system

Abstract

The system according to this embodiment aims to efficiently generate manuals and tutorial videos of work procedures and to communicate them to successors. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, a video generation unit, and a provision unit. The reception unit receives audio. The analysis unit analyzes the audio received by the reception unit and converts it into text. The generation unit generates a manual based on the text generated by the analysis unit. The video generation unit records the work process and generates a tutorial video. The provision unit provides the manual and video generated by the generation unit and the video generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, a great deal of time and labor are required to create manuals and tutorial videos, and there is a problem that it is difficult to efficiently convey the work content to successors.

[0005] The system according to the embodiment aims to efficiently generate work content as a manual and a tutorial video and convey it to successors.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, a video generation unit, and a provision unit. The reception unit receives audio. The analysis unit analyzes the audio received by the reception unit and converts it into text. The generation unit generates a manual based on the text generated by the analysis unit. The video generation unit records the work process and generates a tutorial video. The provision unit provides the manual and video generated by the generation unit and the video generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently generate manuals and tutorial videos of work procedures and transmit them to successors. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The manual creation support system according to an embodiment of the present invention is a system that supports the creation of manuals necessary for handing over tasks to successors and in various situations in daily life. This system provides a mechanism that automatically generates a manual and a tutorial video simply by having the user perform the task to be manualized and explain the task by voice. This system makes it easy to convey the task content, experience, and know-how to successors, and also provides a video to deepen their understanding. For example, the user performs the task to be manualized and explains the task by voice. For example, they explain specific task content such as how to set up a personal computer or the procedure for cooking. This voice is input into the generation AI. Next, the generation AI analyzes the input voice and automatically generates a manual and a tutorial video. The generation AI converts the voice data into text and creates a manual based on that text. It also records the work in a video and edits the video to generate a tutorial video. For example, based on a voice explanation of how to set up a personal computer, a manual that describes the setup procedure in detail and a video recording the setup work are generated. The generated manual and tutorial video are provided to the successor. The successor can understand the task content and know-how by referring to the manual and watching the tutorial video. This allows successors to efficiently take over tasks and maintain the quality of work. This system can be used not only in the workplace but also in many aspects of daily life. For example, by automatically generating manuals and tutorial videos in various situations such as volunteer activities, PTA activities, and community association management, information can be transmitted smoothly and activities can be made more efficient. Furthermore, by using generation AI, anyone can easily create manuals and tutorial videos, ensuring accurate information transmission and improving the quality of life. For example, by generating a detailed recipe manual and a video showing cooking procedures based on audio explanations of cooking recipes, even beginners can easily cook.Thus, the present invention aims to facilitate information transmission and improve the quality of life by automatically generating manuals and tutorial videos using generation AI. As a result, the manual creation support system can assist in creating manuals needed for handover to successors and in various aspects of daily life, thereby facilitating information transmission and improving the quality of life.

[0029] The manual creation support system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, a video generation unit, and a provision unit. The reception unit receives audio. The reception unit receives, for example, audio in which a user describes the work while performing a task that is the subject of the manual. The reception unit can receive audio using a generation AI. The analysis unit analyzes the audio received by the reception unit and converts it into text. The analysis unit converts audio data into text, for example. The analysis unit can convert audio data into text using a generation AI. The generation unit generates a manual based on the text generated by the analysis unit. The generation unit generates a manual based on text, for example. The generation unit can generate a manual based on text using a generation AI. The video generation unit records the work process and generates a tutorial video. The video generation unit records the work process and edits the video to generate a tutorial video, for example. The video generation unit can record the work process and edit the video to generate a tutorial video using a generation AI. The provision unit provides the manual and video generated by the generation unit and the video generation unit. The provisioning unit, for example, provides the generated manual and tutorial video to the successor. The provisioning unit can provide the generated manual and tutorial video to the successor using generation AI. As a result, the manual creation support system according to the embodiment automatically generates a manual and tutorial video based on voice input and provides it to the successor, thereby streamlining the transmission of work content and know-how.

[0030] The reception unit receives voice input. Specifically, it receives voice descriptions of the user's work as they perform tasks covered by the manual. For example, if a user explains, "Next, I will remove this part," while working, the reception unit receives this voice in real time. The reception unit can receive voice input using generative AI. The generative AI utilizes speech recognition technology to capture the user's voice with high accuracy and removes noise and background sounds. This allows the reception unit to receive the user's voice clearly, enabling smooth processing in the subsequent analysis unit. Furthermore, the reception unit can process multiple voice inputs simultaneously, so it can accurately receive the voice of each user even when multiple users are working at the same time. As a result, the reception unit can receive user work details in real time and efficiently collect data for manual creation.

[0031] The analysis unit analyzes the audio received by the reception unit and converts it into text. Specifically, it converts audio data into text. The analysis unit can convert audio data into text using a generation AI. The generation AI uses speech recognition technology to analyze audio data with high accuracy and convert it into text. For example, if a user says, "Next, remove this part," the generation AI analyzes the audio and generates the text data, "Next, remove this part." Furthermore, the analysis unit can understand the context of the audio data and insert appropriate punctuation and line breaks to generate easy-to-read text. This allows the analysis unit to accurately convert the user's voice into text, ensuring smooth processing in the subsequent generation unit. In addition, the analysis unit can extract important keywords and phrases from the audio data and provide information to optimize the structure and content of the manual. This allows the analysis unit to efficiently analyze audio data and generate high-quality text data for manual creation.

[0032] The generation unit generates manuals based on the text generated by the analysis unit. Specifically, it generates manuals based on text. The generation unit can generate manuals based on text using a generation AI. The generation AI utilizes natural language processing technology to analyze text data and automatically generate an appropriate manual structure and content. For example, based on the text data "Next, remove this part" provided by the analysis unit, the generation AI creates a section titled "Part Removal Procedure" and describes the procedure in detail. Furthermore, the generation unit can understand the context of audio data provided by the user and insert appropriate diagrams and photographs to generate a visually easy-to-understand manual. In addition, the generation unit has the function to translate the manual content into multiple languages, making it suitable for international users. As a result, the generation unit can automatically generate high-quality, visually easy-to-understand manuals based on the text data provided by the analysis unit.

