System

Abstract

An object of the system according to the embodiment is to provide a detailed procedure manual or a manual that is easily understood even by a beginner.SOLUTION: A system according to an embodiment includes a text complementing unit, an explanatory image generation unit, an explanatory video generation unit, and a procedure manual generation unit. The text complementing unit analyzes the text of the existing procedure manual or manual and complements the omitted portion. The comment image generator generates a comment image corresponding to each procedure of the procedure manual complemented by the text complementing unit. The explanation video generation unit generates an explanation video corresponding to each procedure of the procedure manual complemented by the text complementing unit. The procedure manual generation unit analyzes a moving image obtained by capturing a state of actual work, and creates a procedure manual from the moving image.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] With conventional technology, many sections of procedures and manuals intended for intermediate and advanced users were omitted, making them difficult for beginners to understand.

[0005] The system according to the embodiment aims to provide detailed instructions and manuals that are easy to understand even for beginners. [Means for solving the problem]

[0006] The system according to the embodiment includes a text completion unit, an explanatory image generation unit, an explanatory video generation unit, and a procedure manual generation unit. The text completion unit analyzes the text of an existing procedure manual or manual and completes omitted parts. The explanatory image generation unit generates an explanatory image corresponding to each procedure in the procedure manual completed by the text completion unit. The explanatory video generation unit generates an explanatory video corresponding to each procedure in the procedure manual completed by the text completion unit. The procedure manual generation unit analyzes a video recorded of an actual operation and creates a procedure manual from the video. [Effects of the Invention]

[0007] The system according to the embodiment can provide detailed instructions and manuals that are easy to understand even for beginners. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The manual creation system according to an embodiment of the present invention uses a generation AI to supplement the small details that are omitted from recipes, business procedures, and manuals intended for intermediate and advanced users, creating ultra-detailed manuals that are easy for beginners to understand. This allows the manual creation system to create ultra-detailed manuals that are easy for even beginners to understand.

[0029] A manual creation system according to an embodiment includes a text completion unit, an explanatory image generation unit, an explanatory video generation unit, and a procedure generation unit. The text completion unit analyzes the text of existing procedure manuals and manuals and completes omitted parts. For example, the text completion unit completes an ambiguous expression such as "add an appropriate amount of salt" in a cooking recipe to specifically "add one teaspoon of salt." The text completion unit also completes an instruction such as "attach parts" in a technical manual to specifically "attach part A to part B." The text completion unit also completes an instruction such as "solve problems" in an educational guide to specifically "solve problem 1." The explanatory image generation unit generates explanatory images corresponding to each step in the completed procedure. For example, the explanatory image generation unit generates an explanatory image that visually illustrates the step "chop an onion" in a cooking procedure manual. The explanatory image generation unit also generates an explanatory image that visually illustrates the step "attach parts" in a technical manual. The explanatory image generation unit generates explanatory images that visually show the procedure of "solving a problem" in the educational guide. The explanatory video generation unit generates explanatory videos corresponding to each procedure in the supplemented procedure manual. For example, the explanatory video generation unit generates an explanatory video that visually shows the procedure of "chopping an onion" in a cooking procedure manual. The explanatory video generation unit generates an explanatory video that visually shows the procedure of "attaching a part" in a technical manual. The explanatory video generation unit generates an explanatory video that visually shows the procedure of "solving a problem" in the educational guide. The procedure manual generation unit analyzes a video of an actual task and creates a procedure manual from the video. For example, the procedure manual generation unit analyzes a video of a cooking task and creates a procedure manual by converting each procedure into text. The procedure manual generation unit analyzes a video of a technical task and creates a procedure manual by converting each procedure into text. The procedure manual generation unit analyzes a video of an educational task and creates a procedure manual by converting each procedure into text. As a result, the manual creation system according to the embodiment can create highly detailed manuals that are easy to understand even for beginners. For example, when a beginner cook cooks while following a recipe, the specific instructions, explanatory images, and explanatory videos allow the cook to proceed smoothly with the work.Furthermore, when handing over work, having detailed procedures can reduce the burden of handing over. Furthermore, even in fields where there is a shortage of successors, having detailed procedures makes it easier to train successors.

[0030] The text completion unit can learn the user's past operation history and preferences and provide individually optimized completion content. For example, the text completion unit uses a generation AI to analyze the user's past operation history and learn specific operation patterns and preferences. For example, in a cooking recipe, the text completion unit provides optimal completion content based on the seasonings and quantities that the user has used in the past. In addition, in a technical manual, the text completion unit provides optimal completion content based on the operation procedures the user has performed in the past. In addition, in an educational guide, the text completion unit provides optimal completion content based on the trends in problems the user has solved in the past. This makes it possible to provide completion content that is optimized for the user.

