system

Abstract

The system according to the embodiment aims to improve the efficiency of creating business manuals. [Solution] A system according to an embodiment includes a recording unit, an analysis unit, and a documentation unit. The recording unit records user operations. The analysis unit analyzes the video recorded by the recording unit and breaks down each operation. The documentation unit documents the operations broken down by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that creating business manuals requires a great deal of time and effort.

[0005] The system according to the embodiment aims to improve the efficiency of creating business manuals. [Means for solving the problem]

[0006] The system according to the embodiment includes a recording unit, an analysis unit, and a documentation unit. The recording unit records user operations. The analysis unit analyzes the video recorded by the recording unit and breaks down each operation. The documentation unit documents the operations broken down by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can improve the efficiency of creating business manuals. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A manual creation support system according to an embodiment of the present invention records, analyzes, and documents user operations. This system records the user's actual screen operations, analyzes the recorded video using image recognition technology, and breaks down each operation into individual steps. It then documents each of the operations, enabling rapid manual creation. This system significantly reduces the effort required for manual creation and enables the efficient creation of high-quality manuals. For example, a user can record software operation procedures. The recorded video is then analyzed using image recognition technology. The image recognition technology detects each operation in the video and breaks each operation down into individual steps. For example, operations such as clicking a button or entering text are detected. Each of the operations is then documented. A generation AI understands the content of each operation and converts it into natural-sounding sentences. For example, a sentence such as "Click the button to open the settings screen" is generated. This allows users to create high-quality manuals without much effort. The manual creation support system automatically records, analyzes, and documents user operations, enabling the efficient creation of high-quality manuals.

[0029] The manual creation support system according to the embodiment includes a recording unit, an analysis unit, and a text generation unit. The recording unit records user operations. The recording unit can, for example, record a user's software operation procedures. The recording unit can also include a storage unit that saves the recorded video. The analysis unit analyzes the video recorded by the recording unit and breaks down each operation. The analysis unit can, for example, use image recognition technology to detect each operation in the video and break down each operation into individual steps. The analysis unit can also include a display unit that displays the analysis results. The text generation unit puts the operations broken down by the analysis unit into text. The text generation unit can, for example, use generative AI to understand the content of each operation and convert it into natural text. As a result, the manual creation support system according to the embodiment can efficiently create manuals by recording, analyzing, and text generation of user operations.

[0030] The recording unit includes a storage unit that stores the recorded video. The storage unit stores the recorded video. For example, the storage unit can store the recorded video in high resolution. The storage unit can also store the recorded video in a compressed format. For example, the storage unit can store the recorded video in MP4 format. Furthermore, the storage unit can store the recorded video in cloud storage. For example, the storage unit can upload the recorded video to cloud storage, allowing it to be reused or viewed later. In this way, by storing the recorded video, it becomes possible to reuse or view it later.

[0031] The analysis unit includes a display unit that displays the analysis results. The display unit displays the analysis results. For example, the display unit can display the analysis results as a graph or chart. The display unit can also display the analysis results in text format. For example, the display unit displays the analysis results in text format, allowing the user to check the analysis content. Furthermore, the display unit can also display the analysis results interactively. For example, the display unit displays the analysis results interactively, allowing the user to check detailed information. In this way, the user can check the analysis content by displaying the analysis results.

[0032] The recording unit automatically adjusts the recording frame rate during recording in accordance with the user's operation speed. The recording unit automatically adjusts the recording frame rate during recording in accordance with the user's operation speed. For example, the recording unit can set a high frame rate when the user is operating at high speed. On the other hand, the recording unit can set a low frame rate when the user is operating at low speed. Furthermore, the recording unit can adjust the frame rate in real time when the user's operation speed fluctuates. This makes it possible to maintain optimal recording quality by adjusting the frame rate according to the user's operation speed.

[0033] The recording unit, when recording, refers to the user's operation history and highlights important operations. The recording unit, when recording, refers to the user's operation history and highlights important operations. For example, the recording unit can highlight operations that the user frequently performs. The recording unit can also highlight important operations that the user performs in specific software. Furthermore, the recording unit can highlight operations in which the user made mistakes in the past. In this way, by highlighting important operations, the user will not miss important operations.

[0034] The recording unit automatically selects appropriate recording settings during recording, taking into account the user's device information. The recording unit automatically selects appropriate recording settings during recording, taking into account the user's device information. For example, if the user is using a high-performance device, the recording unit can record at high resolution. On the other hand, if the user is using a low-performance device, the recording unit can record at low resolution. Furthermore, the recording unit can adjust the recording time taking into account the storage capacity of the user's device. This allows the recording quality to be optimized by selecting the optimal recording settings based on the user's device information.

