system

Abstract

The system according to this embodiment aims to efficiently create business manuals. [Solution] The system according to the embodiment comprises a reception unit, a recording unit, an analysis unit, a document generation unit, an editing unit, and a provision unit. The reception unit receives operations from the user. The recording unit records the operations received by the reception unit. The analysis unit analyzes the operations recorded by the recording unit and breaks them down into individual operations. The document generation unit documents the operations broken down by the analysis unit. The editing unit edits the documents generated by the document generation unit. The provision unit provides the documents edited by the editing unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that creating an operation manual requires time and effort and is difficult to perform efficiently.

[0005] The system according to the embodiment aims to efficiently create an operation manual.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a recording unit, an analysis unit, a document generation unit, an editing unit, and a provision unit. The reception unit receives operations from the user. The recording unit records the operations received by the reception unit. The analysis unit analyzes the operations recorded by the recording unit and breaks them down into individual operations. The document generation unit documents the operations broken down by the analysis unit. The editing unit edits the documents generated by the document generation unit. The provision unit provides the documents edited by the editing unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently create business manuals. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 3 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An embodiment of the present invention provides a business manual creation support system that records user operations and automatically generates a manual based on those records. The business manual creation support system records user operations. Next, the business manual creation support system analyzes the recorded operations using image recognition technology and breaks them down into individual operations. For example, if a user changes software settings, the system analyzes how the operation was performed using image recognition technology and breaks it down into individual steps. Subsequently, the business manual creation support system documents the decomposed operations. For example, specific operations such as "open the settings menu," "select an option," and "click the save button" are generated as text. This text can be freely edited, added to, and deleted by the user. Finally, the user can easily create a business manual. This allows the business manual creation support system to significantly streamline the time-consuming manual creation process. As a result, the business manual creation support system can efficiently record, analyze, document, edit, and provide user operations.

[0029] The business manual creation support system according to this embodiment comprises a reception unit, a recording unit, an analysis unit, a document creation unit, an editing unit, and a provision unit. The reception unit receives operations from the user. The recording unit records the operations received by the reception unit. The analysis unit analyzes the operations recorded by the recording unit and breaks them down into individual operations. The document creation unit documents the operations that have been broken down by the analysis unit. The editing unit edits the documents generated by the document creation unit. The provision unit provides the documents edited by the editing unit. For example, the reception unit receives operations performed by the user. The recording unit, for example, records the operations performed by the user. The analysis unit, for example, analyzes the recorded operations using image recognition technology and breaks them down into individual operations. The document creation unit, for example, documents the broken-down operations. The editing unit, for example, allows the user to freely edit, add to, and delete the generated documents. The provision unit, for example, provides the edited documents. This allows the business manual creation support system to efficiently record, analyze, document, edit, and provide user operations.

[0030] The reception desk receives user input. Specifically, when a user accesses the system and begins operations for creating a business manual, the reception desk receives that input. Users perform operations, for example, through a dedicated interface, and the reception desk receives the details of these operations in real time. The reception desk accurately understands the type and sequence of operations performed by the user and appropriately passes them on to the next processing step. For example, it receives all clicks, input operations, and screen transitions performed by the user to demonstrate business procedures, and ensures that these operations are accurately recorded. In addition to receiving user input, the reception desk also monitors events such as the start and end of operations and the occurrence of errors, and provides feedback to the user as needed. This allows users to proceed with operations smoothly, and the business manual creation process proceeds smoothly.

[0031] The recording unit records operations received by the reception unit. Specifically, it meticulously records user operations and saves them as data for subsequent analysis and documentation. The recording unit accurately records the type and timing of operations, information about the target of the operation, and centrally manages this data. For example, it records the location and name of buttons clicked by the user, the text content entered, and the order of screen transitions. The recording unit saves this operation data with timestamps, enabling accurate reproduction of the order and timing of operations. The recording unit also has an operation recording function, allowing it to record the user's operation screen as a video. This allows for visual confirmation of operations later, improving the accuracy of analysis and documentation. The recording unit securely stores the recorded data and can share it with other departments and systems as needed. In this way, the recording unit accurately and meticulously records user operations and provides data that forms the basis for creating business manuals.

[0032] The analysis unit analyzes the operations recorded by the recording unit and breaks them down into individual operations. Specifically, it analyzes the recorded operation data and breaks it down to understand the meaning and purpose of each operation. The analysis unit uses image recognition technology and natural language processing technology to analyze the operation content in detail. For example, it extracts click operations, input operations, screen transitions, etc., individually from the recorded operations and analyzes the purpose of each operation. The analysis unit logically breaks down the operations, taking into account the order and relationships of the operations, and clarifies the meaning of each operation. For example, if the operation recorded is that a user clicks a specific button and then enters text into an input form, the analysis unit breaks down these operations and understands that the button click triggered the display of the input form. Based on these analysis results, the analysis unit prepares the operation content as data to be passed on to the next document creation unit. In this way, the analysis unit provides basic data for creating business manuals by analyzing the recorded operations in detail and breaking them down into individual operations.

[0033] The text generation unit documents the operations broken down by the analysis unit. Specifically, it converts the operation data provided by the analysis unit into easy-to-understand sentences. The text generation unit uses natural language generation technology to express the operation content in an appropriate context. For example, it documents user clicks and input operations as specific steps. For clicks, it generates instructional sentences such as "Click the next button," and for input operations, it generates explanatory sentences such as "Enter the required information into the text box." The text generation unit organizes the operation content into a consistent document, taking into account the order and relationships of the operations. For example, if multiple operations are performed in sequence, it documents these operations as a series of steps, providing them in a way that is easy for the user to understand. The text generation unit verifies that the generated sentences are accurate and easy to understand, and makes corrections and adjustments as necessary. In this way, the text generation unit converts the analyzed operation content into easy-to-understand sentences, providing the foundational text for business manuals.

[0034] The editorial department edits the text generated by the writing department. Specifically, it allows users to freely edit, add to, and delete the generated text. The editorial department provides an interface that allows users to review the generated text and input corrections or additional information as needed. For example, if there are errors or omissions in the text generated by a user, they can be corrected and accurate information added through the editorial department. The editorial department provides an intuitive and user-friendly interface to enable users to perform editing work efficiently. For example, it includes drag-and-drop functionality and a text editor function to allow users to easily edit text. The editorial department also manages versions of edited text, saving past editing history and allowing users to revert to previous versions as needed. This allows the editorial department to freely edit the text generated by users and keep the content of the work manual accurate and up-to-date.