[0031] The text completion unit can provide more reliable completion content by referring to related scientific data and statistical information. For example, when the generation AI completes a cooking recipe, the text completion unit can refer to nutritional data to provide health-conscious completion content. For example, the text completion unit can complete specific instructions based on appropriate amounts of salt and calories. In addition, the text completion unit can complete optimal operating procedures in technical manuals by referring to product performance data. In addition, in educational guides, the text completion unit can complete effective learning procedures by referring to statistical data on learning effects. This allows for the provision of highly reliable completion content.

[0032] The text completion unit provides completion content that corresponds to different languages ​​and cultures, making it possible to accommodate international users. For example, the text completion unit uses a generation AI to provide completion content that corresponds to different languages. For example, it can complete instruction manuals in multiple languages, such as English, Japanese, and French, making it possible to accommodate international users. The text completion unit also provides completion content that corresponds to different cultures. For example, in cooking recipes, it provides completion content that corresponds to the food culture of each country. The text completion unit also provides completion content that corresponds to the culture of different industries. For example, in technical manuals, it provides completion content that corresponds to the technical standards of each country. This makes it possible to accommodate international users.

[0033] The text completion unit can also accommodate visually impaired or hearing impaired individuals using voice input or voice output. For example, the text completion unit allows visually impaired individuals to complete a procedure manual using voice input from a generation AI. For example, the unit explains the procedure through voice and generates the completed content based on the user's voice input. The text completion unit also uses voice output to allow hearing impaired individuals to complete a procedure manual. For example, the unit outputs the contents of the procedure manual through voice and adjusts it so that it is easy for the user to hear. The text completion unit also provides completed content tailored to the needs of visually impaired or hearing impaired individuals. For example, the unit adjusts the font size or color of the procedure manual to make it easier for visually impaired individuals to read. This makes it possible to accommodate visually impaired or hearing impaired individuals.

[0034] The explanatory image generation unit can use 3D models to provide more three-dimensional, easy-to-understand images. For example, the explanatory image generation unit uses a generation AI to generate three-dimensional explanatory images corresponding to each step in a cooking manual using 3D models. For example, in a cooking manual, the explanatory image generation unit uses a 3D model to show how to cut ingredients. In a technical manual, the explanatory image generation unit also uses a 3D model to show how to install parts. In an educational guide, the explanatory image generation unit also uses a 3D model to show how to solve problems. This makes it possible to provide three-dimensional, easy-to-understand explanatory images.

[0035] The explanatory image generation unit can provide images from the optimal viewpoint, taking into account the user's viewpoint or operation position. For example, the explanatory image generation unit generates explanatory images from the optimal viewpoint, taking into account the user's viewpoint using a generation AI. For example, in a cooking procedure manual, images are provided from a viewpoint that is easy for the user to see. The explanatory image generation unit also generates explanatory images from the optimal viewpoint, taking into account the user's operation position. For example, in a technical manual, images are provided from a viewpoint that is easy for the user to operate. The explanatory image generation unit also tracks the user's movements and generates explanatory images from the optimal viewpoint. For example, in an educational guide, images are provided from a viewpoint that is easy for the user to understand. This makes it possible to provide explanatory images from the optimal viewpoint, taking into account the user's viewpoint and operation position.

[0036] The explanatory video generation unit can automatically adjust the playback speed of the explanatory video according to the user's operation speed. For example, the explanatory video generation unit uses a generation AI to analyze the user's operation speed and automatically adjust the playback speed of the explanatory video according to that speed. For example, in a cooking procedure manual, the playback speed of the video is adjusted to match the user's operation speed. Furthermore, in a technical manual, the explanatory video generation unit adjusts the playback speed of the video to match the user's operation speed. Furthermore, in an educational guide, the explanatory video generation unit adjusts the playback speed of the video to match the user's operation speed. This allows the playback speed of the explanatory video to be automatically adjusted according to the user's operation speed.

[0037] The explanatory video generation unit can update the video in real time according to the progress of the steps. For example, the explanatory video generation unit uses a generation AI to analyze the progress of the steps and update the explanatory video in real time according to the situation. For example, in a cooking instruction manual, the video is updated according to the progress of the steps. Furthermore, in a technical manual, the explanatory video generation unit updates the video according to the progress of the steps. Furthermore, in an educational guide, the explanatory video is updated according to the progress of the steps. This allows the explanatory video to be updated in real time according to the progress of the steps.

[0038] The procedure manual generation unit can track the movements of workers in detail and generate optimal procedure manuals. For example, the procedure manual generation unit uses a generation AI to track the movements of workers in detail and generate optimal procedure manuals based on those movements. For example, in a cooking work video, the unit analyzes in detail how knives are used and how ingredients are cut, and reflects this in the procedure manual. In addition, in a technical work video, the procedure manual generation unit analyzes in detail how tools are used and how parts are installed, and reflects this in the procedure manual. In addition, in an educational work video, the procedure manual generation unit analyzes in detail how problems are solved and how explanations are given, and reflects this in the procedure manual. This makes it possible to track the movements of workers in detail and generate optimal procedure manuals.