[0033] The video generation unit records the work process and generates tutorial videos. Specifically, it records the work process and edits the video to create tutorial videos. The video generation unit can record the work process and edit the video to generate tutorial videos using generation AI. The generation AI utilizes video analysis technology to automatically extract important parts of the work and perform appropriate editing. For example, it records a scene where the user removes a part and creates a section titled "Part Removal Procedure" based on that scene. In addition, the video generation unit can synchronize the user's voice explanation with the video to create tutorial videos that are easy to understand both visually and aurally. Furthermore, the video generation unit has the function to translate the video content into multiple languages, making it suitable for international users. As a result, the video generation unit can record the work process as high-quality video and automatically generate tutorial videos that are easy to understand visually.

[0034] The service provider will provide manuals and videos generated by the generation and video generation units. Specifically, it will provide the generated manuals and tutorial videos to successors. The service provider can use generation AI to provide the generated manuals and tutorial videos to successors. The generation AI will provide the manuals and videos in the most optimal format according to the user's needs and circumstances. For example, if the successor is using a smartphone, the service provider will provide the manuals and videos in a format optimized for smartphones. The service provider can also use cloud storage to securely store the generated manuals and videos, making them accessible whenever needed. Furthermore, the service provider can collect user feedback and continuously improve the content of the manuals and videos. This allows the service provider to efficiently provide the generated manuals and tutorial videos, helping successors quickly and accurately understand the work procedures and know-how.

[0035] The reception unit can analyze the user's past voice input history and select the optimal voice input method. For example, the reception unit can prioritize selecting voice input methods that the user has frequently used in the past. For example, the reception unit can suggest the most efficient voice input method based on the user's past voice input history. For example, the reception unit can select a voice input method appropriate to a specific situation based on the user's past voice input history. This makes the system user-friendly by selecting the optimal voice input method based on past voice input history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's past voice input history data into a generating AI and have the generating AI select the optimal voice input method.

[0036] The reception unit can filter incoming voice messages based on the user's current work status and areas of interest. For example, if the user is performing a specific task, the reception unit will only accept voice messages related to that task. For example, the reception unit can prioritize receiving highly relevant voice messages based on the user's areas of interest. For example, the reception unit can analyze the user's current work status and filter out unnecessary voice messages. This allows the reception unit to receive only highly relevant voice messages by filtering them based on the user's work status and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input user work status data into a generating AI and have the generating AI perform voice filtering.

[0037] The reception unit can prioritize receiving voice messages that are highly relevant, taking into account the user's geographical location information. For example, if the user is in a specific location, the reception unit will prioritize receiving voice messages related to that location. For example, the reception unit can filter highly relevant voice messages based on the user's current location. For example, the reception unit can analyze the user's geographical location information and receive the most appropriate voice messages. This allows for the reception of more appropriate voice messages by prioritizing highly relevant voice messages based on the user's geographical location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information data into a generating AI and have the generating AI perform voice filtering.

[0038] The reception unit can analyze the user's social media activity when receiving audio and receive relevant audio. For example, the reception unit can prioritize receiving relevant audio based on information shared by the user on social media. For example, the reception unit can analyze the user's social media activity and receive audio based on their interests. For example, the reception unit can refer to the user's social media activity history to receive the most suitable audio. In this way, by receiving relevant audio based on the user's social media activity, it is possible to receive audio that matches the user's interests. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI perform audio filtering.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the audio data. For example, the analysis unit can perform a detailed analysis on important audio data. For example, the analysis unit can perform a standard analysis on general audio data. For example, the analysis unit can perform a simplified analysis on unnecessary audio data. By adjusting the level of detail of the analysis based on the importance of the audio, a detailed analysis can be performed on important audio data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the audio data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the category of the audio data during analysis. For example, the analysis unit can apply a specialized analysis algorithm to technical audio data. For example, the analysis unit can apply a standard analysis algorithm to general conversational audio data. For example, the analysis unit can apply an emotion analysis algorithm to emotional audio data. By applying different analysis algorithms depending on the category of the audio, more appropriate analysis results can be obtained. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category of the audio data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0041] The analysis unit can determine the priority of analysis based on the submission date of the audio data. For example, the analysis unit may prioritize the analysis of the most recent audio data. For example, the analysis unit may postpone the analysis of older audio data. For example, the analysis unit can dynamically adjust the analysis priority based on the submission date. This allows for the prioritization of the analysis of the most recent audio data by determining the analysis priority based on the audio submission date. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the submission date of the audio data into a generating AI and have the generating AI determine the analysis priority.

[0042] The analysis unit can adjust the order of analysis based on the relevance of the audio data. For example, the analysis unit can prioritize the analysis of audio data with important relevance. For example, the analysis unit can postpone the analysis of audio data with low relevance. The analysis unit can dynamically adjust the order of analysis based on the relevance of the audio data. This allows for the prioritization of analysis of audio data with important relevance by adjusting the order of analysis based on the relevance of the audio data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the audio data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0043] The generation unit can adjust the level of detail in the manual based on the importance of the text. For example, the generation unit can generate a manual with detailed explanations for important text. For example, the generation unit can generate a manual with standard explanations for general text. For example, the generation unit can generate a manual with simplified explanations for unnecessary text. In this way, by adjusting the level of detail in the manual based on the importance of the text, it is possible to generate a manual with detailed explanations for important text. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the importance of the text into the generation AI and have the generation AI perform the adjustment of the level of detail in the manual.

[0044] The generation unit can apply different generation algorithms depending on the text category when generating manuals. For example, the generation unit can apply a specialized generation algorithm to technical text. For example, the generation unit can apply a standard generation algorithm to general text. For example, the generation unit can apply a sentiment analysis algorithm to emotional text. By applying different generation algorithms depending on the text category, a more appropriate manual can be generated. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the text category into a generation AI and have the generation AI perform the application of the generation algorithm.

[0045] The generation unit can determine the generation priority based on the text submission date during manual generation. For example, the generation unit can prioritize generating the most recent text. For example, the generation unit can postpone generating older texts. For example, the generation unit can dynamically adjust the generation priority based on the submission date. This allows the generation of the most recent text to be prioritized by determining the generation priority based on the text submission date. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the text submission date into the generation AI and have the generation AI determine the generation priority.

[0046] The generation unit can adjust the generation order based on the relevance of the texts during manual generation. For example, the generation unit can prioritize the generation of texts with important relevance. For example, the generation unit can postpone the generation of less relevant texts. The generation unit can dynamically adjust the generation order based on the relevance of the texts. This allows for the priority generation of texts with important relevance by adjusting the generation order based on the relevance of the texts. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the relevance of the texts into a generation AI and have the generation AI perform the adjustment of the generation order.