[0035] The service provider will provide documents edited by the editorial department. Specifically, they will provide users with edited work manuals and quickly share necessary information. The service provider will output the edited documents in formats such as PDF and HTML to make them easily accessible to users. For example, they will output edited work manuals in PDF format so that users can download and print them. They will also output them in HTML format so that users can view the work manuals through a web browser. The service provider will provide search and table of contents functions so that users can quickly find the information they need. For example, they will be able to search for specific keywords within the work manual and quickly find related information. The service provider also has a function to notify users of updates to the work manual, notifying them when a new version is released. In this way, the service provider will quickly and efficiently provide edited work manuals and ensure that users always have access to the latest information they need.

[0036] The reception unit can analyze the user's past operation history and select the optimal reception method. For example, the reception unit may prioritize operations that the user has frequently performed in the past. For example, the reception unit may predict operations to be performed during specific time periods based on the user's past operation history and adjust the reception method accordingly. For example, the reception unit may prioritize input devices (mouse, keyboard, etc.) that the user has used in the past. This improves the efficiency of operations by selecting the optimal reception method based on the user's past operation history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit may input the user's past operation history data into a generating AI and have the generating AI select the optimal reception method.

[0037] The reception unit can filter operations based on the user's current project or area of ​​interest when receiving them. For example, the reception unit may prioritize operations related to the project the user is currently working on. For example, the reception unit may filter and accept operations based on the user's area of ​​interest. For example, the reception unit may prioritize operations related to areas the user has shown interest in in the past. This allows the reception unit to prioritize operations that are highly relevant by filtering operations based on the user's current project and area of ​​interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit may input the user's project data and area of ​​interest data into a generating AI and have the generating AI perform the filtering.

[0038] The reception unit can prioritize operations based on the user's geographical location when receiving an operation request. For example, if the user is in a specific location, the reception unit will prioritize operations related to that location. For example, if the user is on the move, the reception unit will prioritize operations related to movement. For example, if the user is at home, the reception unit will prioritize operations to be performed at home. This improves the efficiency of operations by prioritizing operations based on the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location data into a generating AI and have the generating AI select the most relevant operations.

[0039] The reception unit can analyze the user's social media activity when receiving an operation request and accept relevant operations. For example, the reception unit can prioritize accepting relevant operations based on information the user has shared on social media. For example, the reception unit can prioritize accepting operations related to accounts the user follows on social media. For example, the reception unit can prioritize accepting operations related to topics the user has shown interest in on social media. This improves the efficiency of operations by accepting relevant operations based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI select relevant operations.

[0040] The recording unit can adjust the level of detail in recordings based on the importance of the operations. For example, the recording unit can record important operations in detail and unimportant operations in a simplified manner. For example, the recording unit can adjust the frequency of recordings according to the importance of the operations. For example, the recording unit can record important operations at high resolution and unimportant operations at low resolution. This improves the efficiency of recording by adjusting the level of detail in recordings based on the importance of the operations. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input operation importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in recordings.

[0041] The recording unit can apply different recording algorithms depending on the category of the operation during recording. For example, the recording unit may record setting change operations in detail and data entry operations in a simplified manner. For example, the recording unit may change the format of the recording depending on the category of the operation. For example, the recording unit may change the storage location of the recording depending on the category of the operation. This improves the efficiency of recording by applying a recording algorithm according to the category of the operation. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit may input operation category data into a generating AI and have the generating AI execute the application of the recording algorithm.

[0042] The recording unit can determine the priority of recordings based on the timing of operations. For example, the recording unit prioritizes recording during time periods when important operations are performed. For example, the recording unit adjusts the frequency of recordings according to the timing of operations. For example, the recording unit changes the storage location of records based on the timing of operations. This improves the efficiency of recording by determining the priority of recordings based on the timing of operations. Some or all of the above processes in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input operation timing data into a generating AI and have the generating AI determine the priority of recordings.

[0043] The recording unit can adjust the order of recordings based on the relevance of the operations during recording. For example, the recording unit may record related operations consecutively and postpone less relevant operations. For example, the recording unit may change the order of recordings according to the relevance of the operations. For example, the recording unit may change the storage location of the recordings based on the relevance of the operations. This improves the efficiency of recording by adjusting the order of recordings based on the relevance of the operations. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit may input operation relevance data into a generating AI and have the generating AI perform the adjustment of the recording order.

[0044] The analysis unit can improve the accuracy of the analysis based on the interrelationships of operations during the analysis. For example, the analysis unit improves the accuracy of the analysis by considering the sequence of operations. For example, the analysis unit adjusts the analysis algorithm based on the interrelationships of operations. For example, the analysis unit displays the analysis results considering the interrelationships of operations. In this way, the accuracy of the analysis is improved by considering the interrelationships of operations. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input interrelationship data of operations into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0045] The analysis unit can perform analysis while considering the attribute information of the person performing the operation. The analysis unit can adjust the accuracy of the analysis based on, for example, the skill level of the person performing the operation. The analysis unit can adjust the analysis algorithm based on, for example, the job title of the person performing the operation. The analysis unit can improve the accuracy of the analysis by considering, for example, the past operation history of the person performing the operation. In this way, the accuracy of the analysis is improved by considering the attribute information of the person performing the operation. Some or all of the above processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the attribute information data of the person performing the operation into a generating AI and have the generating AI perform the analysis.

[0046] The analysis unit can perform the analysis while considering the geographical distribution of operations. The analysis unit adjusts the accuracy of the analysis based on, for example, the location where the operations were performed. The analysis unit adjusts the analysis algorithm based on, for example, the geographical distribution of operations. The analysis unit displays the analysis results, for example, while considering the geographical distribution of operations. This improves the accuracy of the analysis by considering the geographical distribution of operations. Some or all of the above processes in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input geographical distribution data of operations into a generating AI and have the generating AI perform the analysis.

[0047] The analysis unit can improve the accuracy of the analysis based on relevant literature for the operation during the analysis. For example, the analysis unit improves the accuracy of the analysis by referring to relevant literature for the operation. For example, the analysis unit adjusts the analysis algorithm based on relevant literature for the operation. For example, the analysis unit displays the analysis results taking relevant literature for the operation into consideration. In this way, the accuracy of the analysis is improved by referring to relevant literature for the operation. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature data for the operation into a generating AI and have the generating AI perform the analysis.

[0048] The document generation unit can adjust the level of detail in the document based on the importance of the operations during the document generation process. For example, the document generation unit can describe important operations in detail and less important operations in a simplified manner. For example, the document generation unit can adjust the length of the document according to the importance of the operations. For example, the document generation unit can describe important operations including specific steps and describe less important operations only in outline. This improves the efficiency of the document by adjusting the level of detail based on the importance of the operations. Some or all of the above processes in the document generation unit may be performed using AI, for example, or without AI. For example, the document generation unit can input operation importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the document.

