System

Abstract

An object of the system according to the embodiment is to interpret the contents of an assembly manual and provide an assembly moving image that is easy for a user to understand.SOLUTION: A system includes a reception unit, an interpretation unit, a generation unit, and an adjustment unit. The receiving unit receives an input of a manual. The interpretation unit interprets the content of the description received by the reception unit. The generation unit generates an assembled moving image based on the content interpreted by the interpretation unit. The adjustment unit adjusts the moving image generated by the generation unit in accordance with a user's viewpoint and an assembly speed.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In conventional technology, assembly instructions can be difficult to understand or visually comprehend, which can make assembly difficult for users.

[0005] The system according to the embodiment aims to interpret the contents of assembly instructions and provide an assembly video that is easy for users to understand. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, an interpretation unit, a generation unit, and an adjustment unit. The receiving unit receives input of instructions. The interpretation unit interprets the content of the instructions received by the receiving unit. The generation unit generates an assembly video based on the content interpreted by the interpretation unit. The adjustment unit adjusts the video generated by the generation unit to suit the user's viewpoint and assembly speed. [Effects of the Invention]

[0007] The system according to the embodiment can interpret the contents of the assembly instructions and provide an assembly video that is easy for the user to understand. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An automatic video generation system according to an embodiment of the present invention interprets the contents of instructions, generates assembly videos, and adjusts them to suit the user's viewpoint and assembly speed. The automatic video generation system accepts input instructions, and a generation AI interprets the contents of the instructions and generates assembly videos. This video can be used as a reference for the user when assembling. For example, a user inputs the instructions included with a product into the automatic video generation system. For example, the automatic video generation system can input the text and images of the instructions by scanning them. This information is input into the generation AI. The automatic video generation system then uses the generation AI to interpret the contents of the input instructions. The generation AI analyzes the text and images in the instructions to understand the assembly steps. For example, the generation AI analyzes instructions such as "attach part A to part B" and understands the specific assembly steps. Next, the automatic video generation system generates an assembly video based on the assembly steps generated by the generation AI. The generation AI then creates an animation of each step according to the analyzed steps. For example, an animation shows the procedure for attaching part A to part B. The animation can be used as a reference for the user when assembling. As a result, the automatic video generation system can reduce the number of people who drop out because they don't know how to assemble, and improve assembly efficiency. Users can intuitively understand the assembly steps without having to read complex instructions. For example, when assembling furniture, users can proceed with the work while checking each step in the video. This reduces assembly errors and allows the work to be done more efficiently. In addition, because the generation AI automatically generates videos, the cost of video creation can be reduced. For example, automatically generating assembly videos for plastic models can reduce the effort required for video creation. In this way, the automatic video generation system can reduce the number of people who drop out because they don't know how to assemble, and improve assembly efficiency. In addition, because it reduces video creation costs, costs can be significantly reduced compared to when videos are created manually by official organizations or volunteers.

[0029] An automatic video generation system according to an embodiment includes a receiving unit, an interpretation unit, a generation unit, and an adjustment unit. The receiving unit receives input of instructions. The input of instructions may include, but is not limited to, text input, voice input, image input, and the like. The receiving unit may, for example, input the text or images of the instructions by scanning them. The interpretation unit uses a generation AI to interpret the content of the instructions received by the receiving unit. The interpretation may be performed using, for example, but not limited to, natural language processing or image recognition technology. For example, the interpretation unit may analyze an instruction in the instructions, such as "attach part A to part B," to understand the specific assembly steps. The generation unit generates an assembly video based on the content interpreted by the interpretation unit. For example, the generation unit uses, for example, a generation AI to represent each step in a video according to the analyzed steps. For example, the generation unit may show a video illustrating the steps of attaching part A to part B. The adjustment unit adjusts the video generated by the generation unit to suit the user's viewpoint and assembly speed. For example, the adjustment unit may adjust the camera angle or zoom level to provide the video from a viewpoint that is easy for the user to view. The adjustment unit can also adjust the playback speed to play the video in accordance with the user's assembly speed. This allows the automatic video generation system according to the embodiment to interpret the contents of the instruction manual, generate an assembly video, and adjust it to match the user's viewpoint and assembly speed, thereby improving assembly efficiency.

[0030] The automatic video generation system includes a scanning unit that scans the text or images of the instructions. The scanning unit scans the text or images of the instructions. For example, OCR technology or image analysis technology is used for the scanning, but is not limited to these examples. For example, the scanning unit reads the text of the instructions with a scanner and converts it into text data using OCR technology. The scanning unit can also read the image of the instructions with a scanner and convert it into image data using image analysis technology. For example, the scanning unit scans the image of the instructions at high resolution and obtains detailed data using image analysis technology. This makes it possible to efficiently input instructions by scanning the text or images of the instructions.

[0031] The automatic video generation system includes a providing unit that provides the generated video to the user. The providing unit provides the generated video to the user. Providing includes, but is not limited to, a method of distributing the video through a web application or a mobile application, for example. For example, the providing unit provides the generated video to the user through a web application. The providing unit can also provide the generated video to the user through a mobile application. For example, the providing unit streams the generated video so that the user can watch it in real time. In this way, providing the generated video to the user allows the user to intuitively understand the assembly procedure.

[0032] The interpretation unit can analyze the contents of the instruction manual using natural language processing or image recognition technology. The interpretation unit analyzes the text of the instruction manual using, for example, natural language processing technology. For example, the interpretation unit can segment the text using morphological analysis, perform grammatical analysis, and perform semantic analysis. The interpretation unit can also analyze images of the instruction manual using image recognition technology. For example, the interpretation unit can identify parts in the image using object detection technology and determine the type of part using image classification technology. Furthermore, the interpretation unit can identify the position of the part in the image using segmentation technology. This allows the content of the instruction manual to be accurately analyzed using natural language processing and image recognition technology.

[0033] The generation unit can represent each step in a video according to the analyzed procedure. For example, the generation unit uses a generative AI to represent each step in a video according to the analyzed procedure. For example, the generation unit uses 3D animation to show a video of the procedure for attaching part A to part B. The generation unit can also show assembly procedures using live-action video. For example, the generation unit films actual assembly work and edits the footage to generate a video. Furthermore, the generation unit can show assembly procedures using a step-by-step guide. For example, the generation unit plays a video while displaying detailed instructions for each step. In this way, by generating a video according to the analyzed procedure, the user can visually understand the assembly procedure.

[0034] The adjustment unit can adjust the video to match the user's viewpoint and assembly speed. The adjustment unit, for example, adjusts the camera angle and zoom level to provide the video from a viewpoint that is easy for the user to view. For example, the adjustment unit changes the camera angle to match the user's viewpoint and zooms in on important parts. The adjustment unit can also adjust the playback speed to play the video to match the user's assembly speed. For example, the adjustment unit slows down the playback speed when the user assembles slowly and speeds up the playback speed when the user assembles quickly. The adjustment unit can also adjust the way the viewpoint moves to make the video easier for the user to watch. For example, the adjustment unit smoothly moves the viewpoint to allow the user to intuitively understand the assembly steps. In this way, the video can be adjusted to match the user's viewpoint and assembly speed, allowing the user to assemble efficiently.

[0035] The reception unit can analyze the user's past instruction input history and select the optimal input method. For example, the reception unit preferentially suggests an input method (voice, text, etc.) that the user has frequently used in the past. For example, if the user has used voice input frequently in the past, the reception unit preferentially suggests voice input. The reception unit can also automatically set the optimal input format based on the format of instructions the user has previously input. For example, if the user has previously input many instructions in PDF format, the reception unit preferentially suggests PDF input. Furthermore, the reception unit can predict and suggest an input method to be used during a specific time period based on the user's past input history. For example, if the user has used voice input frequently at night, the reception unit preferentially suggests voice input at night. In this way, by analyzing the user's past input history, the optimal input method can be suggested, allowing instructions to be input efficiently.

[0036] The reception unit can filter instructions based on the user's current project or area of ​​interest when inputting instructions. For example, the reception unit displays only instructions related to the user's current project. For example, if the user is working on a furniture assembly project, the reception unit displays only instructions related to furniture. The reception unit can also preferentially display highly relevant instructions based on the user's area of ​​interest. For example, if the user is interested in plastic models, the reception unit preferentially displays instructions related to plastic models. Furthermore, the reception unit can suggest related instructions by referring to the user's past project history. For example, if the user has previously worked on a project assembling electronic devices, the reception unit suggests instructions related to electronic devices. In this way, by filtering based on the user's current project or area of ​​interest, highly relevant instructions can be preferentially input.

[0037] When inputting instructions, the reception unit can select the optimal input means depending on the user's input method. For example, if the user selects voice input, the reception unit inputs the instructions using voice recognition technology. For example, the reception unit converts the user's voice into text data using voice recognition software. Furthermore, if the user selects text input, the reception unit can also input the instructions using text analysis technology. For example, the reception unit analyzes the text input by the user using text analysis software to understand the content of the instructions. Furthermore, if the user selects image input, the reception unit can also input the instructions using image recognition technology. For example, the reception unit analyzes the image uploaded by the user using image recognition software to understand the content of the instructions. This allows instructions to be input efficiently by selecting the optimal input means depending on the user's input method.

[0038] When inputting instructions, the reception unit can prioritize inputting highly relevant instructions taking into account the user's geographical location information. The reception unit, for example, prioritizes inputting instructions related to the user's current location. For example, the reception unit displays instructions related to the area where the user is currently located. The reception unit can also prioritize inputting region-specific instructions based on the user's geographical location information. For example, the reception unit prioritizes displaying instructions for products sold in a specific area. Furthermore, the reception unit can also prioritize inputting instructions related to nearby stores or facilities by referring to the user's location information. For example, the reception unit displays instructions for products the user purchased at a nearby store. In this way, highly relevant instructions can be prioritized input by taking into account the user's geographical location information.