[0047] The video generation unit can adjust the level of detail in editing based on the importance of the task during video generation. For example, for important tasks, the video generation unit can perform detailed editing to produce a video that explains even the smallest details. For general tasks, the video generation unit can perform standard editing to produce a video that includes basic explanations. For unnecessary tasks, the video generation unit can perform simplified editing to produce a video that explains only the essentials. This allows for detailed editing of important tasks by adjusting the level of detail in editing based on the importance of the task. Some or all of the above processing in the video generation unit may be performed using AI, for example, or without AI. For example, the video generation unit can input the importance of the task into the generation AI and have the generation AI perform the adjustment of the level of detail in editing.

[0048] The video generation unit can apply different editing algorithms depending on the category of work during video generation. For example, the video generation unit can apply a specialized editing algorithm to technical work. For example, the video generation unit can apply a standard editing algorithm to general work. For example, the video generation unit can apply an emotion analysis algorithm to emotional work. By applying different editing algorithms depending on the category of work, a more appropriate video can be generated. Some or all of the above processing in the video generation unit may be performed using AI, for example, or without AI. For example, the video generation unit can input the category of work into a generation AI and have the generation AI execute the application of the editing algorithm.

[0049] The video generation unit can determine editing priorities based on the submission dates of the work during video generation. For example, the video generation unit can prioritize editing the most recent work. For example, the video generation unit can postpone editing older work. For example, the video generation unit can dynamically adjust the editing priorities based on the submission dates. This allows for prioritizing the editing of the most recent work by determining the editing priorities based on the submission dates of the work. Some or all of the above processes in the video generation unit may be performed using AI, for example, or without AI. For example, the video generation unit can input the submission dates of the work into a generation AI and have the generation AI determine the editing priorities.

[0050] The video generation unit can adjust the editing order based on the relevance of the tasks during video generation. For example, the video generation unit can prioritize editing tasks with important relevance. For example, the video generation unit can postpone tasks with low relevance. For example, the video generation unit can dynamically adjust the editing order based on the relevance of the tasks. This allows for prioritizing the editing of tasks with important relevance by adjusting the editing order based on the relevance of the tasks. Some or all of the above processing in the video generation unit may be performed using AI, for example, or without AI. For example, the video generation unit can input the relevance of the tasks into a generation AI and have the generation AI perform the adjustment of the editing order.

[0051] The service delivery unit can select the optimal delivery method by referring to the user's past usage history at the time of delivery. For example, the service delivery unit can prioritize delivery methods that the user has frequently used in the past. For example, the service delivery unit can propose the most efficient delivery method based on the user's past usage history. For example, the service delivery unit can select a delivery method that suits a specific situation based on the user's past usage history. This makes the system user-friendly by selecting the optimal delivery method based on the user's past usage history. Some or all of the above processing in the service delivery unit may be performed using AI, for example, or without AI. For example, the service delivery unit can input the user's usage history data into a generating AI and have the generating AI perform the selection of the delivery method.

[0052] The delivery unit can select the optimal delivery method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the delivery unit can provide a delivery method that matches the screen size. For example, if the user is using a tablet, the delivery unit can provide a delivery method optimized for a larger screen. For example, if the user is using a smartwatch, the delivery unit can provide a concise and highly visible delivery method. By selecting the optimal delivery method based on the user's device information, the system becomes user-friendly. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's device information into a generating AI and have the generating AI select the delivery method.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The reception unit can be equipped with a filtering function that automatically detects and removes background noise when the user performs voice input. For example, if the user performs voice input in a noisy environment, the reception unit will detect the background noise, remove it, and generate clear audio data. This allows the analysis unit to perform accurate text conversion. Furthermore, the reception unit can automatically turn off the noise reduction filter when the user performs voice input in a quiet environment. In addition, the reception unit can adjust the filtering strength according to the noise level when the user performs voice input. This allows for more accurate analysis of the user's voice input and improves the quality of manuals and tutorial videos.

[0055] The generation unit can refer to the user's past manual creation history when generating a manual and select the most suitable manual format. For example, if the user has previously preferred to create detailed manuals, the generation unit will generate a detailed manual. If the user has previously preferred to create concise manuals, the generation unit can generate a concise manual. The generation unit can select a manual format appropriate to a specific situation based on the user's past manual creation history. By selecting the most suitable manual format based on the user's past manual creation history, it is possible to provide a user-friendly manual. Some or all of the above-described processes in the generation unit may be performed using AI or not.

[0056] The delivery unit can select the optimal delivery method considering the battery level of the user's device. For example, if the user's device battery level is low, it can provide a concise manual and a short video. If the user's device battery level is sufficient, the delivery unit can provide a detailed manual and a longer video. The delivery unit can dynamically adjust the format of the content provided based on the user's device battery level. This makes the system user-friendly by selecting the optimal delivery method based on the user's device battery level. Some or all of the above processing in the delivery unit may be performed using AI or not.

[0057] The reception unit can be equipped with a function to automatically adjust the speed of the voice input when the user speaks. For example, if the user is speaking quickly, the reception unit can slow down the voice speed to make it easier to analyze. If the user is speaking slowly, the reception unit can maintain the original voice speed. Furthermore, the reception unit can dynamically adjust the voice speed according to the user's speaking speed. This improves the accuracy of the analysis based on the voice speed, enabling the generation of more accurate manuals and tutorial videos. Some or all of the above processing in the reception unit may be performed using AI or not.

[0058] The generation unit can adjust the level of detail in the manual generation based on the user's level of expertise. For example, if the user is a beginner, it can generate a manual with detailed explanations. If the user is an intermediate user, it can generate a manual with standard explanations. If the user is an advanced user, it can generate a manual with concise explanations. By adjusting the level of detail based on the user's level of expertise, a more appropriate manual can be generated. Some or all of the above processing in the generation unit may be performed using AI or not.

