve editing efficiency, thereby improving user satisfaction.

[0086] The editorial department can add a function to automatically generate manuals customized for different user groups. For example, the editorial department allows the generation AI to automatically generate manuals customized for different user groups. For example, manuals for beginners and advanced users are generated simultaneously. The editorial department also allows the generation AI to automatically generate manuals customized for different user groups. For example, manuals for the sales team and the technical team are generated simultaneously. The editorial department also allows the generation AI to automatically generate manuals customized for different user groups. For example, manuals for domestic users and manuals for overseas users are generated simultaneously. This improves user convenience by automatically generating manuals customized for different user groups.

[0087] The editorial department can add a function that allows edited content to be shared with other users and enables collaborative editing. For example, the editorial department adds a function that allows the generation AI to share edited content with other users and enables collaborative editing. For example, it allows multiple users to edit a manual simultaneously in real time. The editorial department also adds a function that allows the generation AI to share edited content with other users and enables collaborative editing. For example, it allows sharing of editing history and confirmation of who edited which part. The editorial department also adds a function that allows the generation AI to share edited content with other users and enables collaborative editing. For example, it adds a comment function that allows feedback on edited content to be given in real time. This allows edited content to be shared with other users and enables collaborative editing, improving the efficiency of editing work.

[0088] The editorial department can use the emotion estimation function to identify customization content that will most satisfy the user and propose that content. The editorial department, for example, uses the emotion estimation function to identify customization content that will most satisfy the user and propose that content. For example, the editorial department prioritizes proposing customization content that has a high number of positive emotional responses. The editorial department also uses the emotion estimation function to identify customization content that will most satisfy the user and propose that content. For example, the editorial department proposes customization options that are of high interest to the user. The editorial department also uses the emotion estimation function to identify customization content that will most satisfy the user and propose that content. For example, the editorial department displays customization content that has a high user response in a prominent position. In this way, the editorial department can identify customization content that will most satisfy the user and propose that content, thereby improving user satisfaction.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The recording unit records user operations in the form of video, screenshots, and text descriptions. For example, the recording unit captures the steps a user takes to change software settings in video format and simultaneously takes screenshots. It also records operations such as the text the user enters and the buttons they click. The generation unit generates a step-by-step manual based on the operations recorded by the recording unit. For example, the generation AI analyzes the recorded video and screenshots and adds text descriptions of each operation. The generation AI can also incorporate algorithms that analyze operation procedures and suggest optimal procedures. The update unit automatically updates the manual generated by the generation unit at any time. For example, the generation AI detects new operations performed by the user and updates the existing manual based on those operations. The update unit can also compare new operation procedures with the existing manual and automatically highlight changes when it detects them. The editing unit edits and customizes the manual generated by the generation unit. For example, users can add additional explanations or delete unnecessary parts of the manual automatically generated by the generation AI. The editing unit can also learn the user's editing history and make optimal suggestions for the next editing session. As a result, the AI-driven tool according to the embodiment can record user operations in real time, automatically generate step-by-step manuals, and automatically update and edit them at any time.

[0091] The recording unit can infer the intention of the user's operation and automatically generate supplementary explanations based on that intention. For example, when a user changes software settings, the recording unit's generation AI infers the intention of the operation and automatically generates supplementary explanations such as, "Changing this setting will improve performance." Furthermore, when a user inputs data, the recording unit's generation AI infers the intention of the operation and automatically generates supplementary explanations such as, "This data will be used for monthly reports." Furthermore, when a user saves a file, the recording unit's generation AI infers the intention of the operation and automatically generates supplementary explanations such as, "This file will be used for project progress management." In this way, by inferring the intention of the user's operation and automatically generating supplementary explanations, manuals become easier to understand.