[0039] When inputting the description, the reception unit can analyze the user's social media activity and input the relevant description. The reception unit, for example, inputs a description related to a place where the user has checked in on social media. For example, the reception unit displays a description related to a store or facility where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and input the relevant description. For example, the reception unit displays the relevant description based on the content posted by the user on social media. Furthermore, the reception unit can input the relevant description by referring to the activity of the user's friends on social media. For example, the reception unit displays the relevant description based on information shared by the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to preferentially input highly relevant descriptions.

[0040] The reception unit can customize the input method by reflecting the user's past feedback when inputting instructions. The reception unit, for example, suggests the optimal input method based on feedback provided by the user in the past. For example, if the user has previously preferred voice input, the reception unit preferentially suggests voice input. The reception unit can also customize the input interface by referring to the user's past feedback. For example, if the user has previously provided feedback to improve the text input interface, the reception unit improves the text input interface. Furthermore, the reception unit can also improve the input procedure by reflecting the user's feedback. For example, if the user has previously provided feedback that the input procedure is complicated, the reception unit simplifies the input procedure. In this way, the optimal input method can be suggested by reflecting the user's past feedback, and instructions can be input efficiently.

[0041] During interpretation, the interpretation unit can adjust the level of detail of the interpretation based on the importance of the instructions. For example, in the case of important instructions, the interpretation unit provides a detailed interpretation. For example, the interpretation unit interprets each step of the important instructions in detail and provides it to the user. The interpretation unit can also provide a standard interpretation in the case of general instructions. For example, the interpretation unit interprets the main steps of the general instructions and provides it to the user. Furthermore, the interpretation unit can also provide a concise interpretation in the case of simple instructions. For example, the interpretation unit provides an interpretation that covers the main points of the simple instructions. In this way, by adjusting the level of detail of the interpretation based on the importance of the instructions, interpretation can be performed efficiently.

[0042] During interpretation, the interpretation unit can apply different interpretation algorithms depending on the category of the instruction manual. For example, in the case of furniture instructions, the interpretation unit applies an interpretation algorithm dedicated to assembling furniture. For example, the interpretation unit analyzes the furniture assembly procedures and interprets them using a dedicated algorithm. In addition, in the case of plastic model instructions, the interpretation unit can also apply an interpretation algorithm dedicated to assembling plastic models. For example, the interpretation unit analyzes the plastic model assembly procedures and interprets them using a dedicated algorithm. In addition, in the case of electronic device instructions, the interpretation unit can also apply an interpretation algorithm dedicated to assembling electronic devices. For example, the interpretation unit analyzes the electronic device assembly procedures and interprets them using a dedicated algorithm. In this way, accurate interpretation can be performed by applying different interpretation algorithms depending on the category of the instruction manual.

[0043] During interpretation, the interpretation unit can improve the accuracy of the interpretation by referring to the user's past interpretation results. The interpretation unit improves the accuracy of the interpretation, for example, based on the results of instructions that the user has interpreted in the past. For example, the interpretation unit refers to the history of instructions that the user has interpreted in the past and uses this as a reference when interpreting similar instructions. The interpretation unit can also analyze the user's past interpretation history and optimize the interpretation algorithm. For example, the interpretation unit adjusts the parameters of the interpretation algorithm based on the user's past interpretation history. Furthermore, the interpretation unit can also improve the accuracy of the interpretation by reflecting user feedback. For example, the interpretation unit improves the interpretation algorithm based on feedback provided by the user in the past. In this way, the accuracy of the interpretation can be improved by referring to the user's past interpretation results.

[0044] During interpretation, the interpretation unit can determine the priority of interpretation based on the submission date of the instructions. The interpretation unit, for example, gives priority to interpreting the most recent instructions. For example, the interpretation unit gives priority to analyzing the most recently submitted instructions and provides them to the user. The interpretation unit can also postpone the most recently submitted instructions. For example, the interpretation unit postpones the most recently submitted instructions and gives priority to interpreting the most recent instructions. Furthermore, the interpretation unit can also adjust the order of interpretation based on the submission date. For example, the interpretation unit adjusts the order of interpretation based on the submission date to perform interpretation efficiently. In this way, by determining the priority of interpretation based on the submission date of the instructions, interpretation can be performed efficiently.

[0045] During interpretation, the interpretation unit can adjust the order of interpretation based on the relevance of the instructions. For example, the interpretation unit gives priority to interpreting highly relevant instructions. For example, the interpretation unit gives priority to interpreting instructions related to the user's current project or area of ​​interest. The interpretation unit can also postpone less relevant instructions. For example, the interpretation unit postpones instructions less relevant to the user's current project or area of ​​interest. Furthermore, the interpretation unit can also adjust the order of interpretation based on the relevance of the instructions. For example, the interpretation unit gives priority to interpreting highly relevant instructions and postpones less relevant instructions. In this way, by adjusting the order of interpretation based on the relevance of the instructions, interpretation can be performed efficiently.

[0046] During interpretation, the interpretation unit can adjust the use of technical terms in the interpretation according to the user's level of expertise. For example, if the user has technical expertise, the interpretation unit uses a lot of technical terms. For example, if the user has technical expertise, the interpretation unit provides a detailed interpretation using a lot of technical terms. Furthermore, if the user is a beginner, the interpretation unit can avoid technical terms and use easy-to-understand expressions. For example, if the user is a beginner, the interpretation unit can provide a concise interpretation avoiding technical terms. Furthermore, the interpretation unit can adjust the manner in which the interpretation is expressed according to the user's level of expertise. For example, the interpretation unit adjusts the level of detail and the manner in which the interpretation is expressed according to the user's level of expertise. In this way, by adjusting the use of technical terms in the interpretation according to the user's level of expertise, it is possible to provide an interpretation that is easy for the user to understand.

[0047] When generating a video, the generation unit can adjust the level of detail of the video based on the importance of the assembly steps. For example, in the case of important assembly steps, the generation unit generates a detailed video. For example, the generation unit shows each step of the important assembly steps in a detailed video. The generation unit can also generate a standard video for general assembly steps. For example, the generation unit shows the main steps of the general assembly steps in a video. Furthermore, the generation unit can generate a concise video for simple assembly steps. For example, the generation unit generates a video that highlights the main points of simple assembly steps. In this way, by adjusting the level of detail of the video based on the importance of the assembly steps, videos can be generated efficiently.

[0048] When generating a video, the generation unit can apply different video generation algorithms depending on the category of the assembly steps. For example, in the case of furniture assembly steps, the generation unit applies a video generation algorithm dedicated to furniture assembly. For example, the generation unit analyzes the furniture assembly steps and generates a video using a dedicated algorithm. Furthermore, in the case of plastic model assembly steps, the generation unit can also apply a video generation algorithm dedicated to plastic model assembly. For example, the generation unit analyzes the plastic model assembly steps and generates a video using a dedicated algorithm. Furthermore, in the case of electronic device assembly steps, the generation unit can also apply a video generation algorithm dedicated to electronic device assembly. For example, the generation unit analyzes the electronic device assembly steps and generates a video using a dedicated algorithm. In this way, by applying different video generation algorithms depending on the category of the assembly steps, accurate videos can be generated.

[0049] When generating a video, the generation unit can improve the accuracy of the video by referring to the user's past video generation results. The generation unit improves the accuracy of the video, for example, based on the results of videos generated by the user in the past. For example, the generation unit refers to the history of videos generated by the user in the past and uses this as a reference when creating a video using similar procedures. The generation unit can also analyze the user's past video generation history and optimize the video generation algorithm. For example, the generation unit adjusts parameters of the video generation algorithm based on the user's past video generation history. Furthermore, the generation unit can reflect user feedback to improve the accuracy of the video. For example, the generation unit improves the video generation algorithm based on feedback provided by the user in the past. In this way, the accuracy of the video can be improved by referring to the user's past video generation results.

[0050] When generating videos, the generation unit can determine the priority of the videos based on the submission dates of the assembly instructions. The generation unit, for example, prioritizes the creation of the latest assembly instructions. For example, the generation unit prioritizes the creation of the latest submitted assembly instructions and provides them to the user. The generation unit can also postpone the creation of the oldest submitted assembly instructions. For example, the generation unit postpones the creation of the oldest submitted assembly instructions and prioritizes the creation of the latest assembly instructions. Furthermore, the generation unit can also adjust the order of video generation based on the submission dates. For example, the generation unit adjusts the order of video generation based on the submission dates and efficiently generates videos. In this way, videos can be efficiently generated by determining the priority of the videos based on the submission dates of the assembly instructions.

[0051] When generating a video, the generation unit can adjust the order of the videos based on the relevance of the assembly steps. For example, the generation unit prioritizes animating highly relevant assembly steps. For example, the generation unit prioritizes animating assembly steps related to the user's current project or field of interest. The generation unit can also postpone less relevant assembly steps. For example, the generation unit postpones assembly steps less relevant to the user's current project or field of interest. Furthermore, the generation unit can also adjust the order of video generation based on the relevance of the assembly steps. For example, the generation unit prioritizes animating highly relevant assembly steps and postpones less relevant assembly steps. In this way, videos can be generated efficiently by adjusting the order of the videos based on the relevance of the assembly steps.

[0052] When generating a video, the generation unit can adjust the use of technical terms in the video according to the user's level of expertise. For example, if the user has technical expertise, the generation unit generates a video that uses a lot of technical terms. For example, if the user has technical expertise, the generation unit generates a detailed video that uses a lot of technical terms. Furthermore, if the user is a beginner, the generation unit can generate a video that avoids technical terms and uses easy-to-understand expressions. For example, if the user is a beginner, the generation unit generates a concise video that avoids technical terms. Furthermore, the generation unit can adjust the expression method of the video according to the user's level of expertise. For example, the generation unit adjusts the level of detail and expression method of the video according to the user's level of expertise. In this way, by adjusting the use of technical terms in the video according to the user's level of expertise, it is possible to provide a video that is easy for the user to understand.