[0059] The service provider can select the optimal delivery method considering the user's internet connection status. For example, if the user's internet connection is unstable, it can provide a low-resolution video and a concise manual. If the user's internet connection is stable, it can provide a high-resolution video and a detailed manual. The service provider can dynamically adjust the format of the content provided based on the user's internet connection status. This makes the system user-friendly by selecting the optimal delivery method based on the user's internet connection status. Some or all of the above processing in the service provider may be performed using AI or not.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The reception desk receives voice messages. For example, it receives voice messages from users describing tasks they are performing as described in a manual. The reception desk can receive these voice messages using a generative AI. Step 2: The analysis unit analyzes the audio received by the reception unit and converts it into text. For example, it converts audio data into text. The analysis unit can convert audio data into text using a generation AI. Step 3: The generation unit generates a manual based on the text generated by the analysis unit. For example, it generates a manual based on text. The generation unit can generate a manual based on text using a generation AI. Step 4: The video generation unit records the work process and generates a tutorial video. For example, it records the work process, edits the video, and generates a tutorial video. The video generation unit can use generation AI to record the work process, edit the video, and generate a tutorial video. Step 5: The providing unit provides the manuals and videos generated by the generation unit and the video generation unit. For example, it provides the generated manuals and tutorial videos to the successor. The providing unit can use generation AI to provide the generated manuals and tutorial videos to the successor.

[0062] (Example of form 2) The manual creation support system according to an embodiment of the present invention is a system that supports the creation of manuals necessary for handing over tasks to successors and in various situations in daily life. This system provides a mechanism that automatically generates a manual and a tutorial video simply by having the user perform the task to be manualized and explain the task by voice. This system makes it easy to convey the task content, experience, and know-how to successors, and also provides a video to deepen their understanding. For example, the user performs the task to be manualized and explains the task by voice. For example, they explain specific task content such as how to set up a personal computer or the procedure for cooking. This voice is input into the generation AI. Next, the generation AI analyzes the input voice and automatically generates a manual and a tutorial video. The generation AI converts the voice data into text and creates a manual based on that text. It also records the work in a video and edits the video to generate a tutorial video. For example, based on a voice explanation of how to set up a personal computer, a manual that describes the setup procedure in detail and a video recording the setup work are generated. The generated manual and tutorial video are provided to the successor. The successor can understand the task content and know-how by referring to the manual and watching the tutorial video. This allows successors to efficiently take over tasks and maintain the quality of work. This system can be used not only in the workplace but also in many aspects of daily life. For example, by automatically generating manuals and tutorial videos in various situations such as volunteer activities, PTA activities, and community association management, information can be transmitted smoothly and activities can be made more efficient. Furthermore, by using generation AI, anyone can easily create manuals and tutorial videos, ensuring accurate information transmission and improving the quality of life. For example, by generating a detailed recipe manual and a video showing cooking procedures based on audio explanations of cooking recipes, even beginners can easily cook.Thus, the present invention aims to facilitate information transmission and improve the quality of life by automatically generating manuals and tutorial videos using generation AI. As a result, the manual creation support system can assist in creating manuals needed for handover to successors and in various aspects of daily life, thereby facilitating information transmission and improving the quality of life.

[0063] The manual creation support system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, a video generation unit, and a provision unit. The reception unit receives audio. The reception unit receives, for example, audio in which a user describes the work while performing a task that is the subject of the manual. The reception unit can receive audio using a generation AI. The analysis unit analyzes the audio received by the reception unit and converts it into text. The analysis unit converts audio data into text, for example. The analysis unit can convert audio data into text using a generation AI. The generation unit generates a manual based on the text generated by the analysis unit. The generation unit generates a manual based on text, for example. The generation unit can generate a manual based on text using a generation AI. The video generation unit records the work process and generates a tutorial video. The video generation unit records the work process and edits the video to generate a tutorial video, for example. The video generation unit can record the work process and edit the video to generate a tutorial video using a generation AI. The provision unit provides the manual and video generated by the generation unit and the video generation unit. The provisioning unit, for example, provides the generated manual and tutorial video to the successor. The provisioning unit can provide the generated manual and tutorial video to the successor using generation AI. As a result, the manual creation support system according to the embodiment automatically generates a manual and tutorial video based on voice input and provides it to the successor, thereby streamlining the transmission of work content and know-how.

[0064] The reception unit receives voice input. Specifically, it receives voice descriptions of the user's work as they perform tasks covered by the manual. For example, if a user explains, "Next, I will remove this part," while working, the reception unit receives this voice in real time. The reception unit can receive voice input using generative AI. The generative AI utilizes speech recognition technology to capture the user's voice with high accuracy and removes noise and background sounds. This allows the reception unit to receive the user's voice clearly, enabling smooth processing in the subsequent analysis unit. Furthermore, the reception unit can process multiple voice inputs simultaneously, so it can accurately receive the voice of each user even when multiple users are working at the same time. As a result, the reception unit can receive user work details in real time and efficiently collect data for manual creation.

[0065] The analysis unit analyzes the audio received by the reception unit and converts it into text. Specifically, it converts audio data into text. The analysis unit can convert audio data into text using a generation AI. The generation AI uses speech recognition technology to analyze audio data with high accuracy and convert it into text. For example, if a user says, "Next, remove this part," the generation AI analyzes the audio and generates the text data, "Next, remove this part." Furthermore, the analysis unit can understand the context of the audio data and insert appropriate punctuation and line breaks to generate easy-to-read text. This allows the analysis unit to accurately convert the user's voice into text, ensuring smooth processing in the subsequent generation unit. In addition, the analysis unit can extract important keywords and phrases from the audio data and provide information to optimize the structure and content of the manual. This allows the analysis unit to efficiently analyze audio data and generate high-quality text data for manual creation.

[0066] The generation unit generates manuals based on the text generated by the analysis unit. Specifically, it generates manuals based on text. The generation unit can generate manuals based on text using a generation AI. The generation AI utilizes natural language processing technology to analyze text data and automatically generate an appropriate manual structure and content. For example, based on the text data "Next, remove this part" provided by the analysis unit, the generation AI creates a section titled "Part Removal Procedure" and describes the procedure in detail. Furthermore, the generation unit can understand the context of audio data provided by the user and insert appropriate diagrams and photographs to generate a visually easy-to-understand manual. In addition, the generation unit has the function to translate the manual content into multiple languages, making it suitable for international users. As a result, the generation unit can automatically generate high-quality, visually easy-to-understand manuals based on the text data provided by the analysis unit.