[0049] The document generation unit can apply different document generation algorithms depending on the category of the operation during document generation. For example, the document generation unit can describe setting change operations in detail and data entry operations in a simplified manner. For example, the document generation unit can change the format of the document depending on the category of the operation. For example, the document generation unit can change the location where the document is saved depending on the category of the operation. This improves the efficiency of document generation by applying a document generation algorithm according to the category of the operation. Some or all of the above processing in the document generation unit may be performed using AI, for example, or without AI. For example, the document generation unit can input operation category data into a generating AI and have the generating AI execute the application of the document generation algorithm.

[0050] The document generation unit can determine the priority of documents based on the timing of operations during document generation. For example, the document generation unit prioritizes document generation during time periods when important operations are performed. For example, the document generation unit adjusts the frequency of document generation according to the timing of operations. For example, the document generation unit changes the document storage location based on the timing of operations. This improves the efficiency of document generation by determining the priority of documents based on the timing of operations. Some or all of the above-described processes in the document generation unit may be performed using AI, for example, or without AI. For example, the document generation unit can input operation timing data into a generation AI and have the generation AI determine the priority of documents.

[0051] The text generation unit can adjust the order of sentences based on the relevance of operations during the text generation process. For example, the text generation unit can generate sentences for related operations consecutively, and postpone less relevant operations. For example, the text generation unit can change the order of sentences according to the relevance of operations. For example, the text generation unit can change the location where sentences are saved based on the relevance of operations. This improves the efficiency of the text generation by adjusting the order of sentences based on the relevance of operations. Some or all of the above-described processes in the text generation unit may be performed using AI, for example, or without AI. For example, the text generation unit can input operation relevance data into a generating AI and have the generating AI perform the adjustment of the sentence order.

[0052] The editorial department can select the optimal editing method based on the user's past editing history during editing. For example, the editorial department may prioritize suggesting editing methods the user has used in the past. For example, the editorial department may predict and suggest editing methods to be used at a specific time based on the user's past editing history. For example, the editorial department may prioritize suggesting editing tools the user has used in the past. This improves editing efficiency by selecting the optimal editing method based on the user's past editing history. Some or all of the above processes in the editorial department may be performed using AI, for example, or not using AI. For example, the editorial department can input the user's past editing history data into a generating AI and have the generating AI select the optimal editing method.

[0053] The editorial team can select the optimal editing method based on the user's device information during editing. For example, if the user is using a smartphone, the editorial team can provide an editing method adapted to the screen size. For example, if the user is using a tablet, the editorial team can provide an editing method optimized for a larger screen. For example, if the user is using a desktop, the editorial team can provide detailed editing options. This improves editing efficiency by selecting the optimal editing method based on the user's device information. Some or all of the above processes in the editorial team may be performed using AI, for example, or not. For example, the editorial team can input user device information data into a generating AI and have the generating AI select the optimal editing method.

[0054] The service provider can select the optimal service delivery method based on the user's past usage history at the time of delivery. For example, the service provider can prioritize suggesting information delivery methods that the user has used in the past. For example, the service provider can predict and suggest information delivery methods to be performed during specific time periods based on the user's past usage history. For example, the service provider can prioritize suggesting devices that the user has used in the past. This improves the efficiency of information delivery by selecting the optimal service delivery method based on the user's past usage history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past usage history data into a generating AI and have the generating AI select the optimal service delivery method.

[0055] The information delivery unit can select the optimal delivery method based on the user's device information at the time of delivery. For example, if the user is using a smartphone, the information delivery unit will provide an information delivery method that is adapted to the screen size. For example, if the user is using a tablet, the information delivery unit will provide an information delivery method optimized for a larger screen. For example, if the user is using a desktop, the information delivery unit will provide detailed information delivery options. This improves the efficiency of information delivery by selecting the optimal delivery method based on the user's device information. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the information delivery unit can input user device information data into a generating AI and have the generating AI select the optimal delivery method.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The business manual creation support system can also include a translation unit that translates user operations in real time. For example, if a user performs an operation in English, the translation unit will translate the operation into Japanese. For example, if a user performs an operation in French, the translation unit will translate the operation into English. For example, if a user performs an operation in Chinese, the translation unit will translate the operation into Spanish. This allows the business manual creation support system to automatically generate multilingual manuals, making it usable in international business environments. Some or all of the above-described processes in the translation unit may be performed using AI, for example, or without AI. For example, the translation unit can input the user's operation into a generating AI and have the generating AI perform the translation.

[0058] The reception desk can analyze the user's past operation history and provide optimal operation guides. For example, it can provide detailed guides for operations the user has frequently performed in the past. For example, it can predict operations to be performed at a specific time based on the user's past operation history and provide guides in advance. For example, it can provide optimal operation guides based on the input devices (mouse, keyboard, etc.) the user has used in the past. This improves the efficiency of operations by providing optimal operation guides based on the user's past operation history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past operation history data into a generating AI and have the generating AI perform the task of providing optimal operation guides.

[0059] The reception unit can filter operations based on the user's current health status when receiving them. For example, if the user is tired, simple operations will be prioritized. For example, if the user is healthy, complex operations will be prioritized. For example, if the user is ill, operation acceptance will be temporarily suspended and the system will wait until the user recovers. This allows for prioritizing operations that are highly relevant by filtering them based on the user's health status. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input user health status data into a generating AI and have the generating AI perform the filtering.

[0060] The recording unit can add contextual information about user operations when recording them. For example, it can include the date and time the operation was performed in the record. For example, it can include the location where the operation was performed in the record. For example, it can include information about the device on which the operation was performed in the record. By adding contextual information about the operation, the accuracy of the recording is improved, which is useful for later analysis and documentation. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input contextual information about the operation into a generating AI and have the generating AI perform the generation of additional information for the record.

[0061] The recording unit can adjust the level of detail in the recording based on the skill level of the person performing the operation. For example, it can record beginner operations in detail and advanced operations in a simplified manner. For example, it can adjust the frequency of recording according to the skill level of the operation. For example, it can record beginner operations at high resolution and advanced operations at low resolution. This improves the efficiency of recording by adjusting the level of detail based on the skill level of the operation. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input operation skill level data into a generating AI and have the generating AI perform the adjustment of the level of detail in the recording.

[0062] The recording unit can adjust the recording format based on the job title of the person performing the operation during recording. For example, operations performed by managers can be recorded in detail, while operations performed by general staff can be recorded in a simplified manner. For example, the frequency of recording can be adjusted according to the job title of the operator. For example, operations performed by managers can be recorded in high resolution, while operations performed by general staff can be recorded in low resolution. This improves the efficiency of recording by adjusting the recording format based on the job title of the operator. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input job title data of the operator into a generating AI and have the generating AI perform the adjustment of the recording format.

