System

Abstract

An object of a system according to an embodiment is to convert a manual in a text format into a more easily understandable format.SOLUTION: A system includes a text analysis unit, a cartoon generation unit, a moving image generation unit, and a sound generation unit. The text analysis unit analyzes a manual in a text format. The cartoon generation unit generates a cartoon based on the content analyzed by the text analysis unit. The moving picture generation unit generates a moving picture based on the cartoon generated by the cartoon generation unit. The sound generation unit adds sound to the moving image generated by the moving image generation unit.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] Conventional techniques have had the problem that text-based manuals are difficult to understand.

[0005] The system according to the embodiment aims to convert a text-format manual into a more easily understandable format. [Means for solving the problem]

[0006] The system according to the embodiment includes a text analysis unit, a comic book generation unit, a video generation unit, and an audio generation unit. The text analysis unit analyzes a text manual. The comic book generation unit generates a comic book based on the content analyzed by the text analysis unit. The video generation unit generates a video based on the comic book generated by the comic book generation unit. The audio generation unit adds audio to the video generated by the video generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can convert a text-based manual into a more easily understandable format. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) In the manual generation system according to an embodiment of the present invention, a generation AI analyzes a text-based manual, converts the content into a comic book format, and then generates a video based on the comic book version and adds audio. This makes the manual generation system easier to understand visually and audibly.

[0029] A manual generation system according to an embodiment includes a text analysis unit, a comic book generation unit, a video generation unit, and an audio generation unit. The text analysis unit analyzes a text manual. For example, the text analysis unit analyzes the content of the manual using natural language processing technology. The text analysis unit can also extract important information using text mining technology. The text analysis unit can also analyze sentence structure to make the content of the manual easier to understand. The comic book generation unit generates a comic book based on the content analyzed by the text analysis unit. For example, the comic book generation unit automatically generates character designs. The comic book generation unit can also create a storyboard and divide frames. The comic book generation unit can also generate character dialogue. The video generation unit generates a video based on the comic book generated by the comic book generation unit. For example, the video generation unit generates character movements using animation technology. The video generation unit can also edit the video using video editing software. The video generation unit can also add effects to make the video more appealing. The audio generation unit adds audio to the video generated by the video generation unit. For example, the audio generation unit generates lines for a character using voice synthesis technology. The audio generation unit can also record narration and add it to the video. The audio generation unit can also add sound effects to make the video more realistic. This allows the manual generation system according to the embodiment to make the contents of the manual easier to understand visually and aurally. For example, by reading a manual in comic format, a user can visually understand the operation procedures. Furthermore, combining video and audio makes it easier to understand aurally as well. This allows the user to efficiently learn the contents of the manual.

[0030] The text analysis unit can refer to the user's past operation history and generate comic panels that highlight areas where users frequently make mistakes. For example, the text analysis unit uses a generation AI to analyze the user's past operation history and identify areas where users frequently make mistakes. For example, if a user repeatedly makes the same operation mistake, it generates comic panels that highlight those areas. Furthermore, if the text analysis unit determines that a particular operation procedure is difficult based on the user's operation history data, it generates comic panels that explain that part in detail. For example, it provides easy-to-understand illustrations of complex setting procedures. Furthermore, the text analysis unit uses a generation AI to refer to the operation history and generate comic panels that highlight points where users should pay particular attention. For example, it displays important precautions in a prominent manner. This helps users understand by highlighting areas where they frequently make mistakes.

[0031] The text analysis unit automatically generates characters and stories that match the user's age and occupation, making it possible to create a familiar manga. For example, the text analysis unit uses a generation AI to automatically generate appropriate characters and stories based on the user's age and occupation data. For example, cute characters are used for children, and serious characters for adults. The text analysis unit also generates manga that incorporates technical terms and situations that match the user's occupation. For example, it creates stories using medical terminology for medical professionals. The text analysis unit also automatically generates story developments that match the age group, making it possible to create a familiar manga. For example, it provides an adventure story for young people and a calm story for seniors. This makes it possible to create a familiar manga by automatically generating characters and stories that match the user's age and occupation.

[0032] The text analysis unit generates an interactive quiz-style manga based on the contents of the manual, making it possible to check the user's level of understanding. For example, the text analysis unit uses a generation AI to generate an interactive quiz-style manga based on the contents of the manual. For example, a quiz may be inserted in the middle of an operating procedure to check the user's level of understanding. The text analysis unit also generates a quiz-style manga, where the story progresses as the user selects options. For example, a system is created whereby selecting the correct option advances to the next panel. The text analysis unit also incorporates an interactive quiz into the manga to measure the user's level of understanding. For example, a quiz may be inserted to check the user's level of understanding of the operating procedure. This makes it possible to check the user's level of understanding by generating an interactive quiz-style manga.