[0067] The video generation unit records the work process and generates tutorial videos. Specifically, it records the work process and edits the video to create tutorial videos. The video generation unit can record the work process and edit the video to generate tutorial videos using generation AI. The generation AI utilizes video analysis technology to automatically extract important parts of the work and perform appropriate editing. For example, it records a scene where the user removes a part and creates a section titled "Part Removal Procedure" based on that scene. In addition, the video generation unit can synchronize the user's voice explanation with the video to create tutorial videos that are easy to understand both visually and aurally. Furthermore, the video generation unit has the function to translate the video content into multiple languages, making it suitable for international users. As a result, the video generation unit can record the work process as high-quality video and automatically generate tutorial videos that are easy to understand visually.

[0068] The service provider will provide manuals and videos generated by the generation and video generation units. Specifically, it will provide the generated manuals and tutorial videos to successors. The service provider can use generation AI to provide the generated manuals and tutorial videos to successors. The generation AI will provide the manuals and videos in the most optimal format according to the user's needs and circumstances. For example, if the successor is using a smartphone, the service provider will provide the manuals and videos in a format optimized for smartphones. The service provider can also use cloud storage to securely store the generated manuals and videos, making them accessible whenever needed. Furthermore, the service provider can collect user feedback and continuously improve the content of the manuals and videos. This allows the service provider to efficiently provide the generated manuals and tutorial videos, helping successors quickly and accurately understand the work procedures and know-how.

[0069] The reception unit can estimate the user's emotions and adjust the timing of voice input based on the estimated emotions. For example, if the user is nervous, the reception unit can delay the timing of voice input to help them relax. For example, if the user is in a hurry, the reception unit can speed up the timing to quickly receive the voice. For example, if the user is concentrating, the reception unit can receive the voice at an appropriate time to avoid interrupting their work. By adjusting the timing of voice input according to the user's emotions, voice can be received at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not using AI. For example, the reception unit can input user facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0070] The reception unit can analyze the user's past voice input history and select the optimal voice input method. For example, the reception unit can prioritize selecting voice input methods that the user has frequently used in the past. For example, the reception unit can suggest the most efficient voice input method based on the user's past voice input history. For example, the reception unit can select a voice input method appropriate to a specific situation based on the user's past voice input history. This makes the system user-friendly by selecting the optimal voice input method based on past voice input history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's past voice input history data into a generating AI and have the generating AI select the optimal voice input method.

[0071] The reception unit can filter incoming voice messages based on the user's current work status and areas of interest. For example, if the user is performing a specific task, the reception unit will only accept voice messages related to that task. For example, the reception unit can prioritize receiving highly relevant voice messages based on the user's areas of interest. For example, the reception unit can analyze the user's current work status and filter out unnecessary voice messages. This allows the reception unit to receive only highly relevant voice messages by filtering them based on the user's work status and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input user work status data into a generating AI and have the generating AI perform voice filtering.

[0072] The reception unit can estimate the user's emotions and determine the priority of incoming audio based on the estimated emotions. For example, if the user is stressed, the reception unit will prioritize important audio. If the user is relaxed, the reception unit can prioritize all audio. If the user is in a hurry, the reception unit can prioritize urgent audio. This ensures that important audio is prioritized by determining the priority of audio according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input user emotion data into a generative AI and have the generative AI determine the priority of audio.

[0073] The reception unit can prioritize receiving voice messages that are highly relevant, taking into account the user's geographical location information. For example, if the user is in a specific location, the reception unit will prioritize receiving voice messages related to that location. For example, the reception unit can filter highly relevant voice messages based on the user's current location. For example, the reception unit can analyze the user's geographical location information and receive the most appropriate voice messages. This allows for the reception of more appropriate voice messages by prioritizing highly relevant voice messages based on the user's geographical location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information data into a generating AI and have the generating AI perform voice filtering.

[0074] The reception unit can analyze the user's social media activity when receiving audio and receive relevant audio. For example, the reception unit can prioritize receiving relevant audio based on information shared by the user on social media. For example, the reception unit can analyze the user's social media activity and receive audio based on their interests. For example, the reception unit can refer to the user's social media activity history to receive the most suitable audio. In this way, by receiving relevant audio based on the user's social media activity, it is possible to receive audio that matches the user's interests. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI perform audio filtering.

[0075] The analysis unit can estimate the user's emotions and adjust the method of analyzing the audio data based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis. For example, if the user is in a hurry, the analysis unit can perform a rapid analysis. For example, if the user is nervous, the analysis unit can perform a concise analysis. By adjusting the method of analyzing the audio data according to the user's emotions, more appropriate analysis results can be obtained. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into the generative AI and have the generative AI adjust the method of analyzing the audio data.

[0076] The analysis unit can adjust the level of detail of the analysis based on the importance of the audio data. For example, the analysis unit can perform a detailed analysis on important audio data. For example, the analysis unit can perform a standard analysis on general audio data. For example, the analysis unit can perform a simplified analysis on unnecessary audio data. By adjusting the level of detail of the analysis based on the importance of the audio, a detailed analysis can be performed on important audio data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the audio data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0077] The analysis unit can apply different analysis algorithms depending on the category of the audio data during analysis. For example, the analysis unit can apply a specialized analysis algorithm to technical audio data. For example, the analysis unit can apply a standard analysis algorithm to general conversational audio data. For example, the analysis unit can apply an emotion analysis algorithm to emotional audio data. By applying different analysis algorithms depending on the category of the audio, more appropriate analysis results can be obtained. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category of the audio data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0078] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can display detailed analysis results. For example, if the user is in a hurry, the analysis unit can display concise analysis results. For example, if the user is stressed, the analysis unit can display visually easy-to-understand analysis results. In this way, by adjusting the display method of the analysis results according to the user's emotions, a more appropriate display method can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the display method of the analysis results.

[0079] The analysis unit can determine the priority of analysis based on the submission date of the audio data. For example, the analysis unit may prioritize the analysis of the most recent audio data. For example, the analysis unit may postpone the analysis of older audio data. For example, the analysis unit can dynamically adjust the analysis priority based on the submission date. This allows for the prioritization of the analysis of the most recent audio data by determining the analysis priority based on the audio submission date. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the submission date of the audio data into a generating AI and have the generating AI determine the analysis priority.

[0080] The analysis unit can adjust the order of analysis based on the relevance of the audio data. For example, the analysis unit can prioritize the analysis of audio data with important relevance. For example, the analysis unit can postpone the analysis of audio data with low relevance. The analysis unit can dynamically adjust the order of analysis based on the relevance of the audio data. This allows for the prioritization of analysis of audio data with important relevance by adjusting the order of analysis based on the relevance of the audio data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the audio data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0081] The generation unit can estimate the user's emotions and adjust the manual generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a detailed manual. For example, if the user is in a hurry, the generation unit can generate a concise manual. For example, if the user is nervous, the generation unit can generate a visually easy-to-understand manual. In this way, by adjusting the manual generation method according to the user's emotions, a more appropriate manual can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the manual generation method.