[0063] The recording unit can determine the priority of recordings based on the operator's past operation history. For example, it can prioritize recordings during times when important operations are performed. For example, it can adjust the frequency of recordings based on the operator's past operation history. For example, it can change the storage location of recordings based on the operator's past operation history. This improves the efficiency of recording by determining the priority of recordings based on the operator's past operation history. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the operator's past operation history data into a generating AI and have the generating AI determine the priority of recordings.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The reception desk receives user input. For example, it receives the actions that the user performs. Step 2: The recording unit records the operations received by the reception unit. For example, it records the operations performed by the user. Step 3: The analysis unit analyzes the operations recorded by the recording unit and breaks them down into individual operations. For example, the recorded operations are analyzed using image recognition technology and broken down into individual operations. Step 4: The document creation unit documents the operations that have been broken down by the analysis unit. For example, it documents the decomposed operations. Step 5: The editorial team edits the text generated by the writing team. For example, they enable users to freely edit, add to, and delete the generated text. Step 6: The providing department provides the edited text. For example, they provide the edited text.

[0066] (Example of form 2) An embodiment of the present invention provides a business manual creation support system that records user operations and automatically generates a manual based on those records. The business manual creation support system records user operations. Next, the business manual creation support system analyzes the recorded operations using image recognition technology and breaks them down into individual operations. For example, if a user changes software settings, the system analyzes how the operation was performed using image recognition technology and breaks it down into individual steps. Subsequently, the business manual creation support system documents the decomposed operations. For example, specific operations such as "open the settings menu," "select an option," and "click the save button" are generated as text. This text can be freely edited, added to, and deleted by the user. Finally, the user can easily create a business manual. This allows the business manual creation support system to significantly streamline the time-consuming manual creation process. As a result, the business manual creation support system can efficiently record, analyze, document, edit, and provide user operations.

[0067] The business manual creation support system according to this embodiment comprises a reception unit, a recording unit, an analysis unit, a document creation unit, an editing unit, and a provision unit. The reception unit receives operations from the user. The recording unit records the operations received by the reception unit. The analysis unit analyzes the operations recorded by the recording unit and breaks them down into individual operations. The document creation unit documents the operations that have been broken down by the analysis unit. The editing unit edits the documents generated by the document creation unit. The provision unit provides the documents edited by the editing unit. For example, the reception unit receives operations performed by the user. The recording unit, for example, records the operations performed by the user. The analysis unit, for example, analyzes the recorded operations using image recognition technology and breaks them down into individual operations. The document creation unit, for example, documents the broken-down operations. The editing unit, for example, allows the user to freely edit, add to, and delete the generated documents. The provision unit, for example, provides the edited documents. This allows the business manual creation support system to efficiently record, analyze, document, edit, and provide user operations.

[0068] The reception desk receives user input. Specifically, when a user accesses the system and begins operations for creating a business manual, the reception desk receives that input. Users perform operations, for example, through a dedicated interface, and the reception desk receives the details of these operations in real time. The reception desk accurately understands the type and sequence of operations performed by the user and appropriately passes them on to the next processing step. For example, it receives all clicks, input operations, and screen transitions performed by the user to demonstrate business procedures, and ensures that these operations are accurately recorded. In addition to receiving user input, the reception desk also monitors events such as the start and end of operations and the occurrence of errors, and provides feedback to the user as needed. This allows users to proceed with operations smoothly, and the business manual creation process proceeds smoothly.

[0069] The recording unit records operations received by the reception unit. Specifically, it meticulously records user operations and saves them as data for subsequent analysis and documentation. The recording unit accurately records the type and timing of operations, information about the target of the operation, and centrally manages this data. For example, it records the location and name of buttons clicked by the user, the text content entered, and the order of screen transitions. The recording unit saves this operation data with timestamps, enabling accurate reproduction of the order and timing of operations. The recording unit also has an operation recording function, allowing it to record the user's operation screen as a video. This allows for visual confirmation of operations later, improving the accuracy of analysis and documentation. The recording unit securely stores the recorded data and can share it with other departments and systems as needed. In this way, the recording unit accurately and meticulously records user operations and provides data that forms the basis for creating business manuals.

[0070] The analysis unit analyzes the operations recorded by the recording unit and breaks them down into individual operations. Specifically, it analyzes the recorded operation data and breaks it down to understand the meaning and purpose of each operation. The analysis unit uses image recognition technology and natural language processing technology to analyze the operation content in detail. For example, it extracts click operations, input operations, screen transitions, etc., individually from the recorded operations and analyzes the purpose of each operation. The analysis unit logically breaks down the operations, taking into account the order and relationships of the operations, and clarifies the meaning of each operation. For example, if the operation recorded is that a user clicks a specific button and then enters text into an input form, the analysis unit breaks down these operations and understands that the button click triggered the display of the input form. Based on these analysis results, the analysis unit prepares the operation content as data to be passed on to the next document creation unit. In this way, the analysis unit provides basic data for creating business manuals by analyzing the recorded operations in detail and breaking them down into individual operations.

[0071] The text generation unit documents the operations broken down by the analysis unit. Specifically, it converts the operation data provided by the analysis unit into easy-to-understand sentences. The text generation unit uses natural language generation technology to express the operation content in an appropriate context. For example, it documents user clicks and input operations as specific steps. For clicks, it generates instructional sentences such as "Click the next button," and for input operations, it generates explanatory sentences such as "Enter the required information into the text box." The text generation unit organizes the operation content into a consistent document, taking into account the order and relationships of the operations. For example, if multiple operations are performed in sequence, it documents these operations as a series of steps, providing them in a way that is easy for the user to understand. The text generation unit verifies that the generated sentences are accurate and easy to understand, and makes corrections and adjustments as necessary. In this way, the text generation unit converts the analyzed operation content into easy-to-understand sentences, providing the foundational text for business manuals.

[0072] The editorial department edits the text generated by the writing department. Specifically, it allows users to freely edit, add to, and delete the generated text. The editorial department provides an interface that allows users to review the generated text and input corrections or additional information as needed. For example, if there are errors or omissions in the text generated by a user, they can be corrected and accurate information added through the editorial department. The editorial department provides an intuitive and user-friendly interface to enable users to perform editing work efficiently. For example, it includes drag-and-drop functionality and a text editor function to allow users to easily edit text. The editorial department also manages versions of edited text, saving past editing history and allowing users to revert to previous versions as needed. This allows the editorial department to freely edit the text generated by users and keep the content of the work manual accurate and up-to-date.

[0073] The service provider will provide documents edited by the editorial department. Specifically, they will provide users with edited work manuals and quickly share necessary information. The service provider will output the edited documents in formats such as PDF and HTML to make them easily accessible to users. For example, they will output edited work manuals in PDF format so that users can download and print them. They will also output them in HTML format so that users can view the work manuals through a web browser. The service provider will provide search and table of contents functions so that users can quickly find the information they need. For example, they will be able to search for specific keywords within the work manual and quickly find related information. The service provider also has a function to notify users of updates to the work manual, notifying them when a new version is released. In this way, the service provider will quickly and efficiently provide edited work manuals and ensure that users always have access to the latest information they need.