[0033] The text analysis unit can generate a 3D model based on the contents of the manual and add scenes in the manga in which a character operates the 3D model. For example, the text analysis unit uses a generation AI to generate a 3D model based on the contents of the manual and add scenes in the manga in which a character operates the 3D model. For example, the operating procedures for a machine are shown using a 3D model. The text analysis unit also converts the contents of the manual into a 3D model and incorporates scenes in the manga in which a character uses the model to demonstrate the operation. For example, the operation of a setting screen is reproduced using a 3D model. The text analysis unit also uses a generation AI to generate a 3D model and add scenes in the manga in which a character uses the model to perform specific operations. For example, the internal structure of a product is shown using a 3D model. In this way, the operating procedures can be shown more specifically using 3D models.

[0034] The text analysis unit can automatically simplify technical terms or difficult parts and convert them into easier-to-understand dialogue. For example, when the generation AI analyzes text, the text analysis unit automatically simplifies technical terms or difficult parts and converts them into easier-to-understand dialogue. For example, technical terms are replaced with everyday language. In addition, to simplify difficult parts, the generation AI analyzes the text and generates dialogue that is easy for users to understand. For example, complex operating procedures are explained in simple terms. The text analysis unit also builds a system that automatically simplifies technical terms and converts them into easier-to-understand dialogue. For example, medical terms are replaced with common words. This simplifies technical terms and difficult parts, making it easier for users to understand.

[0035] The text analysis unit can generate dialogue or panel layouts that correspond to the user's learning style (visual, auditory, tactile). For example, the generation AI in the text analysis unit analyzes the user's learning style and generates dialogue and panel layouts that correspond to it. For example, it may use a lot of illustrations for visual learners and add audio guides for auditory learners. The text analysis unit also builds a system that generates appropriate dialogue and panel layouts based on the user's learning style. For example, it may add interactive elements for tactile learners. The text analysis unit also uses the generation AI to analyze the user's data to generate dialogue and panel layouts that correspond to the learning style. For example, it may provide colorful illustrations for visual learners. This can aid comprehension by generating dialogue and panel layouts that correspond to the user's learning style.

[0036] The text analysis unit generates an interactive storybook-style comic based on the contents of the manual, and the story progresses as the user selects options. For example, the text analysis unit uses a generation AI to generate an interactive storybook-style comic based on the contents of the manual. For example, it creates a system in which the story progresses as the user selects options. The text analysis unit also generates a storybook-style comic so that the user can learn operating procedures by selecting options. For example, selecting the correct option moves to the next step. The text analysis unit also generates an interactive storybook-style comic, and builds a system in which the story progresses as the user selects options. For example, it provides options to check the level of understanding of the operating procedures. In this way, the generation of an interactive storybook-style comic can help the user understand.

[0037] The text analysis unit generates a comic that explains the contents of the manual in the form of a dialogue between characters, based on the contents of the manual, allowing the user to deepen their understanding through the dialogue. For example, the text analysis unit generates a comic that explains the contents of the manual in the form of a dialogue between characters using a generation AI. For example, characters explain operating procedures through dialogue. The text analysis unit also generates a comic in the form of a dialogue, allowing the user to understand the operating procedures through conversations between characters. For example, explanations are given in the form of questions and answers. The text analysis unit also generates a comic that explains the contents of the manual in the form of a dialogue between characters, building a system that deepens the user's understanding through dialogue. For example, characters explain operating procedures in an easy-to-understand manner. This allows the user to deepen their understanding by explaining them in the form of a dialogue between characters.

[0038] The video generation unit can refer to the user's past viewing history and apply a preferred animation style. For example, the video generation unit uses a generation AI to analyze the user's past viewing history and identify the user's preferred animation style. For example, the video generation unit applies the user's preferred animation style to the video. The video generation unit also builds a system that selects an appropriate animation style based on the user's viewing history data and generates videos. For example, it reflects the user's preferred color usage and character designs. The video generation unit also uses a generation AI to refer to the viewing history and apply an animation style that matches the user's preferences. For example, it adds animation movements and effects that the user prefers. This allows the viewing experience to be improved by applying the user's preferred animation style.