[0082] The generation unit can adjust the level of detail in the manual based on the importance of the text. For example, the generation unit can generate a manual with detailed explanations for important text. For example, the generation unit can generate a manual with standard explanations for general text. For example, the generation unit can generate a manual with simplified explanations for unnecessary text. In this way, by adjusting the level of detail in the manual based on the importance of the text, it is possible to generate a manual with detailed explanations for important text. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the importance of the text into the generation AI and have the generation AI perform the adjustment of the level of detail in the manual.

[0083] The generation unit can apply different generation algorithms depending on the text category when generating manuals. For example, the generation unit can apply a specialized generation algorithm to technical text. For example, the generation unit can apply a standard generation algorithm to general text. For example, the generation unit can apply a sentiment analysis algorithm to emotional text. By applying different generation algorithms depending on the text category, a more appropriate manual can be generated. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the text category into a generation AI and have the generation AI perform the application of the generation algorithm.

[0084] The generation unit can estimate the user's emotions and adjust the length of the manual based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a detailed manual. For example, if the user is in a hurry, the generation unit can generate a concise manual. For example, if the user is nervous, the generation unit can generate a visually easy-to-understand manual. By adjusting the length of the manual according to the user's emotions, a more appropriate manual can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the length of the manual.

[0085] The generation unit can determine the generation priority based on the text submission date during manual generation. For example, the generation unit can prioritize generating the most recent text. For example, the generation unit can postpone generating older texts. For example, the generation unit can dynamically adjust the generation priority based on the submission date. This allows the generation of the most recent text to be prioritized by determining the generation priority based on the text submission date. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the text submission date into the generation AI and have the generation AI determine the generation priority.

[0086] The generation unit can adjust the generation order based on the relevance of the texts during manual generation. For example, the generation unit can prioritize the generation of texts with important relevance. For example, the generation unit can postpone the generation of less relevant texts. The generation unit can dynamically adjust the generation order based on the relevance of the texts. This allows for the priority generation of texts with important relevance by adjusting the generation order based on the relevance of the texts. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the relevance of the texts into a generation AI and have the generation AI perform the adjustment of the generation order.

[0087] The video generation unit can estimate the user's emotions and adjust the video editing method based on the estimated emotions. For example, if the user is relaxed, the video generation unit can generate a video that progresses at a leisurely pace. For example, if the user is in a hurry, the video generation unit can generate a video that emphasizes the shortest route. For example, if the user is excited, the video generation unit can generate a video with visually stimulating effects. This allows for the generation of more appropriate videos by adjusting the video editing method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the video generation unit may be performed using AI or not. For example, the video generation unit can input user emotion data into the generative AI and have the generative AI adjust the video editing method.

[0088] The video generation unit can adjust the level of detail in editing based on the importance of the task during video generation. For example, for important tasks, the video generation unit can perform detailed editing to produce a video that explains even the smallest details. For general tasks, the video generation unit can perform standard editing to produce a video that includes basic explanations. For unnecessary tasks, the video generation unit can perform simplified editing to produce a video that explains only the essentials. This allows for detailed editing of important tasks by adjusting the level of detail in editing based on the importance of the task. Some or all of the above processing in the video generation unit may be performed using AI, for example, or without AI. For example, the video generation unit can input the importance of the task into the generation AI and have the generation AI perform the adjustment of the level of detail in editing.

[0089] The video generation unit can apply different editing algorithms depending on the category of work during video generation. For example, the video generation unit can apply a specialized editing algorithm to technical work. For example, the video generation unit can apply a standard editing algorithm to general work. For example, the video generation unit can apply an emotion analysis algorithm to emotional work. By applying different editing algorithms depending on the category of work, a more appropriate video can be generated. Some or all of the above processing in the video generation unit may be performed using AI, for example, or without AI. For example, the video generation unit can input the category of work into a generation AI and have the generation AI execute the application of the editing algorithm.

[0090] The video generation unit can estimate the user's emotions and adjust the video length based on the estimated emotions. For example, if the user is relaxed, the video generation unit can generate a longer video with detailed explanations. For example, if the user is in a hurry, the video generation unit can generate a short, concise video. For example, if the user is excited, the video generation unit can generate a video with visually stimulating effects. By adjusting the video length according to the user's emotions, a more appropriate video can be generated. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the video generation unit may be performed using AI or not. For example, the video generation unit can input user emotion data into the generative AI and have the generative AI adjust the video length.

[0091] The video generation unit can determine editing priorities based on the submission dates of the work during video generation. For example, the video generation unit can prioritize editing the most recent work. For example, the video generation unit can postpone editing older work. For example, the video generation unit can dynamically adjust the editing priorities based on the submission dates. This allows for prioritizing the editing of the most recent work by determining the editing priorities based on the submission dates of the work. Some or all of the above processes in the video generation unit may be performed using AI, for example, or without AI. For example, the video generation unit can input the submission dates of the work into a generation AI and have the generation AI determine the editing priorities.

[0092] The video generation unit can adjust the editing order based on the relevance of the tasks during video generation. For example, the video generation unit can prioritize editing tasks with important relevance. For example, the video generation unit can postpone tasks with low relevance. For example, the video generation unit can dynamically adjust the editing order based on the relevance of the tasks. This allows for prioritizing the editing of tasks with important relevance by adjusting the editing order based on the relevance of the tasks. Some or all of the above processing in the video generation unit may be performed using AI, for example, or without AI. For example, the video generation unit can input the relevance of the tasks into a generation AI and have the generation AI perform the adjustment of the editing order.

[0093] The service provider can estimate the user's emotions and adjust the display method of the manuals and videos based on the estimated emotions. For example, if the user is relaxed, the service provider can provide a detailed display method. For example, if the user is in a hurry, the service provider can provide a concise display method. For example, if the user is nervous, the service provider can provide a visually easy-to-understand display method. By adjusting the display method according to the user's emotions, a more appropriate display method can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0094] The service delivery unit can select the optimal delivery method by referring to the user's past usage history at the time of delivery. For example, the service delivery unit can prioritize delivery methods that the user has frequently used in the past. For example, the service delivery unit can propose the most efficient delivery method based on the user's past usage history. For example, the service delivery unit can select a delivery method that suits a specific situation based on the user's past usage history. This makes the system user-friendly by selecting the optimal delivery method based on the user's past usage history. Some or all of the above processing in the service delivery unit may be performed using AI, for example, or without AI. For example, the service delivery unit can input the user's usage history data into a generating AI and have the generating AI perform the selection of the delivery method.