[0053] The adjustment unit can select the optimal display method by referring to the user's past operation history when displaying a video. The adjustment unit, for example, proposes the optimal display method based on a display method previously selected by the user. For example, the adjustment unit refers to a display method previously selected by the user and proposes a similar display method. The adjustment unit can also analyze the user's past operation history and optimize the display method. For example, the adjustment unit adjusts parameters of the display method based on the user's past operation history. Furthermore, the adjustment unit can improve the display method by reflecting user feedback. For example, the adjustment unit improves the display method based on feedback provided by the user in the past. In this way, the optimal display method can be provided by referring to the user's past operation history.

[0054] The adjustment unit can customize the display content according to the user's current task when displaying the video. For example, the adjustment unit prioritizes displaying information related to the task the user is currently performing. For example, the adjustment unit displays information related to a project the user is currently performing. The adjustment unit can also customize the display content based on the user's current task. For example, the adjustment unit displays information related to the work the user is currently performing. Furthermore, the adjustment unit can display related information by referring to the user's past task history. For example, the adjustment unit displays information related to tasks the user has performed in the past. In this way, by customizing the display content according to the user's current task, information can be provided efficiently.

[0055] The adjustment unit can improve the display method by reflecting user feedback when displaying a video. The adjustment unit improves the display method, for example, based on feedback provided by the user. For example, the adjustment unit improves the display method by referring to feedback provided by the user. The adjustment unit can also customize the display interface by referring to past user feedback. For example, the adjustment unit improves the display interface based on feedback provided by the user in the past. Furthermore, the adjustment unit can also optimize the display procedure by reflecting user feedback. For example, the adjustment unit improves the display procedure based on feedback provided by the user in the past. In this way, an optimal display method can be provided by reflecting user feedback.

[0056] The adjustment unit can select the optimal display method when displaying a video by taking into consideration device information of the user. For example, when the user is using a smartphone, the adjustment unit provides a display method that matches the screen size. For example, the adjustment unit provides a display method optimized for the smartphone screen size. Furthermore, when the user is using a tablet, the adjustment unit can also provide a display method optimized for a large screen. For example, the adjustment unit provides a display method optimized for the tablet screen size. Furthermore, when the user is using a smartwatch, the adjustment unit can also provide a simple and highly visible display method. For example, the adjustment unit provides a display method optimized for the smartwatch screen size. In this way, the optimal display method can be provided by taking into consideration device information of the user.

[0057] The adjustment unit can make the display content multilingual in accordance with the user's language setting when displaying a video. The adjustment unit automatically sets the display content based on, for example, the language setting of the user's device. For example, the adjustment unit automatically sets the display content based on the language setting of the user's device. The adjustment unit can also provide a language switching function when the user uses multiple languages. For example, the adjustment unit provides a language switching function when the user uses multiple languages. Furthermore, the adjustment unit can also provide the display content in a specific language when the user selects that language. For example, the adjustment unit provides the display content in that language when the user selects a specific language. In this way, by making the display content multilingual in accordance with the user's language setting, it is possible to provide a display that is easy for the user to understand.

[0058] The adjustment unit can customize the display method according to the visual and auditory characteristics of the user when displaying a video. For example, the adjustment unit provides audio guidance when the user is visually impaired. For example, the adjustment unit provides audio guidance when the user is visually impaired. The adjustment unit can also provide subtitles when the user is hearing impaired. For example, the adjustment unit provides subtitles when the user is hearing impaired. Furthermore, the adjustment unit can customize the display method according to the visual and auditory characteristics of the user. For example, the adjustment unit adjusts display colors according to the color vision characteristics of the user. In this way, by customizing the display method according to the visual and auditory characteristics of the user, it is possible to provide a display that is easy for the user to understand.

[0059] The scanning unit can adjust the level of detail of the scan based on the importance of the instructions when scanning. For example, in the case of important instructions, the scanning unit performs a detailed scan. For example, the scanning unit scans each page of the important instructions at high resolution to obtain detailed data. The scanning unit can also perform a standard scan in the case of general instructions. For example, the scanning unit scans the main pages of the general instructions to obtain standard data. Furthermore, the scanning unit can also perform a concise scan in the case of simple instructions. For example, the scanning unit scans pages that summarize the main points of the simple instructions to obtain concise data. In this way, by adjusting the level of detail of the scan based on the importance of the instructions, scanning can be performed efficiently.

[0060] When scanning, the scanning unit can apply different scanning algorithms depending on the category of the instruction manual. For example, in the case of furniture instructions, the scanning unit applies a scanning algorithm dedicated to furniture. For example, the scanning unit scans the furniture instructions and analyzes the data using the dedicated algorithm. Furthermore, in the case of instructions for plastic models, the scanning unit can also apply a scanning algorithm dedicated to plastic models. For example, the scanning unit scans the plastic model instructions and analyzes the data using the dedicated algorithm. Furthermore, in the case of instructions for electronic devices, the scanning unit can also apply a scanning algorithm dedicated to electronic devices. For example, the scanning unit scans the electronic device instructions and analyzes the data using the dedicated algorithm. In this way, by applying different scanning algorithms depending on the category of the instructions, accurate scanning can be performed.

[0061] When scanning, the scanning unit can determine the scanning priority based on the submission date of the instructions. For example, the scanning unit prioritizes scanning the most recent instructions. For example, the scanning unit prioritizes scanning the most recently submitted instructions and provides them to the user. The scanning unit can also postpone the most recently submitted instructions. For example, the scanning unit postpones the most recently submitted instructions and prioritizes scanning the most recent instructions. Furthermore, the scanning unit can also adjust the scanning order based on the submission date. For example, the scanning unit adjusts the scanning order based on the submission date to perform scanning efficiently. In this way, by determining the scanning priority based on the submission date of the instructions, scanning can be performed efficiently.

[0062] The scanning unit can adjust the scanning order based on the relevance of the instructions during scanning. For example, the scanning unit prioritizes scanning highly relevant instructions. For example, the scanning unit prioritizes scanning instructions related to the user's current project or area of ​​interest. The scanning unit can also postpone scanning less relevant instructions. For example, the scanning unit postpones scanning instructions less relevant to the user's current project or area of ​​interest. Furthermore, the scanning unit can adjust the scanning order based on the relevance of the instructions. For example, the scanning unit prioritizes scanning highly relevant instructions and postpones scanning instructions less relevant. In this way, by adjusting the scanning order based on the relevance of the instructions, scanning can be performed efficiently.

[0063] When providing a video, the providing unit can select the optimal providing method by referring to the user's past viewing history. The providing unit, for example, suggests an optimal video based on the history of videos the user has viewed in the past. For example, the providing unit refers to the history of videos the user has viewed in the past and suggests similar videos. The providing unit can also analyze the user's past viewing history and optimize the providing method. For example, the providing unit adjusts parameters of the providing method based on the user's past viewing history. Furthermore, the providing unit can improve the providing method by reflecting user feedback. For example, the providing unit improves the providing method based on feedback provided by the user in the past. In this way, the optimal providing method can be provided by referring to the user's past viewing history.

[0064] The providing unit can customize the content to be provided according to the user's current task when providing videos. For example, the providing unit preferentially provides videos related to the task the user is currently performing. For example, the providing unit provides videos related to a project the user is currently performing. The providing unit can also customize the content to be provided based on the user's current task. For example, the providing unit provides videos related to the work the user is currently performing. Furthermore, the providing unit can also provide related videos by referring to the user's past task history. For example, the providing unit provides videos related to tasks the user has performed in the past. In this way, by customizing the content to be provided according to the user's current task, information can be provided efficiently.

[0065] The providing unit can improve the providing method by reflecting user feedback when providing a video. The providing unit improves the providing method, for example, based on feedback provided by the user. For example, the providing unit improves the providing method by referring to feedback provided by the user. The providing unit can also customize the providing interface by referring to past feedback from the user. For example, the providing unit improves the providing interface based on feedback provided by the user in the past. Furthermore, the providing unit can also optimize the providing procedure by reflecting user feedback. For example, the providing unit improves the providing procedure based on feedback provided by the user in the past. In this way, the optimal providing method can be provided by reflecting user feedback.

[0066] When providing a video, the providing unit can select the optimal providing method by taking into consideration device information of the user. For example, if the user is using a smartphone, the providing unit provides a video that matches the screen size. For example, the providing unit provides a video optimized for the smartphone screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a video optimized for a larger screen. For example, the providing unit provides a video optimized for the tablet screen size. Furthermore, if the user is using a smartwatch, the providing unit can also provide a concise video with high visibility. For example, the providing unit provides a video optimized for the smartwatch screen size. In this way, the optimal providing method can be provided by taking into consideration device information of the user.

[0067] When providing a video, the providing unit can make the provided content multilingual in accordance with the user's language setting. The providing unit, for example, automatically sets the language of the video based on the language setting of the user's device. For example, the providing unit automatically sets the language of the video based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. For example, the providing unit provides a language switching function when the user uses multiple languages. Furthermore, the providing unit can also provide the video in a specific language when the user selects that language. For example, the providing unit provides the video in that language when the user selects a specific language. In this way, by making the provided content multilingual in accordance with the user's language setting, it is possible to provide a video that is easy for the user to understand.

[0068] The providing unit can customize the method of providing a video according to the visual and auditory characteristics of the user. For example, if the user has a visual impairment, the providing unit provides an audio guide. For example, if the user has a visual impairment, the providing unit provides an audio guide. The providing unit can also provide subtitles if the user has a hearing impairment. For example, if the user has a hearing impairment, the providing unit provides subtitles. Furthermore, the providing unit can also customize the method of providing a video according to the visual and auditory characteristics of the user. For example, the providing unit adjusts display colors according to the color vision characteristics of the user. In this way, by customizing the method of providing a video according to the visual and auditory characteristics of the user, it is possible to provide a video that is easy for the user to understand.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit can prioritize and suggest input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also automatically set the optimal input format based on the format of instructions that the user has previously input. Furthermore, the reception unit can predict and suggest the input method that will be used during a specific time period based on the user's past input history. In this way, by analyzing the user's past input history, the optimal input method can be suggested, allowing instructions to be input efficiently.