[0039] The procedure manual generation unit can generate procedure manuals that are appropriate for the environment, taking into account background information about the work environment. For example, the procedure manual generation unit uses a generation AI to analyze background information about the work environment and generate procedure manuals based on that information. For example, for a video of work in a factory, the procedure manual generation unit generates procedure manuals that are appropriate for the work environment. Furthermore, for a video of work in an office environment, the procedure manual generation unit generates procedure manuals that are appropriate for the work environment. Furthermore, for a video of work outdoors, the procedure manual generation unit generates procedure manuals that are appropriate for the work environment. In this way, it is possible to generate procedure manuals that are appropriate for the environment, taking into account background information about the work environment.

[0040] The procedure manual generation unit can refer to work videos from different industries or fields and generate procedure manuals that incorporate knowledge from other fields. For example, the generation AI of the procedure manual generation unit refers to work videos from different industries and generates procedure manuals based on that knowledge. For example, the procedure manual generation unit refers to work videos from the manufacturing industry and generates procedure manuals that incorporate knowledge from other fields. Furthermore, the procedure manual generation unit refers to work videos from the medical field and generates procedure manuals that incorporate knowledge from other fields. Furthermore, the procedure manual generation unit refers to work videos from the education field and generates procedure manuals that incorporate knowledge from other fields. This makes it possible to generate procedure manuals that incorporate knowledge from other fields.

[0041] The procedure manual generation unit can update the contents of the procedure manual in real time and generate procedure manuals based on the latest information. For example, the procedure manual generation unit uses a generation AI to analyze work videos in real time and generate procedure manuals based on the latest information. For example, it generates procedure manuals that incorporate new technologies and methods. The procedure manual generation unit also updates the procedure manual in real time based on updated information in the database. For example, it generates procedure manuals that reflect new product information and technical data. The procedure manual generation unit also updates the procedure manual in real time based on sensor information. For example, it generates procedure manuals that reflect the situation on site. This makes it possible to generate procedure manuals based on the latest information in real time.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The manual creation system can also be equipped with customization functions according to the user's learning style. For example, it can make extensive use of explanatory images and videos for visual learners, and enhance audio explanations for auditory learners. It can also provide interactive simulations for hands-on learners. This allows the system to provide manuals optimized for the user's learning style.

[0044] The text completion unit can learn the user's past operation history and preferences and provide individually optimized completion content. For example, in a cooking recipe, the unit provides optimal completion content based on the seasonings and quantities that the user has used in the past. In a technical manual, the unit provides optimal completion content based on the operation procedures the user has performed in the past. In an educational guide, the unit provides optimal completion content based on the trends in problems the user has solved in the past. This makes it possible to provide completion content that is optimized for the user.

[0045] The text completion unit provides completion content that corresponds to different languages ​​and cultures, making it possible to accommodate international users. For example, the generation AI provides completion content that corresponds to different languages. For example, it can complete instruction manuals in multiple languages, such as English, Japanese, and French, to accommodate international users. It also provides completion content that corresponds to different cultures. For example, in cooking recipes, it provides completion content that corresponds to the food culture of each country. It also provides completion content that corresponds to the culture of different industries. For example, in technical manuals, it provides completion content that corresponds to the technical standards of each country. This makes it possible to accommodate international users.

[0046] The explanatory image generation unit can use 3D models to provide more three-dimensional, easy-to-understand images. For example, the generation AI uses 3D models to generate three-dimensional explanatory images corresponding to each step in a cooking manual. For example, in a cooking manual, a 3D model may be used to show how to cut ingredients. In a technical manual, a 3D model may be used to show how to install parts. In an educational guide, a 3D model may be used to show how to solve a problem. This allows the provision of three-dimensional, easy-to-understand explanatory images.

[0047] The explanatory video generation unit can automatically adjust the playback speed of the explanatory video according to the user's operation speed. For example, the generation AI analyzes the user's operation speed and automatically adjusts the playback speed of the explanatory video according to that speed. For example, in a cooking procedure manual, the video playback speed is adjusted to match the user's operation speed. Also, in a technical manual, the video playback speed is adjusted to match the user's operation speed. Also, in an educational guide, the video playback speed is adjusted to match the user's operation speed. This allows the playback speed of the explanatory video to be automatically adjusted according to the user's operation speed.