[0074] The reception unit can estimate the user's emotions and adjust the timing of operation acceptance based on the estimated emotions. For example, if the user is stressed, the reception unit will delay accepting the operation and wait until the user is relaxed. For example, if the user is focused, the reception unit will accept the operation immediately to support smooth operation. For example, if the user is tired, the reception unit will accept the operation slowly to match the user's pace. This allows for more appropriate operation acceptance by adjusting the timing of operation acceptance according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not using AI. For example, the reception unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0075] The reception unit can analyze the user's past operation history and select the optimal reception method. For example, the reception unit may prioritize operations that the user has frequently performed in the past. For example, the reception unit may predict operations to be performed during specific time periods based on the user's past operation history and adjust the reception method accordingly. For example, the reception unit may prioritize input devices (mouse, keyboard, etc.) that the user has used in the past. This improves the efficiency of operations by selecting the optimal reception method based on the user's past operation history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit may input the user's past operation history data into a generating AI and have the generating AI select the optimal reception method.

[0076] The reception unit can filter operations based on the user's current project or area of ​​interest when receiving them. For example, the reception unit may prioritize operations related to the project the user is currently working on. For example, the reception unit may filter and accept operations based on the user's area of ​​interest. For example, the reception unit may prioritize operations related to areas the user has shown interest in in the past. This allows the reception unit to prioritize operations that are highly relevant by filtering operations based on the user's current project and area of ​​interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit may input the user's project data and area of ​​interest data into a generating AI and have the generating AI perform the filtering.

[0077] The reception unit can estimate the user's emotions and determine the priority of operations to accept based on the estimated emotions. For example, if the user is nervous, the reception unit will prioritize simple operations. For example, if the user is relaxed, the reception unit will prioritize complex operations. For example, if the user is in a hurry, the reception unit will prioritize important operations. This allows for more appropriate operation acceptance by determining the priority of operations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not using AI. For example, the reception unit can input user emotion data into a generative AI and have the generative AI determine the priority of operations.

[0078] The reception unit can prioritize operations based on the user's geographical location when receiving an operation request. For example, if the user is in a specific location, the reception unit will prioritize operations related to that location. For example, if the user is on the move, the reception unit will prioritize operations related to movement. For example, if the user is at home, the reception unit will prioritize operations to be performed at home. This improves the efficiency of operations by prioritizing operations based on the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location data into a generating AI and have the generating AI select the most relevant operations.

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

[0080] The recording unit can estimate the user's emotions and adjust the recording method based on the estimated emotions. For example, if the user is relaxed, the recording unit will perform a detailed recording. For example, if the user is in a hurry, the recording unit will perform a simplified recording. For example, if the user is stressed, the recording unit will pause the recording and wait until the user relaxes. This allows for more appropriate recording by adjusting the recording method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recording unit may be performed using AI or not using AI. For example, the recording unit can input user emotion data into a generative AI and have the generative AI adjust the recording method.

[0081] The recording unit can adjust the level of detail in recordings based on the importance of the operations. For example, the recording unit can record important operations in detail and unimportant operations in a simplified manner. For example, the recording unit can adjust the frequency of recordings according to the importance of the operations. For example, the recording unit can record important operations at high resolution and unimportant operations at low resolution. This improves the efficiency of recording by adjusting the level of detail in recordings based on the importance of the operations. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input operation importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in recordings.

[0082] The recording unit can apply different recording algorithms depending on the category of the operation during recording. For example, the recording unit may record setting change operations in detail and data entry operations in a simplified manner. For example, the recording unit may change the format of the recording depending on the category of the operation. For example, the recording unit may change the storage location of the recording depending on the category of the operation. This improves the efficiency of recording by applying a recording algorithm according to the category of the operation. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit may input operation category data into a generating AI and have the generating AI execute the application of the recording algorithm.

[0083] The recording unit can estimate the user's emotions and adjust the recording length based on the estimated emotions. For example, if the user is relaxed, the recording unit will record for a longer duration. If the user is in a hurry, the recording unit will record for a shorter duration. If the user is stressed, the recording unit will pause recording and wait until the user relaxes. This allows for more appropriate recording by adjusting the recording length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input user emotion data into a generative AI and have the generative AI adjust the recording length.

[0084] The recording unit can determine the priority of recordings based on the timing of operations. For example, the recording unit prioritizes recording during time periods when important operations are performed. For example, the recording unit adjusts the frequency of recordings according to the timing of operations. For example, the recording unit changes the storage location of records based on the timing of operations. This improves the efficiency of recording by determining the priority of recordings based on the timing of operations. Some or all of the above processes in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input operation timing data into a generating AI and have the generating AI determine the priority of recordings.

[0085] The recording unit can adjust the order of recordings based on the relevance of the operations during recording. For example, the recording unit may record related operations consecutively and postpone less relevant operations. For example, the recording unit may change the order of recordings according to the relevance of the operations. For example, the recording unit may change the storage location of the recordings based on the relevance of the operations. This improves the efficiency of recording by adjusting the order of recordings based on the relevance of the operations. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit may input operation relevance data into a generating AI and have the generating AI perform the adjustment of the recording order.

[0086] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated emotions. For example, if the user is relaxed, the analysis unit performs a detailed analysis. For example, if the user is in a hurry, the analysis unit performs a simplified analysis. For example, if the user is stressed, the analysis unit pauses the analysis and waits until the user is relaxed. This allows for a more appropriate analysis by adjusting the analysis criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the analysis criteria.

[0087] The analysis unit can improve the accuracy of the analysis based on the interrelationships of operations during the analysis. For example, the analysis unit improves the accuracy of the analysis by considering the sequence of operations. For example, the analysis unit adjusts the analysis algorithm based on the interrelationships of operations. For example, the analysis unit displays the analysis results considering the interrelationships of operations. In this way, the accuracy of the analysis is improved by considering the interrelationships of operations. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input interrelationship data of operations into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0088] The analysis unit can perform analysis while considering the attribute information of the person performing the operation. The analysis unit can adjust the accuracy of the analysis based on, for example, the skill level of the person performing the operation. The analysis unit can adjust the analysis algorithm based on, for example, the job title of the person performing the operation. The analysis unit can improve the accuracy of the analysis by considering, for example, the past operation history of the person performing the operation. In this way, the accuracy of the analysis is improved by considering the attribute information of the person performing the operation. Some or all of the above processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the attribute information data of the person performing the operation into a generating AI and have the generating AI perform the analysis.