[0039] The video generation unit can automatically adjust the video playback speed according to the user's learning pace. For example, the generation AI of the video generation unit analyzes the user's learning pace and automatically adjusts the video playback speed accordingly. For example, if the user studies slowly, the playback speed is slowed down. The video generation unit also builds a system that adjusts the video playback speed based on the user's learning pace. For example, if the user studies quickly, the playback speed is increased. The video generation unit also incorporates a playback speed adjustment function according to the learning pace into the generation AI, allowing the user to study at an optimal speed. For example, the playback speed is adjusted based on the user's reaction speed. This makes it possible to improve learning efficiency by adjusting the video playback speed according to the user's learning pace.

[0040] The video generation unit generates an interactive video based on the contents of the manual, and the progress of the video can change depending on the user's selection of options. For example, the video generation unit uses a generation AI to generate an interactive video based on the contents of the manual. For example, it creates a system in which the progress of the video changes depending on the user's selection of options. The video generation unit also generates interactive videos that allow users to learn operating procedures by selecting options. For example, selecting the correct option will move on to the next step. The video generation unit also generates interactive videos, and builds a system in which the progress of the video changes depending on the user's selection of options. For example, it provides options to check the user's understanding of the operating procedures. In this way, the generation of interactive videos can help the user understand.

[0041] The video generation unit generates an AR-compatible video based on the contents of the manual, allowing the user to check the operating procedures in an actual environment. In the video generation unit, for example, a generation AI generates an AR-compatible video based on the contents of the manual. For example, it allows the user to check the operating procedures in an actual environment. In addition, the video generation unit generates an AR-compatible video to build a system that allows the user to check the operating procedures in an actual environment. For example, the operating procedures are displayed using a smartphone or tablet. In addition, the video generation unit generates an AR-compatible video using a generation AI to enable the user to check the operating procedures in an actual environment. For example, it displays the installation procedures for a product in AR. In this way, by generating an AR-compatible video, the user can check the operating procedures in an actual environment.

[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 generation system can further include a learning plan generation unit that analyzes a user's learning history and provides an optimized learning plan for each individual user. For example, the learning plan generation unit analyzes which operating procedures the user has had difficulty with in the past and creates a plan that focuses on those parts. The learning plan generation unit can also adjust the learning content according to the user's learning speed and level of understanding. For example, it can speed up the progress for users who understand quickly and add detailed explanations for users who understand slowly. The learning plan generation unit can also customize the learning content based on the user's interests and concerns. For example, a user who is interested in a particular function can be directed to focus on learning content related to that function. This allows the user to study efficiently based on a learning plan that is optimized for them.

[0044] The text analysis unit can also automatically generate characters and stories that match the user's learning style, creating a familiar manga. For example, it can use a lot of illustrations for visual learners and add audio guides for auditory learners. It can also add interactive elements for tactile learners. For example, it can incorporate simulations that allow users to actually try out operating procedures. Learning effectiveness can also be enhanced by generating appropriate characters and stories based on the user's learning style. For example, it can use colorful characters for visual learners and characters that provide audio explanations for auditory learners. In this way, it is possible to create a familiar manga by generating characters and stories that match the user's learning style.

[0045] The text analysis unit can also generate an interactive quiz-style comic based on the user's learning history to check the user's level of understanding. For example, a quiz can be inserted in the middle of an operation procedure to check the user's level of understanding. It is also possible to generate a quiz-style comic and create a mechanism in which the story progresses as the user selects options. For example, selecting the correct option will move on to the next panel. It is also possible to incorporate an interactive quiz into the comic to measure the user's level of understanding. For example, a quiz can be inserted to check the user's level of understanding of an operation procedure. In this way, an interactive quiz-style comic can be generated to check the user's level of understanding.

[0046] The text analysis unit can also generate 3D models based on the contents of the manual and add scenes in the manga in which a character operates the 3D model. For example, the operating procedures for a machine can be shown using a 3D model. The contents of the manual can also be converted into a 3D model, and scenes in the manga in which a character uses the model to demonstrate the operation can be incorporated. For example, the operations on a setting screen can be reproduced using a 3D model. The generation AI can also generate a 3D model and add scenes in the manga in which a character uses the model to perform specific operations. For example, the internal structure of a product can be shown using a 3D model. This makes it possible to use 3D models to more specifically illustrate operating procedures.

[0047] The text analysis unit can further automatically simplify technical or difficult parts and convert them into easier-to-understand dialogue. For example, technical terms can be replaced with everyday language. To simplify difficult parts, a generative AI can also analyze the text and generate dialogue that is easier for users to understand. For example, complex operating procedures can be explained in simple terms. It is also possible to build a system that automatically simplifies technical terms and converts them into easier-to-understand dialogue. For example, medical terms can be replaced with common words. This simplifies technical terms and difficult parts, making them easier for users to understand.