[0035] When recording, the recording unit automatically detects the user's operating environment and adds it to the recorded content as metadata. When recording, the recording unit automatically detects the user's operating environment and adds it to the recorded content as metadata. The recording unit can, for example, add version information of the software used by the user as metadata. The recording unit can also add the user's operating environment (e.g., OS version) as metadata. Furthermore, the recording unit can add the user's device information (e.g., hardware specifications) as metadata. By adding the user's operating environment as metadata, detailed information about the recorded content can be provided.

[0036] The analysis unit applies a different analysis algorithm to each type of operation during analysis. The analysis unit applies a different analysis algorithm to each type of operation during analysis. For example, the analysis unit can apply a click-specific analysis algorithm to a click operation. The analysis unit can also apply a text input-specific analysis algorithm to a text input operation. The analysis unit can also apply a drag-and-drop-specific analysis algorithm to a drag-and-drop operation. In this way, analysis accuracy is improved by applying an appropriate analysis algorithm to each type of operation.

[0037] During analysis, the analysis unit emphasizes the relationship between steps by taking into account the continuity of operations. During analysis, the analysis unit emphasizes the relationship between steps by taking into account the continuity of operations. The analysis unit can, for example, analyze consecutive click operations as a series of steps. The analysis unit can also analyze text input and subsequent click operations by associating them with each other. Furthermore, the analysis unit can analyze a drag-and-drop operation by associating it with the operations before and after it. In this way, by taking into account the continuity of operations, the relationship between steps is clarified, making it easier to understand the analysis results.

[0038] During analysis, the analysis unit refers to the user's operation history and compares it with similar past operations. During analysis, the analysis unit refers to the user's operation history and compares it with similar past operations. The analysis unit can, for example, compare a current operation with operations performed in the past by the user. The analysis unit can also extract and compare similar operations from the user's past operation history. Furthermore, the analysis unit can evaluate the current operation based on the success rate of operations performed in the past by the user. This makes it easier to evaluate the current operation and identify areas for improvement by comparing it with the past operation history.

[0039] The analysis unit automatically evaluates the importance level based on the frequency of the operation during analysis. The analysis unit automatically evaluates the importance level based on the frequency of the operation during analysis. For example, the analysis unit can evaluate an operation that is frequently performed by the user as having a high importance level. Furthermore, the analysis unit can evaluate an operation that is rarely performed by the user as having a low importance level. Furthermore, the analysis unit can dynamically adjust the importance level based on the frequency of the user's operation. In this way, by evaluating the importance level based on the frequency of the operation, it is possible to analyze important operations without missing any of them.

[0040] The text generation unit adjusts the level of detail of the text based on the importance of the operation when generating the text. The text generation unit adjusts the level of detail of the text based on the importance of the operation when generating the text. For example, the text generation unit can add detailed explanations to important operations. Also, the text generation unit can add concise explanations to operations with low importance. Furthermore, the text generation unit can dynamically adjust the level of detail of the text according to the importance of the operation. In this way, by adjusting the level of detail of the text according to the importance of the operation, an appropriate level of detailed explanation can be provided.

[0041] The text generation unit applies different text generation algorithms depending on the category of the operation when generating text. The text generation unit applies different text generation algorithms depending on the category of the operation when generating text. For example, the text generation unit can apply a text generation algorithm dedicated to clicks to a click operation. Furthermore, the text generation unit can apply a text generation algorithm dedicated to text input to a text input operation. Furthermore, the text generation unit can apply a text generation algorithm dedicated to drag and drop to a drag and drop operation. In this way, by applying a text generation algorithm depending on the category of the operation, more appropriate text can be generated.

[0042] The text generation unit determines the priority of text based on the time of execution of an operation when generating text. The text generation unit determines the priority of text based on the time of execution of an operation when generating text. The text generation unit can, for example, prioritize the most recent operation when generating text. The text generation unit can also prioritize important past operations when generating text. Furthermore, the text generation unit can dynamically adjust the priority of text based on the time of execution of an operation. In this way, by determining the priority of text based on the time of execution of an operation, important operations can be prioritized when generating text.

[0043] The text generation unit adjusts the order of the text based on the relevance of the operations when generating text. The text generation unit adjusts the order of the text based on the relevance of the operations when generating text. The text generation unit can, for example, generate consecutive operations as a series of sentences. The text generation unit can also generate text by grouping related operations together. Furthermore, the text generation unit can dynamically adjust the order of the sentences based on the relevance of the operations. In this way, by adjusting the order of the sentences based on the relevance of the operations, it is possible to create a manual that is easier to understand.

[0044] The storage unit automatically selects a storage format based on the importance of the data when saving. The storage unit automatically selects a storage format based on the importance of the data when saving. For example, the storage unit can save important data in a high-quality format. The storage unit can also save less important data in a compressed format. Furthermore, the storage unit can dynamically adjust the storage format according to the importance of the data. This makes it possible to provide an optimal storage format by selecting a storage format based on the importance of the data.