[0092] The recording unit can evaluate the efficiency of operations based on the recorded operation data and make improvement suggestions. For example, when a user performs an operation to open a file through multiple steps, the recording unit allows the generating AI to evaluate the efficiency of the operation and make improvement suggestions such as, "Using shortcut keys will speed up the operation." When a user manually inputs data, the recording unit allows the generating AI to evaluate the efficiency of the operation and make improvement suggestions such as, "Using the auto-entry function will reduce the input time." When a user switches between multiple windows, the recording unit allows the generating AI to evaluate the efficiency of the operation and make improvement suggestions such as, "Using the window rearrangement function will make the operation smoother." By evaluating the efficiency of operations and making improvement suggestions, the user's work efficiency is improved.

[0093] The recording unit can use the emotion estimation function to record the user's emotional state and provide operation advice according to changes in emotion. For example, if the user feels stressed while operating the device, the emotion estimation function detects this state and provides advice such as, "We recommend that you take a break to relax." If the user feels confused while operating the device, the emotion estimation function detects this state and provides advice such as, "Please refer to the help documentation, which will help you solve the problem." If the user loses concentration while operating the device, the emotion estimation function detects this state and provides advice such as, "You will be more efficient if you break the task into smaller pieces." By recording the user's emotional state and providing advice according to changes in emotion, the system reduces the user's stress and improves work efficiency.

[0094] The recording unit can simultaneously record operations on different devices and generate cross-platform manuals. For example, the recording unit generates a cross-platform manual by recording operations performed by a user on a smartphone and simultaneously recording operations on a PC. For example, setting changes on a smartphone and data entry on a PC can be combined into a single manual. The recording unit also generates a cross-platform manual by recording operations performed by a user on a tablet and simultaneously recording operations on a desktop. For example, app operations on a tablet and file management on a desktop can be combined into a single manual. The recording unit also generates a cross-platform manual by recording operations performed by a user on a laptop and simultaneously recording operations on a smartwatch. For example, sending an email on a laptop and setting notifications on a smartwatch can be combined into a single manual. This allows operations on different devices to be simultaneously recorded and a cross-platform manual can be generated, improving user convenience.

[0095] The recording unit allows team members to share operations in real time, thereby improving the efficiency of collaborative work. For example, the recording unit shares operations performed by team members in real time, allowing other members to instantly check those operations. For example, task updates in a project management tool are shared in real time. The recording unit also shares operations performed by team members in real time, allowing other members to refer to those operations and proceed with their work. For example, editing operations in a design tool are shared in real time. The recording unit also shares operations performed by team members in real time, allowing other members to instantly provide feedback on those operations. For example, code reviews are shared in real time, and feedback is provided instantly. This allows team members to share operations in real time, thereby improving the efficiency of collaborative work.

[0096] The recording unit can use the emotion estimation function to identify stress points felt by the user during operation and suggest solutions to those stress points. For example, the recording unit can use the emotion estimation function to identify stress points felt by the user during operation and suggest solutions such as, "We recommend using a shortcut key to simplify this operation." The recording unit can also use the emotion estimation function to identify stress points felt by the user during operation and suggest solutions such as, "We recommend using a macro to automate this procedure." The recording unit can also use the emotion estimation function to identify stress points felt by the user during operation and suggest solutions such as, "We recommend using a template to streamline this operation." In this way, by identifying stress points felt by the user during operation and suggesting solutions to those stress points, the user's stress is reduced and work efficiency is improved.

[0097] The generation unit can introduce an algorithm that analyzes operation procedures and proposes the optimal procedure. For example, the generation unit introduces an algorithm in which the generation AI analyzes operation procedures and proposes the optimal procedure. For example, it compares multiple operation procedures and selects the most efficient procedure. The generation unit also introduces an algorithm in which the generation AI analyzes operation procedures and proposes the optimal procedure. For example, it proposes the optimal procedure based on the user's past operation history. The generation unit also introduces an algorithm in which the generation AI analyzes operation procedures and proposes the optimal procedure. For example, it proposes the optimal procedure based on operation data of other users. In this way, by analyzing operation procedures and proposing the optimal procedure, the user's work efficiency is improved.