[0089] The analysis unit can estimate the user's emotions and adjust the order in which the analysis results are displayed based on the estimated emotions. For example, if the user is relaxed, the analysis unit will prioritize displaying detailed analysis results. For example, if the user is in a hurry, the analysis unit will prioritize displaying concise analysis results. For example, if the user is stressed, the analysis unit will prioritize displaying simplified analysis results. By adjusting the display order of the analysis results according to the user's emotions, more appropriate analysis results are provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the display order of the analysis results.

[0090] The analysis unit can perform the analysis while considering the geographical distribution of operations. The analysis unit adjusts the accuracy of the analysis based on, for example, the location where the operations were performed. The analysis unit adjusts the analysis algorithm based on, for example, the geographical distribution of operations. The analysis unit displays the analysis results, for example, while considering the geographical distribution of operations. This improves the accuracy of the analysis by considering the geographical distribution of operations. Some or all of the above processes in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input geographical distribution data of operations into a generating AI and have the generating AI perform the analysis.

[0091] The analysis unit can improve the accuracy of the analysis based on relevant literature for the operation during the analysis. For example, the analysis unit improves the accuracy of the analysis by referring to relevant literature for the operation. For example, the analysis unit adjusts the analysis algorithm based on relevant literature for the operation. For example, the analysis unit displays the analysis results taking relevant literature for the operation into consideration. In this way, the accuracy of the analysis is improved by referring to relevant literature for the operation. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature data for the operation into a generating AI and have the generating AI perform the analysis.

[0092] The text generation unit can estimate the user's emotions and adjust the expression of the text based on the estimated emotions. For example, if the user is relaxed, the text generation unit will use detailed expressions. If the user is in a hurry, the text generation unit will use concise expressions. If the user is stressed, the text generation unit will pause the text generation and wait until the user is relaxed. By adjusting the expression of the text according to the user's emotions, more appropriate text is generated. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the text generation unit may be performed using AI, or not using AI. For example, the text generation unit can input user emotion data into the generative AI and have the generative AI adjust the expression of the text.

[0093] The document generation unit can adjust the level of detail in the document based on the importance of the operations during the document generation process. For example, the document generation unit can describe important operations in detail and less important operations in a simplified manner. For example, the document generation unit can adjust the length of the document according to the importance of the operations. For example, the document generation unit can describe important operations including specific steps and describe less important operations only in outline. This improves the efficiency of the document by adjusting the level of detail based on the importance of the operations. Some or all of the above processes in the document generation unit may be performed using AI, for example, or without AI. For example, the document generation unit can input operation importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the document.

[0094] The document generation unit can apply different document generation algorithms depending on the category of the operation during document generation. For example, the document generation unit can describe setting change operations in detail and data entry operations in a simplified manner. For example, the document generation unit can change the format of the document depending on the category of the operation. For example, the document generation unit can change the location where the document is saved depending on the category of the operation. This improves the efficiency of document generation by applying a document generation algorithm according to the category of the operation. Some or all of the above processing in the document generation unit may be performed using AI, for example, or without AI. For example, the document generation unit can input operation category data into a generating AI and have the generating AI execute the application of the document generation algorithm.

[0095] The text generation unit can estimate the user's emotions and adjust the length of the text based on the estimated emotions. For example, if the user is relaxed, the text generation unit will generate a long, detailed text. For example, if the user is in a hurry, the text generation unit will generate a short, concise text. For example, if the user is stressed, the text generation unit will pause text generation and wait until the user is relaxed. This allows for the generation of more appropriate text by adjusting the length of the text according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the text generation unit may be performed using AI or not. For example, the text generation unit can input user emotion data into the generative AI and have the generative AI adjust the length of the text.

[0096] The document generation unit can determine the priority of documents based on the timing of operations during document generation. For example, the document generation unit prioritizes document generation during time periods when important operations are performed. For example, the document generation unit adjusts the frequency of document generation according to the timing of operations. For example, the document generation unit changes the document storage location based on the timing of operations. This improves the efficiency of document generation by determining the priority of documents based on the timing of operations. Some or all of the above-described processes in the document generation unit may be performed using AI, for example, or without AI. For example, the document generation unit can input operation timing data into a generation AI and have the generation AI determine the priority of documents.

[0097] The text generation unit can adjust the order of sentences based on the relevance of operations during the text generation process. For example, the text generation unit can generate sentences for related operations consecutively, and postpone less relevant operations. For example, the text generation unit can change the order of sentences according to the relevance of operations. For example, the text generation unit can change the location where sentences are saved based on the relevance of operations. This improves the efficiency of the text generation by adjusting the order of sentences based on the relevance of operations. Some or all of the above-described processes in the text generation unit may be performed using AI, for example, or without AI. For example, the text generation unit can input operation relevance data into a generating AI and have the generating AI perform the adjustment of the sentence order.

[0098] The editorial team can estimate the user's emotions and adjust the editing method based on the estimated emotions. For example, if the user is relaxed, the editorial team can provide detailed editing options. If the user is in a hurry, for example, the editorial team can provide simplified editing options. If the user is stressed, for example, the editorial team can pause editing and wait until the user relaxes. This allows for more appropriate editing by adjusting the editing method according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the editorial team may be performed using AI or not. For example, the editorial team can input user emotion data into a generative AI and have the generative AI adjust the editing method.

[0099] The editorial department can select the optimal editing method based on the user's past editing history during editing. For example, the editorial department may prioritize suggesting editing methods the user has used in the past. For example, the editorial department may predict and suggest editing methods to be used at a specific time based on the user's past editing history. For example, the editorial department may prioritize suggesting editing tools the user has used in the past. This improves editing efficiency by selecting the optimal editing method based on the user's past editing history. Some or all of the above processes in the editorial department may be performed using AI, for example, or not using AI. For example, the editorial department can input the user's past editing history data into a generating AI and have the generating AI select the optimal editing method.

[0100] The editorial team can estimate the user's emotions and determine editing priorities based on those estimates. For example, if the user is relaxed, the editorial team will prioritize detailed editing. If the user is in a hurry, the editorial team will prioritize simplified editing. If the user is stressed, the editorial team will pause editing and wait until the user relaxes. This allows for more appropriate editing by prioritizing editing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the editorial team may be performed using AI or not. For example, the editorial team can input user emotion data into a generative AI and have the generative AI determine editing priorities.

[0101] The editorial team can select the optimal editing method based on the user's device information during editing. For example, if the user is using a smartphone, the editorial team can provide an editing method adapted to the screen size. For example, if the user is using a tablet, the editorial team can provide an editing method optimized for a larger screen. For example, if the user is using a desktop, the editorial team can provide detailed editing options. This improves editing efficiency by selecting the optimal editing method based on the user's device information. Some or all of the above processes in the editorial team may be performed using AI, for example, or not. For example, the editorial team can input user device information data into a generating AI and have the generating AI select the optimal editing method.