[0045] The storage unit automatically selects a storage location for data at the time of saving based on the user's device information. The storage unit automatically selects a storage location for data at the time of saving based on the user's device information. The storage unit can select a storage location, for example, taking into account the storage capacity of the user's device. The storage unit can also select a storage location taking into account the performance of the user's device. Furthermore, the storage unit can select a storage location taking into account the usage status of the user's device. In this way, by selecting a storage location based on the user's device information, it is possible to provide an optimal storage location.

[0046] The display unit, when displaying, selects the optimal display method by referring to the user's past operation history. The display unit, when displaying, selects the optimal display method by referring to the user's past operation history. The display unit can, for example, preferentially provide a display method that the user has used favorably in the past. The display unit can also suggest an optimal display layout based on the user's past operation history. Furthermore, the display unit can provide a customized display method based on the display settings that the user has used in the past. In this way, the optimal display method can be provided for the user by selecting the optimal display method based on the user's past operation history.

[0047] The display unit selects an appropriate display method in consideration of the user's device information when displaying. The display unit selects an appropriate display method in consideration of the user's device information when displaying. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the display unit can provide a simple and highly visible display method. As a result, by selecting the optimal display method based on the user's device information, it is possible to provide a display that is optimized for the device.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The recording unit can not only record the user's operations, but also the audio generated during the user's operations. For example, if a user performs an operation while being verbally explained the operation procedure, recording the audio can be useful for checking the operation procedure later. The recording unit can also analyze the recorded audio and extract keywords related to the operation procedure. Furthermore, the recording unit can convert the recorded audio into text and use it to document the operation procedure. This allows for more detailed recording of the user's operation procedure and improves the accuracy of manual creation.

[0050] The storage unit can simultaneously store not only recorded video but also error logs and system logs that occur during user operations. For example, if an error occurs during user operation, storing the error log makes it easier to identify the cause of the error later. The storage unit can also analyze the system log and extract information related to the operation procedure. Furthermore, the storage unit can store error logs and system logs in cloud storage and share them with other users. This allows for quick resolution of problems that occur during operation and improves the quality of manuals.

[0051] The display unit not only displays the analysis results but also plays back the operation procedures in video format to make it easier for the user to confirm the operation procedures. For example, the operation procedures can be generated as a video based on the analysis results, and the user can intuitively understand the operation procedures by watching the video. The display unit can also provide an interactive function that allows the user to skip or rewind specific operations while the video is playing. Furthermore, the display unit can highlight important operations while the video is playing to ensure that the user does not miss important points. This allows the user to learn the operation procedures more effectively and increases the usefulness of the manual.

[0052] When recording, the recording unit not only automatically adjusts the recording frame rate according to the speed of the user's operations, but also automatically adjusts the recording resolution according to the user's operations. For example, if the user is performing detailed operations, it can record at high resolution. On the other hand, if the user is performing general operations, it can record at low resolution. Furthermore, the recording unit can automatically adjust the recording bit rate according to the user's operations. This makes it possible to optimize the recording quality according to the user's operations and provide optimal recording data.

[0053] During recording, the recording unit not only references the user's operation history and highlights important operations, but also optimizes operation procedures based on the user's operation history. For example, it can analyze the operation procedures a user has performed in the past and suggest the most efficient operation procedures. The recording unit can also point out areas for improvement in operation procedures based on the user's operation history. Furthermore, the recording unit can provide a function that allows a user's operation history to be shared with other users and allows them to learn best practices. This can optimize user operation procedures and improve the quality of manuals.

[0054] When recording, the recording unit not only automatically selects appropriate recording settings taking into account the user's device information, but also automatically selects the upload method for recorded data taking into account the user's network environment. For example, if the user is in a high-speed network environment, high-resolution recorded data can be uploaded quickly. On the other hand, if the user is in a slow network environment, the recorded data can be compressed and uploaded. Furthermore, the recording unit can adjust the upload schedule for recorded data depending on the user's network environment. This allows the optimal upload method to be provided depending on the user's network environment, making recorded data management more efficient.

[0055] The recording unit not only automatically detects the user's operating environment and adds it to the recording as metadata during recording, but also automatically classifies the recording based on the user's operating environment. For example, if a user uses different software, the recording can be classified by each software. Also, if a user uses different devices, the recording can be classified by each device. Furthermore, the recording unit can tag the recording based on the user's operating environment, making it easy to search for later. This allows for efficient management of recordings according to the user's operating environment and quick finding of required information.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The recording unit records the user's operations. For example, the user can record the software operation procedure. The recording unit may also include a storage unit for storing the recorded video. Step 2: The analysis unit analyzes the video recorded by the recording unit and breaks down each operation. For example, it may use image recognition technology to detect each operation in the video and break down each operation into individual steps. The analysis unit may also be equipped with a display unit that displays the analysis results. Step 3: The text generation unit converts the operations broken down by the analysis unit into text. For example, it uses a generative AI to understand the content of each operation and convert it into natural-sounding text.