[0071] The interpretation unit can adjust the use of technical terms in the interpretation according to the user's level of expertise. For example, if the user has technical expertise, the interpretation unit can provide an interpretation that uses a lot of technical terms. Alternatively, if the user is a beginner, the interpretation unit can avoid technical terms and use easy-to-understand expressions. Furthermore, the interpretation unit can adjust the level of detail and expression of the interpretation according to the user's level of expertise. In this way, by adjusting the use of technical terms in the interpretation according to the user's level of expertise, it is possible to provide an interpretation that is easy for the user to understand.

[0072] When generating a video, the generation unit can adjust the level of detail of the video based on the importance of the assembly steps. For example, for important assembly steps, a detailed video can be generated. For general assembly steps, a standard video can be generated. Furthermore, for simple assembly steps, a concise video can be generated. In this way, by adjusting the level of detail of the video based on the importance of the assembly steps, videos can be generated efficiently.

[0073] The adjustment unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, a display method that matches the screen size can be provided. Also, if the user is using a tablet, a display method optimized for a large screen can be provided. Furthermore, if the user is using a smartwatch, a simple and highly visible display method can be provided. In this way, the optimal display method can be provided by taking into account the user's device information.

[0074] The providing unit can select the optimal providing method by referring to the user's past viewing history. For example, the optimal video is suggested based on the history of videos the user has previously viewed. The providing unit can also analyze the user's past viewing history and optimize the providing method. Furthermore, the providing method can be improved by reflecting user feedback. In this way, the optimal providing method can be provided by referring to the user's past viewing history.

[0075] The processing flow of the first embodiment will be briefly explained below.

[0076] Step 1: The reception unit receives input of instructions. The input of instructions includes text input, voice input, image input, etc. For example, the text or image of the instructions can be scanned and input. Step 2: The interpretation unit uses the generation AI to interpret the contents of the instructions received by the reception unit. Interpretation is performed using natural language processing and image recognition technology. For example, the interpretation unit analyzes instructions in the instructions, such as "Attach part A to part B," and understands the specific assembly steps. Step 3: The generator generates an assembly video based on the content interpreted by the interpreter. Using a generative AI, the generator creates a video of each step according to the analyzed procedure. For example, the video shows the procedure for attaching part A to part B. Step 4: The adjustment unit adjusts the video generated by the generation unit to suit the user's viewpoint and assembly speed. For example, it adjusts the camera angle and zoom level to provide the video from a viewpoint that is easy for the user to view. It can also adjust the playback speed to play the video to suit the user's assembly speed.

[0077] (Example 2) An automatic video generation system according to an embodiment of the present invention interprets the contents of instructions, generates assembly videos, and adjusts them to suit the user's viewpoint and assembly speed. The automatic video generation system accepts input instructions, and a generation AI interprets the contents of the instructions and generates assembly videos. This video can be used as a reference for the user when assembling. For example, a user inputs the instructions included with a product into the automatic video generation system. For example, the automatic video generation system can input the text and images of the instructions by scanning them. This information is input into the generation AI. The automatic video generation system then uses the generation AI to interpret the contents of the input instructions. The generation AI analyzes the text and images in the instructions to understand the assembly steps. For example, the generation AI analyzes instructions such as "attach part A to part B" and understands the specific assembly steps. Next, the automatic video generation system generates an assembly video based on the assembly steps generated by the generation AI. The generation AI then creates an animation of each step according to the analyzed steps. For example, an animation shows the procedure for attaching part A to part B. The animation can be used as a reference for the user when assembling. As a result, the automatic video generation system can reduce the number of people who drop out because they don't know how to assemble, and improve assembly efficiency. Users can intuitively understand the assembly steps without having to read complex instructions. For example, when assembling furniture, users can proceed with the work while checking each step in the video. This reduces assembly errors and allows the work to be done more efficiently. In addition, because the generation AI automatically generates videos, the cost of video creation can be reduced. For example, automatically generating assembly videos for plastic models can reduce the effort required for video creation. In this way, the automatic video generation system can reduce the number of people who drop out because they don't know how to assemble, and improve assembly efficiency. In addition, because it reduces video creation costs, costs can be significantly reduced compared to when videos are created manually by official organizations or volunteers.

[0078] An automatic video generation system according to an embodiment includes a receiving unit, an interpretation unit, a generation unit, and an adjustment unit. The receiving unit receives input of instructions. The input of instructions may include, but is not limited to, text input, voice input, image input, and the like. The receiving unit may, for example, input the text or images of the instructions by scanning them. The interpretation unit uses a generation AI to interpret the content of the instructions received by the receiving unit. The interpretation may be performed using, for example, but not limited to, natural language processing or image recognition technology. For example, the interpretation unit may analyze an instruction in the instructions, such as "attach part A to part B," to understand the specific assembly steps. The generation unit generates an assembly video based on the content interpreted by the interpretation unit. For example, the generation unit uses, for example, a generation AI to represent each step in a video according to the analyzed steps. For example, the generation unit may show a video illustrating the steps of attaching part A to part B. The adjustment unit adjusts the video generated by the generation unit to suit the user's viewpoint and assembly speed. For example, the adjustment unit may adjust the camera angle or zoom level to provide the video from a viewpoint that is easy for the user to view. The adjustment unit can also adjust the playback speed to play the video in accordance with the user's assembly speed. This allows the automatic video generation system according to the embodiment to interpret the contents of the instruction manual, generate an assembly video, and adjust it to match the user's viewpoint and assembly speed, thereby improving assembly efficiency.

[0079] The automatic video generation system includes a scanning unit that scans the text or images of the instructions. The scanning unit scans the text or images of the instructions. For example, OCR technology or image analysis technology is used for the scanning, but is not limited to these examples. For example, the scanning unit reads the text of the instructions with a scanner and converts it into text data using OCR technology. The scanning unit can also read the image of the instructions with a scanner and convert it into image data using image analysis technology. For example, the scanning unit scans the image of the instructions at high resolution and obtains detailed data using image analysis technology. This makes it possible to efficiently input instructions by scanning the text or images of the instructions.

[0080] The automatic video generation system includes a providing unit that provides the generated video to the user. The providing unit provides the generated video to the user. Providing includes, but is not limited to, a method of distributing the video through a web application or a mobile application, for example. For example, the providing unit provides the generated video to the user through a web application. The providing unit can also provide the generated video to the user through a mobile application. For example, the providing unit streams the generated video so that the user can watch it in real time. In this way, providing the generated video to the user allows the user to intuitively understand the assembly procedure.

[0081] The interpretation unit can analyze the contents of the instruction manual using natural language processing or image recognition technology. The interpretation unit analyzes the text of the instruction manual using, for example, natural language processing technology. For example, the interpretation unit can segment the text using morphological analysis, perform grammatical analysis, and perform semantic analysis. The interpretation unit can also analyze images of the instruction manual using image recognition technology. For example, the interpretation unit can identify parts in the image using object detection technology and determine the type of part using image classification technology. Furthermore, the interpretation unit can identify the position of the part in the image using segmentation technology. This allows the content of the instruction manual to be accurately analyzed using natural language processing and image recognition technology.

[0082] The generation unit can represent each step in a video according to the analyzed procedure. For example, the generation unit uses a generative AI to represent each step in a video according to the analyzed procedure. For example, the generation unit uses 3D animation to show a video of the procedure for attaching part A to part B. The generation unit can also show assembly procedures using live-action video. For example, the generation unit films actual assembly work and edits the footage to generate a video. Furthermore, the generation unit can show assembly procedures using a step-by-step guide. For example, the generation unit plays a video while displaying detailed instructions for each step. In this way, by generating a video according to the analyzed procedure, the user can visually understand the assembly procedure.

[0083] The adjustment unit can adjust the video to match the user's viewpoint and assembly speed. The adjustment unit, for example, adjusts the camera angle and zoom level to provide the video from a viewpoint that is easy for the user to view. For example, the adjustment unit changes the camera angle to match the user's viewpoint and zooms in on important parts. The adjustment unit can also adjust the playback speed to play the video to match the user's assembly speed. For example, the adjustment unit slows down the playback speed when the user assembles slowly and speeds up the playback speed when the user assembles quickly. The adjustment unit can also adjust the way the viewpoint moves to make the video easier for the user to watch. For example, the adjustment unit smoothly moves the viewpoint to allow the user to intuitively understand the assembly steps. In this way, the video can be adjusted to match the user's viewpoint and assembly speed, allowing the user to assemble efficiently.

[0084] The reception unit can estimate the user's emotions and adjust the timing of inputting instructions based on the estimated user emotions. The reception unit, for example, captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on changes in facial expression and determines whether the user is stressed or relaxed. The reception unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the reception unit analyzes the tone and speed of the voice and calculates the emotion score. Furthermore, the reception unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit calculates the emotion score based on heart rate fluctuations. This allows the user to reduce stress and input instructions efficiently by adjusting the timing of inputting instructions according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0085] The reception unit can analyze the user's past instruction input history and select the optimal input method. For example, the reception unit preferentially suggests an input method (voice, text, etc.) that the user has frequently used in the past. For example, if the user has used voice input frequently in the past, the reception unit preferentially suggests voice input. The reception unit can also automatically set the optimal input format based on the format of instructions the user has previously input. For example, if the user has previously input many instructions in PDF format, the reception unit preferentially suggests PDF input. Furthermore, the reception unit can predict and suggest an input method to be used during a specific time period based on the user's past input history. For example, if the user has used voice input frequently at night, the reception unit preferentially suggests voice input at night. In this way, by analyzing the user's past input history, the optimal input method can be suggested, allowing instructions to be input efficiently.