[0048] The text analysis unit can also generate dialogue or panel layouts that correspond to the user's learning style (visual, auditory, tactile). For example, it can use more illustrations for visual learners and add audio guides for auditory learners. It can also add interactive elements for tactile learners. For example, it can incorporate a simulation that allows users to try out operating procedures. It can also build a system that generates appropriate dialogue and panel layouts based on the user's learning style. For example, it can provide colorful illustrations for visual learners. This can help with comprehension by generating dialogue and panel layouts that correspond to the user's learning style.

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

[0050] Step 1: The text analysis unit analyzes the text manual. For example, it uses natural language processing technology to analyze the contents of the manual and text mining technology to extract important information. It also analyzes the structure of the sentences to make the contents of the manual easier to understand. Step 2: The manga generation unit generates a manga based on the content analyzed by the text analysis unit. For example, it automatically generates character designs, creates a storyboard, divides the frames, and generates character dialogue. Step 3: The video generation unit generates a video based on the cartoon generated by the cartoon generation unit. For example, it generates character movements using animation techniques, edits the video using video editing software, and adds effects to make the video more appealing. Step 4: The audio generation unit adds audio to the video generated by the video generation unit. For example, it uses voice synthesis technology to generate character lines, records narration and adds it to the video, and adds sound effects to make the video more immersive.

[0051] (Example 2) In the manual generation system according to an embodiment of the present invention, a generation AI analyzes a text-based manual, converts the content into a comic book format, and then generates a video based on the comic book version and adds audio. This makes the manual generation system easier to understand visually and audibly.

[0052] A manual generation system according to an embodiment includes a text analysis unit, a comic book generation unit, a video generation unit, and an audio generation unit. The text analysis unit analyzes a text manual. For example, the text analysis unit analyzes the content of the manual using natural language processing technology. The text analysis unit can also extract important information using text mining technology. The text analysis unit can also analyze sentence structure to make the content of the manual easier to understand. The comic book generation unit generates a comic book based on the content analyzed by the text analysis unit. For example, the comic book generation unit automatically generates character designs. The comic book generation unit can also create a storyboard and divide frames. The comic book generation unit can also generate character dialogue. The video generation unit generates a video based on the comic book generated by the comic book generation unit. For example, the video generation unit generates character movements using animation technology. The video generation unit can also edit the video using video editing software. The video generation unit can also add effects to make the video more appealing. The audio generation unit adds audio to the video generated by the video generation unit. For example, the audio generation unit generates lines for a character using voice synthesis technology. The audio generation unit can also record narration and add it to the video. The audio generation unit can also add sound effects to make the video more realistic. This allows the manual generation system according to the embodiment to make the contents of the manual easier to understand visually and aurally. For example, by reading a manual in comic format, a user can visually understand the operation procedures. Furthermore, combining video and audio makes it easier to understand aurally as well. This allows the user to efficiently learn the contents of the manual.

[0053] The text analysis unit can refer to the user's past operation history and generate comic panels that highlight areas where users frequently make mistakes. For example, the text analysis unit uses a generation AI to analyze the user's past operation history and identify areas where users frequently make mistakes. For example, if a user repeatedly makes the same operation mistake, it generates comic panels that highlight those areas. Furthermore, if the text analysis unit determines that a particular operation procedure is difficult based on the user's operation history data, it generates comic panels that explain that part in detail. For example, it provides easy-to-understand illustrations of complex setting procedures. Furthermore, the text analysis unit uses a generation AI to refer to the operation history and generate comic panels that highlight points where users should pay particular attention. For example, it displays important precautions in a prominent manner. This helps users understand by highlighting areas where they frequently make mistakes.

[0054] The text analysis unit automatically generates characters and stories that match the user's age and occupation, making it possible to create a familiar manga. For example, the text analysis unit uses a generation AI to automatically generate appropriate characters and stories based on the user's age and occupation data. For example, cute characters are used for children, and serious characters for adults. The text analysis unit also generates manga that incorporates technical terms and situations that match the user's occupation. For example, it creates stories using medical terminology for medical professionals. The text analysis unit also automatically generates story developments that match the age group, making it possible to create a familiar manga. For example, it provides an adventure story for young people and a calm story for seniors. This makes it possible to create a familiar manga by automatically generating characters and stories that match the user's age and occupation.