[0058] (Example 2) A manual creation support system according to an embodiment of the present invention records, analyzes, and documents user operations. This system records the user's actual screen operations, analyzes the recorded video using image recognition technology, and breaks down each operation into individual steps. It then documents each of the operations, enabling rapid manual creation. This system significantly reduces the effort required for manual creation and enables the efficient creation of high-quality manuals. For example, a user can record software operation procedures. The recorded video is then analyzed using image recognition technology. The image recognition technology detects each operation in the video and breaks each operation down into individual steps. For example, operations such as clicking a button or entering text are detected. Each of the operations is then documented. A generation AI understands the content of each operation and converts it into natural-sounding sentences. For example, a sentence such as "Click the button to open the settings screen" is generated. This allows users to create high-quality manuals without much effort. The manual creation support system automatically records, analyzes, and documents user operations, enabling the efficient creation of high-quality manuals.

[0059] The manual creation support system according to the embodiment includes a recording unit, an analysis unit, and a text generation unit. The recording unit records user operations. The recording unit can, for example, record a user's software operation procedures. The recording unit can also include a storage unit that saves the recorded video. The analysis unit analyzes the video recorded by the recording unit and breaks down each operation. The analysis unit can, for example, use image recognition technology to detect each operation in the video and break down each operation into individual steps. The analysis unit can also include a display unit that displays the analysis results. The text generation unit puts the operations broken down by the analysis unit into text. The text generation unit can, for example, use generative AI to understand the content of each operation and convert it into natural text. As a result, the manual creation support system according to the embodiment can efficiently create manuals by recording, analyzing, and text generation of user operations.

[0060] The recording unit includes a storage unit that stores the recorded video. The storage unit stores the recorded video. For example, the storage unit can store the recorded video in high resolution. The storage unit can also store the recorded video in a compressed format. For example, the storage unit can store the recorded video in MP4 format. Furthermore, the storage unit can store the recorded video in cloud storage. For example, the storage unit can upload the recorded video to cloud storage, allowing it to be reused or viewed later. In this way, by storing the recorded video, it becomes possible to reuse or view it later.

[0061] The analysis unit includes a display unit that displays the analysis results. The display unit displays the analysis results. For example, the display unit can display the analysis results as a graph or chart. The display unit can also display the analysis results in text format. For example, the display unit displays the analysis results in text format, allowing the user to check the analysis content. Furthermore, the display unit can also display the analysis results interactively. For example, the display unit displays the analysis results interactively, allowing the user to check detailed information. In this way, the user can check the analysis content by displaying the analysis results.

[0062] The recording unit estimates the user's emotion and adjusts the start timing of recording based on the estimated emotion. The recording unit estimates the user's emotion and adjusts the start timing of recording based on the estimated emotion. For example, if the user is nervous, the recording unit can delay the start of recording until the user is relaxed. Furthermore, if the user is concentrating, the recording unit can start recording immediately. Furthermore, if the user is tired, the recording unit can start recording after a break. In this way, by adjusting the start timing of recording according to the user's emotion, recording can be started at a more appropriate time. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0063] The recording unit automatically adjusts the recording frame rate during recording in accordance with the user's operation speed. The recording unit automatically adjusts the recording frame rate during recording in accordance with the user's operation speed. For example, the recording unit can set a high frame rate when the user is operating at high speed. On the other hand, the recording unit can set a low frame rate when the user is operating at low speed. Furthermore, the recording unit can adjust the frame rate in real time when the user's operation speed fluctuates. This makes it possible to maintain optimal recording quality by adjusting the frame rate according to the user's operation speed.

[0064] The recording unit, when recording, refers to the user's operation history and highlights important operations. The recording unit, when recording, refers to the user's operation history and highlights important operations. For example, the recording unit can highlight operations that the user frequently performs. The recording unit can also highlight important operations that the user performs in specific software. Furthermore, the recording unit can highlight operations in which the user made mistakes in the past. In this way, by highlighting important operations, the user will not miss important operations.

[0065] The recording unit estimates the user's emotion and adjusts the timing to end recording based on the estimated emotion. The recording unit estimates the user's emotion and adjusts the timing to end recording based on the estimated emotion. For example, the recording unit can end recording earlier if the user is tired. Furthermore, the recording unit can continue recording for a longer period if the user is concentrating. Furthermore, the recording unit can end recording at an appropriate time if the user is satisfied. In this way, by adjusting the timing to end recording according to the user's emotion, recording can be ended at a more appropriate time. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0066] The recording unit automatically selects appropriate recording settings during recording, taking into account the user's device information. The recording unit automatically selects appropriate recording settings during recording, taking into account the user's device information. For example, if the user is using a high-performance device, the recording unit can record at high resolution. On the other hand, if the user is using a low-performance device, the recording unit can record at low resolution. Furthermore, the recording unit can adjust the recording time taking into account the storage capacity of the user's device. This allows the recording quality to be optimized by selecting the optimal recording settings based on the user's device information.