[0098] The generation unit can automatically add background information and related knowledge for operations. For example, the generation AI analyzes operation procedures and automatically adds background information and related knowledge. For example, it adds an explanation such as, "Changing this setting will improve security." The generation unit also analyzes operation procedures and automatically adds background information and related knowledge. For example, it adds an explanation such as, "This operation is necessary to back up data." The generation unit also analyzes operation procedures and automatically adds background information and related knowledge. For example, it adds an explanation such as, "This procedure is important for optimizing system performance." In this way, the background information and related knowledge for operations are automatically added, deepening the user's understanding.

[0099] The generation unit can use the emotion estimation function to select expressions that are easiest for the user to understand and reflect them in the manual. For example, the generation unit uses the emotion estimation function to select expressions that are easiest for the user to understand and reflect them in the manual. For example, if the user is confused, a more specific example is added. The generation unit also uses the emotion estimation function to select expressions that are easiest for the user to understand and reflect them in the manual. For example, if the user is feeling stressed, a concise and clear expression is used. The generation unit also uses the emotion estimation function to select expressions that are easiest for the user to understand and reflect them in the manual. For example, if the user is concentrating, a detailed technical explanation is added. In this way, by selecting expressions that are easiest for the user to understand and reflecting them in the manual, the manual can be more easily understood.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The recorder records user actions in the form of video, screenshots, and text descriptions. For example, the recorder captures video of the steps a user takes to change software settings and simultaneously takes screenshots. It also records user actions such as the text typed and the buttons clicked. Step 2: The generator generates a step-by-step manual based on the operations recorded by the recorder. For example, the generator analyzes the recorded video and screenshots and adds textual explanations of each operation. The generator can also incorporate an algorithm that analyzes the operation procedures and suggests optimal procedures. Step 3: The update unit automatically updates the manual generated by the generation unit at any time. For example, the generation AI detects new operations performed by the user and updates the existing manual based on those operations. The update unit can also compare new operating procedures detected with the existing manual and automatically highlight changes. Step 4: The editorial department edits and customizes the manual generated by the generation department. For example, the user can add additional explanations to the manual automatically generated by the generation AI or delete unnecessary parts. The editorial department can also learn the user's editing history and make optimal suggestions for the next time they edit.

[0102] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0103] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0104] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0111] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0112] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0113] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0115] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0122] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0123] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0124] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0126] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0127] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0128] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0130] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0131] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0132] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0134] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0135] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0139] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0141] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0142] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0143] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0144] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0145] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0146] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0147] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0148] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0149] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0150] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0151] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0152] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0153] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0154] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0155] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0156] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0157] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0158] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0159] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0160] 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.

[0161] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0162] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0163] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0164] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0165] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0166] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0167] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0168] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0169] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a recording unit that records user operations in the form of videos, screenshots, and text descriptions; a generating unit that generates a step-by-step manual based on the operations recorded by the recording unit; an update unit that automatically updates the manual generated by the generation unit at any timing; an editing unit that edits and customizes the manual generated by the generation unit; A system characterized by:

2. The recording unit The intention of the user's operation is estimated, and a supplementary explanation based on the intention is automatically generated.

2. The system of claim 1.

3. The recording unit Evaluate the efficiency of the operation based on the recorded operation data and make suggestions for improvement 2. The system of claim 1.

4. The recording unit Recording the emotional state of the user and providing advice on the operation according to changes in the user's emotions 2. The system of claim 1.

5. The recording unit The operations on different devices are also recorded simultaneously to generate a cross-platform manual.

2. The system of claim 1.

6. The recording unit Share the above operations among team members in real time to improve the efficiency of collaboration.

2. The system of claim 1.

7. The recording unit Identify the stress points felt by the user during operation and suggest ways to resolve them 2. The system of claim 1.

8. The generation unit Introducing an algorithm that analyzes operational procedures and suggests optimal procedures 2. The system of claim 1.

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