[0048] The procedure manual generation unit can reference work videos from different industries or fields and generate procedure manuals that incorporate knowledge from other fields. For example, the generation AI references work videos from different industries and generates procedure manuals based on that knowledge. For example, it references work videos from the manufacturing industry and generates procedure manuals that incorporate knowledge from other fields. It can also reference work videos from the medical field and generate procedure manuals that incorporate knowledge from other fields. It can also reference work videos from the education field and generate procedure manuals that incorporate knowledge from other fields. This makes it possible to generate procedure manuals that incorporate knowledge from other fields.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The text completion unit analyzes the text of existing procedures and manuals and completes omitted parts. For example, in a cooking recipe, an ambiguous expression such as "add an appropriate amount of salt" is completed with the specific "add one teaspoon of salt." In a technical manual, an instruction such as "attach parts" is completed with the detailed "attach part A to part B." Furthermore, in an educational guide, an instruction such as "solve a problem" is completed with the specific "solve problem 1." Step 2: The explanatory image generator generates explanatory images corresponding to each step in the completed instruction manual. For example, in a cooking instruction manual, an explanatory image is generated that visually shows the step of "chopping an onion." In a technical manual, an explanatory image is generated that visually shows the step of "attaching a part." In an educational guide, an explanatory image is generated that visually shows the step of "solving a problem." Step 3: The instructional video generator generates an instructional video corresponding to each step in the completed instruction manual. For example, an instructional video is generated that visually shows the step of "chopping an onion" in a cooking instruction manual. Also, an instructional video is generated that visually shows the step of "attaching a part" in a technical manual. Furthermore, an instructional video is generated that visually shows the step of "solving a problem" in an educational guide. Step 4: The procedure manual generator analyzes videos of actual work and creates a procedure manual from the video. For example, it analyzes videos of cooking work and converts each step into text to create a procedure manual. It also analyzes videos of technical work and converts each step into text to create a procedure manual. It also analyzes videos of work used for training and converts each step into text to create a procedure manual.

[0051] (Example 2) The manual creation system according to an embodiment of the present invention uses a generation AI to supplement the small details that are omitted from recipes, business procedures, and manuals intended for intermediate and advanced users, creating ultra-detailed manuals that are easy for beginners to understand. This allows the manual creation system to create ultra-detailed manuals that are easy for even beginners to understand.

[0052] A manual creation system according to an embodiment includes a text completion unit, an explanatory image generation unit, an explanatory video generation unit, and a procedure generation unit. The text completion unit analyzes the text of existing procedure manuals and manuals and completes omitted parts. For example, the text completion unit completes an ambiguous expression such as "add an appropriate amount of salt" in a cooking recipe to specifically "add one teaspoon of salt." The text completion unit also completes an instruction such as "attach parts" in a technical manual to specifically "attach part A to part B." The text completion unit also completes an instruction such as "solve problems" in an educational guide to specifically "solve problem 1." The explanatory image generation unit generates explanatory images corresponding to each step in the completed procedure. For example, the explanatory image generation unit generates an explanatory image that visually illustrates the step "chop an onion" in a cooking procedure manual. The explanatory image generation unit also generates an explanatory image that visually illustrates the step "attach parts" in a technical manual. The explanatory image generation unit generates explanatory images that visually show the procedure of "solving a problem" in the educational guide. The explanatory video generation unit generates explanatory videos corresponding to each procedure in the supplemented procedure manual. For example, the explanatory video generation unit generates an explanatory video that visually shows the procedure of "chopping an onion" in a cooking procedure manual. The explanatory video generation unit generates an explanatory video that visually shows the procedure of "attaching a part" in a technical manual. The explanatory video generation unit generates an explanatory video that visually shows the procedure of "solving a problem" in the educational guide. The procedure manual generation unit analyzes a video of an actual task and creates a procedure manual from the video. For example, the procedure manual generation unit analyzes a video of a cooking task and creates a procedure manual by converting each procedure into text. The procedure manual generation unit analyzes a video of a technical task and creates a procedure manual by converting each procedure into text. The procedure manual generation unit analyzes a video of an educational task and creates a procedure manual by converting each procedure into text. As a result, the manual creation system according to the embodiment can create highly detailed manuals that are easy to understand even for beginners. For example, when a beginner cook cooks while following a recipe, the specific instructions, explanatory images, and explanatory videos allow the cook to proceed smoothly with the work.Furthermore, when handing over work, having detailed procedures can reduce the burden of handing over. Furthermore, even in fields where there is a shortage of successors, having detailed procedures makes it easier to train successors.

[0053] The text completion unit can learn the user's past operation history and preferences and provide individually optimized completion content. For example, the text completion unit uses a generation AI to analyze the user's past operation history and learn specific operation patterns and preferences. For example, in a cooking recipe, the text completion unit provides optimal completion content based on the seasonings and quantities that the user has used in the past. In addition, in a technical manual, the text completion unit provides optimal completion content based on the operation procedures the user has performed in the past. In addition, in an educational guide, the text completion unit provides optimal completion content based on the trends in problems the user has solved in the past. This makes it possible to provide completion content that is optimized for the user.