[0067] When recording, the recording unit automatically detects the user's operating environment and adds it to the recorded content as metadata. When recording, the recording unit automatically detects the user's operating environment and adds it to the recorded content as metadata. The recording unit can, for example, add version information of the software used by the user as metadata. The recording unit can also add the user's operating environment (e.g., OS version) as metadata. Furthermore, the recording unit can add the user's device information (e.g., hardware specifications) as metadata. By adding the user's operating environment as metadata, detailed information about the recorded content can be provided.

[0068] The analysis unit estimates the user's emotions and adjusts the analysis priority based on the estimated emotions. The analysis unit estimates the user's emotions and adjusts the analysis priority based on the estimated emotions. For example, when the user is in a hurry, the analysis unit can prioritize analyzing important operations. Furthermore, when the user is relaxed, the analysis unit can analyze all operations equally. Furthermore, when the user is feeling anxious, the analysis unit can prioritize analyzing operations that require particular attention. In this way, by adjusting the analysis priority according to the user's emotions, more important operations can be analyzed preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0069] The analysis unit applies a different analysis algorithm to each type of operation during analysis. The analysis unit applies a different analysis algorithm to each type of operation during analysis. For example, the analysis unit can apply a click-specific analysis algorithm to a click operation. The analysis unit can also apply a text input-specific analysis algorithm to a text input operation. The analysis unit can also apply a drag-and-drop-specific analysis algorithm to a drag-and-drop operation. In this way, analysis accuracy is improved by applying an appropriate analysis algorithm to each type of operation.

[0070] During analysis, the analysis unit emphasizes the relationship between steps by taking into account the continuity of operations. During analysis, the analysis unit emphasizes the relationship between steps by taking into account the continuity of operations. The analysis unit can, for example, analyze consecutive click operations as a series of steps. The analysis unit can also analyze text input and subsequent click operations by associating them with each other. Furthermore, the analysis unit can analyze a drag-and-drop operation by associating it with the operations before and after it. In this way, by taking into account the continuity of operations, the relationship between steps is clarified, making it easier to understand the analysis results.

[0071] The analysis unit estimates the user's emotion and adjusts the display method of the analysis results based on the estimated emotion. The analysis unit estimates the user's emotion and adjusts the display method of the analysis results based on the estimated emotion. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. In this way, by adjusting the display method according to the user's emotion, it is possible to provide an optimal display method for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0072] During analysis, the analysis unit refers to the user's operation history and compares it with similar past operations. During analysis, the analysis unit refers to the user's operation history and compares it with similar past operations. The analysis unit can, for example, compare a current operation with operations performed in the past by the user. The analysis unit can also extract and compare similar operations from the user's past operation history. Furthermore, the analysis unit can evaluate the current operation based on the success rate of operations performed in the past by the user. This makes it easier to evaluate the current operation and identify areas for improvement by comparing it with the past operation history.

[0073] The analysis unit automatically evaluates the importance level based on the frequency of the operation during analysis. The analysis unit automatically evaluates the importance level based on the frequency of the operation during analysis. For example, the analysis unit can evaluate an operation that is frequently performed by the user as having a high importance level. Furthermore, the analysis unit can evaluate an operation that is rarely performed by the user as having a low importance level. Furthermore, the analysis unit can dynamically adjust the importance level based on the frequency of the user's operation. In this way, by evaluating the importance level based on the frequency of the operation, it is possible to analyze important operations without missing any of them.

[0074] The sentence generation unit estimates the user's emotion and adjusts the tone of the sentence based on the estimated emotion. The sentence generation unit estimates the user's emotion and adjusts the tone of the sentence based on the estimated emotion. For example, if the user is nervous, the sentence generation unit can generate sentences in a calm tone. Furthermore, if the user is relaxed, the sentence generation unit can generate sentences in a friendly tone. Furthermore, if the user is in a hurry, the sentence generation unit can generate sentences in a concise and to-the-point tone. In this way, by adjusting the tone of the sentence according to the user's emotion, more appropriate sentences can be generated. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0075] The text generation unit adjusts the level of detail of the text based on the importance of the operation when generating the text. The text generation unit adjusts the level of detail of the text based on the importance of the operation when generating the text. For example, the text generation unit can add detailed explanations to important operations. Also, the text generation unit can add concise explanations to operations with low importance. Furthermore, the text generation unit can dynamically adjust the level of detail of the text according to the importance of the operation. In this way, by adjusting the level of detail of the text according to the importance of the operation, an appropriate level of detailed explanation can be provided.