[0086] The reception unit can filter instructions based on the user's current project or area of ​​interest when inputting instructions. For example, the reception unit displays only instructions related to the user's current project. For example, if the user is working on a furniture assembly project, the reception unit displays only instructions related to furniture. The reception unit can also preferentially display highly relevant instructions based on the user's area of ​​interest. For example, if the user is interested in plastic models, the reception unit preferentially displays instructions related to plastic models. Furthermore, the reception unit can suggest related instructions by referring to the user's past project history. For example, if the user has previously worked on a project assembling electronic devices, the reception unit suggests instructions related to electronic devices. In this way, by filtering based on the user's current project or area of ​​interest, highly relevant instructions can be preferentially input.

[0087] When inputting instructions, the reception unit can select the optimal input means depending on the user's input method. For example, if the user selects voice input, the reception unit inputs the instructions using voice recognition technology. For example, the reception unit converts the user's voice into text data using voice recognition software. Furthermore, if the user selects text input, the reception unit can also input the instructions using text analysis technology. For example, the reception unit analyzes the text input by the user using text analysis software to understand the content of the instructions. Furthermore, if the user selects image input, the reception unit can also input the instructions using image recognition technology. For example, the reception unit analyzes the image uploaded by the user using image recognition software to understand the content of the instructions. This allows instructions to be input efficiently by selecting the optimal input means depending on the user's input method.

[0088] The reception unit can estimate the user's emotions and determine the priority of the instructions to be input based on the estimated user emotions. For example, the reception unit captures the user's facial expression with a camera and estimates the emotions using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on changes in facial expression and determines whether the user is stressed or relaxed. The reception unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the reception unit analyzes the tone and speed of the voice and calculates the emotion score. Furthermore, the reception unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the emotions using an emotion estimation algorithm. For example, the reception unit calculates the emotion score based on heart rate fluctuations. This allows the instructions to be input efficiently by determining the priority of the instructions according to the user's emotions. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0089] When inputting instructions, the reception unit can prioritize inputting highly relevant instructions taking into account the user's geographical location information. The reception unit, for example, prioritizes inputting instructions related to the user's current location. For example, the reception unit displays instructions related to the area where the user is currently located. The reception unit can also prioritize inputting region-specific instructions based on the user's geographical location information. For example, the reception unit prioritizes displaying instructions for products sold in a specific area. Furthermore, the reception unit can also prioritize inputting instructions related to nearby stores or facilities by referring to the user's location information. For example, the reception unit displays instructions for products the user purchased at a nearby store. In this way, highly relevant instructions can be prioritized input by taking into account the user's geographical location information.

[0090] When inputting the description, the reception unit can analyze the user's social media activity and input the relevant description. The reception unit, for example, inputs a description related to a place where the user has checked in on social media. For example, the reception unit displays a description related to a store or facility where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and input the relevant description. For example, the reception unit displays the relevant description based on the content posted by the user on social media. Furthermore, the reception unit can input the relevant description by referring to the activity of the user's friends on social media. For example, the reception unit displays the relevant description based on information shared by the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to preferentially input highly relevant descriptions.

[0091] The reception unit can customize the input method by reflecting the user's past feedback when inputting instructions. The reception unit, for example, suggests the optimal input method based on feedback provided by the user in the past. For example, if the user has previously preferred voice input, the reception unit preferentially suggests voice input. The reception unit can also customize the input interface by referring to the user's past feedback. For example, if the user has previously provided feedback to improve the text input interface, the reception unit improves the text input interface. Furthermore, the reception unit can also improve the input procedure by reflecting the user's feedback. For example, if the user has previously provided feedback that the input procedure is complicated, the reception unit simplifies the input procedure. In this way, the optimal input method can be suggested by reflecting the user's past feedback, and instructions can be input efficiently.

[0092] The interpretation unit can estimate the user's emotions and adjust the way the interpretation is expressed based on the estimated user emotions. For example, the interpretation unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the interpretation unit calculates an emotion score based on changes in facial expressions and determines whether the user is stressed or relaxed. The interpretation unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the interpretation unit analyzes the tone and speed of the voice and calculates an emotion score. Furthermore, the interpretation unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the emotions using an emotion estimation algorithm. For example, the interpretation unit calculates an emotion score based on heart rate fluctuations. This allows the interpretation to be adjusted according to the user's emotions, thereby providing an interpretation that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0093] During interpretation, the interpretation unit can adjust the level of detail of the interpretation based on the importance of the instructions. For example, in the case of important instructions, the interpretation unit provides a detailed interpretation. For example, the interpretation unit interprets each step of the important instructions in detail and provides it to the user. The interpretation unit can also provide a standard interpretation in the case of general instructions. For example, the interpretation unit interprets the main steps of the general instructions and provides it to the user. Furthermore, the interpretation unit can also provide a concise interpretation in the case of simple instructions. For example, the interpretation unit provides an interpretation that covers the main points of the simple instructions. In this way, by adjusting the level of detail of the interpretation based on the importance of the instructions, interpretation can be performed efficiently.

[0094] During interpretation, the interpretation unit can apply different interpretation algorithms depending on the category of the instruction manual. For example, in the case of furniture instructions, the interpretation unit applies an interpretation algorithm dedicated to assembling furniture. For example, the interpretation unit analyzes the furniture assembly procedures and interprets them using a dedicated algorithm. In addition, in the case of plastic model instructions, the interpretation unit can also apply an interpretation algorithm dedicated to assembling plastic models. For example, the interpretation unit analyzes the plastic model assembly procedures and interprets them using a dedicated algorithm. In addition, in the case of electronic device instructions, the interpretation unit can also apply an interpretation algorithm dedicated to assembling electronic devices. For example, the interpretation unit analyzes the electronic device assembly procedures and interprets them using a dedicated algorithm. In this way, accurate interpretation can be performed by applying different interpretation algorithms depending on the category of the instruction manual.

[0095] During interpretation, the interpretation unit can improve the accuracy of the interpretation by referring to the user's past interpretation results. The interpretation unit improves the accuracy of the interpretation, for example, based on the results of instructions that the user has interpreted in the past. For example, the interpretation unit refers to the history of instructions that the user has interpreted in the past and uses this as a reference when interpreting similar instructions. The interpretation unit can also analyze the user's past interpretation history and optimize the interpretation algorithm. For example, the interpretation unit adjusts the parameters of the interpretation algorithm based on the user's past interpretation history. Furthermore, the interpretation unit can also improve the accuracy of the interpretation by reflecting user feedback. For example, the interpretation unit improves the interpretation algorithm based on feedback provided by the user in the past. In this way, the accuracy of the interpretation can be improved by referring to the user's past interpretation results.

[0096] The interpretation unit can estimate the user's emotion and adjust the length of the interpretation based on the estimated user emotion. For example, the interpretation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the interpretation unit calculates an emotion score based on changes in facial expression and determines whether the user is stressed or relaxed. The interpretation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the interpretation unit analyzes the tone and speed of the voice and calculates an emotion score. Furthermore, the interpretation unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the emotion using an emotion estimation algorithm. For example, the interpretation unit calculates an emotion score based on heart rate fluctuations. This allows the length of the interpretation to be adjusted according to the user's emotion, thereby providing an interpretation that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0097] During interpretation, the interpretation unit can determine the priority of interpretation based on the submission date of the instructions. The interpretation unit, for example, gives priority to interpreting the most recent instructions. For example, the interpretation unit gives priority to analyzing the most recently submitted instructions and provides them to the user. The interpretation unit can also postpone the most recently submitted instructions. For example, the interpretation unit postpones the most recently submitted instructions and gives priority to interpreting the most recent instructions. Furthermore, the interpretation unit can also adjust the order of interpretation based on the submission date. For example, the interpretation unit adjusts the order of interpretation based on the submission date to perform interpretation efficiently. In this way, by determining the priority of interpretation based on the submission date of the instructions, interpretation can be performed efficiently.

[0098] During interpretation, the interpretation unit can adjust the order of interpretation based on the relevance of the instructions. For example, the interpretation unit gives priority to interpreting highly relevant instructions. For example, the interpretation unit gives priority to interpreting instructions related to the user's current project or area of ​​interest. The interpretation unit can also postpone less relevant instructions. For example, the interpretation unit postpones instructions less relevant to the user's current project or area of ​​interest. Furthermore, the interpretation unit can also adjust the order of interpretation based on the relevance of the instructions. For example, the interpretation unit gives priority to interpreting highly relevant instructions and postpones less relevant instructions. In this way, by adjusting the order of interpretation based on the relevance of the instructions, interpretation can be performed efficiently.

[0099] During interpretation, the interpretation unit can adjust the use of technical terms in the interpretation according to the user's level of expertise. For example, if the user has technical expertise, the interpretation unit uses a lot of technical terms. For example, if the user has technical expertise, the interpretation unit provides a detailed interpretation using a lot of technical terms. Furthermore, if the user is a beginner, the interpretation unit can avoid technical terms and use easy-to-understand expressions. For example, if the user is a beginner, the interpretation unit can provide a concise interpretation avoiding technical terms. Furthermore, the interpretation unit can adjust the manner in which the interpretation is expressed according to the user's level of expertise. For example, the interpretation unit adjusts the level of detail and the manner in which the interpretation is expressed according to the user's level of expertise. In this way, by adjusting the use of technical terms in the interpretation according to the user's level of expertise, it is possible to provide an interpretation that is easy for the user to understand.