[0054] The text completion unit can provide more reliable completion content by referring to related scientific data and statistical information. For example, when the generation AI completes a cooking recipe, the text completion unit can refer to nutritional data to provide health-conscious completion content. For example, the text completion unit can complete specific instructions based on appropriate amounts of salt and calories. In addition, the text completion unit can complete optimal operating procedures in technical manuals by referring to product performance data. In addition, in educational guides, the text completion unit can complete effective learning procedures by referring to statistical data on learning effects. This allows for the provision of highly reliable completion content.

[0055] The text completion unit uses an emotion estimation function to identify parts where the user feels anxious or uncertain, and can provide particularly detailed completion for those parts. For example, the text completion unit uses a generation AI to analyze the user's facial expressions and voice when entering text and identify parts where the user feels anxious or uncertain. For example, it provides particularly detailed completion for steps where the user shows a confused expression. The text completion unit also analyzes behavioral data when the user enters text and identifies parts where the user feels anxious or uncertain. For example, it provides particularly detailed completion for steps where the user interrupts an operation. The text completion unit also analyzes user feedback and identifies parts where the user feels anxious or uncertain. For example, it provides particularly detailed completion for steps about which users have many questions. This allows it to provide particularly detailed completion for parts where the user feels anxious or uncertain.

[0056] The text completion unit provides completion content that corresponds to different languages ​​and cultures, making it possible to accommodate international users. For example, the text completion unit uses a generation AI to provide completion content that corresponds to different languages. For example, it can complete instruction manuals in multiple languages, such as English, Japanese, and French, making it possible to accommodate international users. The text completion unit also provides completion content that corresponds to different cultures. For example, in cooking recipes, it provides completion content that corresponds to the food culture of each country. The text completion unit also provides completion content that corresponds to the culture of different industries. For example, in technical manuals, it provides completion content that corresponds to the technical standards of each country. This makes it possible to accommodate international users.

[0057] The text completion unit can also accommodate visually impaired or hearing impaired individuals using voice input or voice output. For example, the text completion unit allows visually impaired individuals to complete a procedure manual using voice input from a generation AI. For example, the unit explains the procedure through voice and generates the completed content based on the user's voice input. The text completion unit also uses voice output to allow hearing impaired individuals to complete a procedure manual. For example, the unit outputs the contents of the procedure manual through voice and adjusts it so that it is easy for the user to hear. The text completion unit also provides completed content tailored to the needs of visually impaired or hearing impaired individuals. For example, the unit adjusts the font size or color of the procedure manual to make it easier for visually impaired individuals to read. This makes it possible to accommodate visually impaired or hearing impaired individuals.

[0058] The text completion unit can use the emotion estimation function to prioritize completion content related to topics in which the user is most interested. For example, the generation AI uses the emotion estimation function to identify topics in which the user is most interested and prioritizes providing completion content related to those topics. For example, it provides particularly detailed completion for steps in which the user has shown interest. The text completion unit also analyzes user behavior data to identify topics in which the user is most interested. For example, it provides particularly detailed completion for steps in which the user has spent a lot of time. The text completion unit also analyzes user feedback to identify topics in which the user is most interested. For example, it provides particularly detailed completion for steps that the user has given a high rating. This allows completion content related to topics in which the user is most interested to be prioritized.

[0059] The explanatory image generation unit can use 3D models to provide more three-dimensional, easy-to-understand images. For example, the explanatory image generation unit uses a generation AI to generate three-dimensional explanatory images corresponding to each step in a cooking manual using 3D models. For example, in a cooking manual, the explanatory image generation unit uses a 3D model to show how to cut ingredients. In a technical manual, the explanatory image generation unit also uses a 3D model to show how to install parts. In an educational guide, the explanatory image generation unit also uses a 3D model to show how to solve problems. This makes it possible to provide three-dimensional, easy-to-understand explanatory images.

[0060] The explanatory image generation unit can provide images from the optimal viewpoint, taking into account the user's viewpoint or operation position. For example, the explanatory image generation unit generates explanatory images from the optimal viewpoint, taking into account the user's viewpoint using a generation AI. For example, in a cooking procedure manual, images are provided from a viewpoint that is easy for the user to see. The explanatory image generation unit also generates explanatory images from the optimal viewpoint, taking into account the user's operation position. For example, in a technical manual, images are provided from a viewpoint that is easy for the user to operate. The explanatory image generation unit also tracks the user's movements and generates explanatory images from the optimal viewpoint. For example, in an educational guide, images are provided from a viewpoint that is easy for the user to understand. This makes it possible to provide explanatory images from the optimal viewpoint, taking into account the user's viewpoint and operation position.