[0076] The text generation unit applies different text generation algorithms depending on the category of the operation when generating text. The text generation unit applies different text generation algorithms depending on the category of the operation when generating text. For example, the text generation unit can apply a text generation algorithm dedicated to clicks to a click operation. Furthermore, the text generation unit can apply a text generation algorithm dedicated to text input to a text input operation. Furthermore, the text generation unit can apply a text generation algorithm dedicated to drag and drop to a drag and drop operation. In this way, by applying a text generation algorithm depending on the category of the operation, more appropriate text can be generated.

[0077] The sentence generator estimates the user's emotion and adjusts the length of the sentence based on the estimated emotion. The sentence generator estimates the user's emotion and adjusts the length of the sentence based on the estimated emotion. For example, if the user is in a hurry, the sentence generator can generate short, to-the-point sentences. Furthermore, if the user is relaxed, the sentence generator can generate longer sentences with detailed explanations. Furthermore, if the user is excited, the sentence generator can generate sentences with visually stimulating effects. In this way, by adjusting the length of the sentences according to the user's emotion, more appropriate sentences can be generated. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0078] The text generation unit determines the priority of text based on the time of execution of an operation when generating text. The text generation unit determines the priority of text based on the time of execution of an operation when generating text. The text generation unit can, for example, prioritize the most recent operation when generating text. The text generation unit can also prioritize important past operations when generating text. Furthermore, the text generation unit can dynamically adjust the priority of text based on the time of execution of an operation. In this way, by determining the priority of text based on the time of execution of an operation, important operations can be prioritized when generating text.

[0079] The text generation unit adjusts the order of the text based on the relevance of the operations when generating text. The text generation unit adjusts the order of the text based on the relevance of the operations when generating text. The text generation unit can, for example, generate consecutive operations as a series of sentences. The text generation unit can also generate text by grouping related operations together. Furthermore, the text generation unit can dynamically adjust the order of the sentences based on the relevance of the operations. In this way, by adjusting the order of the sentences based on the relevance of the operations, it is possible to create a manual that is easier to understand.

[0080] The storage unit estimates the user's emotions and determines the priority of data to be saved based on the estimated emotions. The storage unit estimates the user's emotions and determines the priority of data to be saved based on the estimated emotions. For example, when the user is in a hurry, the storage unit can prioritize saving important data. Furthermore, when the user is relaxed, the storage unit can equally save all data. Furthermore, when the user is feeling anxious, the storage unit can prioritize saving data that requires particular attention. In this way, by determining the priority of data to be saved according to the user's emotions, important data can be saved preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0081] The storage unit automatically selects a storage format based on the importance of the data when saving. The storage unit automatically selects a storage format based on the importance of the data when saving. For example, the storage unit can save important data in a high-quality format. The storage unit can also save less important data in a compressed format. Furthermore, the storage unit can dynamically adjust the storage format according to the importance of the data. This makes it possible to provide an optimal storage format by selecting a storage format based on the importance of the data.

[0082] The storage unit estimates the user's emotion and adjusts the compression rate of the data to be saved based on the estimated emotion. The storage unit estimates the user's emotion and adjusts the compression rate of the data to be saved based on the estimated emotion. For example, when the user is in a hurry, the storage unit can increase the compression rate to shorten the saving time. Also, when the user is relaxed, the storage unit can decrease the compression rate to save high-quality data. Furthermore, when the user is feeling anxious, the storage unit can adjust the compression rate to balance saving time and quality. In this way, by adjusting the data compression rate according to the user's emotion, it is possible to balance saving time and quality. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0083] The storage unit automatically selects a storage location for data at the time of saving based on the user's device information. The storage unit automatically selects a storage location for data at the time of saving based on the user's device information. The storage unit can select a storage location, for example, taking into account the storage capacity of the user's device. The storage unit can also select a storage location taking into account the performance of the user's device. Furthermore, the storage unit can select a storage location taking into account the usage status of the user's device. In this way, by selecting a storage location based on the user's device information, it is possible to provide an optimal storage location.

[0084] The display unit estimates the user's emotion and adjusts the priority of the display content based on the estimated emotion. The display unit estimates the user's emotion and adjusts the priority of the display content based on the estimated emotion. For example, when the user is in a hurry, the display unit can prioritize displaying important information. Furthermore, when the user is relaxed, the display unit can display all information evenly. Furthermore, when the user is feeling anxious, the display unit can prioritize displaying information that requires particular attention. In this way, by adjusting the priority of the display content according to the user's emotion, important information can be prioritized and displayed. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0085] The display unit, when displaying, selects the optimal display method by referring to the user's past operation history. The display unit, when displaying, selects the optimal display method by referring to the user's past operation history. The display unit can, for example, preferentially provide a display method that the user has used favorably in the past. The display unit can also suggest an optimal display layout based on the user's past operation history. Furthermore, the display unit can provide a customized display method based on the display settings that the user has used in the past. In this way, the optimal display method can be provided for the user by selecting the optimal display method based on the user's past operation history.