[0100] The generation unit can estimate the user's emotions and adjust the expression method of the video generation based on the estimated user emotions. For example, the generation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on changes in facial expression and determines whether the user is stressed or relaxed. The generation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the generation unit analyzes the tone and speed of the voice and calculates the emotion score. Furthermore, the generation unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the emotion using an emotion estimation algorithm. For example, the generation unit calculates the emotion score based on heart rate fluctuations. This allows the user to adjust the expression method of the video generation according to the user's emotions, thereby providing a video that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0101] When generating a video, the generation unit can adjust the level of detail of the video based on the importance of the assembly steps. For example, in the case of important assembly steps, the generation unit generates a detailed video. For example, the generation unit shows each step of the important assembly steps in a detailed video. The generation unit can also generate a standard video for general assembly steps. For example, the generation unit shows the main steps of the general assembly steps in a video. Furthermore, the generation unit can generate a concise video for simple assembly steps. For example, the generation unit generates a video that highlights the main points of simple assembly steps. In this way, by adjusting the level of detail of the video based on the importance of the assembly steps, videos can be generated efficiently.

[0102] When generating a video, the generation unit can apply different video generation algorithms depending on the category of the assembly steps. For example, in the case of furniture assembly steps, the generation unit applies a video generation algorithm dedicated to furniture assembly. For example, the generation unit analyzes the furniture assembly steps and generates a video using a dedicated algorithm. Furthermore, in the case of plastic model assembly steps, the generation unit can also apply a video generation algorithm dedicated to plastic model assembly. For example, the generation unit analyzes the plastic model assembly steps and generates a video using a dedicated algorithm. Furthermore, in the case of electronic device assembly steps, the generation unit can also apply a video generation algorithm dedicated to electronic device assembly. For example, the generation unit analyzes the electronic device assembly steps and generates a video using a dedicated algorithm. In this way, by applying different video generation algorithms depending on the category of the assembly steps, accurate videos can be generated.

[0103] When generating a video, the generation unit can improve the accuracy of the video by referring to the user's past video generation results. The generation unit improves the accuracy of the video, for example, based on the results of videos generated by the user in the past. For example, the generation unit refers to the history of videos generated by the user in the past and uses this as a reference when creating a video using similar procedures. The generation unit can also analyze the user's past video generation history and optimize the video generation algorithm. For example, the generation unit adjusts parameters of the video generation algorithm based on the user's past video generation history. Furthermore, the generation unit can reflect user feedback to improve the accuracy of the video. For example, the generation unit improves the video generation algorithm based on feedback provided by the user in the past. In this way, the accuracy of the video can be improved by referring to the user's past video generation results.

[0104] The generation unit can estimate the user's emotions and adjust the length of the video based on the estimated user emotions. For example, the generation unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on changes in facial expressions and determines whether the user is stressed or relaxed. The generation unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the generation unit analyzes the tone and speed of the voice and calculates an emotion score. Furthermore, the generation unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the emotions using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on heart rate fluctuations. This allows the video to be easily understood by adjusting the length of the video according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0105] When generating videos, the generation unit can determine the priority of the videos based on the submission dates of the assembly instructions. The generation unit, for example, prioritizes the creation of the latest assembly instructions. For example, the generation unit prioritizes the creation of the latest submitted assembly instructions and provides them to the user. The generation unit can also postpone the creation of the oldest submitted assembly instructions. For example, the generation unit postpones the creation of the oldest submitted assembly instructions and prioritizes the creation of the latest assembly instructions. Furthermore, the generation unit can also adjust the order of video generation based on the submission dates. For example, the generation unit adjusts the order of video generation based on the submission dates and efficiently generates videos. In this way, videos can be efficiently generated by determining the priority of the videos based on the submission dates of the assembly instructions.

[0106] When generating a video, the generation unit can adjust the order of the videos based on the relevance of the assembly steps. For example, the generation unit prioritizes animating highly relevant assembly steps. For example, the generation unit prioritizes animating assembly steps related to the user's current project or field of interest. The generation unit can also postpone less relevant assembly steps. For example, the generation unit postpones assembly steps less relevant to the user's current project or field of interest. Furthermore, the generation unit can also adjust the order of video generation based on the relevance of the assembly steps. For example, the generation unit prioritizes animating highly relevant assembly steps and postpones less relevant assembly steps. In this way, videos can be generated efficiently by adjusting the order of the videos based on the relevance of the assembly steps.

[0107] When generating a video, the generation unit can adjust the use of technical terms in the video according to the user's level of expertise. For example, if the user has technical expertise, the generation unit generates a video that uses a lot of technical terms. For example, if the user has technical expertise, the generation unit generates a detailed video that uses a lot of technical terms. Furthermore, if the user is a beginner, the generation unit can generate a video that avoids technical terms and uses easy-to-understand expressions. For example, if the user is a beginner, the generation unit generates a concise video that avoids technical terms. Furthermore, the generation unit can adjust the expression method of the video according to the user's level of expertise. For example, the generation unit adjusts the level of detail and expression method of the video according to the user's level of expertise. In this way, by adjusting the use of technical terms in the video according to the user's level of expertise, it is possible to provide a video that is easy for the user to understand.

[0108] The adjustment unit can estimate the user's emotion and adjust the video display method based on the estimated user emotion. For example, the adjustment unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the adjustment unit calculates an emotion score based on changes in facial expression and determines whether the user is stressed or relaxed. The adjustment unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the adjustment unit analyzes the tone and speed of the voice and calculates an emotion score. Furthermore, the adjustment unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the adjustment unit calculates an emotion score based on heart rate fluctuations. This allows the video display method to be adjusted according to the user's emotion, thereby providing a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0109] The adjustment unit can select the optimal display method by referring to the user's past operation history when displaying a video. The adjustment unit, for example, proposes the optimal display method based on a display method previously selected by the user. For example, the adjustment unit refers to a display method previously selected by the user and proposes a similar display method. The adjustment unit can also analyze the user's past operation history and optimize the display method. For example, the adjustment unit adjusts parameters of the display method based on the user's past operation history. Furthermore, the adjustment unit can improve the display method by reflecting user feedback. For example, the adjustment unit improves the display method based on feedback provided by the user in the past. In this way, the optimal display method can be provided by referring to the user's past operation history.

[0110] The adjustment unit can customize the display content according to the user's current task when displaying the video. For example, the adjustment unit prioritizes displaying information related to the task the user is currently performing. For example, the adjustment unit displays information related to a project the user is currently performing. The adjustment unit can also customize the display content based on the user's current task. For example, the adjustment unit displays information related to the work the user is currently performing. Furthermore, the adjustment unit can display related information by referring to the user's past task history. For example, the adjustment unit displays information related to tasks the user has performed in the past. In this way, by customizing the display content according to the user's current task, information can be provided efficiently.

[0111] The adjustment unit can improve the display method by reflecting user feedback when displaying a video. The adjustment unit improves the display method, for example, based on feedback provided by the user. For example, the adjustment unit improves the display method by referring to feedback provided by the user. The adjustment unit can also customize the display interface by referring to past user feedback. For example, the adjustment unit improves the display interface based on feedback provided by the user in the past. Furthermore, the adjustment unit can also optimize the display procedure by reflecting user feedback. For example, the adjustment unit improves the display procedure based on feedback provided by the user in the past. In this way, an optimal display method can be provided by reflecting user feedback.

[0112] The adjustment unit can estimate the user's emotions and adjust the video operation procedures based on the estimated user emotions. For example, the adjustment unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the adjustment unit calculates an emotion score based on changes in facial expressions and determines whether the user is stressed or relaxed. The adjustment unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the adjustment unit analyzes the tone and speed of the voice and calculates an emotion score. Furthermore, the adjustment unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the emotions using an emotion estimation algorithm. For example, the adjustment unit calculates an emotion score based on heart rate fluctuations. This allows the video operation procedures to be adjusted according to the user's emotions, thereby providing operation procedures that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0113] The adjustment unit can select the optimal display method when displaying a video by taking into consideration device information of the user. For example, when the user is using a smartphone, the adjustment unit provides a display method that matches the screen size. For example, the adjustment unit provides a display method optimized for the smartphone screen size. Furthermore, when the user is using a tablet, the adjustment unit can also provide a display method optimized for a large screen. For example, the adjustment unit provides a display method optimized for the tablet screen size. Furthermore, when the user is using a smartwatch, the adjustment unit can also provide a simple and highly visible display method. For example, the adjustment unit provides a display method optimized for the smartwatch screen size. In this way, the optimal display method can be provided by taking into consideration device information of the user.

[0114] The adjustment unit can make the display content multilingual in accordance with the user's language setting when displaying a video. The adjustment unit automatically sets the display content based on, for example, the language setting of the user's device. For example, the adjustment unit automatically sets the display content based on the language setting of the user's device. The adjustment unit can also provide a language switching function when the user uses multiple languages. For example, the adjustment unit provides a language switching function when the user uses multiple languages. Furthermore, the adjustment unit can also provide the display content in a specific language when the user selects that language. For example, the adjustment unit provides the display content in that language when the user selects a specific language. In this way, by making the display content multilingual in accordance with the user's language setting, it is possible to provide a display that is easy for the user to understand.

[0115] The adjustment unit can customize the display method according to the visual and auditory characteristics of the user when displaying a video. For example, the adjustment unit provides audio guidance when the user is visually impaired. For example, the adjustment unit provides audio guidance when the user is visually impaired. The adjustment unit can also provide subtitles when the user is hearing impaired. For example, the adjustment unit provides subtitles when the user is hearing impaired. Furthermore, the adjustment unit can customize the display method according to the visual and auditory characteristics of the user. For example, the adjustment unit adjusts display colors according to the color vision characteristics of the user. In this way, by customizing the display method according to the visual and auditory characteristics of the user, it is possible to provide a display that is easy for the user to understand.