[0095] The service provider can estimate the user's emotions and determine the priority of manuals and videos to provide based on the estimated emotions. For example, if the user is stressed, the service provider can prioritize providing important manuals and videos. For example, if the user is relaxed, the service provider can provide all manuals and videos equally. For example, if the user is in a hurry, the service provider can prioritize providing manuals and videos of high urgency. This ensures that important information is provided preferentially by determining the priority of manuals and videos according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform the priority determination.

[0096] The delivery unit can select the optimal delivery method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the delivery unit can provide a delivery method that matches the screen size. For example, if the user is using a tablet, the delivery unit can provide a delivery method optimized for a larger screen. For example, if the user is using a smartwatch, the delivery unit can provide a concise and highly visible delivery method. By selecting the optimal delivery method based on the user's device information, the system becomes user-friendly. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's device information into a generating AI and have the generating AI select the delivery method.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The reception unit can be equipped with a filtering function that automatically detects and removes background noise when the user performs voice input. For example, if the user performs voice input in a noisy environment, the reception unit will detect the background noise, remove it, and generate clear audio data. This allows the analysis unit to perform accurate text conversion. Furthermore, the reception unit can automatically turn off the noise reduction filter when the user performs voice input in a quiet environment. In addition, the reception unit can adjust the filtering strength according to the noise level when the user performs voice input. This allows for more accurate analysis of the user's voice input and improves the quality of manuals and tutorial videos.

[0099] The analysis unit can analyze the tone and pitch of the user's voice data to estimate the user's emotions. For example, if the user speaks in a high tone, the analysis unit can estimate that the user is excited. If the user speaks in a low tone, the analysis unit can estimate that the user is calm. The analysis unit can analyze the pitch fluctuations of the user's voice to estimate stress and tension. This allows the analysis results to be adjusted based on the user's emotions, enabling the generation of more appropriate manuals and tutorial videos. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the above processing in the analysis unit may be performed using AI or not.

[0100] The generation unit can refer to the user's past manual creation history when generating a manual and select the most suitable manual format. For example, if the user has previously preferred to create detailed manuals, the generation unit will generate a detailed manual. If the user has previously preferred to create concise manuals, the generation unit can generate a concise manual. The generation unit can select a manual format appropriate to a specific situation based on the user's past manual creation history. By selecting the most suitable manual format based on the user's past manual creation history, it is possible to provide a user-friendly manual. Some or all of the above-described processes in the generation unit may be performed using AI or not.

[0101] The video generation unit can estimate the user's emotions and adjust the video narration based on the estimated emotions. For example, if the user is relaxed, a calm tone of narration is used. If the user is in a hurry, the video generation unit can use a fast-paced tone of narration. If the user is excited, the video generation unit can use an energetic tone of narration. By adjusting the video narration according to the user's emotions, a more appropriate video can be generated. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the above-described processes in the video generation unit may be performed using AI or not.

[0102] The delivery unit can select the optimal delivery method considering the battery level of the user's device. For example, if the user's device battery level is low, it can provide a concise manual and a short video. If the user's device battery level is sufficient, the delivery unit can provide a detailed manual and a longer video. The delivery unit can dynamically adjust the format of the content provided based on the user's device battery level. This makes the system user-friendly by selecting the optimal delivery method based on the user's device battery level. Some or all of the above processing in the delivery unit may be performed using AI or not.

[0103] The reception unit can be equipped with a function to automatically adjust the speed of the voice input when the user speaks. For example, if the user is speaking quickly, the reception unit can slow down the voice speed to make it easier to analyze. If the user is speaking slowly, the reception unit can maintain the original voice speed. Furthermore, the reception unit can dynamically adjust the voice speed according to the user's speaking speed. This improves the accuracy of the analysis based on the voice speed, enabling the generation of more accurate manuals and tutorial videos. Some or all of the above processing in the reception unit may be performed using AI or not.

[0104] The analysis unit can estimate the user's emotions and adjust the feedback method of the analysis results based on the estimated user emotions. For example, if the user is relaxed, it can provide detailed feedback. If the user is in a hurry, for example, the analysis unit can provide concise feedback. If the user is stressed, for example, the analysis unit can provide visually easy-to-understand feedback. By adjusting the feedback method of the analysis results according to the user's emotions, more appropriate feedback can be provided. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI.

[0105] The generation unit can adjust the level of detail in the manual generation based on the user's level of expertise. For example, if the user is a beginner, it can generate a manual with detailed explanations. If the user is an intermediate user, it can generate a manual with standard explanations. If the user is an advanced user, it can generate a manual with concise explanations. By adjusting the level of detail based on the user's level of expertise, a more appropriate manual can be generated. Some or all of the above processing in the generation unit may be performed using AI or not.

[0106] The video generation unit can estimate the user's emotions and adjust the video's subtitle display method based on the estimated emotions. For example, if the user is relaxed, it can display detailed subtitles. If the user is in a hurry, the video generation unit can display concise subtitles. If the user is excited, the video generation unit can display subtitles with visually stimulating effects. By adjusting the video's subtitle display method according to the user's emotions, it is possible to generate more appropriate videos. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the above-described processes in the video generation unit may be performed using AI or not.