[0086] The display unit estimates the user's emotion and adjusts the display layout based on the estimated emotion. The display unit estimates the user's emotion and adjusts the display layout based on the estimated emotion. For example, when the user is nervous, the display unit can provide a simple, highly visible layout. When the user is relaxed, the display unit can provide a layout including detailed information. When the user is in a hurry, the display unit can provide a layout that focuses on the main points. In this way, by adjusting the display layout according to the user's emotion, a highly visible display can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0087] The display unit selects an appropriate display method in consideration of the user's device information when displaying. The display unit selects an appropriate display method in consideration of the user's device information when displaying. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the display unit can provide a simple and highly visible display method. As a result, by selecting the optimal display method based on the user's device information, it is possible to provide a display that is optimized for the device. === Hard Collateral 1-1 === Each of the multiple elements including the recording unit, analysis unit, and text generation unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the recording unit records user operations using the camera 42 and microphone 38B of the smart device 14, and saves the recorded video by the specific processing unit 290 of the data processing device 12. For example, the analysis unit analyzes the recorded video by the specific processing unit 290 of the data processing device 12 and breaks down each operation. For example, the text generation unit puts the operations broken down by the specific processing unit 290 of the data processing device 12 into text and converts them into natural-looking text using a generation AI. === Hard Collateral 1-2 === Each of the multiple elements including the recording unit, analysis unit, and text generation unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the recording unit records user operations using the camera 42 and microphone 238 of the smart glasses 214, and saves the recorded video by the specific processing unit 290 of the data processing device 12. For example, the analysis unit analyzes the recorded video by the specific processing unit 290 of the data processing device 12 and breaks down each operation. For example, the text generation unit puts the operations broken down by the specific processing unit 290 of the data processing device 12 into text and converts them into natural-looking text using a generation AI. === Hard Collateral 1-3 === Each of the multiple elements including the recording unit, analysis unit, and text generation unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the recording unit records user operations using the camera 42 and microphone 238 of the headset type terminal 314, and saves the recorded video by the specific processing unit 290 of the data processing device 12. For example, the analysis unit analyzes the recorded video by the specific processing unit 290 of the data processing device 12 and breaks down each operation. For example, the text generation unit puts the operations broken down by the specific processing unit 290 of the data processing device 12 into text and converts it into natural-looking text using a generation AI. === Hard Collateral 1-4 === Each of the multiple elements including the recording unit, analysis unit, and text generation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the recording unit records the user's operations using the camera 42 and microphone 238 of the robot 414, and saves the recorded video by the specific processing unit 290 of the data processing device 12. For example, the analysis unit analyzes the recorded video by the specific processing unit 290 of the data processing device 12 and breaks down each operation. For example, the text generation unit puts the operations broken down by the specific processing unit 290 of the data processing device 12 into text and converts it into natural-looking text using a generation AI.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The recording unit can not only record the user's operations, but also the audio generated during the user's operations. For example, if a user performs an operation while being verbally explained the operation procedure, recording the audio can be useful for checking the operation procedure later. The recording unit can also analyze the recorded audio and extract keywords related to the operation procedure. Furthermore, the recording unit can convert the recorded audio into text and use it to document the operation procedure. This allows for more detailed recording of the user's operation procedure and improves the accuracy of manual creation.

[0090] The storage unit can simultaneously store not only recorded video but also error logs and system logs that occur during user operations. For example, if an error occurs during user operation, storing the error log makes it easier to identify the cause of the error later. The storage unit can also analyze the system log and extract information related to the operation procedure. Furthermore, the storage unit can store error logs and system logs in cloud storage and share them with other users. This allows for quick resolution of problems that occur during operation and improves the quality of manuals.

[0091] The display unit not only displays the analysis results but also plays back the operation procedures in video format to make it easier for the user to confirm the operation procedures. For example, the operation procedures can be generated as a video based on the analysis results, and the user can intuitively understand the operation procedures by watching the video. The display unit can also provide an interactive function that allows the user to skip or rewind specific operations while the video is playing. Furthermore, the display unit can highlight important operations while the video is playing to ensure that the user does not miss important points. This allows the user to learn the operation procedures more effectively and increases the usefulness of the manual.

[0092] The recording unit not only estimates the user's emotions and adjusts the start timing of recording based on the estimated emotions, but also monitors changes in the user's emotions in real time while recording. For example, if the user feels stressed while operating the device, the recording unit can detect that emotion and pause recording. Alternatively, if the user feels relaxed, the recording unit can resume recording. Furthermore, the recording unit can automatically adjust the frame rate and resolution of recording according to changes in the user's emotions. This allows the quality of recording to be optimized according to the user's emotions, allowing recording to be performed at more appropriate times.