[0116] The scanning unit can estimate the user's emotions and adjust the timing of scanning based on the estimated user emotions. For example, the scanning unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the scanning unit calculates an emotion score based on changes in facial expressions and determines whether the user is stressed or relaxed. The scanning unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the scanning unit analyzes the tone and speed of the voice and calculates an emotion score. Furthermore, the scanning unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the emotions using an emotion estimation algorithm. For example, the scanning unit calculates an emotion score based on heart rate fluctuations. This allows for efficient scanning by adjusting the timing of scanning according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0117] The scanning unit can adjust the level of detail of the scan based on the importance of the instructions when scanning. For example, in the case of important instructions, the scanning unit performs a detailed scan. For example, the scanning unit scans each page of the important instructions at high resolution to obtain detailed data. The scanning unit can also perform a standard scan in the case of general instructions. For example, the scanning unit scans the main pages of the general instructions to obtain standard data. Furthermore, the scanning unit can also perform a concise scan in the case of simple instructions. For example, the scanning unit scans pages that summarize the main points of the simple instructions to obtain concise data. In this way, by adjusting the level of detail of the scan based on the importance of the instructions, scanning can be performed efficiently.

[0118] When scanning, the scanning unit can apply different scanning algorithms depending on the category of the instruction manual. For example, in the case of furniture instructions, the scanning unit applies a scanning algorithm dedicated to furniture. For example, the scanning unit scans the furniture instructions and analyzes the data using the dedicated algorithm. Furthermore, in the case of instructions for plastic models, the scanning unit can also apply a scanning algorithm dedicated to plastic models. For example, the scanning unit scans the plastic model instructions and analyzes the data using the dedicated algorithm. Furthermore, in the case of instructions for electronic devices, the scanning unit can also apply a scanning algorithm dedicated to electronic devices. For example, the scanning unit scans the electronic device instructions and analyzes the data using the dedicated algorithm. In this way, by applying different scanning algorithms depending on the category of the instructions, accurate scanning can be performed.

[0119] The scanning unit can estimate the user's emotions and determine scanning priorities based on the estimated user emotions. For example, the scanning unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the scanning unit calculates an emotion score based on changes in facial expressions and determines whether the user is stressed or relaxed. The scanning unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the scanning unit analyzes the tone and speed of the voice and calculates an emotion score. Furthermore, the scanning unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the emotions using an emotion estimation algorithm. For example, the scanning unit calculates an emotion score based on heart rate fluctuations. This allows for efficient scanning by determining scanning priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generative AI.

[0120] When scanning, the scanning unit can determine the scanning priority based on the submission date of the instructions. For example, the scanning unit prioritizes scanning the most recent instructions. For example, the scanning unit prioritizes scanning the most recently submitted instructions and provides them to the user. The scanning unit can also postpone the most recently submitted instructions. For example, the scanning unit postpones the most recently submitted instructions and prioritizes scanning the most recent instructions. Furthermore, the scanning unit can also adjust the scanning order based on the submission date. For example, the scanning unit adjusts the scanning order based on the submission date to perform scanning efficiently. In this way, by determining the scanning priority based on the submission date of the instructions, scanning can be performed efficiently.

[0121] The scanning unit can adjust the scanning order based on the relevance of the instructions during scanning. For example, the scanning unit prioritizes scanning highly relevant instructions. For example, the scanning unit prioritizes scanning instructions related to the user's current project or area of ​​interest. The scanning unit can also postpone scanning less relevant instructions. For example, the scanning unit postpones scanning instructions less relevant to the user's current project or area of ​​interest. Furthermore, the scanning unit can adjust the scanning order based on the relevance of the instructions. For example, the scanning unit prioritizes scanning highly relevant instructions and postpones scanning instructions less relevant. In this way, by adjusting the scanning order based on the relevance of the instructions, scanning can be performed efficiently.

[0122] The providing unit can estimate the user's emotions and adjust the video presentation method based on the estimated user emotions. For example, the providing unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on changes in facial expressions and determines whether the user is stressed or relaxed. The providing unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the providing unit analyzes the tone and speed of the voice and calculates an emotion score. Furthermore, the providing unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the emotions using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on heart rate fluctuations. This allows the video presentation method to be adjusted according to the user's emotions, thereby providing a video that is easy for the user to understand. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0123] When providing a video, the providing unit can select the optimal providing method by referring to the user's past viewing history. The providing unit, for example, suggests an optimal video based on the history of videos the user has viewed in the past. For example, the providing unit refers to the history of videos the user has viewed in the past and suggests similar videos. The providing unit can also analyze the user's past viewing history and optimize the providing method. For example, the providing unit adjusts parameters of the providing method based on the user's past viewing history. Furthermore, the providing unit can improve the providing method by reflecting user feedback. For example, the providing unit improves the providing method based on feedback provided by the user in the past. In this way, the optimal providing method can be provided by referring to the user's past viewing history.

[0124] The providing unit can customize the content to be provided according to the user's current task when providing videos. For example, the providing unit preferentially provides videos related to the task the user is currently performing. For example, the providing unit provides videos related to a project the user is currently performing. The providing unit can also customize the content to be provided based on the user's current task. For example, the providing unit provides videos related to the work the user is currently performing. Furthermore, the providing unit can also provide related videos by referring to the user's past task history. For example, the providing unit provides videos related to tasks the user has performed in the past. In this way, by customizing the content to be provided according to the user's current task, information can be provided efficiently.

[0125] The providing unit can improve the providing method by reflecting user feedback when providing a video. The providing unit improves the providing method, for example, based on feedback provided by the user. For example, the providing unit improves the providing method by referring to feedback provided by the user. The providing unit can also customize the providing interface by referring to past feedback from the user. For example, the providing unit improves the providing interface based on feedback provided by the user in the past. Furthermore, the providing unit can also optimize the providing procedure by reflecting user feedback. For example, the providing unit improves the providing procedure based on feedback provided by the user in the past. In this way, the optimal providing method can be provided by reflecting user feedback.

[0126] The providing unit can estimate the user's emotions and adjust the order in which videos are provided based on the estimated user emotions. For example, the providing unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on changes in facial expressions and determines whether the user is stressed or relaxed. The providing unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the providing unit analyzes the tone and speed of the voice and calculates an emotion score. Furthermore, the providing unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the emotions using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on heart rate fluctuations. This allows for efficient video provision by adjusting the order in which videos are provided based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0127] When providing a video, the providing unit can select the optimal providing method by taking into consideration device information of the user. For example, if the user is using a smartphone, the providing unit provides a video that matches the screen size. For example, the providing unit provides a video optimized for the smartphone screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a video optimized for a larger screen. For example, the providing unit provides a video optimized for the tablet screen size. Furthermore, if the user is using a smartwatch, the providing unit can also provide a concise video with high visibility. For example, the providing unit provides a video optimized for the smartwatch screen size. In this way, the optimal providing method can be provided by taking into consideration device information of the user.

[0128] When providing a video, the providing unit can make the provided content multilingual in accordance with the user's language setting. The providing unit, for example, automatically sets the language of the video based on the language setting of the user's device. For example, the providing unit automatically sets the language of the video based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. For example, the providing unit provides a language switching function when the user uses multiple languages. Furthermore, the providing unit can also provide the video in a specific language when the user selects that language. For example, the providing unit provides the video in that language when the user selects a specific language. In this way, by making the provided content multilingual in accordance with the user's language setting, it is possible to provide a video that is easy for the user to understand.

[0129] The providing unit can customize the method of providing a video according to the visual and auditory characteristics of the user. For example, if the user has a visual impairment, the providing unit provides an audio guide. For example, if the user has a visual impairment, the providing unit provides an audio guide. The providing unit can also provide subtitles if the user has a hearing impairment. For example, if the user has a hearing impairment, the providing unit provides subtitles. Furthermore, the providing unit can also customize the method of providing a video according to the visual and auditory characteristics of the user. For example, the providing unit adjusts display colors according to the color vision characteristics of the user. In this way, by customizing the method of providing a video according to the visual and auditory characteristics of the user, it is possible to provide a video that is easy for the user to understand. === Hard Collateral 1-1 === Each of the multiple elements, including the above-described reception unit, interpretation unit, generation unit, adjustment unit, scanning unit, and providing unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can receive input of instructions using the reception device 38 of the smart device 14 or the communication I / F 26 of the data processing device 12. For example, the interpretation unit can interpret the content of the instructions using a generation AI by the specific processing unit 290 of the data processing device 12. For example, the generation unit can generate an assembly video using a generation AI by the specific processing unit 290 of the data processing device 12. For example, the adjustment unit can adjust the video generated by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12 to match the user's viewpoint and assembly speed. For example, the scanning unit can scan text and images of the instructions using the camera 42 of the smart device 14 or the communication I / F 26 of the data processing device 12. For example, the providing unit can provide the generated video to the user using the output device 40 of the smart device 14 or the communication I / F 26 of the data processing device 12. For example, the scanning unit can estimate the user's emotions using the camera 42 of the smart device 14 or the communication I / F 26 of the data processing device 12, and adjust the timing of scanning. For example, the providing unit can estimate the user's emotions using the camera 42 of the smart device 14 or the communication I / F 26 of the data processing device 12, and adjust the method of providing the video. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described reception unit, interpretation unit, generation unit, adjustment unit, scanning unit, and providing unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can receive input of instructions using the microphone 238 of the smart glasses 214 or the communication I / F 26 of the data processing device 12. For example, the interpretation unit can interpret the content of the instructions using a generation AI by the specific processing unit 290 of the data processing device 12. For example, the generation unit can generate an assembly video using a generation AI by the specific processing unit 290 of the data processing device 12. For example, the adjustment unit can adjust the video generated by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 to match the user's viewpoint and assembly speed. For example, the scanning unit can scan text and images of the instructions using the camera 42 of the smart glasses 214 or the communication I / F 26 of the data processing device 12. For example, the providing unit can provide the user with a video generated using the speaker 240 of the smart glasses 214 or the communication I / F 26 of the data processing device 12. For example, the scanning unit can estimate the user's emotions using the camera 42 of the smart glasses 214 or the communication I / F 26 of the data processing device 12, and adjust the timing of scanning. For example, the providing unit can estimate the user's emotions using the camera 42 of the smart glasses 214 or the communication I / F 26 of the data processing device 12, and adjust the method of providing the video. === Hard Collateral 1-3 === Each of the multiple elements including the above-described reception unit, interpretation unit, generation unit, adjustment unit, scanning unit, and providing unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit can receive input of instructions using the microphone 238 of the headset-type terminal 314 or the communication I / F 26 of the data processing device 12. For example, the interpretation unit can interpret the content of the instructions using a generation AI by the specific processing unit 290 of the data processing device 12. For example, the generation unit can generate an assembly video using a generation AI by the specific processing unit 290 of the data processing device 12. For example, the adjustment unit can adjust the video generated by the control unit 46A of the headset-type terminal 314 or the specific processing unit 290 of the data processing device 12 to match the user's viewpoint and assembly speed. For example, the scanning unit can scan text and images of the instructions using the camera 42 of the headset-type terminal 314 or the communication I / F 26 of the data processing device 12. For example, the providing unit can provide the user with a video generated using the display 343 of the headset type terminal 314 or the communication I / F 26 of the data processing device 12. For example, the scanning unit can estimate the user's emotions using the camera 42 of the headset type terminal 314 or the communication I / F 26 of the data processing device 12, and adjust the timing of scanning. For example, the providing unit can estimate the user's emotions using the camera 42 of the headset type terminal 314 or the communication I / F 26 of the data processing device 12, and adjust the method of providing the video. === Hard Collateral 1-4 === Each of the multiple elements, including the above-described reception unit, interpretation unit, generation unit, adjustment unit, scanning unit, and providing unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can receive input of instructions using the microphone 238 of the robot 414 or the communication I / F 26 of the data processing device 12. For example, the interpretation unit can interpret the content of the instructions using a generation AI by the specific processing unit 290 of the data processing device 12. For example, the generation unit can generate an assembly video using a generation AI by the specific processing unit 290 of the data processing device 12. For example, the adjustment unit can adjust the video generated by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12 to match the user's viewpoint and assembly speed. For example, the scanning unit can scan text and images of the instructions using the camera 42 of the robot 414 or the communication I / F 26 of the data processing device 12. For example, the providing unit can provide the generated video to the user using the speaker 240 of the robot 414 or the communication I / F 26 of the data processing device 12. For example, the scanning unit can estimate the user's emotions using the camera 42 of the robot 414 or the communication I / F 26 of the data processing device 12, and adjust the timing of the scan. For example, the providing unit can estimate the user's emotions using the camera 42 of the robot 414 or the communication I / F 26 of the data processing device 12, and adjust the method of providing the video.