[0107] The service provider can select the optimal delivery method considering the user's internet connection status. For example, if the user's internet connection is unstable, it can provide a low-resolution video and a concise manual. If the user's internet connection is stable, it can provide a high-resolution video and a detailed manual. The service provider can dynamically adjust the format of the content provided based on the user's internet connection status. This makes the system user-friendly by selecting the optimal delivery method based on the user's internet connection status. Some or all of the above processing in the service provider may be performed using AI or not.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The reception desk receives voice messages. For example, it receives voice messages from users describing tasks they are performing as described in a manual. The reception desk can receive these voice messages using a generative AI. Step 2: The analysis unit analyzes the audio received by the reception unit and converts it into text. For example, it converts audio data into text. The analysis unit can convert audio data into text using a generation AI. Step 3: The generation unit generates a manual based on the text generated by the analysis unit. For example, it generates a manual based on text. The generation unit can generate a manual based on text using a generation AI. Step 4: The video generation unit records the work process and generates a tutorial video. For example, it records the work process, edits the video, and generates a tutorial video. The video generation unit can use generation AI to record the work process, edit the video, and generate a tutorial video. Step 5: The providing unit provides the manuals and videos generated by the generation unit and the video generation unit. For example, it provides the generated manuals and tutorial videos to the successor. The providing unit can use generation AI to provide the generated manuals and tutorial videos to the successor.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0112] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0113] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, video generation unit, and provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit receives voice using the microphone 38B of the smart device 14 and generates voice data by the control unit 46A. The analysis unit converts the voice data into text using the specific processing unit 290 of the data processing unit 12. The generation unit generates a manual based on the text using the specific processing unit 290 of the data processing unit 12. The video generation unit records the work using the camera 42 of the smart device 14 and edits the video using the control unit 46A to generate a tutorial video. The provision unit provides the manual and tutorial video generated by the specific processing unit 290 of the data processing unit 12 to the successor. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0120] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0121] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0122] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0123] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0124] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0126] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0128] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, video generation unit, and provision unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit receives voice using the microphone 238 of the smart glasses 214 and generates voice data by the control unit 46A. The analysis unit converts the voice data into text using the specific processing unit 290 of the data processing unit 12. The generation unit generates a manual based on the text using the specific processing unit 290 of the data processing unit 12. The video generation unit records the work using the camera 42 of the smart glasses 214 and edits the video using the control unit 46A to generate a tutorial video. The provision unit provides the manual and tutorial video generated by the specific processing unit 290 of the data processing unit 12 to the successor. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, video generation unit, and provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit receives voice using the microphone 238 of the headset terminal 314 and generates voice data using the control unit 46A. The analysis unit converts the voice data into text using the specific processing unit 290 of the data processing unit 12. The generation unit generates a manual based on the text using the specific processing unit 290 of the data processing unit 12. The video generation unit records the work using the camera 42 of the headset terminal 314 and edits the video using the control unit 46A to generate a tutorial video. The provision unit provides the manual and tutorial video generated by the specific processing unit 290 of the data processing unit 12 to the successor. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0154] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0155] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, video generation unit, and provision unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the reception unit receives voice using the microphone 238 of the robot 414 and generates voice data using the control unit 46A. The analysis unit converts the voice data into text using the specific processing unit 290 of the data processing unit 12. The generation unit generates a manual based on the text using the specific processing unit 290 of the data processing unit 12. The video generation unit records the work using the camera 42 of the robot 414 and edits the video using the control unit 46A to generate a tutorial video. The provision unit provides the manual and tutorial video generated by the specific processing unit 290 of the data processing unit 12 to the successor. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0163] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0164] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0165] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0166] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0167] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0168] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0170] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0171] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0172] 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.

[0173] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0174] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0175] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0176] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0177] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0178] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0179] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0180] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0181] (Note 1) The reception desk that accepts voice messages, An analysis unit analyzes the audio received by the reception unit and converts it into text, A generation unit that generates a manual based on the text generated by the analysis unit, A video generation unit that records the work process and generates a tutorial video, The system comprises a generating unit and a providing unit that provides the manual and video generated by the generating unit and the video generating unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of voice reception based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is The system analyzes the user's past voice input history and selects the optimal voice input method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is When receiving voice messages, filtering is performed based on the user's current work status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It estimates the user's emotions and determines the priority of the voice messages to accept based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is When receiving voice messages, the system prioritizes receiving messages that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When receiving audio, the system analyzes the user's social media activity and accepts relevant audio. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the audio data analysis method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, When analyzing audio data, adjust the level of detail of the analysis based on the importance of the audio. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, When analyzing audio data, different analysis algorithms are applied depending on the audio category. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, When analyzing audio data, the analysis priority is determined based on when the audio was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, When analyzing audio data, the order of analysis is adjusted based on the relevance of the audio. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is We estimate the user's emotions and adjust the manual generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating manuals, adjust the level of detail based on the importance of the text. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating manuals, different generation algorithms are applied depending on the text category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is The system estimates the user's emotions and adjusts the length of the manual based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating manuals, the generation priority is determined based on when the text was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is When generating manuals, the generation order is adjusted based on the relevance of the text. The system described in Appendix 1, characterized by the features described herein. (Note 20) The video generation unit, It estimates the user's emotions and adjusts the video editing method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The video generation unit, When generating the video, adjust the level of detail in the editing based on the importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 22) The video generation unit, When generating videos, different editing algorithms are applied depending on the category of work. The system described in Appendix 1, characterized by the features described herein. (Note 23) The video generation unit, It estimates the user's emotions and adjusts the video length based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The video generation unit, When generating the video, prioritize editing based on the submission deadline. The system described in Appendix 1, characterized by the features described herein. (Note 25) The video generation unit, When generating the video, adjust the editing order based on the relevance of the tasks. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, It estimates the user's emotions and adjusts how manuals and videos are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing the service, the optimal delivery method is selected by referring to the user's past usage history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates user sentiment and determines the priority of manuals and videos to provide based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception desk that accepts voice messages, An analysis unit analyzes the audio received by the reception unit and converts it into text, A generation unit that generates a manual based on the text generated by the analysis unit, A video generation unit that records the work process and generates a tutorial video, The system comprises a generating unit and a providing unit that provides the manual and video generated by the generating unit and the video generating unit. A system characterized by the following features.

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

3. The aforementioned reception unit is The system analyzes the user's past voice input history and selects the optimal voice input method. The system according to feature 1.

4. The aforementioned reception unit is When receiving voice messages, filtering is performed based on the user's current work status and areas of interest. The system according to feature 1.

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

6. The aforementioned reception unit is When receiving voice messages, the system prioritizes receiving messages that are highly relevant, taking into account the user's geographical location. The system according to feature 1.

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

8. The aforementioned analysis unit, The system estimates the user's emotions and adjusts the audio data analysis method based on the estimated emotions. The system according to feature 1.

9. The aforementioned analysis unit, When analyzing audio data, adjust the level of detail of the analysis based on the importance of the audio. The system according to feature 1.

10. The aforementioned analysis unit, When analyzing audio data, different analysis algorithms are applied depending on the audio category. The system according to feature 1.

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