[0055] The text analysis unit can use the emotion estimation function to generate comic panels that highlight parts of the content that the user is particularly interested in. For example, the text analysis unit uses the emotion estimation function to identify parts of the content that the user is interested in and generate comic panels that highlight those parts. For example, it describes in detail the operation procedures that the user is interested in. The text analysis unit also generates comic panels that highlight parts of the content that the user is particularly interested in based on the user's emotion data. For example, it highlights scenes in which the user is surprised or happy. The text analysis unit also utilizes the emotion estimation function to create a comic centered on content that the user is interested in. For example, it creates a story development that the user will enjoy reading. This can help the user understand the content by highlighting parts that the user is particularly interested in.

[0056] The text analysis unit generates an interactive quiz-style manga based on the contents of the manual, making it possible to check the user's level of understanding. For example, the text analysis unit uses a generation AI to generate an interactive quiz-style manga based on the contents of the manual. For example, a quiz may be inserted in the middle of an operating procedure to check the user's level of understanding. The text analysis unit also generates a quiz-style manga, where the story progresses as the user selects options. For example, a system is created whereby selecting the correct option advances to the next panel. The text analysis unit also incorporates an interactive quiz into the manga to measure the user's level of understanding. For example, a quiz may be inserted to check the user's level of understanding of the operating procedure. This makes it possible to check the user's level of understanding by generating an interactive quiz-style manga.

[0057] The text analysis unit can generate a 3D model based on the contents of the manual and add scenes in the manga in which a character operates the 3D model. For example, the text analysis unit uses a generation AI to generate a 3D model based on the contents of the manual and add scenes in the manga in which a character operates the 3D model. For example, the operating procedures for a machine are shown using a 3D model. The text analysis unit also converts the contents of the manual into a 3D model and incorporates scenes in the manga in which a character uses the model to demonstrate the operation. For example, the operation of a setting screen is reproduced using a 3D model. The text analysis unit also uses a generation AI to generate a 3D model and add scenes in the manga in which a character uses the model to perform specific operations. For example, the internal structure of a product is shown using a 3D model. In this way, the operating procedures can be shown more specifically using 3D models.

[0058] The text analysis unit can use the emotion estimation function to analyze the emotions of a user while reading a manga in real time and provide feedback within the manga that corresponds to the emotions. The text analysis unit, for example, uses the emotion estimation function to analyze the emotions of a user while reading a manga in real time and provide feedback within the manga that corresponds to the emotions. For example, if the user is confused, additional explanations are displayed. The text analysis unit also builds a system that provides appropriate feedback within the manga based on the user's emotion data. For example, information related to parts that the user showed interest in is added. The text analysis unit also utilizes the emotion estimation function to provide feedback within the manga that corresponds to the user's emotions. For example, if the user is enjoying the manga, hints for taking the next step are displayed. In this way, providing feedback that corresponds to the user's emotions can help with understanding.

[0059] The text analysis unit can automatically simplify technical terms or difficult parts and convert them into easier-to-understand dialogue. For example, when the generation AI analyzes text, the text analysis unit automatically simplifies technical terms or difficult parts and converts them into easier-to-understand dialogue. For example, technical terms are replaced with everyday language. In addition, to simplify difficult parts, the generation AI analyzes the text and generates dialogue that is easy for users to understand. For example, complex operating procedures are explained in simple terms. The text analysis unit also builds a system that automatically simplifies technical terms and converts them into easier-to-understand dialogue. For example, medical terms are replaced with common words. This simplifies technical terms and difficult parts, making it easier for users to understand.

[0060] The text analysis unit can generate dialogue or panel layouts that correspond to the user's learning style (visual, auditory, tactile). For example, the generation AI in the text analysis unit analyzes the user's learning style and generates dialogue and panel layouts that correspond to it. For example, it may use a lot of illustrations for visual learners and add audio guides for auditory learners. The text analysis unit also builds a system that generates appropriate dialogue and panel layouts based on the user's learning style. For example, it may add interactive elements for tactile learners. The text analysis unit also uses the generation AI to analyze the user's data to generate dialogue and panel layouts that correspond to the learning style. For example, it may provide colorful illustrations for visual learners. This can aid comprehension by generating dialogue and panel layouts that correspond to the user's learning style.

[0061] The text analysis unit can use the emotion estimation function to generate lines that are easy for the user to empathize with emotionally, making it easier for the user to understand. The text analysis unit, for example, uses the emotion estimation function to generate lines that are easy for the user to empathize with emotionally, making it easier for the user to understand. For example, if the user is confused, words of encouragement are added. The text analysis unit also builds a system that generates lines that are easy for the user to empathize with based on the user's emotion data. For example, emotional lines are added to parts that the user shows interest in. The text analysis unit also utilizes the emotion estimation function to generate lines that are easy for the user to empathize with emotionally, making it easier for the user to understand. For example, if the user is having fun, positive lines are added. In this way, lines that are easy for the user to empathize with emotionally can be generated, helping the user understand.