[0130] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0131] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit can prioritize and suggest input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also automatically set the optimal input format based on the format of instructions that the user has previously input. Furthermore, the reception unit can predict and suggest the input method that will be used during a specific time period based on the user's past input history. In this way, by analyzing the user's past input history, the optimal input method can be suggested, allowing instructions to be input efficiently.

[0132] The interpretation unit can adjust the use of technical terms in the interpretation according to the user's level of expertise. For example, if the user has technical expertise, the interpretation unit can provide an interpretation that uses a lot of technical terms. Alternatively, if the user is a beginner, the interpretation unit can avoid technical terms and use easy-to-understand expressions. Furthermore, the interpretation unit can adjust the level of detail and expression of the interpretation according to the user's level of expertise. In this way, by adjusting the use of technical terms in the interpretation according to the user's level of expertise, it is possible to provide an interpretation that is easy for the user to understand.

[0133] When generating a video, the generation unit can adjust the level of detail of the video based on the importance of the assembly steps. For example, for important assembly steps, a detailed video can be generated. For general assembly steps, a standard video can be generated. Furthermore, for simple assembly steps, a concise video can be generated. In this way, by adjusting the level of detail of the video based on the importance of the assembly steps, videos can be generated efficiently.

[0134] The adjustment unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, a display method that matches the screen size can be provided. Also, if the user is using a tablet, a display method optimized for a large screen can be provided. Furthermore, if the user is using a smartwatch, a simple and highly visible display method can be provided. In this way, the optimal display method can be provided by taking into account the user's device information.

[0135] The providing unit can select the optimal providing method by referring to the user's past viewing history. For example, the optimal video is suggested based on the history of videos the user has previously viewed. The providing unit can also analyze the user's past viewing history and optimize the providing method. Furthermore, the providing method can be improved by reflecting user feedback. In this way, the optimal providing method can be provided by referring to the user's past viewing history.

[0136] The reception unit can estimate the user's emotions and adjust the timing of inputting instructions based on the estimated user emotions. For example, the user's facial expression can be captured with a camera and the emotion can be estimated using an emotion estimation algorithm. The user's voice can also be recorded and the emotion can be estimated using voice analysis technology. Furthermore, the user's biometric data (heart rate and electrodermal activity) can be collected with a sensor and the emotion can be estimated using an emotion estimation algorithm. In this way, adjusting the timing of inputting instructions according to the user's emotions reduces the user's stress and allows the user to input instructions efficiently.

[0137] The interpretation unit can estimate the user's emotion and adjust the way the interpretation is expressed based on the estimated user emotion. For example, the user's facial expression can be captured with a camera and the emotion can be estimated using an emotion estimation algorithm. The user's voice can also be recorded and the emotion can be estimated using voice analysis technology. Furthermore, the user's biometric data (heart rate and electrodermal activity) can be collected with a sensor and the emotion can be estimated using an emotion estimation algorithm. In this way, the way the interpretation is expressed can be adjusted according to the user's emotion, making it possible to provide an interpretation that is easy for the user to understand.

[0138] The generation unit can estimate the user's emotions and adjust the expression method for generating videos based on the estimated user emotions. For example, the user's facial expressions can be captured with a camera and the emotion can be estimated using an emotion estimation algorithm. The user's voice can also be recorded and the emotion can be estimated using voice analysis technology. Furthermore, the user's biometric data (heart rate and electrodermal activity) can be collected with a sensor and the emotion can be estimated using an emotion estimation algorithm. In this way, the expression method for generating videos can be adjusted according to the user's emotions, making it possible to provide videos that are easy for the user to understand.

[0139] The adjustment unit can estimate the user's emotions and adjust the video display method based on the estimated user emotions. For example, the user's facial expressions can be captured with a camera and the emotions can be estimated using an emotion estimation algorithm. The user's voice can also be recorded and the emotions can be estimated using voice analysis technology. Furthermore, the user's biometric data (heart rate and electrodermal activity) can be collected with a sensor and the emotions can be estimated using an emotion estimation algorithm. In this way, the video display method can be adjusted according to the user's emotions, making it possible to provide a display that is easy for the user to understand.

[0140] The providing unit can estimate the user's emotions and adjust the video providing method based on the estimated user emotions. For example, the user's facial expressions can be captured with a camera and the emotions can be estimated using an emotion estimation algorithm. The user's voice can also be recorded and the emotions can be estimated using voice analysis technology. Furthermore, the user's biometric data (heart rate and electrodermal activity) can be collected with a sensor and the emotions can be estimated using an emotion estimation algorithm. In this way, by adjusting the video providing method according to the user's emotions, it is possible to provide videos that are easy for the user to understand.

[0141] The processing flow of the second embodiment will be briefly explained below.

[0142] Step 1: The reception unit receives input of instructions. The input of instructions includes text input, voice input, image input, etc. For example, the text or image of the instructions can be scanned and input. Step 2: The interpretation unit uses the generation AI to interpret the contents of the instructions received by the reception unit. Interpretation is performed using natural language processing and image recognition technology. For example, the interpretation unit analyzes instructions in the instructions, such as "Attach part A to part B," and understands the specific assembly steps. Step 3: The generator generates an assembly video based on the content interpreted by the interpreter. Using a generative AI, the generator creates a video of each step according to the analyzed procedure. For example, the video shows the procedure for attaching part A to part B. Step 4: The adjustment unit adjusts the video generated by the generation unit to suit the user's viewpoint and assembly speed. For example, it adjusts the camera angle and zoom level to provide the video from a viewpoint that is easy for the user to view. It can also adjust the playback speed to play the video to suit the user's assembly speed.

[0143] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0145] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0148] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0150] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

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

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0153] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0154] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0157] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0159] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0161] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0162] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0163] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0164] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0165] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0166] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0167] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0168] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0169] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0170] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0171] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0172] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0173] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0174] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0175] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0176] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0177] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0178] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0179] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0180] 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.

[0181] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0182] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0183] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0184] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0185] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0186] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0187] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0188] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0189] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0190] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0191] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0192] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0193] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0194] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0195] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0196] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0197] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0198] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0199] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0200] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0201] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0202] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0203] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0204] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0205] 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.

[0206] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0207] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0208] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0209] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0210] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0211] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0212] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0213] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0214] [Explanation of symbols]

[0215] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input of instructions; an interpretation unit that interprets the content of the instruction manual received by the reception unit; a generation unit that generates an assembly animation based on the content interpreted by the interpretation unit; an adjustment unit that adjusts the video generated by the generation unit to suit a user's viewpoint and assembly speed. A system characterized by:

2. Equipped with a scanning unit to scan the text or images of the instructions 2. The system of claim 1.

3. A providing unit that provides the generated video to the user 2. The system of claim 1.

4. The interpretation unit Analyze the contents of the instructions using natural language processing or image recognition technology 2. The system of claim 1.

5. The generation unit Following the analyzed procedure, each step is shown in a video 2. The system of claim 1.

6. The adjustment unit Adjust the video to suit the user's viewpoint and assembly speed 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the timing of inputting instructions based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's previous instruction input history and select the optimal input method 2. The system of claim 1.

9. The reception unit Filtering based on your current project or area of ​​interest as you enter your description 2. The system of claim 1.

Images