[0102] The information delivery unit can estimate the user's emotions and adjust the delivery method based on the estimated emotions. For example, if the user is relaxed, the information delivery unit will provide detailed information. For example, if the user is in a hurry, the information delivery unit will provide concise information. For example, if the user is stressed, the information delivery unit will pause and wait until the user is relaxed. This allows for more appropriate information delivery by adjusting the delivery method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information delivery unit may be performed using AI or not using AI. For example, the information delivery unit can input user emotion data into a generative AI and have the generative AI adjust the delivery method.

[0103] The service provider can select the optimal service delivery method based on the user's past usage history at the time of delivery. For example, the service provider can prioritize suggesting information delivery methods that the user has used in the past. For example, the service provider can predict and suggest information delivery methods to be performed during specific time periods based on the user's past usage history. For example, the service provider can prioritize suggesting devices that the user has used in the past. This improves the efficiency of information delivery by selecting the optimal service delivery method based on the user's past usage history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past usage history data into a generating AI and have the generating AI select the optimal service delivery method.

[0104] The service provider can estimate the user's emotions and determine the priority of information delivery based on the estimated emotions. For example, if the user is relaxed, the service provider will prioritize providing detailed information. If the user is in a hurry, the service provider will prioritize providing concise information. If the user is stressed, the service provider will pause providing information and wait until the user is relaxed. This allows for more appropriate information delivery by determining the priority of information delivery according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI determine the priority of information delivery.

[0105] The information delivery unit can select the optimal delivery method based on the user's device information at the time of delivery. For example, if the user is using a smartphone, the information delivery unit will provide an information delivery method that is adapted to the screen size. For example, if the user is using a tablet, the information delivery unit will provide an information delivery method optimized for a larger screen. For example, if the user is using a desktop, the information delivery unit will provide detailed information delivery options. This improves the efficiency of information delivery by selecting the optimal delivery method based on the user's device information. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the information delivery unit can input user device information data into a generating AI and have the generating AI select the optimal delivery method.

[0106] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0107] The business manual creation support system can also include a translation unit that translates user operations in real time. For example, if a user performs an operation in English, the translation unit will translate the operation into Japanese. For example, if a user performs an operation in French, the translation unit will translate the operation into English. For example, if a user performs an operation in Chinese, the translation unit will translate the operation into Spanish. This allows the business manual creation support system to automatically generate multilingual manuals, making it usable in international business environments. Some or all of the above-described processes in the translation unit may be performed using AI, for example, or without AI. For example, the translation unit can input the user's operation into a generating AI and have the generating AI perform the translation.

[0108] The reception unit can estimate the user's emotions and adjust the difficulty of operations based on the estimated emotions. For example, if the user is stressed, simple operations will be prioritized. For example, if the user is relaxed, complex operations will be prioritized. For example, if the user is concentrating, the difficulty of operations can be increased. This allows for more appropriate operation acceptance by adjusting the difficulty of operations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, or not using AI. For example, the reception unit can input user emotion data into a generative AI and have the generative AI adjust the difficulty of operations.

[0109] The reception desk can analyze the user's past operation history and provide optimal operation guides. For example, it can provide detailed guides for operations the user has frequently performed in the past. For example, it can predict operations to be performed at a specific time based on the user's past operation history and provide guides in advance. For example, it can provide optimal operation guides based on the input devices (mouse, keyboard, etc.) the user has used in the past. This improves the efficiency of operations by providing optimal operation guides based on the user's past operation history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past operation history data into a generating AI and have the generating AI perform the task of providing optimal operation guides.

[0110] The reception unit can filter operations based on the user's current health status when receiving them. For example, if the user is tired, simple operations will be prioritized. For example, if the user is healthy, complex operations will be prioritized. For example, if the user is ill, operation acceptance will be temporarily suspended and the system will wait until the user recovers. This allows for prioritizing operations that are highly relevant by filtering them based on the user's health status. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input user health status data into a generating AI and have the generating AI perform the filtering.

[0111] The reception unit can estimate the user's emotions and provide operational feedback based on the estimated emotions. For example, if the user is nervous, it can provide positive feedback. For example, if the user is relaxed, it can provide detailed feedback. For example, if the user is in a hurry, it can provide concise feedback. This allows for more appropriate operational support by providing operational feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not using AI. For example, the reception unit can input user emotion data into a generative AI and have the generative AI provide operational feedback.

[0112] The recording unit can add contextual information about user operations when recording them. For example, it can include the date and time the operation was performed in the record. For example, it can include the location where the operation was performed in the record. For example, it can include information about the device on which the operation was performed in the record. By adding contextual information about the operation, the accuracy of the recording is improved, which is useful for later analysis and documentation. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input contextual information about the operation into a generating AI and have the generating AI perform the generation of additional information for the record.

[0113] The recording unit can estimate the user's emotions and adjust the recording format based on the estimated emotions. For example, if the user is relaxed, a detailed recording is made. For example, if the user is in a hurry, a simplified recording is made. For example, if the user is stressed, the recording is paused and waited until the user relaxes. This allows for more appropriate recording by adjusting the recording format according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recording unit may be performed using AI or not using AI. For example, the recording unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the recording format.

[0114] The recording unit can adjust the level of detail in the recording based on the skill level of the person performing the operation. For example, it can record beginner operations in detail and advanced operations in a simplified manner. For example, it can adjust the frequency of recording according to the skill level of the operation. For example, it can record beginner operations at high resolution and advanced operations at low resolution. This improves the efficiency of recording by adjusting the level of detail based on the skill level of the operation. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input operation skill level data into a generating AI and have the generating AI perform the adjustment of the level of detail in the recording.

[0115] The recording unit can adjust the recording format based on the job title of the person performing the operation during recording. For example, operations performed by managers can be recorded in detail, while operations performed by general staff can be recorded in a simplified manner. For example, the frequency of recording can be adjusted according to the job title of the operator. For example, operations performed by managers can be recorded in high resolution, while operations performed by general staff can be recorded in low resolution. This improves the efficiency of recording by adjusting the recording format based on the job title of the operator. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input job title data of the operator into a generating AI and have the generating AI perform the adjustment of the recording format.

[0116] The recording unit can determine the priority of recordings based on the operator's past operation history. For example, it can prioritize recordings during times when important operations are performed. For example, it can adjust the frequency of recordings based on the operator's past operation history. For example, it can change the storage location of recordings based on the operator's past operation history. This improves the efficiency of recording by determining the priority of recordings based on the operator's past operation history. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the operator's past operation history data into a generating AI and have the generating AI determine the priority of recordings.

[0117] The following briefly describes the processing flow for example form 2.