[0062] The text analysis unit generates an interactive storybook-style comic based on the contents of the manual, and the story progresses as the user selects options. For example, the text analysis unit uses a generation AI to generate an interactive storybook-style comic based on the contents of the manual. For example, it creates a system in which the story progresses as the user selects options. The text analysis unit also generates a storybook-style comic so that the user can learn operating procedures by selecting options. For example, selecting the correct option moves to the next step. The text analysis unit also generates an interactive storybook-style comic, and builds a system in which the story progresses as the user selects options. For example, it provides options to check the level of understanding of the operating procedures. In this way, the generation of an interactive storybook-style comic can help the user understand.

[0063] The text analysis unit generates a comic that explains the contents of the manual in the form of a dialogue between characters, based on the contents of the manual, allowing the user to deepen their understanding through the dialogue. For example, the text analysis unit generates a comic that explains the contents of the manual in the form of a dialogue between characters using a generation AI. For example, characters explain operating procedures through dialogue. The text analysis unit also generates a comic in the form of a dialogue, allowing the user to understand the operating procedures through conversations between characters. For example, explanations are given in the form of questions and answers. The text analysis unit also generates a comic that explains the contents of the manual in the form of a dialogue between characters, building a system that deepens the user's understanding through dialogue. For example, characters explain operating procedures in an easy-to-understand manner. This allows the user to deepen their understanding by explaining them in the form of a dialogue between characters.

[0064] The text analysis unit can use the emotion estimation function to generate dialogue and panel layouts that emphasize parts of the user's interest. For example, the text analysis unit uses the emotion estimation function to generate dialogue and panel layouts that emphasize parts of the user's interest. For example, it describes in detail the operating procedures that the user is interested in. The text analysis unit also builds a system that generates dialogue and panel layouts that emphasize parts of the user's interest based on the user's emotion data. For example, it highlights scenes in which the user is surprised or happy. The text analysis unit also utilizes the emotion estimation function to generate dialogue and panel layouts that match the user's interests. For example, it creates a story development that the user will enjoy reading. This can help the user understand the text by emphasizing parts of the text that the user is particularly interested in.

[0065] The video generation unit can refer to the user's past viewing history and apply a preferred animation style. For example, the video generation unit uses a generation AI to analyze the user's past viewing history and identify the user's preferred animation style. For example, the video generation unit applies the user's preferred animation style to the video. The video generation unit also builds a system that selects an appropriate animation style based on the user's viewing history data and generates videos. For example, it reflects the user's preferred color usage and character designs. The video generation unit also uses a generation AI to refer to the viewing history and apply an animation style that matches the user's preferences. For example, it adds animation movements and effects that the user prefers. This allows the viewing experience to be improved by applying the user's preferred animation style.

[0066] The video generation unit can automatically adjust the video playback speed according to the user's learning pace. For example, the generation AI of the video generation unit analyzes the user's learning pace and automatically adjusts the video playback speed accordingly. For example, if the user studies slowly, the playback speed is slowed down. The video generation unit also builds a system that adjusts the video playback speed based on the user's learning pace. For example, if the user studies quickly, the playback speed is increased. The video generation unit also incorporates a playback speed adjustment function according to the learning pace into the generation AI, allowing the user to study at an optimal speed. For example, the playback speed is adjusted based on the user's reaction speed. This makes it possible to improve learning efficiency by adjusting the video playback speed according to the user's learning pace.

[0067] The video generation unit can use the emotion estimation function to generate animation and audio that highlights parts of the video that are of particular interest to the user. For example, the video generation unit uses the emotion estimation function to generate animation and audio that highlights parts of the video that are of particular interest to the user. For example, it depicts in detail an operation procedure that the user is interested in. The video generation unit also builds a system that generates animation and audio that highlights parts of the video that are of particular interest to the user based on the user's emotion data. For example, it highlights scenes that make the user feel surprised or happy. The video generation unit also utilizes the emotion estimation function to generate animation and audio according to the user's interests. For example, it creates a storyline that the user can enjoy watching. This can improve the viewing experience by highlighting parts of the video that the user is particularly interested in.