[0061] The explanatory video generation unit can automatically adjust the playback speed of the explanatory video according to the user's operation speed. For example, the explanatory video generation unit uses a generation AI to analyze the user's operation speed and automatically adjust the playback speed of the explanatory video according to that speed. For example, in a cooking procedure manual, the playback speed of the video is adjusted to match the user's operation speed. Furthermore, in a technical manual, the explanatory video generation unit adjusts the playback speed of the video to match the user's operation speed. Furthermore, in an educational guide, the explanatory video generation unit adjusts the playback speed of the video to match the user's operation speed. This allows the playback speed of the explanatory video to be automatically adjusted according to the user's operation speed.

[0062] The explanatory video generation unit can update the video in real time according to the progress of the steps. For example, the explanatory video generation unit uses a generation AI to analyze the progress of the steps and update the explanatory video in real time according to the situation. For example, in a cooking instruction manual, the video is updated according to the progress of the steps. Furthermore, in a technical manual, the explanatory video generation unit updates the video according to the progress of the steps. Furthermore, in an educational guide, the explanatory video is updated according to the progress of the steps. This allows the explanatory video to be updated in real time according to the progress of the steps.

[0063] The explanatory video generation unit can use the emotion estimation function to provide explanatory videos that give a sense of security to a user for steps that make the user feel particularly anxious. For example, the explanatory video generation unit uses the emotion estimation function to identify steps that make the user feel particularly anxious, and provides explanatory videos that give a sense of security to that step. For example, a particularly detailed explanatory video is provided for a step at which the user shows a confused expression. The explanatory video generation unit also analyzes user behavior data to identify steps that make the user feel particularly anxious. For example, a particularly detailed explanatory video is provided for a step at which the user stopped operating. The explanatory video generation unit also analyzes user feedback to identify steps that make the user feel particularly anxious. For example, a particularly detailed explanatory video is provided for a step at which the user has many questions. This makes it possible to provide explanatory videos that give a sense of security to a user for steps that make the user feel particularly anxious.

[0064] The procedure manual generation unit can track the movements of workers in detail and generate optimal procedure manuals. For example, the procedure manual generation unit uses a generation AI to track the movements of workers in detail and generate optimal procedure manuals based on those movements. For example, in a cooking work video, the unit analyzes in detail how knives are used and how ingredients are cut, and reflects this in the procedure manual. In addition, in a technical work video, the procedure manual generation unit analyzes in detail how tools are used and how parts are installed, and reflects this in the procedure manual. In addition, in an educational work video, the procedure manual generation unit analyzes in detail how problems are solved and how explanations are given, and reflects this in the procedure manual. This makes it possible to track the movements of workers in detail and generate optimal procedure manuals.

[0065] The procedure manual generation unit can generate procedure manuals that are appropriate for the environment, taking into account background information about the work environment. For example, the procedure manual generation unit uses a generation AI to analyze background information about the work environment and generate procedure manuals based on that information. For example, for a video of work in a factory, the procedure manual generation unit generates procedure manuals that are appropriate for the work environment. Furthermore, for a video of work in an office environment, the procedure manual generation unit generates procedure manuals that are appropriate for the work environment. Furthermore, for a video of work outdoors, the procedure manual generation unit generates procedure manuals that are appropriate for the work environment. In this way, it is possible to generate procedure manuals that are appropriate for the environment, taking into account background information about the work environment.

[0066] The procedure manual generation unit can use the emotion estimation function to identify parts that the worker finds particularly difficult and generate particularly detailed procedure manuals for those parts. For example, the procedure manual generation unit uses the emotion estimation function by a generation AI to identify parts that the worker finds particularly difficult and generate particularly detailed procedure manuals for those parts. For example, a particularly detailed procedure manual is generated for a procedure where the worker shows a confused expression. The procedure manual generation unit also analyzes the worker's behavioral data to identify parts that are particularly difficult. For example, a particularly detailed procedure manual is generated for a procedure where the worker stopped operating. The procedure manual generation unit also analyzes the worker's feedback to identify parts that are particularly difficult. For example, a particularly detailed procedure manual is generated for a procedure about which the worker has many questions. In this way, a particularly detailed procedure manual can be generated for parts that the worker finds particularly difficult.

[0067] The procedure manual generation unit can refer to work videos from different industries or fields and generate procedure manuals that incorporate knowledge from other fields. For example, the generation AI of the procedure manual generation unit refers to work videos from different industries and generates procedure manuals based on that knowledge. For example, the procedure manual generation unit refers to work videos from the manufacturing industry and generates procedure manuals that incorporate knowledge from other fields. Furthermore, the procedure manual generation unit refers to work videos from the medical field and generates procedure manuals that incorporate knowledge from other fields. Furthermore, the procedure manual generation unit refers to work videos from the education field and generates procedure manuals that incorporate knowledge from other fields. This makes it possible to generate procedure manuals that incorporate knowledge from other fields.