[0118] Step 1: The reception desk receives user input. For example, it receives the actions that the user performs. Step 2: The recording unit records the operations received by the reception unit. For example, it records the operations performed by the user. Step 3: The analysis unit analyzes the operations recorded by the recording unit and breaks them down into individual operations. For example, the recorded operations are analyzed using image recognition technology and broken down into individual operations. Step 4: The document creation unit documents the operations that have been broken down by the analysis unit. For example, it documents the decomposed operations. Step 5: The editorial team edits the text generated by the writing team. For example, they enable users to freely edit, add to, and delete the generated text. Step 6: The providing department provides the edited text. For example, they provide the edited text.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0121] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives user operations. The recording unit records the user's operations using the camera 42 of the smart device 14, for example. The analysis unit analyzes the recorded operations using image recognition technology with the specific processing unit 290 of the data processing device 12 and breaks them down into individual operations. The document generation unit documents the operations broken down by the specific processing unit 290 of the data processing device 12. The editing unit allows the user to freely edit, add to, and delete the document generated by the control unit 46A of the smart device 14, for example. The provisioning unit provides the edited document using the output device 40 of the smart device 14, for example. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0123] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0124] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0126] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0127] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0129] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0130] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0131] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0132] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0133] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0137] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0138] For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives user input. The recording unit records the user's input using the camera 42 of the smart glasses 214. The analysis unit analyzes the recorded input using image recognition technology with the specific processing unit 290 of the data processing device 12 and breaks it down into individual inputs. The text generation unit documents the operations broken down by the specific processing unit 290 of the data processing device 12. The editing unit allows the user to freely edit, add to, and delete the text generated by the control unit 46A of the smart glasses 214. The provisioning unit provides the edited text using the speaker 240 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0139] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0140] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0141] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0142] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0143] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0145] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0146] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0147] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0148] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0149] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0151] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0153] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0154] For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives user input. The recording unit records the user's input using the camera 42 of the headset terminal 314. The analysis unit analyzes the recorded input using image recognition technology by the specific processing unit 290 of the data processing device 12 and breaks it down into individual inputs. The text generation unit documents the operations broken down by the specific processing unit 290 of the data processing device 12. The editing unit allows the user to freely edit, add to, and delete the text generated by the control unit 46A of the headset terminal 314. The provisioning unit provides the edited text using the display 343 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0155] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0156] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0158] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0159] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0161] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0162] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0163] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0164] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0165] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0166] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0167] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0168] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0169] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0170] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0171] For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives user input. The recording unit records the user's input using the camera 42 of the robot 414. The analysis unit analyzes the recorded input using image recognition technology, for example, by the specific processing unit 290 of the data processing device 12, and breaks it down into individual inputs. The document creation unit documents the inputs broken down by the specific processing unit 290 of the data processing device 12. The editing unit allows the user to freely edit, add to, and delete the document generated by the control unit 46A of the robot 414. The provisioning unit provides the edited document using the speaker 240 of the robot 414. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0172] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0173] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0174] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0175] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0176] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0177] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0178] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0179] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0180] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0181] 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.

[0182] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0183] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0184] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0185] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0186] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0187] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0188] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0189] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0190] (Note 1) A reception desk that receives operations from users, A recording unit that records the operations received by the reception unit, An analysis unit analyzes the operations recorded by the recording unit and breaks them down into individual operations, A documenting unit that converts the operation details decomposed by the analysis unit into text, An editing unit that edits the text generated by the aforementioned text generation unit, A providing unit that provides text edited by the aforementioned editorial unit, A system characterized by the following features. (Note 2) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of accepting actions based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is Analyze the user's past operation history and select the optimal reception method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is When an operation is received, filtering is performed based on the user's current project or area of ​​interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It estimates the user's emotions and determines the priority of the actions to accept based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is When receiving an operation request, the system prioritizes accepting operations that are highly relevant based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When an operation is requested, the system analyzes the user's social media activity and accepts relevant operations. The system described in Appendix 1, characterized by the features described herein. (Note 8) The recording unit is, The system estimates the user's emotions and adjusts the recording method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The recording unit is, During recording, adjust the level of detail based on the importance of the operation. The system described in Appendix 1, characterized by the features described herein. (Note 10) The recording unit is, During recording, different recording algorithms are applied depending on the category of the operation. The system described in Appendix 1, characterized by the features described herein. (Note 11) The recording unit is, The system estimates the user's emotions and adjusts the length of the recording based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The recording unit is, During recording, prioritize recordings based on when the operations were performed. The system described in Appendix 1, characterized by the features described herein. (Note 13) The recording unit is, During recording, adjust the recording order based on the relevance of the operations. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, We estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, improve the accuracy of the analysis based on the interrelationships between operations. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During the analysis, the analysis is performed based on the attribute information of the person who performed the operation. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and adjusts the order in which the analysis results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During the analysis, the analysis will be performed based on the geographical distribution of operations. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, improve the accuracy of the analysis based on relevant literature for the procedure. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned document creation unit, It estimates the user's emotions and adjusts the way the text is written based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned document creation unit, When writing, adjust the level of detail in the text based on the importance of the operation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned document creation unit, When creating a document, different document creation algorithms are applied depending on the category of the operation. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned document creation unit, It estimates the user's emotions and adjusts the length of the text based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned document creation unit, When writing, prioritize the sentences based on when the operations were performed. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned document creation unit, When writing, adjust the order of sentences based on the relevance of the operations. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned editorial department, It estimates the user's emotions and adjusts the editing method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned editorial department, During editing, the system selects the optimal editing method based on the user's past editing history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned editorial department, It estimates the user's emotions and determines editing priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned editorial department, During editing, the system selects the optimal editing method based on the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, It estimates the user's emotions and adjusts the delivery method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected based on the user's past usage history. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of offerings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, When providing the service, the optimal delivery method is selected based on the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that receives operations from users, A recording unit that records the operations received by the reception unit, An analysis unit analyzes the operations recorded by the recording unit and breaks them down into individual operations, A documenting unit that converts the operation details decomposed by the analysis unit into text, An editing unit that edits the text generated by the aforementioned text generation unit, A providing unit that provides text edited by the aforementioned editorial unit, A system characterized by the following features.

2. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of accepting actions based on the estimated emotions. The system according to feature 1.

3. The aforementioned reception unit is Analyze the user's past operation history and select the optimal reception method. The system according to feature 1.

4. The aforementioned reception unit is When an operation is received, filtering is performed based on the user's current project or area of ​​interest. The system according to feature 1.

5. The aforementioned reception unit is It estimates the user's emotions and determines the priority of the actions to accept based on the estimated user emotions. The system according to feature 1.

6. The aforementioned reception unit is When receiving an operation request, the system prioritizes accepting operations that are highly relevant based on the user's geographical location. The system according to feature 1.

7. The aforementioned reception unit is When an operation is requested, the system analyzes the user's social media activity and accepts relevant operations. The system according to feature 1.

8. The recording unit is, The system estimates the user's emotions and adjusts the recording method based on those estimated emotions. The system according to feature 1.

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