[0068] The video generation unit generates an interactive video based on the contents of the manual, and the progress of the video can change depending on the user's selection of options. For example, the video generation unit uses a generation AI to generate an interactive video based on the contents of the manual. For example, it creates a system in which the progress of the video changes depending on the user's selection of options. The video generation unit also generates interactive videos that allow users to learn operating procedures by selecting options. For example, selecting the correct option will move on to the next step. The video generation unit also generates interactive videos, and builds a system in which the progress of the video changes depending on the user's selection of options. For example, it provides options to check the user's understanding of the operating procedures. In this way, the generation of interactive videos can help the user understand.

[0069] The video generation unit generates an AR-compatible video based on the contents of the manual, allowing the user to check the operating procedures in an actual environment. In the video generation unit, for example, a generation AI generates an AR-compatible video based on the contents of the manual. For example, it allows the user to check the operating procedures in an actual environment. In addition, the video generation unit generates an AR-compatible video to build a system that allows the user to check the operating procedures in an actual environment. For example, the operating procedures are displayed using a smartphone or tablet. In addition, the video generation unit generates an AR-compatible video using a generation AI to enable the user to check the operating procedures in an actual environment. For example, it displays the installation procedures for a product in AR. In this way, by generating an AR-compatible video, the user can check the operating procedures in an actual environment.

[0070] The video generation unit can use the emotion estimation function to analyze the emotions of a user while watching a video in real time and provide feedback within the video that corresponds to the emotions. For example, the video generation unit can use the emotion estimation function to analyze the emotions of a user while watching a video in real time and provide feedback within the video that corresponds to the emotions. For example, if the user is confused, additional explanations can be displayed. The video generation unit also builds a system that provides appropriate feedback within the video based on the user's emotion data. For example, information related to parts that the user showed interest in can be added. The video generation unit also utilizes the emotion estimation function to provide feedback within the video that corresponds to the user's emotions. For example, if the user is enjoying the video, a hint for taking the next step can be displayed. In this way, the viewing experience can be improved by providing feedback that corresponds to the user's emotions.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The manual generation system can further include a learning plan generation unit that analyzes a user's learning history and provides an optimized learning plan for each individual user. For example, the learning plan generation unit analyzes which operating procedures the user has had difficulty with in the past and creates a plan that focuses on those parts. The learning plan generation unit can also adjust the learning content according to the user's learning speed and level of understanding. For example, it can speed up the progress for users who understand quickly and add detailed explanations for users who understand slowly. The learning plan generation unit can also customize the learning content based on the user's interests and concerns. For example, a user who is interested in a particular function can be directed to focus on learning content related to that function. This allows the user to study efficiently based on a learning plan that is optimized for them.

[0073] The text analysis unit can further estimate the user's emotions and, based on the estimated emotions, generate comic panels that emphasize points that the user should pay particular attention to. For example, if the user is feeling anxious, a comic panel that explains that part in detail can be generated. Also, by highlighting parts that the user is interested in, it is possible to deepen understanding. For example, it can describe in detail the operating procedures that interest the user. Also, if the user is confused, it can generate comic panels that explain those parts in an easy-to-understand manner. In this way, by generating comic panels that correspond to the user's emotions, it is possible to aid understanding.

[0074] The text analysis unit can also automatically generate characters and stories that match the user's learning style, creating a familiar manga. For example, it can use a lot of illustrations for visual learners and add audio guides for auditory learners. It can also add interactive elements for tactile learners. For example, it can incorporate simulations that allow users to actually try out operating procedures. Learning effectiveness can also be enhanced by generating appropriate characters and stories based on the user's learning style. For example, it can use colorful characters for visual learners and characters that provide audio explanations for auditory learners. In this way, it is possible to create a familiar manga by generating characters and stories that match the user's learning style.

[0075] The text analysis unit can further use the emotion estimation function to generate comic panels that highlight parts of the user's interest. For example, it can describe in detail the operating procedures that the user is interested in. It can also generate comic panels that highlight parts of the user's interest based on the user's emotion data. For example, it can highlight scenes in which the user is surprised or happy. The emotion estimation function can also be used to create a comic centered on content that the user is interested in. For example, it can create a story development that the user will enjoy reading. This can help the user understand the content by highlighting the parts that the user is particularly interested in.

[0076] The text analysis unit can also generate an interactive quiz-style comic based on the user's learning history to check the user's level of understanding. For example, a quiz can be inserted in the middle of an operation procedure to check the user's level of understanding. It is also possible to generate a quiz-style comic and create a mechanism in which the story progresses as the user selects options. For example, selecting the correct option will move on to the next panel. It is also possible to incorporate an interactive quiz into the comic to measure the user's level of understanding. For example, a quiz can be inserted to check the user's level of understanding of an operation procedure. In this way, an interactive quiz-style comic can be generated to check the user's level of understanding.