[0068] The procedure manual generation unit can update the contents of the procedure manual in real time and generate procedure manuals based on the latest information. For example, the procedure manual generation unit uses a generation AI to analyze work videos in real time and generate procedure manuals based on the latest information. For example, it generates procedure manuals that incorporate new technologies and methods. The procedure manual generation unit also updates the procedure manual in real time based on updated information in the database. For example, it generates procedure manuals that reflect new product information and technical data. The procedure manual generation unit also updates the procedure manual in real time based on sensor information. For example, it generates procedure manuals that reflect the situation on site. This makes it possible to generate procedure manuals based on the latest information in real time.

[0069] The procedure manual generation unit can use the emotion estimation function to generate particularly detailed procedure manuals for procedures in which the worker is most interested. For example, the procedure manual generation unit uses the emotion estimation function to identify procedures in which the worker is most interested and generates particularly detailed procedure manuals for those procedures. For example, a particularly detailed procedure manual is generated for a procedure in which the worker showed interest. The procedure manual generation unit also analyzes behavioral data of the worker to identify procedures in which the worker is most interested. For example, a particularly detailed procedure manual is generated for a procedure in which the worker spent a lot of time. The procedure manual generation unit also analyzes feedback from the worker to identify procedures in which the worker is most interested. For example, a particularly detailed procedure manual is generated for a procedure that the worker gave a high rating. In this way, a particularly detailed procedure manual can be generated for procedures in which the worker is most interested.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The manual creation system can also be equipped with customization functions according to the user's learning style. For example, it can make extensive use of explanatory images and videos for visual learners, and enhance audio explanations for auditory learners. It can also provide interactive simulations for hands-on learners. This allows the system to provide manuals optimized for the user's learning style.

[0072] The text completion unit can estimate the user's emotions and, based on the estimated emotions, provide completion content related to topics in which the user is particularly interested with priority. For example, particularly detailed completion is provided for steps in which the user has shown interest. Furthermore, the text completion unit analyzes the user's behavioral data to identify topics in which the user is most interested. For example, particularly detailed completion is provided for steps in which the user has spent a lot of time. Furthermore, the text completion unit analyzes the user's feedback to identify topics in which the user is most interested. For example, particularly detailed completion is provided for steps that the user has given a high rating. This makes it possible to provide completion content related to topics in which the user is most interested with priority.

[0073] The text completion unit can learn the user's past operation history and preferences and provide individually optimized completion content. For example, in a cooking recipe, the unit provides optimal completion content based on the seasonings and quantities that the user has used in the past. In a technical manual, the unit provides optimal completion content based on the operation procedures the user has performed in the past. In an educational guide, the unit provides optimal completion content based on the trends in problems the user has solved in the past. This makes it possible to provide completion content that is optimized for the user.

[0074] The text completion unit uses the emotion estimation function to identify parts where the user feels anxious or uncertain, and can provide particularly thorough completion to those parts. For example, it analyzes the facial expression and voice of the user when inputting text to identify parts where the user feels anxious or uncertain. For example, it provides particularly detailed completion to steps where the user shows a confused expression. It also analyzes behavioral data when the user is inputting text to identify parts where the user feels anxious or uncertain. For example, it provides particularly detailed completion to steps where the user interrupts an operation. It also analyzes user feedback to identify parts where the user feels anxious or uncertain. For example, it provides particularly detailed completion to steps about which users have many questions. This makes it possible to provide particularly thorough completion to parts where the user feels anxious or uncertain.

[0075] The text completion unit provides completion content that corresponds to different languages ​​and cultures, making it possible to accommodate international users. For example, the generation AI provides completion content that corresponds to different languages. For example, it can complete instruction manuals in multiple languages, such as English, Japanese, and French, to accommodate international users. It also provides completion content that corresponds to different cultures. For example, in cooking recipes, it provides completion content that corresponds to the food culture of each country. It also provides completion content that corresponds to the culture of different industries. For example, in technical manuals, it provides completion content that corresponds to the technical standards of each country. This makes it possible to accommodate international users.

[0076] The explanatory image generation unit can use 3D models to provide more three-dimensional, easy-to-understand images. For example, the generation AI uses 3D models to generate three-dimensional explanatory images corresponding to each step in a cooking manual. For example, in a cooking manual, a 3D model may be used to show how to cut ingredients. In a technical manual, a 3D model may be used to show how to install parts. In an educational guide, a 3D model may be used to show how to solve a problem. This allows the provision of three-dimensional, easy-to-understand explanatory images.

[0077] The explanatory video generation unit can use the emotion estimation function to provide explanatory videos that give a sense of security to a user for steps that make the user feel particularly anxious. For example, the generation AI can use the emotion estimation function to identify steps that make the user feel particularly anxious, and provide explanatory videos that give a sense of security to those steps. For example, a particularly detailed explanatory video can be provided for a step at which the user looks confused. Furthermore, user behavior data can be analyzed to identify steps that make the user feel particularly anxious. For example, a particularly detailed explanatory video can be provided for a step at which the user stops operating. Furthermore, user feedback can be analyzed to identify steps that make the user feel particularly anxious. For example, a particularly detailed explanatory video can be provided for a step at which the user has many questions. This makes it possible to provide explanatory videos that give a sense of security to a user for steps that make the user feel particularly anxious.