[0093] When recording, the recording unit not only automatically adjusts the recording frame rate according to the speed of the user's operations, but also automatically adjusts the recording resolution according to the user's operations. For example, if the user is performing detailed operations, it can record at high resolution. On the other hand, if the user is performing general operations, it can record at low resolution. Furthermore, the recording unit can automatically adjust the recording bit rate according to the user's operations. This makes it possible to optimize the recording quality according to the user's operations and provide optimal recording data.

[0094] During recording, the recording unit not only references the user's operation history and highlights important operations, but also optimizes operation procedures based on the user's operation history. For example, it can analyze the operation procedures a user has performed in the past and suggest the most efficient operation procedures. The recording unit can also point out areas for improvement in operation procedures based on the user's operation history. Furthermore, the recording unit can provide a function that allows a user's operation history to be shared with other users and allows them to learn best practices. This can optimize user operation procedures and improve the quality of manuals.

[0095] The recording unit can estimate the user's emotions and adjust the timing of recording termination based on the estimated emotions, as well as automatically edit the recorded content based on the user's emotions after recording has finished. For example, if the user is satisfied, the recorded content can be saved as is. On the other hand, if the user is dissatisfied, the recorded content can be re-edited to cut out unnecessary parts. Furthermore, the recording unit can add annotations to the recorded content based on the user's emotions. This allows the recorded content to be optimized according to the user's emotions, making it possible to create more effective manuals.

[0096] When recording, the recording unit not only automatically selects appropriate recording settings taking into account the user's device information, but also automatically selects the upload method for recorded data taking into account the user's network environment. For example, if the user is in a high-speed network environment, high-resolution recorded data can be uploaded quickly. On the other hand, if the user is in a slow network environment, the recorded data can be compressed and uploaded. Furthermore, the recording unit can adjust the upload schedule for recorded data depending on the user's network environment. This allows the optimal upload method to be provided depending on the user's network environment, making recorded data management more efficient.

[0097] The recording unit not only automatically detects the user's operating environment and adds it to the recording as metadata during recording, but also automatically classifies the recording based on the user's operating environment. For example, if a user uses different software, the recording can be classified by each software. Also, if a user uses different devices, the recording can be classified by each device. Furthermore, the recording unit can tag the recording based on the user's operating environment, making it easy to search for later. This allows for efficient management of recordings according to the user's operating environment and quick finding of required information.

[0098] The analysis unit can estimate the user's emotions and adjust the analysis priority based on the estimated emotions, as well as customize the analysis results based on the user's emotions. For example, if the user is in a hurry, it can display only important operations concisely. On the other hand, if the user is relaxed, it can provide detailed analysis results. Furthermore, the analysis unit can adjust the display format of the analysis results based on the user's emotions and provide visually easy-to-understand graphs and charts. This allows the analysis results to be optimized according to the user's emotions, and more effective information to be provided.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The recording unit records the user's operations. For example, the user can record the software operation procedure. The recording unit may also include a storage unit for storing the recorded video. Step 2: The analysis unit analyzes the video recorded by the recording unit and breaks down each operation. For example, it may use image recognition technology to detect each operation in the video and break down each operation into individual steps. The analysis unit may also be equipped with a display unit that displays the analysis results. Step 3: The text generation unit converts the operations broken down by the analysis unit into text. For example, it uses a generative AI to understand the content of each operation and convert it into natural-sounding text.

[0101] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0103] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0104] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

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

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

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

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

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

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

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

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

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

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

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

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

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

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

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, a 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.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0145] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0156] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0157] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0158] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0161] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0162] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0163] 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.

[0164] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0165] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0166] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0167] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0169] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0170] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0171] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0172] [Explanation of symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a recording unit that records user operations; an analysis unit that analyzes the video recorded by the recording unit and breaks down each operation; a text generation unit that generates text from the operations decomposed by the analysis unit; Equipped with A system characterized by:

2. The recording unit Equipped with a storage unit for storing recorded video The system of claim 1 .

3. The analysis unit Equipped with a display that displays the analysis results The system of claim 1 .

4. The recording unit Estimate the user's emotions and adjust the start timing of recording based on the estimated emotions. The system of claim 1 .

5. The recording unit When recording, the recording frame rate is automatically adjusted according to the user's operation speed. The system of claim 1 .

6. The recording unit When recording, refer to the user's operation history and highlight important operations The system of claim 1 .

7. The recording unit Estimate the user's emotions and adjust the timing to end recording based on the estimated emotions. The system of claim 1 .

8. The recording unit When recording, the appropriate recording settings are automatically selected based on the user's device information. The system of claim 1 .

9. The recording unit When recording, the user's operating environment is automatically detected and added as metadata to the recording. The system of claim 1 .

10. The analysis unit Inferring user emotions and adjusting analysis priorities based on the inferred emotions The system of claim 1 .

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