[0077] The text analysis unit can also generate 3D models based on the contents of the manual and add scenes in the manga in which a character operates the 3D model. For example, the operating procedures for a machine can be shown using a 3D model. The contents of the manual can also be converted into a 3D model, and scenes in the manga in which a character uses the model to demonstrate the operation can be incorporated. For example, the operations on a setting screen can be reproduced using a 3D model. The generation AI can also generate a 3D model and add scenes in the manga in which a character uses the model to perform specific operations. For example, the internal structure of a product can be shown using a 3D model. This makes it possible to use 3D models to more specifically illustrate operating procedures.

[0078] The text analysis unit can further use the emotion estimation function to analyze the user's emotions in real time while reading the manga and provide feedback within the manga that corresponds to the emotions. For example, if the user is confused, additional explanations can be displayed. It is also possible to build a system that provides appropriate feedback within the manga based on the user's emotion data. For example, information related to parts that the user showed interest in can be added. The emotion estimation function can also be used to provide feedback within the manga that corresponds to the user's emotions. For example, if the user is enjoying the manga, hints on how to proceed to the next step can be displayed. This makes it possible to provide feedback that corresponds to the user's emotions and aid understanding.

[0079] The text analysis unit can further automatically simplify technical or difficult parts and convert them into easier-to-understand dialogue. For example, technical terms can be replaced with everyday language. To simplify difficult parts, a generative AI can also analyze the text and generate dialogue that is easier for users to understand. For example, complex operating procedures can be explained in simple terms. It is also possible to build a system that automatically simplifies technical terms and converts them into easier-to-understand dialogue. For example, medical terms can be replaced with common words. This simplifies technical terms and difficult parts, making them easier for users to understand.

[0080] The text analysis unit can also generate dialogue or panel layouts that correspond to the user's learning style (visual, auditory, tactile). For example, it can use more illustrations for visual learners and add audio guides for auditory learners. It can also add interactive elements for tactile learners. For example, it can incorporate a simulation that allows users to try out operating procedures. It can also build a system that generates appropriate dialogue and panel layouts based on the user's learning style. For example, it can provide colorful illustrations for visual learners. This can help with comprehension by generating dialogue and panel layouts that correspond to the user's learning style.

[0081] The text analysis unit can further use the emotion estimation function to generate lines that are easy for the user to empathize with, making it easier for the user to understand. For example, if the user is confused, words of encouragement can be added. It is also possible to build a system that generates lines that are easy for the user to empathize with, based on the user's emotion data. For example, emotional lines can be added to parts that the user has shown interest in. The emotion estimation function can also be used to generate lines that are easy for the user to empathize with, making it easier for the user to understand. For example, if the user is having fun, positive lines can be added. In this way, lines that are easy for the user to empathize with can be generated, helping the user understand.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The text analysis unit analyzes the text manual. For example, it uses natural language processing technology to analyze the contents of the manual and text mining technology to extract important information. It also analyzes the structure of the sentences to make the contents of the manual easier to understand. Step 2: The manga generation unit generates a manga based on the content analyzed by the text analysis unit. For example, it automatically generates character designs, creates a storyboard, divides the frames, and generates character dialogue. Step 3: The video generation unit generates a video based on the cartoon generated by the cartoon generation unit. For example, it generates character movements using animation techniques, edits the video using video editing software, and adds effects to make the video more appealing. Step 4: The audio generation unit adds audio to the video generated by the video generation unit. For example, it uses voice synthesis technology to generate character lines, records narration and adds it to the video, and adds sound effects to make the video more immersive.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0097] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0112] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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]

[0151] 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 analysis unit that analyzes a text format manual; a comic book generation unit that generates a comic book based on the content analyzed by the text analysis unit; a moving image generating unit that generates a moving image based on the cartoon generated by the cartoon generating unit; an audio generation unit that adds audio to the video generated by the video generation unit; A system characterized by:

2. The text analysis unit It references the user's past operation history and generates comic panels that highlight frequently made mistakes.

2. The system of claim 1.

3. The text analysis unit Automatically generate characters and stories based on the user's age and occupation to create a familiar manga 2. The system of claim 1.

4. The text analysis unit Generate manga panels that highlight the parts that are of particular interest to the user 2. The system of claim 1.

5. The text analysis unit Generates an interactive quiz-style comic based on the contents of a manual to check the user's understanding 2. The system of claim 1.

Images