[0078] The explanatory video generation unit can automatically adjust the playback speed of the explanatory video according to the user's operation speed. For example, the generation AI analyzes the user's operation speed and automatically adjusts the playback speed of the explanatory video according to that speed. For example, in a cooking procedure manual, the video playback speed is adjusted to match the user's operation speed. Also, in a technical manual, the video playback speed is adjusted to match the user's operation speed. Also, in an educational guide, the video playback speed is adjusted to match the user's operation speed. This allows the playback speed of the explanatory video to be automatically adjusted according to the user's operation speed.

[0079] The procedure manual generation unit can use the emotion estimation function to identify parts that a worker finds particularly difficult and generate particularly detailed procedure manuals for those parts. For example, the generation AI can use the emotion estimation function to identify parts that a worker finds particularly difficult and generate particularly detailed procedure manuals for those parts. For example, a particularly detailed procedure manual is generated for a procedure where a worker shows a confused expression. The unit can also analyze the worker's behavioral data to identify parts that are particularly difficult. For example, a particularly detailed procedure manual is generated for a procedure where a worker stops operating. The unit can also analyze the worker's feedback to identify parts that are particularly difficult. For example, a particularly detailed procedure manual is generated for a procedure where a worker has many questions. This makes it possible to generate particularly detailed procedure manuals for parts that a worker finds particularly difficult.

[0080] The procedure manual generation unit can reference work videos from different industries or fields and generate procedure manuals that incorporate knowledge from other fields. For example, the generation AI references work videos from different industries and generates procedure manuals based on that knowledge. For example, it references work videos from the manufacturing industry and generates procedure manuals that incorporate knowledge from other fields. It can also reference work videos from the medical field and generate procedure manuals that incorporate knowledge from other fields. It can also reference work videos from the education field and generate procedure manuals that incorporate knowledge from other fields. This makes it possible to generate procedure manuals that incorporate knowledge from other fields.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The text completion unit analyzes the text of existing procedures and manuals and completes omitted parts. For example, in a cooking recipe, an ambiguous expression such as "add an appropriate amount of salt" is completed with the specific "add one teaspoon of salt." In a technical manual, an instruction such as "attach parts" is completed with the detailed "attach part A to part B." Furthermore, in an educational guide, an instruction such as "solve a problem" is completed with the specific "solve problem 1." Step 2: The explanatory image generator generates explanatory images corresponding to each step in the completed instruction manual. For example, in a cooking instruction manual, an explanatory image is generated that visually shows the step of "chopping an onion." In a technical manual, an explanatory image is generated that visually shows the step of "attaching a part." In an educational guide, an explanatory image is generated that visually shows the step of "solving a problem." Step 3: The instructional video generator generates an instructional video corresponding to each step in the completed instruction manual. For example, an instructional video is generated that visually shows the step of "chopping an onion" in a cooking instruction manual. Also, an instructional video is generated that visually shows the step of "attaching a part" in a technical manual. Furthermore, an instructional video is generated that visually shows the step of "solving a problem" in an educational guide. Step 4: The procedure manual generator analyzes videos of actual work and creates a procedure manual from the video. For example, it analyzes videos of cooking work and converts each step into text to create a procedure manual. It also analyzes videos of technical work and converts each step into text to create a procedure manual. It also analyzes videos of work used for training and converts each step into text to create a procedure manual.

[0083] 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.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0096] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0150] 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 text completion unit that analyzes the text of an existing procedure or manual and completes omitted parts; an explanation image generating unit that generates an explanation image corresponding to each procedure in the procedure manual completed by the text completion unit; an instructional video generation unit that generates an instructional video corresponding to each procedure in the procedure manual completed by the text completion unit; and a procedure manual generation unit that analyzes a video of the actual work being performed and creates a procedure manual from the video. A system characterized by:

2. The text completion unit Accommodates international users by providing completion content that is compatible with different languages ​​and cultures 2. The system of claim 1.

3. The commentary image generation unit Using the 3D model, we provide a more three-dimensional and easy-to-understand image.

2. The system of claim 1.

4. The explanation video generation unit The playback speed of the explanatory video is automatically adjusted according to the user's operation speed.

2. The system of claim 1.

5. The procedure manual generation unit Tracking the worker's movements in detail and generating the optimal procedure manual 2. The system of claim 1.

6. The text completion unit Identify areas where users feel uneasy or unsure, and provide particularly detailed assistance.

2. The system of claim 1.

Images